AN ABSTRACT OF THE DISSERTATION OF
Martin C. Quincke for the degree of Doctor of Philosophy in Crop Science
presented on June 19, 2009.
Title: Phenotypic Response and Quantitative Trait Loci for Resistance to
Cephalosporium gramineum in Winter Wheat.
Abstract approved:
____________________________________________________________
C. James Peterson
Christopher C. Mundt
Cephalosporium stripe (Cephalosporium gramineum) is an important
disease limiting adoption of conservation tillage practices in the Pacific
Northwest. The disease can cause severe loss of grain yield and quality in
winter wheat (Triticum aestivum L.). Modified cultural practices can reduce
disease incidence, but are not always dependable because of variation in
climatic conditions and conflicts with soil conservations goals. The most
desirable method of control would be planting of resistant cultivars. Improving
disease resistance is problematic, however, as infection and disease response
are environmentally dependent. Little is known about inheritance and level of
resistance required to minimize grain yield loss. A combination of field trials
and molecular genotyping was used to investigate the genetics and
inheritance of resistance to Cephalosporium gramineum. Yield loss and impact
on kernel characteristics was examined using 12 cultivars in field trials
comparing inoculated and non-inoculated treatments. Negligible reduction in
grain yield or test weight was observed with disease ratings of less than 5%
whiteheads (sterile heads caused by pathogen infection). However, grain yield
of the susceptible cultivar Stephens was reduced by 1.7 t ha-1 with addition of
inoculum. Response to Cephalosporium stripe was positively correlated with
uniformity in kernel size, possibly a function of reduced number of seeds per
spike. A recombinant inbred line (RIL) population derived from a cross
between two commercially grown winter wheat cultivars was studied.
Whiteheads and kernel characteristics were measured on each RIL.
Quantitative trait loci (QTL) analysis identified seven regions associated with
resistance to Cephalosporium stripe, with approximately equal effects, four
derived from the susceptible parent (Brundage) and three from the resistant
parent (Coda). Additivity of QTL effects was confirmed through regression
analysis. Two resistance QTL were found to be related to head morphology
traits. A promising QTL located on chromosome 5B could be related to toxin
insensitivity genes described for other wheat pathogens. This study confirms
the importance of Cephalosporium stripe as a threat to grain yield for wheat
growers in the Pacific Northwest. Molecular markers can be effectively used to
identify and combine QTL and provide higher levels of genetic resistance than
available in commercial cultivars.
©Copyright by Martin C. Quincke
June 19, 2009
All Rights Reserved
Phenotypic Response and Quantitative Trait Loci for Resistance to
Cephalosporium gramineum in Winter Wheat
by
Martin C. Quincke
A DISSERTATION
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Doctor of Philosophy
Presented June 19, 2009
Commencement June 2010
Doctor of Philosophy dissertation of Martin C. Quincke presented on June 19,
2009.
APPROVED:
____________________________________________________________
Co-Major Professor, representing Crop Science
____________________________________________________________
Co-Major Professor, representing Crop Science
____________________________________________________________
Head of the Department of Crop and Soil Science
____________________________________________________________
Dean of the Graduate School
I understand that my dissertation will become part of the permanent collection
of Oregon State University libraries. My signature below authorizes release of
my dissertation to any reader upon request.
____________________________________________________________
Martin C. Quincke, Author
ACKNOWLEDGEMENTS
I would like to express my sincere appreciation and gratitude to my
major Professors, Dr. C. James Peterson and Dr. Christopher C. Mundt, for
their guidance, help, and continuous support throughout my graduate
program. Appreciation is extended to Dr. Oscar Riera-Lizarazu, Dr. Patrick
Hayes and Dr. Edward Peachey (Graduate School representative), for serving
on my graduate committee.
This research was possible through funds granted by USDA, STEEP
program.
Special acknowledgment is due to LaRae Wallace and Kathryn Sackett
in Chris Mundt’s lab for their assistance in field plantings; to Jeron Chatelain
and Karl Rhinhart from the Columbia Basin Agricultural Research Center, for
their field assistance. Special thanks to Dr. Robert Zemetra from the University
of Idaho for sharing the mapping population and providing valuable marker
data. My sincere gratitude is extended to all the members of the Wheat
Research Project, Mary Verhoeven, Mark Larson, Adam Heesacker, Edward
Simons, Jari von Zitzewitz, Chris Gaynor, Dolores Vazquez, and summer
crews, for their help and expert assistance with field work, cooperation, and
friendship.
I would like to express my gratitude to my authorities at the Uruguayan
National Research Institute (INIA), who provided the opportunity and support
for my graduate program here at Oregon State University. Special thanks to
my colleagues and friends at La Estanzuela, INIA, Uruguay.
My warmest thanks to my fellow graduate students for their friendship
and assistance. It was a pleasure working and sharing this time with all of you.
To the many friends I made, who helped me get to the finish line, thank you
very much. I have memories of all of you that I will never forget.
My sincere gratitude also goes to Dr. Mohan Kohli, for his passion and
enthusiasm in wheat breeding, and for laying the corner-stones of my incipient
career as a wheat breeder.
I would like to especially thank my parents, brothers and sister, both of
my grandmothers, well in their nineties, for their endless love and support, and
for being there when it was needed.
I have no words to describe the immense gratitude I have for my wife
Alenka, my daughters Analía and Rocío, and my son Sebastián. You made
this happen. I’m eternally indebted with you for all your love, patience, support
and encouragement since the very first day we started this journey.
TABLE OF CONTENTS
Page
GENERAL INTRODUCTION............................................................................ 1
The disease .................................................................................................. 1
Disease resistance ..................................................................................... 21
Molecular markers and marker assisted selection ...................................... 34
Thesis Objectives ....................................................................................... 54
Quantitative Trait Loci Analysis for Resistance to Cephalosporium stripe, a
Vascular Wilt Disease of Wheat ..................................................................... 55
Abstract ...................................................................................................... 55
Introduction ................................................................................................. 56
Materials and Methods ............................................................................... 60
Results........................................................................................................ 66
Discussion .................................................................................................. 72
References ................................................................................................. 83
Relationship between incidence of Cephalosporium stripe and yield loss in
winter wheat ................................................................................................. 100
Abstract .................................................................................................... 100
Introduction ............................................................................................... 101
Materials and methods ............................................................................. 104
Results...................................................................................................... 109
Discussion ................................................................................................ 112
References ............................................................................................... 119
TABLE OF CONTENTS (Continued)
Page
Characterization of field resistance to Cephalosporium stripe in four related
wheat RIL populations. ................................................................................. 130
Abstract .................................................................................................... 130
Introduction ............................................................................................... 131
Objectives ................................................................................................. 132
Materials and methods ............................................................................. 132
Results and discussion ............................................................................. 134
Conclusions .............................................................................................. 135
References ............................................................................................... 137
GENERAL CONCLUSIONS ......................................................................... 146
BIBLIOGRAPHY .......................................................................................... 149
APPENDIX ................................................................................................... 169
LIST OF TABLES
Table
Page
1.1. Features of the four main kinds of resistance.......................................... 27
2.1. Analyses of variance, coefficients of variation (CV), and heritability
estimates (h2) for percentage whiteheads, kernel weight, and kernel
weight standard deviation (SD) for a population of 268 recombinant
inbred wheat lines (RIL) exposed to Cephalosporium stripe disease in
three environments (Moro and Pendleton 2007 and Pendleton 2008) ... 89
2.2. Means and standard errors (SE) for percentage whiteheads (square roottransformed), kernel weight, and kernel weight standard deviation (SD)
for parents (Coda and Brundage) and a population of 268 recombinant
inbred lines (RIL) of wheat exposed to Cephalosporium stripe disease in
three environments ................................................................................ 90
2.3. Results of composite interval mapping (CIM) of Cephalosporium stripe
response (square root of percentage whiteheads), kernel weight, and
kernel weight SD combined over three environments ............................ 91
3.1. Analysis of variance for % whiteheads (square root-transformed) and
grain parameters for wheat genotypes grown in plots inoculated or not
inoculated with Cephalosporium gramineum in Pendleton 2006 and Moro
2007 ..................................................................................................... 122
3.2. Percent whiteheads, grain yield, test weight and grain protein of 10 wheat
genotypes non-inoculated (U) and inoculated (I) with Cephalosporium
gramineum in field plots in Pendleton, Oregon, 2006 ........................... 123
3.3. Kernel parameters of 10 wheat genotypes grown non-inoculated (U) and
inoculated (I) with Cephalosporium gramineum in field plots in Pendleton,
Oregon, 2006 ....................................................................................... 124
3.4. Percent whiteheads, grain yield, test weight and grain protein of 12 wheat
genotypes non-inoculated (U) and inoculated (I) with Cephalosporium
gramineum in field plots in Moro, Oregon, 2007................................... 125
3.5. Kernel parameters of 12 wheat genotypes grown non-inoculated (U) and
inoculated (I) with Cephalosporium gramineum in field plots in Moro,
Oregon, 2007 ....................................................................................... 126
LIST OF TABLES (Continued)
Table
Page
3.6. Pearson correlation coefficients among Cephalosporium stripe rating,
grain yield, test weight, and several kernel traits of 10 and 12 cultivars
tested in Pendleton 2006 (below diagonal) and Moro 2007 (above
diagonal) under inoculated and non-inoculated conditions .................. 127
4.1. Pedigree and number of lines per population used in the study. ........... 138
4.2. Mean square values from analyses of variance for percentage
whiteheads, kernel weight, and kernel diameter of progeny from four
wheat crosses exposed to Cephalosporium stripe disease in Pendleton,
Oregon, in two years. ........................................................................... 138
4.3. Broad sense heritability (h2) estimates for disease response (whiteheads)
at Pendleton 2005 and 2006, in four related RIL populations............... 139
4.4. Pearson’s coefficients of correlation between disease scores and kernel
quality traits at Pendleton 2006, and significance ................................ 140
LIST OF FIGURES
Figure
Page
2.1. Frequency distributions of Cephalosporium stripe response (square root
of percentage whiteheads) for 268 recombinant inbred lines of wheat
derived from a cross between the cultivars Coda and Brundage, in three
environments; the cultivar Stephens was a susceptible check. .............. 94
2.2. Frequency distributions of kernel weight for 268 recombinant inbred lines
derived from a cross between the wheat cultivars Coda and Brundage in
the presence of Cephalosporium stripe in three environments. ............. 95
2.3. Frequency distributions of kernel weight standard deviation (SD) for 268
recombinant inbred lines derived from the cross of the wheat cultivars
Coda and Brundage evaluated in the presence of Cephalosporium stripe
in three environments. ............................................................................ 96
2.4. Partial linkage map of the Coda x Brundage recombinant inbred line
population (n=268), with graphical presentation of significant QTL
revealed by CIM on combined data of disease response (square root of
% whiteheads), kernel weight and kernel weight standard deviation (SD)
over three environments......................................................................... 97
2.5. Regression of least squares means of % whiteheads caused by
Cephalosporium stripe on number of resistance alleles present at 7 QTL
for a population of 268 recombinant inbred lines derived from a cross
between the wheat cultivars Coda (C) and Brundage (B). ..................... 99
3.1. Yield loss caused by inoculation of wheat genotypes with Cephalosporium
gramineum in Pendleton 2006 (10 wheat genotypes) and Moro 2007 (12
wheat genotypes) ................................................................................. 128
3.2. Reduction of grain test weight caused by inoculation of wheat genotypes
with Cephalosporium gramineum in Pendleton 2006 (10 wheat
genotypes) and Moro 2007 (12 wheat genotypes) ............................... 129
4.1. a-d. Frequency distributions of lines for four segregating populations (A-D)
evaluated for % whiteheads caused by Cephalosporium gramineum in
artificially inoculated field trials at Pendleton in 2005 and 2006. .......... 142
LIST OF APPENDIX TABLES
Table
Page
5.1. Means, standard error (SE) and range for percentage whiteheads (square
root-transformed), kernel weight, and kernel weight standard deviation
(SD) for a population of 268 recombinant inbred lines (RIL) of wheat and
their parents evaluated in Moro 2007, Pendleton 2007 and Pendleton
2008. .................................................................................................... 170
5.2. Pearson correlation coefficients between disease scores for
Cephalosporium stripe and kernel physical quality traits in a 268
recombinant inbred line (RIL) population of winter wheat..................... 171
5.3. Results of composite interval mapping of Cephalosporium stripe response
(percentage whiteheads), kernel weight, and kernel weight standard
deviation (SD) for Moro 2007, Pendleton 2007 and Pendleton 2008. .. 172
PHENOTYPIC RESPONSE AND QUANTITATIVE TRAIT LOCI FOR
RESISTANCE TO CEPHALOSPORIUM GRAMINEUM IN WINTER WHEAT
GENERAL INTRODUCTION
The disease
Cephalosporium stripe of wheat is caused by the soil-borne fungal pathogen
Cephalosporium gramineum Nisikado and Ikata (syn. Hymenula cerealis Ellis
& Everh.) (Bruehl, 1956; Ellis and Everhart, 1894; Nisikado et al., 1934). The
fungus has a wide range of hosts, mainly winter cereals (wheat, oats, barley
and rye), but it is also pathogenic on other grass species (i.e. Bromus, Dactylis
and Poa) (Bruehl, 1957; Willis and Shively, 1974). However, Cephalosporium
gramineum is of economic importance only in winter wheat. It is the only
known, true vascular wilt of wheat (Wiese, 1987).
Cephalosporium stripe was first observed and thoroughly described in Japan
(Nisikado et al., 1934) and more than 20 years later found in the Palouse
region in the state of Washington, U.S. (Bruehl, 1956). Today, C. gramineum
is found in winter wheat growing areas in every continent with the exception of
Oceania (Gray and Noble, 1960; Hawksworth and Waller, 1976; Kobayashi
and Ui, 1979; Slope and Bardner, 1965). In North America it is widespread
throughout the Pacific Northwest, where it is a chronic yield reducing disease,
and in western Provinces of Canada (Bruehl, 1957; Mundt, 2002a; Murray,
2006; Wiese, 1987). It is frequent in Montana, in the Midwest (Kansas and
2
Nebraska), in some eastern states like the Virginias and New York
(Hawksworth and Waller, 1976; Mathre and Johnston, 1975b; Willis and
Shively, 1974).
Life Cycle
C. gramineum is an asexual ascomycete. As with many soil-borne pathogens,
no sexual stage has been reported. It reproduces asexually by means of
sporodochia that produce copious phialospores, phialides on mycelium, or
blastogenously in host xylem vessels (Bruehl, 1963; Wiese and Ravenscroft,
1978). The fungus survives and overwinters between host crops
saprophytically as mycelium and conidia in association with host residues on
or near the soil surface (Lai and Bruehl, 1966). Profuse sporulation occurs on
the senescent tissue during cool, wet periods in fall. Unicellular conidia are
produced on masses of conidiophores called sporodochia on the infected crop
residue (Bruehl, 1968; Wiese and Ravenscroft, 1978). Sporodochia are flat
and black when dry and raised and yellow-brown when moist. Conidia are the
primary inoculum source. They are hyaline, oval shaped, nonseptate and very
small (4-7 µm x 2-3 µm).
Water carries the spores to the root surface of the host plants (Bruehl, 1957;
Bruehl, 1963; Wiese, 1987; Zillinsky, 1983). Conidia of C. gramineum enter
wheat roots through wounds caused by freeze injury, freeze heaving of soil,
root feeding insects (wireworm and nematodes), or other mechanical injury.
3
However infection can also occur in the absence of root-wounding (Bailey et
al., 1982; Douhan and Murray, 2001; Slope and Bardner, 1965; Stiles and
Murray, 1996).
Once inside the roots conidia germinate and the fungus becomes established
in the vascular system of the host plant. With crop growth in the spring the
fungus moves upward through the xylem vessels into leaves and elongating
tillers, were it can extend for several internodes up the stem. It continues
multiplying and potentially colonizes the entire plant. Additionally C.
gramineum produces toxic metabolites which block the vascular system, thus
preventing normal movement of water and nutrients. This creates a
considerable amount of potential inoculum for the next season, which is key to
its survival and future spread (Bruehl, 1957; Bruehl and Lai, 1966; Lai and
Bruehl, 1966; Spalding et al., 1961; Wiese, 1987).
C. gramineum can survive saprophytically on undisturbed host crop residue for
as long as three years, but cannot survive unprotected in soil for more than a
few months. The survival of the propagules, free in the soil, is temperature dependent and limited. They have a half-life of 0.5 – 2.5 weeks at 23°C in
autumn-collected field soil, and a half-life of 17 weeks if the soil is allowed to
dry at 7°C (Wiese and Ravenscroft, 1975). Soil pH and matric potential also
influence conidial survival (Specht and Murray, 1989). Conidia produced in the
top layer of soil on crop stubble and released during cool and moist weather
4
conditions during fall and winter are washed down into the root zone and
represent the infective propagules for the next crop (Mathre and Johnston,
1975b; Wiese and Ravenscroft, 1973).
Though the primary source of inoculum for Cephalosporium stripe is infested
residue from previous crops, transmission of C. gramineum through seed
could represent a significant source of inoculum, especially in fields where the
pathogen does not occur or where other control measures such as crop
rotations are being used (Murray, 2006). Since the first reports on
Cephalosporium stripe, seed has been implicated as a potential source of
inoculum (Nisikado et al., 1934). In his initial study on Cephalosporium stripe,
Bruehl (1957) could not isolate C. gramineum from seeds of the winter wheat
Elmar harvested from a naturally infected crop. In his second attempt he
succeeded and concluded that the pathogen was seed-transmitted, but at a
very low rate and not enough to produce an epidemic. More recently, Murray
(2006) found that Cephalosporium stripe developed in up to 0.55 % of plants
grown from seed lots infected by C. gramineum. In his conclusions he states
that: “Although the rate of seed transmission for C. gramineum is too low to
start an epidemic in the first year of introduction, it is great enough to allow the
pathogen to become established in fields where it is not present and become a
significant problem in subsequent crops.”
Symptoms
5
C. gramineum limits the movement of water and nutrients within stems and
leaves. The pathogen was given its common name of Cephalosporium stripe
due to the characteristic stripes that develop on host leaves (Bruehl, 1957).
Under high fungal populations a seedling blight phase may occur. Infected
seedlings first show a mild mosaic-like yellowing and then wilt and die (Wiese,
1972). Most typical and recognizable chlorotic leaf striping is apparent during
jointing and heading. One or two sharp, bright yellow, lengthwise stripes with a
narrow brown center stripe appear on leaf blades that may continue on
sheaths and stems. Symptoms are most obvious on the younger, upper leaves
since the lower leaves may have died prematurely. Stripes may not develop
on all the leaves of an infected plant or on all tillers. Lower nodes on stems
may be darkened as plants mature. Likewise, severely infected stems are
stunted and prematurely ripen, producing a white and usually sterile head,
containing sometimes just a few shriveled seeds. It is at this level of infection
where the greatest amount of yield loss is observed (Johnston and Mathre,
1972; Mathre and Johnston, 1975a; Morton et al., 1980; Nisikado et al., 1934;
Wiese, 1987).
Role of Toxin in Cephalosporium stripe development
Production of toxins or similar compounds by pathogens is not uncommon. For
some fungi, the relationship of toxins to pathogenesis is well established
(Wolpert et al., 2002). The symptoms of Cephalosporium stripe suggest a
pathogen-produced toxin or xylem-plugging compound may be involved in its
6
pathogenesis. This has led to investigations on the production of antibiotics,
toxins, and extra-cellular polysaccharides by the pathogen. Bruehl (1957)
suggested that toxic metabolites of the fungus may play a role in
pathogenesis. It was then concluded that an extra-cellular polysaccharide
produced by the fungus resulted in xylary plugging (Spalding et al., 1961). It
was later reported that occlusions were due to fungal proliferation that
developed only after lateral extension of leaf striping (Wiese, 1972).
Graminin A was isolated and characterized from culture filtrates of C.
gramineum by Kobayashi and Ui (1977). It was later reported that this toxic
compound was found to cause yellowing at concentrations of 25 µg ml-1 in
excised leaves (Kobayashi and Ui, 1979). Graminin A possesses antimicrobial
activity and has been shown to affect stomatal function in the same manner as
infection by C. gramineum (Creatura et al., 1981). However, pathogenicity and
virulence of C. gramineum has been found to be independent of in vitro
production of extra-cellular polysaccharides and Graminin A (Van Wert and
Fulbright, 1986).
Epidemiology
o Role of inoculum on disease development
Conidia produced on infested crop residue are the primary inoculum source.
The importance of the quantity of primary inoculum in disease development
has been demonstrated for several monocyclic soilborne pathogens (Fry,
1982). In general, as the quantity of inoculum increases, disease increases
7
until relatively high incidences are reached (Bruehl et al., 1986; Mathre and
Johnston, 1975a). Therefore, decreasing initial inoculum density or inoculum
efficiency is an important control strategy for monocyclic diseases.
The incidence of Cephalosporium stripe is largely a function of inoculum
density. However, the relative importance of reducing soil inoculum levels in
controlling Cephalosporium stripe disease is unclear. Both linear and
logarithmic relationships between inoculum density and disease incidence
have been reported. For resistant varieties, the response to varying levels of
C. gramineum inoculum, measured as either percent infection or grain yield
reduction, followed a linear function, while for susceptible genotypes of winter
wheat a logarithmic relationship was found to fit better (Mathre and Johnston,
1975a). According to greenhouse studies, a logarithmic relationship exists
between disease incidence and inoculum density (Specht and Murray, 1990).
This implies that large reductions in inoculum are necessary in order to affect
the prevalence of the disease. If the amount of naturally infested straw is only
partially reduced by rotation and other measures, its impact on disease
occurrence is likely to be small. Instead the most important determinants of
disease development will be environmental conditions (e.g. temperature,
moisture, soil pH and other epidemiological factors such as root wounding)
(Specht and Murray, 1990).
o Influence of environment on disease development
8
The survival and spread of C. gramineum is greatly determined by its ability to
infest the host plant through the xylem vessels from the base of the plant to
the tip of the rachis, creating a considerable reservoir of the infected material
upon death of the host. Infection of wheat by C. gramineum and the
development of disease are highly influenced by environmental factors (Bruehl
and Lai, 1968; Martin et al., 1989; Pool and Sharp, 1969). Cephalosporium
stripe is most severe in cool, wet soils with low pH (Blank and Murray, 1998).
Several authors reported that higher severity levels are attained in soils with
pH 4.5 to 5.5 than in soils with pH 6.0 or greater (Anderegg and Murray, 1988;
Love and Bruehl, 1987; Murray et al., 1992; Specht and Murray, 1989). The
mechanisms by which soil pH influences disease are unknown. One plausible
explanation is that low pH favors growth, sporulation and survival of C.
gramineum in soil, and, hence, the subsequent development of disease
(Murray, 1988; Murray and Walter, 1991; Specht and Murray, 1989).
Increased survival of C. gramineum in infested wheat straw at low pH has
been attributed to elevated antibiotic production under such conditions (Bruehl
et al., 1972). Murray and Walter (1991) found that sporulation by C.
gramineum on colonized oat kernels and wheat straw was two to three times
greater at pH 4.5 to 5.5 than at pH 6.5 to 7.5. Growth in vitro is also greatest
on media with low pH (Murray, 1988). However, these increases in survival
and sporulation are probably not sufficiently significant to explain the five-fold
or more increase in disease at acidic pH levels (Anderegg and Murray, 1988;
Blank and Murray, 1998; Love and Bruehl, 1987; Specht and Murray, 1990).
9
Interestingly, Blank and Murray (1998) report a lack of a pH effect on spore
germination. It was proposed by Specht and Murray (1990) that acid soil may
favor pathogenicity by promoting increased host susceptibility to root infection,
possibly as a result of greater root stress and damage and/or slower woundhealing in acid than in neutral or alkaline soil. Soil pH has the greatest
influence on Cephalosporium stripe incidence under field conditions in years
when root injury is relatively minor. Disease severity, in contrast to infection, is
not significantly affected by soil pH (Murray et al., 1992; Stiles and Murray,
1996). Instead, severity may be more the result of temperature and rainfall in
the autumn and winter, or cultural practices, as suggested by Murray (1992).
High soil moisture is associated with increased disease incidence in field and
greenhouse studies (Anderegg and Murray, 1988; Bruehl, 1957; Bruehl and
Lai, 1968; Specht and Murray, 1989). It is well documented that
Cephalosporium stripe is greatest in years when autumn weather is cool and
wet and soils have high moisture content (Anderegg and Murray, 1988; Love
and Bruehl, 1987). This condition is often associated with increased soil
heaving, which results from freeze-thaw in soils with high moisture, creating
more wounds for infection. In growth-chamber studies, sporulation of C.
gramineum on artificially colonized oat kernels was significantly greater on wet
soil (Specht and Murray, 1989). Other research on the effect of matric
potential on C. gramineum is not consistent with the increase in
Cephalosporium stripe at high soil moisture content. Murray and Walter (1991)
10
buried colonized oat kernels or straw in soil and found sporulation increased
as matric potential decreased from -0.001 to -0.07 MPa, but significant
sporulation still occurred in nearly saturated soil (-0.001 MPa). Similar results
for germination of conidia were reported by Blank and Murray (1998). A 1.4- to
2.0- fold increase in percent germination of conidia in soil was observed as soil
moisture decreased from -0.007 to -0.064 MPa. Soil matric potential also has
a major effect on conidial longevity, with greatest survival in drier soils. Poor
survival of conidia at -0.03 and -0.01 MPa probably was due partly to the
physical environment associated with very wet soil (i.e. low oxygen
concentration) (Specht and Murray, 1989). Moreover, sporodochia of H.
cerealis are favored by wet, cool weather (Wiese and Ravenscroft, 1975). It
was also suggested that wetter soils may increase host susceptibility (Pool
and Sharp, 1969).
Lower temperatures are conducive to growth and survival of the fungus
(Murray, 1988; Murray and Walter, 1991). It was observed by Murray and
Walter (1991) that sporulation per unit area was greater and hyphal growth
was less at lower temperatures (5°C) than at higher temperatures (20°C),
where hyphal growth was abundant. They found sporulation on oat kernels or
straw in soil was 28 to 50 times greater at 5°C than at 15°C and attributed the
reduced sporulation at the elevated temperature to biological competition.
11
Host genotype may affect the production of inoculum by C. gramineum on
infested residue. It was hypothesized that a decline in incidence of the disease
could result from reduced inoculum production on infested residue originating
from moderately resistant cultivars (Shefelbine and Bockus, 1990). Results on
five winter wheat cultivars confirmed the differential capacity of their diseased
residue to support inoculum production. However, this study also suggested
that disease decline is not exclusively the result of reduced ability of infested
residue to produce inoculum, because infested residue of all cultivars
supported profuse sporulation.
Infection process
Mathre and Johnston (1975b) were the first to study etiology of C. gramineum
as related to the infection process. They hypothesized that inoculum
penetration is passive. They reported that infection may occur when conidia
are “vacuumed” into xylem vessels of freeze-damaged roots. They also
observed that mycelial growth and sporulation were not limited by soil per se,
but by a fungistatic factor operating in natural soil. More data supporting the
hypothesis of passive penetration and the dependence of root damage for
effective infection were given by Morton and Mathre (1980a). They planted
three red winter wheat cultivars with varying levels of resistance to C.
gramineum in environments in which mechanical root breakage did not occur.
Despite artificial inoculation after planting, less than 2% infection was
observed. Under the assumption that soil frost heaving causes root breakage,
12
they concluded that root wounding is necessary for successful pathogenesis
by C. gramineum.
Bailey et al. (1982) questioned the passive infection theory and found that the
epidermis and root cortex did not appear to serve as barriers to colonization.
Freeze-induced root exudates stimulated germination of conidia and mycelium
production of C. gramineum which penetrated the host roots. Little colonization
was found on non-frozen roots, and exudates from cut roots did not stimulate
germination. This work provides evidence that C. gramineum can infect wheat
roots by direct penetration. In agreement with other researchers, it was
concluded that freeze-stress is an important factor predisposing wheat plants
to active penetration and infection. In addition, they suggested that the number
of potential root infections would be much greater for direct than for passive
penetration, due to a higher effectiveness of freeze-induced root exudates in
stimulating active penetration in relation to the number of wounds created by
frost heaving permitting passive ingress of conidia. In the absence of frost
heaving, mechanically broken roots or damage caused by other means such
as root-feeding insects still provide a major avenue of infection for C.
gramineum (Slope and Bardner, 1965; Specht and Murray, 1990). In
greenhouse experiments, however, Anderegg and Murray (1988)
demonstrated that root breakage is not a prerequisite for disease development
and that severe disease can occur without soil freezing under conditions of low
pH and high soil moisture.
13
No precise evidence supporting the proposed hypothesis of infection process
was presented in the literature until Douhan and Murray (2001) embarked in a
complex study to elucidate the timing and mode of penetration of winter wheat
by C. gramineum under field conditions and also to determine whether
differences existed among resistant and susceptible cultivars for these
processes. To visually determine how C. gramineum infects and colonizes
wheat, a strain of the fungus was transformed with the β-glucuronidase (GUS)
reporter gene and used as the only isolate in the experiment. The GUStransformed isolate of C. gramineum behaved normally with respect to
pathogenicity, physiological growth and reproduction.
An important finding of Douhan and Murray (2001) is that C. gramineum
colonized vascular and nonvascular stem and root tissues of field grown plants
well before soil freezing occurred, providing additional evidence that soil
freeze-thaw cycles are not required for infection. Colonization of viable root
epidermis and cortical cells occurred as soon as 15 days post-inoculation and
the pathogen was found in the vascular tissues by 20 days post-inoculation,
well before freezing soil temperatures occurred. The study also revealed that
the pathogen can penetrate and colonize stems directly through the epidermis
of leaf sheaths or wounds where tillers emerge. Other observations support
earlier conclusions that adventitious roots are the most important infection
courts of the root system. The pathogen also penetrated through root cap cells
14
and colonized meristematic tissues near root tips to gain access to the
vascular system. Appressorium-like structures, which appeared to aid
penetration of cell walls, were often found within cells of both roots and stems
after initial colonization. The mechanisms of resistance were not apparent, but
there are indications that resistance to Cephalosporium stripe may be a
function of the ability of the host to retard colonization of the crown. Less
colonization occurred in resistant than in susceptible cultivars.
Pathogen variability
Understanding genetic variability of a pathogen is important to determine the
most effective strategy for breeding host resistance. First, it helps understand
some aspects of the population structure of the pathogen. Second, it would
improve the usefulness of resistance screening procedures, since the number
of isolates used for screening should depend on the variation among them.
Third, better predictions about the durability of host resistance might be made,
since higher levels of variation in the pathogen offers greater evolutionary
potential to overcome resistance genes (Keller et al., 2000; Stuthman et al.,
2007).
Little is known about the extent of genetic polymorphism in C. gramineum.
Mathre et al. (1977) determined the pathogenicity of 25 isolates of C.
gramineum from various areas in North America on winter wheat. Most of the
isolates (18 out of 25) were highly virulent, causing yield reductions of 50% or
15
more in susceptible wheat cultivars. A few isolates from Montana and New
York were weakly virulent. Yields were reduced because seeds per head and
especially kernel weight were reduced, but not because number of heads was
reduced. Differences in virulence were also expressed in grain and flour
quality tests, with increased virulence resulting in an overall decrease in
quality. Following these findings, Van Wert et al. (1984) tested six isolates of
C. gramineum on 15 winter wheat lines and noted apparently different
virulence patterns produced by two wild-type Michigan isolates, suggesting
that races of C. gramineum may exist.
Cowger and Mundt (1998) conducted two growth-chamber experiments to
assess whether isolates from different regions would rank cultivars differently.
Winter wheat cultivars from the U.S. Southern Plains and the Pacific
Northwest were inoculated with isolates from both regions in a complete
factorial design. No significant cultivar X inoculum-source interactions were
found, suggesting low variability in virulence among the limited number of
isolates surveyed. It was concluded that these initial results provided no
evidence of substantial pathogenic variability.
Two aspects of the life cycle of C. gramineum suggest limited potential for
variability. First, C. gramineum has no known sexual stage. It is, therefore,
likely to be highly clonal with a limited spectrum of virulence within each
lineage (Anderson and Kohn, 1995). Second, C. gramineum is a facultative
16
parasite, and encounters living hosts only in alternate years in regions where
summer fallow wheat is grown. Facultative parasites may develop less
pathogenic variability than obligate parasites (Ellingboe, 1983; Parlevliet,
2002).
Yield and quality loss
Cephalosporium stripe is a potentially devastating disease and may result in
loss of both grain yield and grain quality. In areas conducive to
Cephalosporium stripe, up to 80% yield reduction from a generalized infection
on a susceptible cultivar is common (Bockus et al., 1994; Johnston and
Mathre, 1972; Mathre et al., 1977; Morton and Mathre, 1980a; Richardson and
Rennie, 1970). Early reports indicate that yield loss in naturally infected plants
in the field was observed to be as high as 78% and that the grain from such
plants was small and shriveled (Slope and Bardner, 1965). Another
investigation found that infected plants of the susceptible cultivar Cappelle
Desprez yielded only 30% that of healthy plants. The loss of yield was due in
part to a smaller number of fertile florets and a smaller seed size (Richardson
and Rennie, 1970). The authors stressed that the actual yield of infected
plants might even be lower due to losses that occur during seed processing.
Johnston and Mathre (1972) found that plants with Cephalosporium stripe
were stunted, had reduced yield, and had higher levels of protein than healthy
plants in greenhouse experiments. The yield components most affected by the
17
disease were number and weight of seeds per head. Number of heads per
plant is generally not affected by this pathogen. Loss of kernel weight
intensifies overall yield loss since many of the light kernels would be expelled
from the combine during harvest. Kernel shriveling alters the carbohydrate to
protein ratio and results in a relative increase in percent protein. Similar results
were obtained by Morton and Mathre (1980a) with field experiments. Spikelet
number was unaltered by disease, while seed number was reduced and
thousand kernel weight was sharply reduced in susceptible cultivars. Based on
the yield components most affected, the authors concluded that the effects of
pathogenesis are not pronounced until after anthesis and during grain filling.
Martin et al. (1989) found that yield loss in Marias or Redwin (susceptible
cultivars) ranged from 82% to 0% in different years with a uniform inoculum
source. The environmental conditions which were most related to the largest
yield reduction were a warm fall followed by a cool spring, with numerous
freeze-thaw cycles. Similar year effects were observed by Bockus et al.
(1994).
Mathre et al. (1977) determined the effect of C. gramineum on grain and flour
quality of four lines with varying levels of resistance. Yield components were
affected as expected based on previous research. Grain and flour quality
characteristics were determined on a plot basis and included traits like test
weight, flour yield, protein content and physical dough properties. When
quality means of all cultivars were averaged, changes induced by the
18
pathogen were noted in 7 of the 13 quality traits considered. Overall, quality
deteriorated as a consequence of the incidence of Cephalosporium stripe.
Evidence was given by reductions in test weight, flour yield and dough water
absorption, as well as increases in flour ash, dough strength (farinograph peak
time), farinographic stability time and volume. A trend toward increased wheat
grain protein was noted, but it was not significant. However, these effects were
not great enough to significantly affect baking parameters (loaf volume or grain
and texture characteristics).
Cultural methods of control
Cephalosporium stripe cannot be controlled with fungicides. However, this
disease can be partially controlled by cultural practices such as adequate crop
rotations, residue management, adjusting planting date, altering soil pH with
lime applications, and fertilizer management (Bockus et al., 1983; Latin et al.,
1982; Martin et al., 1989; Mathre and Johnston, 1975b; Murray et al., 1992;
Pool and Sharp, 1969; Raymond and Bockus, 1984).
Winter wheat suffers only minor damage when grown in rotations with spring
cereals, with non-host crops such as legumes or corn, or with a weed free
fallow. In areas prone to Cephalosporium stripe it is strongly recommended to
rotate out of winter wheat for at least two years. In a study in the Palouse area,
rotations that had winter wheat every third year resulted in disease incidence
of less than 10%, and in most cases less than 3% (Latin et al., 1982).
19
As with crop rotation, destruction of infested residue effectively reduces
inoculum density and, therefore, disease incidence. In Kansas a three year
field experiment was conducted to compare the effect of five different wheat
residue management practices on the incidence of Cephalosporium stripe
(Bockus et al., 1983). Burning wheat stubble was the most effective method,
followed by deep plowing, to minimize disease incidence after a severe
outbreak under a continuous winter wheat production regime. However,
Cephalosporium stripe incidence after three consecutive years of moldboard
plowing (3.6%) was the same as after three years of burning (3.0%). This is
consistent with previous research, which found reduced incidence of
Cephalosporium stripe after conventional plowing (Bockus et al., 1983; Latin et
al., 1982; Pool and Sharp, 1969; Wiese and Ravenscroft, 1975). Severe
disease incidence in no-till cropping systems was also consistently reported
(Bockus et al., 1983; Latin et al., 1982). Therefore, residue management
practices applied to control Cephalosporium stripe should destroy, remove or
reduce the amount of straw left in the top layer of soil to limit disease
incidence during the next cropping season. These recommended practices,
however, conflict with current soil conservation practices.
Delayed fall planting also has been recommended for Cephalosporium stripe
control (Bruehl, 1968; Mathre and Johnston, 1975b; Pool and Sharp, 1969;
Raymond and Bockus, 1984). It results in plants with smaller root systems that
20
are less susceptible to winter root injury, and therefore less likely to be
infected. Mathre and Johnston (1975a), reported that early fall seeding in
Montana increases the number of infected tillers and white head counts under
natural conditions, verifying earlier findings by Pool and Sharp (1969) and
Bruehl (1968). In a two-year trial in Kansas, Raymond and Bockus (1984)
found a significant reduction in Cephalosporium stripe incidence due to late
planting in the first year but not in the second. Furthermore, results from the
study showed a 13.7% yield reduction for non-inoculated plots with every
week of delay past the optimum planting date. For best economic return,
careful crop management decisions must consider these and other factors that
might also limit crop productivity. In the Pacific Northwest delayed seeding and
residue burning are discouraged because of the increased potential for soil
erosion.
Liming can reduce disease severity by adjusting soil pH. Murray et al.(1992)
conducted a study for four consecutive years in Washington to determine
disease response to changes in soil pH in the field. The incidence of
Cephalosporium stripe was reduced significantly by liming to raise soil pH in
two of four years. Grain yield and test weight increased after liming in three of
the four years. However, the rates of lime necessary to raise pH levels from
around 5.0 to 6.5 or 7.5 in the study were 5,080 and 12,000 kg/ha
respectively, making this practice economically unfeasible to control
Cephalosporium stripe.
21
Pool and Sharp (1969) found that fall fertilization increased disease incidence,
due to an increase in root length which probably increases the number of
potential infection sites. The general effect of fall fertilization was to increase
infection by C. gramineum regardless of planting dates, decrease yield in the
early plantings, and increase yield in the later plantings.
Cultural practices are not always dependable because they rely upon
favorable climatic conditions for successful implementation and are influenced
by economic factors. The most effective and desirable control method would
be planting of resistant cultivars (Morton and Mathre, 1980b).
Disease resistance
Gene-for-gene interactions
One of the basic concepts of host resistance was elucidated by H. H. Flor
(1955) studying the genetics of resistance in the flax - flax rust pathosystem.
He was the first plant pathologist to study the inheritance of resistance in the
host and virulence in the pathogen simultaneously. Based on his results he
proposed the gene-for-gene hypothesis, in which successful infection depends
on genetic factors in both the host and the pathogen. In the gene-for-gene
model, resistance (incompatibility) is a recognition process between an active
gene product from the avirulent pathogen (elicitor of plant defense, avr genes)
and an active gene product from the resistant host (receptor associated with R
22
genes). At infection the elicitor is specifically recognized by the corresponding
resistance genes’ receptor, inducing a complex series of events, including
rapid and localized host cell death (programmed cell death), that leads to
resistance, the hypersensitive reaction. It is this elicitor-receptor interaction
that gives many host-pathogen interactions their genetic specificity (Bent and
Mackey, 2007; Jones and Dangl, 2006).
Dominant genes for resistance in the host often provide complete or nearly
complete resistance against races of the pathogen that have a dominant
avirulence gene that matches the host resistance gene but not against
pathogen races lacking the matching avirulence gene. It can be generalized,
thus, that for each gene for race-specific resistance in the host, there is a
corresponding gene for avirulence in the avirulent races of pathogen. Also, in
a typical gene-for-gene relationship, every gene for race-specific resistance
can be overcome by a virulence gene that is either present or potentially
present in some members of the pathogen population (Stuthman et al., 2007).
Any mutation in the avirulence gene leading to non-recognition by the
corresponding resistance gene (loss mutation) restores pathogenicity because
of failure to elicit the incompatible reaction. Pathogens can sometimes afford
the loss of many avirulences without loss of fitness (Parlevliet, 2002).
Gene-for-gene relationships are usually found with pathogens that are highly
specialized biotrophic or hemibiotrophic parasites which reproduce poorly or
23
not at all outside living hosts, and the resistance against avirulent races is
usually highly effective. However, this type of resistance is typically not
durable. Examples of crop-pathogen systems with many race-specific, nondurable resistance genes and many pathogen races known include the rust
and powdery mildew diseases of cereals, potato late blight, rice blast and
some bacterial diseases (Parlevliet, 2002; Stuthman et al., 2007). There are
exceptions, where host resistance is more durable. These pathogens have
host ranges varying from fairly narrow to fairly wide, they present few or no
races and only a few race-specific resistance genes are known. The vascular
wilt fungi, apple scab and many viruses belong to this group (Parlevliet, 2002).
Systems of race-specific resistance and virulence are generally characterized
by the combination of qualitatively inherited resistance in the host that can be
overcome completely, or nearly completely, by qualitatively inherited virulence
in the pathogen (Stuthman et al., 2007).
When host-selective toxins are responsible for pathogenesis, the interaction
between these and their hosts has been described as a “gene-for-gene II” or
as an “inverse gene-for-gene” model, where sensitivity or susceptibility is
usually conferred by a single dominant gene (Wolpert et al., 2002).
Qualitative vs. quantitative resistance
Disease resistance terminology is controversial, and many attempts have
been made to attain consensus within the scientific community. The list of
24
resistance-related terms is long, but following Van der Plank (1984), can still
be divided into two groups. The first group includes terms like race-specific,
major gene, monogenic, vertical and ‘R-gene’, and refers to the type of
resistance that is often highly effective against avirulent races, is controlled by
one gene (or a few), fits the gene-for-gene model and triggers the
hypersensitive reaction. This group is frequently referred to as qualitative
resistance, because resistance reactions (phenotypes) can be placed in
distinct categories (+ or -, or all or nothing). Qualitative resistance genes
behave in a simple Mendelian fashion and usually have a large effect on
resistance against specific races of the pathogen (Keller et al., 2000;
Parlevliet, 1979; Schumann and D'Arcy, 2006).
The terms belonging to the second group are non-race specific, minor gene,
polygenic, horizontal and field resistance, as clear opposites of the terms in
the first group. This type of resistance is characterized as being controlled by
multiple genes, is partially effective against all races, activates a broad concert
of defense responses in the host and is usually associated with durability (Van
der Plank, 1984). This type of resistance is also called “quantitative” because it
shows continuous variation and more complex inheritance. Progeny of crosses
between resistant and susceptible individuals show a continuous range of
phenotypes. This type of resistance tends to be comprised of multiple, minor
genes, with one often accounting for a larger portion of the resistance, while
each of the other genes is contributing smaller additive effect to the overall
25
resistance (Parlevliet, 1989; Schumann and D'Arcy, 2006; Stuthman et al.,
2007). Quantitative resistance is present to nearly all pathogens at low to
moderate levels in most commercial cultivars (Parlevliet, 2002). A defining
feature of qualitative resistance is that the ranking of cultivars from least to
most resistant depends on the pathogen genotype (race) used. In contrast, a
host cultivar with quantitative resistance shows the same relative level of
resistance to all pathogen isolates.
From an epidemiological stand point, resistance can be viewed as vertical or
horizontal (Van der Plank, 1984). Where vertical resistance reduces initial
inoculum in an epidemic by screening out avirulent races in the pathogen
population, yet it has no effect on the rate of increase of the virulent races.
Horizontal resistance, on the other hand, reduces the rate of pathogen
increase by being at least partially effective against all races. Horizontal
resistance is responsible for keeping most minor plant diseases at low levels
and controlling many major ones well enough (Knott, 1988).
Durable resistance is often equated with quantitative resistance, which is
based upon the additive effects of some to several genes and the resulting
resistance being of another nature than the hypersensitive reaction.
Resistance to specialized fungi and bacteria often presents durable and
quantitative effects, in addition to the characteristic monogenic resistance
which elicits the hypersensitive reaction. Monogenic resistance that is durable
26
is also frequent and is usually of a non-hypersensitive reaction type.
Resistances against viruses have also been reported to be durable, even
though these are based on monogenic, race-specific resistances (Parlevliet,
2002; Parlevliet, 1993; Simmonds, 1991).
Simmonds (1988) recognized four general kinds of disease resistance that are
broadly applicable to all classes of pathogens (virus, bacteria, fungi and
animal): (1) vertical resistance as defined by Van der Plank, based on major
genes and race-specificity, (2) race non-specific major gene resistance
(monogenic resistance lacking race specificity), (3) horizontal polygenic
resistance (race-nonspecific), and (4) interaction resistance, which relies on
the use of mixed populations of host lines with different genes for vertical
resistance to avoid rapid selection of races virulent to any single resistance
gene in the mixture. The main features of the four broad kinds of resistance
are summarized in Table 1 (adapted from Simmonds 1988). A more detailed
discussion is presented in the original publication.
27
Table 1.1. Features of the four main kinds of resistance
Kind of resistance
1. Race-specific or
vertical resistance
Specificity
Very high
Genetics
Oligogenic
2. Race-nonspecific
major gene resistance
3. Horizontal
resistance
4. Interaction or
mixture resistance
Nil
Oligogenic
Nil/low
Polygenic
Some
Heterogeneous
oligogenic
Durability
(1) mobile pathogens:
durability usually poor
(2) immobile
pathogens: durability
may be good
Good but is rather rare
or specialized
High
Probably good
Simmonds (1988) reinforced the concept that, in all plant breeding programs
dealing with disease resistance, the general objective must be to provide
adequate levels of resistance that are reliable over years. If varieties are
expected to remain in cultivation for many years, the need for durability is
implied. In such situations the breeding strategy is clear and points to the use
of horizontal resistance or interaction resistance. When immobile pathogens
are of concern, then a certain degree of durability can be achieved with
vertical resistance. However for mobile pathogens (i.e. cereal rusts and
powdery mildew), this type of resistance only offers short term solutions, and
the risks involved in using race-specific resistance are well known (Johnson,
1981; Van der Plank, 1984). For annual crops, vertical resistance is in general
a risky choice, if durability is the objective. There is evidence that pathogen
virulence can evolve more quickly than plant breeders can incorporate single
resistance genes into new varieties (Parlevliet, 1977).
28
The main limitation for the use of quantitative resistance is that it requires
extensive and accurate field testing, which makes it more difficult to select for
in plant breeding programs. Other strategies have been developed and
proposed to overcome the risk of rapid break down of major gene resistance
or for cases where horizontal resistance is not an option. Cultivar mixtures
and multilines are two examples of these strategies that have been studied
and used extensively (Allan et al., 1993; Allan et al., 1983; Dileone and Mundt,
1994; Mundt, 2002a; Mundt, 2002b; Wolfe, 1985).
Pyramiding genes for race-specific resistance has been a common method as
a way to increase the likelihood of resistance durability. One potential
mechanism of durability provided by pyramids is that the probability of a
simultaneous mutation affecting all avirulence genes in the pathogen to
overcome the pyramided resistance genes in the host is greatly reduced
(Chen et al., 2008; Huang et al., 1997; Mundt, 1990; Mundt, 1991; Singh et al.,
2001).
Castro et al. (2003a; 2003b) pyramided three quantitative resistance genes for
barley stripe rust in one common genetic background and confirmed their
effects in reducing disease severity. However, the impact on durability is still
unknown. Following the same logic, qualitative and quantitative resistance
genes against barley stripe rust were combined in a susceptible genotype.
This strategy is based on the hypothesis that in the case of failure of the
29
qualitative resistance gene due to a new race, the quantitative resistances
present will act as a resistance backup. The results from Castro et al. (2003c)
and Richardson et al. (2006) indicate that the quantitative resistance genes
were effective in decreasing disease severity in the absence of the major
resistance allele in the new genetic background and that such a combination
confers potentially higher and more durable levels of resistance.
Development of techniques to genetically transform plants has opened new
possibilities for crop protection. Transgenic crop acreage has increased rapidly
world-wide, driven primarily by cotton, corn and soybeans. The 2007 global
acreage planted with genetically modified crops was 114.3 million hectares in
23 countries (http://www.isaaa.org/). Herbicide tolerance and insect resistance
are the dominating traits. Commercial releases of cultivars transgenic for
resistance to diseases are limited to a relatively few with transgenic resistance
to viruses. Virus resistance has been obtained by transforming plants with coat
protein genes from the virus itself. The mechanism responsible for the
resistance in these plants is posttranscriptional gene silencing. Other
approaches are to add genes for additional receptors that can recognize
pathogen invasion more efficiently or to enhance the passive or active
defenses already present in the plant (Cornelissen and Schram, 2000;
Schumann and D'Arcy, 2006). Allele mining and ecotilling are examples of
new strategies being used in the context of molecular characterization of
wheat disease resistance. Allele mining was followed to study the occurrence
30
of known resistance alleles at the wheat Pm3 powdery mildew resistance
locus in a set of 1320 landraces. The potential and reach of the analysis was
demonstrated with the identification of a group of resistant lines where new
functional alleles could be present (Kaur et al., 2008).
Resistance to Cephalosporium gramineum in wheat
Management of soil-borne pathogens has relied on reducing inoculum in the
soil via cultural controls such as crop rotation and management of crop
residues (already discussed), and application of fungicides; however, these
practices are only partially effective in reducing the incidence and severity of
disease (Li et al., 2008). Resistant cultivars are generally considered to be a
cost-effective method of reducing yield losses caused by biotic pests in
general and of soil-borne diseases in particular (Schumann and D'Arcy, 2006).
The development of host resistance currently offers the best hope for control
of Cephalosporium stripe in the Pacific Northwest.
The identification of genetic variation in wheat for reaction to C. gramineum by
Bruehl (1957) lead to efforts to find a reliable source of resistance that can be
broadly used in breeding programs. He identified four resistant cultivars by
using hypodermic inoculations of a liquid conidial suspension into wheat culms
above the crown. These later proved to be susceptible in the field under
conditions of natural infection (Rivera and Bruehl, 1963). Expression of
resistance is occasionally incomplete and environmentally dependent (Martin
31
et al., 1986). Artificial inoculation techniques were generally inadequate and
inconsistent until the development of C. gramineum inoculum on oat kernels.
A measured quantity of sterile oat kernels infested with C. gramineum was
added with the seed at planting (Mathre and Johnston, 1975a). Using this
inoculation technique over 1000 hard red winter wheat cultivars from the major
winter wheat growing areas of the world were screened for resistance in the
field (Mathre et al., 1977). Although results revealed that most lines were
highly susceptible, 29 cultivars showing either a low infection percentage or
restricted symptom development in infected plants were selected for further
characterization. Four lines with highest tolerance to disease in terms of least
reduction in yield, kernel weight, and kernels per head were considered to be
useful to include in breeding programs. None of the tested lines were immune
(Martin et al., 1983; Mathre et al., 1985).
Seven hard red winter wheat cultivars varying in their susceptibility to
Cephalosporium stripe were used in a study to identify the types of resistance
exhibited by resistant cultivars (Morton and Mathre, 1980b). Expression of
resistance to Cephalosporium stripe was scored 30 days after heading
according to the number of symptomatic tillers per row and the number of
symptomatic tillers per plant. Two types of resistance were observed:
exclusion of the pathogen, expressed as a reduction in the percentage of
diseased plants, and restriction of spread of the pathogen after successful
colonization of the host expressed as a reduction in the percentage of
32
diseased tillers per infected plant and also as a reduced rate and severity of
symptom development. The latter is caused by restricted pathogen movement
in the plant. Both types of resistance were expressed independently, leading
the authors to conclude that maximum resistance would be attained if both
types of resistance were incorporated into a single genotype.
Although variation in the degree of resistance among cultivars has been
confirmed, complete resistance to C. gramineum has not been found (Bruehl
et al., 1986; Martin et al., 1983; Mathre et al., 1977; Morton and Mathre,
1980b). Whereas high levels of resistance do not occur within T. aestivum,
such resistance does occur in wheat relatives such as tall wheatgrass,
Thinopyrum ponticum (Podp.) Barkworth and D. R. Dewey [syn. Agropyron
elongatum (Host) Beauvois, Elytrigia elongata (Host) Nevski] and
intermediate wheatgrass, Th. intermedium (Host) Barkworth and D. R. Dewey
[syn. A. intermedium (Host) Beauvois, E. intermedia (Host) Nevski] (Cox et al.,
2002; Mathre et al., 1985). In greenhouse tests and with artificial inoculation
several accessions of these species have shown an extremely high level of
resistance to C. gramineum. Perennial wheat lines derived from crosses
between wheat and these wheatgrass species also proved to have high to
moderate levels of resistance to the disease (Cox et al., 2002). Further
investigation with related wheat breeding lines, with special interest in the
characterization of the Th. ponticum chromatin conferring resistance to
Cephalosporium stripe, revealed that the Th. Ponticum chromosome, 6Ae#2,
33
was responsible for conferring resistance to the disease in a cultivated wheat
background (Cai et al., 1996). Results also indicate that this chromosome
showed close homoeology with wheat chromosome 6A.
Research to elucidate the mechanisms of resistance and (or) tolerance to this
pathogen indicate that factors inherent to roots, particularly crown tissue of
winter wheat plants and its wild relatives, might be directly associated with
disease response. The difference between the highly resistant wheat relatives
and wheat was in the movement of C. gramineum through the transition zone
from roots into the culm tissues. Movement of the pathogen in roots also
showed differences, though these were not as distinct. Coincidently, in other
vascular wilt diseases, the transition zone between root and above ground
tissue is also the site of resistance. It was also suggested that a differential
wound healing rate after root damage occurs could be partially responsible for
resistance (Mathre and Johnston, 1975b; Mathre and Johnston, 1990; Morton
and Mathre, 1980b).
Shefelbine and Bockus (1989) looked at the impact of monoculture of winter
wheat cultivars with varying levels of resistance on the intensity and progress
rate of Cephalosporium stripe. After the three year experiment, it was
concluded that repeated planting of moderately resistant cultivars can reduce
both the incidence and severity of Cephalosporium stripe over years.
34
Furthermore, the authors suggested that levels of resistance available were
adequate to reduce inoculum overtime and control disease in the long term.
Molecular markers and marker assisted selection
Recent advances in biotechnology have resulted in increased understanding
and characterization of various genes at the molecular level that are
associated with traits of biological and economic importance (William et al.,
2005). The discipline of plant breeding has been adopting some of these new
approaches to develop improved cultivars and increase crop yields, enabled
by an increased efficiency to generate accurate and unique phenotypes and
genotypes in well-characterized environments (Landjeva et al., 2007). The
central technology involved in these novel genetic tools is molecular markerassisted selection (MAS), using sequences and/or banding patterns of DNA
that have been shown through linkage mapping to be located in or near genes
that affect the phenotype (Edwards and McCouch, 2007). The availability of
molecular markers linked to specific resistance genes and of information on
their genetic location has supported resistance breeding by simplifying the
detection of specific genes in parental and segregating material. The selection
process is enhanced by making it faster and more cost effective. In addition,
genes for resistance can be pyramided in one common genetic background
(Feuillet and Keller, 2005).
Molecular markers
35
Broadly defined, a genetic marker is a polymorphism between two plant lines
that is inherited in a simple Mendelian way in the progeny of a cross between
these lines. The use of polymorphisms as genetic markers started with plant
morphology traits and biochemical markers (isozymes), but it was not until the
use of DNA sequences that it reached full potential (Keller et al., 2000). DNA
markers are the most widely used type of marker predominantly due to their
abundance. Molecular markers are powerful tools for identifying quantitative
traits and dissecting these complex traits into Mendelian factors in the form of
quantitative trait loci (QTL) as well as for establishing the genomic locations of
such genetic loci (Bernardo, 2008; Collard et al., 2005; Lörz and Wenzel,
2005).
Restriction fragment length polymorphism (RFLP) was the first type of
molecular markers applied. These are based on hybridization with probes from
DNA libraries, where polymorphic DNA fragments are detected after genomic
DNA is digested with a restriction enzyme. RFLP markers behave as
Mendelian genes and are inherited codominantly. They are robust, reliable
and can be transferred across different populations as they may serve as
anchor loci for genetic maps. RFLPs require a large amount of DNA and are
time-consuming, laborious, and expensive, thus making them unsuitable for
high-throughput analyses (Landjeva et al., 2007; Tanksley et al., 1989).
However, RFLP markers are uniquely appropriate since they detect
homoeologous loci. RFLP markers have been found to be linked with disease
36
resistance genes in many species such as lettuce, flax, potato, tomato, maize,
rice, wheat and barley (Keller et al., 2000). In wheat, the application of RFLP
markers has been limited due to low levels of polymorphisms and inability to
detect single-base mutations (Landjeva et al., 2007). However, there are some
cases in which RFLPs have played an essential role in marker-assisted
selection, genome mapping, and the characterization and isolation of various
disease resistance genes in wheat (Feuillet et al., 2003; Lagudah et al., 2006).
The development of markers based on the polymerase chain reaction (PCR)
accelerated the development and use of molecular markers. The detection of
PCR markers is faster than with the more complex RFLPs. In addition, the
important steps of the reaction and also of detection can be automated, saving
costs and increasing the number of samples that can be analyzed (Keller et
al., 2000).
The first type of PCR-based marker described was random amplified
polymorphic DNAs (RAPD) (Williams et al., 1990). In this application, the PCR
reaction amplifies DNA fragments from a genomic template between short
arbitrary oligonucleotide primers. They allow the detection of polymorphisms
resulting from insertions, deletions and single base changes that alter the
binding sequence. RAPD markers are inherited as dominant genes, and
therefore do not detect heterozygous loci. Although this class of markers is
simple, quick and inexpensive to implement, and multiple loci can be detected
37
from a single primer, its reproducibility is not high and results are generally not
transferable between different laboratories. The application of RAPD markers
in breeding programs has been limited (Collard et al., 2005; Keller et al., 2000;
Landjeva et al., 2007). However, in combination with Bulked Segregant
Analysis they have been used successfully to detect markers linked to
resistance genes or other traits of interest. In wheat, RAPD markers were
found linked to the leaf rust resistance genes Lr9 and Lr24 (Schachermayr et
al., 1994; Schachermayr et al., 1995).
An alternative to overcome some of the problems mentioned above was to
convert RFLP and RAPD markers into sequence characterized amplified
regions (SCAR) or sequence-tagged sited (STS) by cloning, sequencing and
designing specific primers to amplify the new marker. SCAR markers are
robust and reliable. They detect single loci and are codominant (Paran and
Michelmore, 1993). These PCR-based markers are technically simpler, less
time-consuming, and cheaper. STS markers have been highly useful tools for
the screening of natural genetic variation as well as for tagging of resistance
genes and QTL in several crops including wheat (Lagudah et al., 2006; Liu et
al., 2002). Numerous reports on the successful development of SCAR and
STS markers, in particular in relation to disease resistance genes, can be
found in the literature [for a detailed list of references see Keller et al. (2000)
and Landjeva et al. (2007)].
38
Cleaved amplified polymorphic sequence (CAPS) is another class of
converted markers that is codominant and simplifies the screening of large
numbers of individuals. In this case the target DNA fragment is amplified by
PCR and the product is digested with an appropriate restriction enzyme.
Polymorphisms are visualized as band size differences on agarose gel.
Helguera et al. (2000) developed a CAPS marker for the wheat leaf rust
resistance gene Lr47 from an RFLP allele on chromosome 7A, to facilitate the
transfer of this resistance gene into elite cultivars. The time-consuming post
PCR digestion step limits its application for small-scale experiments (Landjeva
et al., 2007; Mohler and Schwarz, 2005).
Further development of marker techniques gave rise to amplified fragment
length polymorphism (Vos et al., 1995), which combines the robustness of
RFLPs and the simplicity of RAPDs. This method is based on the selective
PCR amplification of restriction fragments from genomic DNA, after a double
digestion process with a pair of restriction enzymes (rare and frequent cutter).
Prior knowledge of nucleotide sequence is not needed. High resolution
electrophoresis systems are necessary to separate AFLP products. Due to its
capacity to reveal many polymorphic bands in a single reaction, the AFLP
technique has been used with plant DNA for the construction of whole genome
linkage maps, and to investigate biodiversity in several crops [refer to Mohler
and Schwarz (2005) for a list of references]. In high throughput, AFLPs can be
detected with an automated DNA sequencer by using fluorescently labeled
39
primers. This makes the process very fast, but also expensive (Edwards and
McCouch, 2007). The markers are reliable and reproducible, but generally the
inheritance is dominant which represents a considerable restriction for the
application of AFLP markers in mapping studies (Landjeva et al., 2007). In
wheat, however, diagnostic markers for different resistance genes have been
developed using this technique (Adhikari et al., 2003; William et al., 2003).
Tables 1, 2 and 3, in pages 355-359, in Feuillet and Keller (2005) provide a
detailed listing of markers developed for different monogenic and quantitative
traits with their corresponding location, donor source, marker type and
reference. Conversion of AFLP to SCAR markers is feasible, reducing their
cost and improving their usefulness in marker-assisted selection (Landjeva et
al., 2007).
Microsatellites, also known as simple sequence repeats (SSR) are tandem
repeats of short nucleotide sequence motifs (di-, tri-, tetra-nucleotide units).
They are abundant and a common feature of most eukaryotic genomes,
however, their genomic distribution may vary. SSR loci are extremely variable
in the number of repeat units among individuals of a given species, which
results in PCR products of varying lengths. The flanking sequences at each
SSR locus are highly conserved and are used to design the specific primers.
Polymorphism is revealed as fragments of different molecular weight are
separated by electrophoresis on polyacrylamide gels or using capillary DNA
sequencers that provide sufficient resolution. SSR markers are highly variable,
40
they are codominant, multiallelic and stably inherited, and they are amenable
for automation and high throughput analysis. SSRs discovered in non-coding
sequence often have a higher rate of polymorphism than SSRs discovered in
genes. The results are reproducible and the markers are easily shared among
researchers simply by distributing primer sequences. With their high level of
allelic diversity, microsatellites are valuable as molecular markers, particularly
for studies of closely related individuals (Edwards and McCouch, 2007). For
most agriculturally important crops, a huge number of SSR markers are
already publicly available for research. For a summarized list of references
see page 25 in Mohler and Schwarz (2005). Microsatellite markers are ideally
suited for genetic mapping of genes of agronomic interest in segregating
populations.
Microsatellite markers are highly polymorphic in wheat and are now widely
used in wheat genetics for diversity studies, genotype identification (Prasad et
al., 2000), marker-assisted selection, mapping, identification and tagging of
disease resistance genes (Adhikari et al., 2003; Chague et al., 1999; Liu et al.,
2002; Miranda et al., 2006). A special advantage in wheat is the genome
specificity of most microsatellite markers, which allows the analysis of the
three homoeologous genomes individually. In addition, microsatellite markers
can be easily transferred between wheat mapping populations, since they
identify a specific locus in various genetic backgrounds (Röder et al., 2005).
41
SSR markers were developed for a number of important major genes and
associated with many QTL. References are compiled in Landjeva et al. (2007)
and Röder et al. (2005) table 2, page 258. Recently, Tar et al. (2008) reported
two SSR markers linked to the leaf rust resistance gene Lr52. While Santra et
al. (2008) studied the high temperature adult plant (HTAP) resistance to stripe
rust in the cultivar ‘Stephens’ and found two major QTL on chromosome 6B
and reported the presence of linked SSR markers to each QTL. Validation
tests confirmed these findings and markers were suggested for use in markerassisted breeding programs to efficiently incorporate HTAP resistance into
new wheat cultivars. One major pre-harvest sprouting resistance QTL was
reported by Liu et al. (2008) that explained up to 41.0% of the total phenotypic
variation in three greenhouse experiments. A population of 171 recombinant
inbred lines was used and polymorphic SSR markers were detected through
bulked segregant analysis. Microsatellite markers linked to the QTL have
potential for use in high-throughput marker-assisted selection of wheat
cultivars with improved pre-harvest sprouting resistance.
Single base changes in the DNA sequence, called single nucleotide
polymorphisms (SNPs), are an abundant source of variation in plant and
animal genomes. This molecular marker system is based on direct analysis of
sequence variation and is considered the most robust form of analyzing
genomic variation (Xu and Crouch, 2008). Due to the high density of SNPs
across the genome there is a high probability of finding a marker within the
42
gene of interest (Syvänen, 2005). This constitutes an important advance for
MAS programs because it can minimize the loss of associations between trait
and marker due to recombination. The di-allelic character and stable
inheritance make SNPs amenable to automated, high-throughput genotyping
and, therefore, a powerful tool for QTL mapping studies and marker-assisted
selection in plant breeding programs (Mohler and Schwarz, 2005). In addition,
rapid advances in SNP marker detection technologies are reducing associated
costs (Bai et al., 2007). In wheat, a number of SNP-based markers were
identified for gamma-gliadins (Zhang et al., 2003), the waxy starch gene Wx-D
(Yanagisawa et al., 2003), Lr1 (Tyrka et al., 2004), Glu-D3 (Zhao et al., 2007)
and a karnal bunt resistance QTL (Brooks et al., 2006). The capabilities and
challenges related to the application of SNP markers in plant molecular
breeding are extensively reviewed by Gupta et al. (2001).
Gene-derived functional markers (FM) offer great potential for marker-assisted
breeding in crops, since they are derived from polymorphisms within genes
that affect phenotypic trait variation. Their utility in wheat remains restricted
due to limited availability of genes that control agronomic characters and the
high level of linkage disequilibrium found in elite materials (Bagge et al., 2007).
Some of the most recent developments in molecular techniques combine QTL
mapping with methods in functional genomics to study gene expression.
These techniques include expressed sequence tag (EST) and microarray
43
analysis, which can be utilized to develop markers from genes themselves
(Gupta et al., 2001; Morgante and Salamini, 2003). EST markers represent
only expressed genes. An EST is a DNA segment, representing the sequence
from a cDNA clone that corresponds to a messenger RNA molecule or a part
of it (Gupta et al., 1999). The wheat EST collection is an enormous resource
for genome analysis as the number is increasing constantly and in 2008 they
exceeded one million in the NCBI database (http://www.ncbi.nlm.nih.gov/). An
important advantage of using expressed genes is that if an EST marker is
found to be genetically associated with a trait of interest, it may be possible
that this could be the gene affecting the trait directly (Thiel et al., 2003).
Microsatellites also occur in EST sequences, thus allowing the generation of
EST-derived SSR markers, which has become a valuable complement to
existing SSR collections (Yu et al., 2004). However, in several crops the level
of polymorphism found in EST-SSRs is considerably lower compared with
genomic SSR markers (Cho et al., 2000; Eujayl et al., 2002; Thiel et al., 2003).
Single feature polymorphisms (SFPs) are markers based on indel loci and are
particularly amenable to microarray-based genotyping. Each SFP is scored by
the presence or absence of a hybridization signal with its corresponding
oligonucleotide probe on the array. The advantage of microarray platforms for
genotyping is that they are highly parallel, and they are well suited for
applications such as QTL analysis, where whole genome coverage with many
markers is desirable (Edwards and McCouch, 2007).
44
In the last few years a new marker platform was developed to enable wholegenome profiling without the need of sequence information (Jaccoud et al.,
2001). Diversity array technology (DArT) is a modification of the AFLP
procedure, and is based on microarray hybridizations that detect the presence
versus absence of individual DNA fragments in genomic representations
generated from samples of genomic DNA. First developed for rice, a diploid
model crop with a simple genome, DArTs were then successfully applied to
barley creating a medium-density genetic map (Wenzl et al., 2004). The
results showed the potential of DArT as a generic technique for highthroughput genome profiling of plants. It is a low-cost, robust system with
minimal DNA sample requirements. This technology simultaneously types
several thousand loci in a single assay. Recently, DArT markers were also
developed for the hexaploid genome of bread wheat (Akbari et al., 2006) and
the tetraploid genome of durum wheat (Mantovani et al., 2008) and were
successfully used for preparing genetic maps.
White et al. (2008) estimated the genetic diversity within and among UK, US
and Australian wheat germplasm collections using DArT markers. Two
hundred-and-forty cultivars from all three regions spanning the period of
modern plant breeding were genotyped with DArT markers and the diversity
study was based on 379 polymorphic loci distributed along the three genomes.
The three populations were confirmed to be genetically distinct at the whole
45
genome level. The benefits of using a high-density genotyping platform with a
common marker set allowed direct comparison between the diversities of the
national populations and the fluctuation of diversity over time. DArT markers
are being combined with selected SSRs to construct higher density maps for
QTL analysis of different traits, i.e. disease resistance factors (Lillemo et al.,
2008).
Resistance gene analogues (RGAs) constitute a marker class designed
specifically for molecular breeding for disease resistance, which has been
extensively used in some plant species given the substantial molecular
information available on various plant disease resistance genes. Based on
sequence information of cloned resistance genes, primers are designed
corresponding to conserved regions to isolate RGAs (Feuillet and Keller,
2005). In wheat a number of RGA markers have been identified linked to
resistance genes for yellow rust [Yr5, (Yan et al., 2003); Yr9, (Shi et al.,
2001)], leaf rust [Lr1, (Ling et al., 2003)] and powdery mildew [Pm21, (Chen et
al., 2006); Pm31, (Xie et al., 2004)].
Molecular markers for disease resistance
Molecular markers can be used to increase the efficiency with which
qualitative and quantitative disease resistance genes are manipulated in
breeding programs (Horvath et al., 1995). In this context, the most commonly
used markers in wheat are RFLP and RAPD, and their corresponding
46
converted STS or SCAR markers, and more recently PCR-based SSR and
AFLP are the markers of choice. With the increasing amount of sequence
information (ESTs) and technological development for SNP discovery, there is
no doubt that in the future such markers will also be integrated in molecular
breeding programs enhancing its potential and possibilities to detect markerresistance factor associations (Edwards and McCouch, 2007; Feuillet and
Keller, 2005).
Applications of molecular markers in plant breeding programs
Molecular breeding tools promise to facilitate selection for complex and
quantitative traits by shifting the basis of selection from phenotype to
genotype. Since their development, molecular markers have been used for
many purposes. The most common applications are: (1) assessment of
genetic diversity in germplasm collections, i.e. taxonomic studies, where
results are often presented as a dendogram; (2) fingerprinting for cultivar
identification and enforcement of legal protection; (3) construction of linkage
maps based on recombination frequencies; (4) QTL (quantitative trait loci)
analysis, were existing linkage maps are combined with phenotypic
information and used to identify chromosome regions that affect a quantitative
trait; and (5) marker-assisted selection (MAS) which is probably the most
useful application of molecular markers. It is broadly defined as the indirect
selection of seedlings with the desired combination of genes based on the
47
presence of closely linked molecular markers (Collard et al., 2005; Lörz and
Wenzel, 2005).
QTL analysis is an application of molecular markers by itself; however, most
importantly it is a basic step preceding the implementation of molecular
marker-assisted selection. The simple principle behind QTL analysis is the
detection of association between phenotype and the genotype, as determined
by markers. The use of molecular markers and QTL analysis procedures has
provided tools for relating complex traits, such as quantitative resistance, to
specific regions on the genome (Doerge, 2002). Knowledge of markers that
are linked to a QTL or that actually represent the QTL allows breeders to
determine the presence or absence of specific QTL alleles for the trait of
interest (Bernardo, 2002). Three widely used methods that allow statistical
analyses of the association between phenotype and genotype for the purpose
of understanding and dissecting genomic regions that affect complex traits are
single-marker analysis, simple interval mapping and composite interval
mapping (Doerge, 2002; Liu, 1998; Tanksley, 1993).
Marker Assisted Selection
The major breeding applications of mapping and tagging genes are to perform
indirect selection for desired traits using the linkage between the target gene
and a molecular marker(s). Basically, MAS consists of identification of a tight
linkage between genes controlling agronomic traits and molecular markers,
48
and the use of these markers to improve selected germplasm by incorporating
the target traits (Dudley, 1993). The increasing popularity of MAS comes from
its potential to accelerate plant breeding through precise transfer of genomic
regions involved in the expression of target traits, and speeding the recovery
of the recurrent parent genome in marker-assisted backcrossing.
Nevertheless, as some chromosomal regions may have multiple effects on
different traits, it is important to consider that the marker-aided selection for a
certain trait should not have adverse effects on other agronomically important
characters (Marshall et al., 2001).
Selection based on DNA markers can increase screening efficiency in several
ways. The most notable are the ability to screen in the juvenile stage for traits
that are expressed late in the life of the organism (i.e. grain quality in cereals);
ability to screen for traits that have low penetrance or are difficult, expensive or
time consuming to score phenotypically, like resistance to quarantined pests
or quantitatively inherited traits; and ability to maintain recessive alleles during
backcrossing or for speeding up backcross breeding. Identifying the
recombinants with the least amount of donor DNA flanking the genes of
interest is enhanced by the use of molecular markers. MAS offers a powerful
strategy for making efficient use of natural genetic variation harbored in
landraces and wild species of cultivated food crops (Tanksley and McCouch,
1997). Simultaneous MAS can be performed for several characters at one
time (i.e. pyramiding), even in combination with phenotypic selection. MAS
49
offers the potential to assemble target traits in the same genotype more
precisely, with less unintentional losses and in fewer selection cycles, than
with conventional breeding (Edwards and McCouch, 2007; Gupta et al., 1999;
Xu and Crouch, 2008).
A few critical factors need to be considered for the effective implementation of
molecular MAS, besides the availability of a high density genetic map. First,
close linkage between molecular markers and the gene of interest, or QTL, is
fundamental. Recombination events between the marker loci and the gene
can limit the use of MAS, since the later can be lost even though the marker is
present. The efficiency of MAS can be increased by the co-selection of two or
more markers flanking the target gene instead of using a single linked marker
(Peng et al., 1988). This practice reduces the probability of losing the QTL by
recombination and controls linkage drag, which has been a problem
associated with the conventional backcrossing technique used extensively to
introgress specific traits (i.e. disease resistance genes) into adapted cultivars
(Tanksley et al., 1989). Second, the validation of marker/trait associations in
different genetic backgrounds is of substantial importance for their general
application in MAS (Gupta et al., 1999; Lörz and Wenzel, 2005; Sharp et al.,
2001). In recent research (Gupta et al., 2006) molecular markers were
developed for the A. elongatum derived leaf rust resistance gene Lr24 using a
red seeded receptor line. However, some of the markers were ineffective for
MAS because they showed poor linkage with the resistance gene in a white
50
seeded genetic background. It may be more difficult to conduct marker
validation for quantitative traits where there is major genotype x environment
interaction and it is difficult to accurately assess phenotypes (Gupta et al.,
1999). Third, a marker system suitable for high-throughput and large scale
deployment, capable of analyzing thousands of plants in a timely and costeffective manner is necessary for successful implementation of MAS (Gupta et
al., 1999; Gupta et al., 2008; Landjeva et al., 2007). With the increasing
availability of SSRs for wheat since the late nineties, locus specificity and high
level of polymorphisms associated with microsatellites made them the marker
system of choice for molecular-aided selection in practical plant breeding
(Gupta et al., 1999). SNP markers and microarray platforms are promising
tools perfectly suitable for automated data interpretation (Landjeva et al.,
2007; Xu and Crouch, 2008).
The present focus of MAS is on: (1) accelerated selection of traits that show
complex inheritance or have strong environmental influence; (2) selection for
traits of economic importance, particularly in cases where the biological
assays are unreliable or expensive; and (3) pyramiding disease resistance
genes (Landjeva et al., 2007; Xu and Crouch, 2008). Selection for improved
Fusarium head blight (FHB) resistance in wheat is an excellent example where
marker-assisted breeding exploits its full potential. It is an economic important
disease causing huge grain yield and quality losses. Inheritance of FHB
resistance is quantitative and expression is environmental dependant. Several
51
QTL were identified on different chromosomal regions. Miedaner et al. (2006)
reported the pyramiding of three QTL for FHB resistance into elite European
spring wheat lines, reducing overall FHB severity in selected lines compared
to susceptible controls. Wilde et al. (2007) proposed the combination of MAS
with phenotypic selection to achieve even higher levels of resistance to FHB.
Marker-based selection is also helpful in attempts to transfer genes from wild
relatives into cultivated lines. Miranda et al., (2006) reported the successful
transfer of a novel powdery mildew resistance gene (Pm34) from A. tauschii
into common wheat. SSR markers were used to map and tag the resistance
gene. Three co-dominant microsatellites markers were identified as closely
linked and were proposed to be used in MAS to incorporate the novel gene
into susceptible cultivars or by pyramiding effective resistance genes. Further
examples of effective and successful integration of molecular MAS with
conventional wheat breeding practices are presented elsewhere (Dubcovsky,
2004; Kuchel et al., 2007; Landjeva et al., 2007; Sorrells, 2007; William et al.,
2007; Xu and Crouch, 2008).
Implementation and practical benefits from MAS are taking longer than
expected. According to Landjeva et al. (2007), the main reasons for the delay
are the need for better quality markers, the high costs of DNA assays and the
complexity of quantitative traits. Mapping and MAS for complex traits was
indicated as one of the most immediate challenges wheat breeding programs
52
are facing (Sorrells, 2007). As primary constraints to successful MAS, Xu and
Crouch (2008) suggest the need for accurate validation of published markers
in a range of populations representative of breeding material; the necessity to
develop simple, quick and cheap technical protocols for DNA extraction; and
genotyping and data collection (high throughput genotyping) that remain
reliable and precise over time in large scale systems. To help breeders make
rapid but accurate selections, powerful decision-support tools are required that
can manage the increasing volume of information being generated.
Challenges in the wheat-Cephalosporium pathosystem
Cephalosporium stripe is an important and limiting factor in many winter wheat
production areas. In the Pacific Northwest, wheat growers in erosion-prone
areas are particularly affected when early plantings and reduced or no tillage
are practiced. Cultural control methods are only partially effective and often
conflict with soil conservation practices. Screening germplasm for resistance
to Cephalosporium stripe is difficult. While expression of the disease is
inconsistent and environmentally dependent, it also requires artificial
inoculation to obtain uniform disease pressure. As a result, only a limited
number of advanced lines can be screened for Cephalosporium stripe
resistance in field trials every year. Incorporating genetic resistance into new
wheat cultivars through conventional breeding remains difficult and
challenging. To date, wheat cultivars in the PNW have shown limited
resistance to the disease. Previous work shows that inheritance of the
53
resistance to Cephalosporium stripe is quantitative. At present, no molecular
breeding approaches have been attempted to tackle this disease. Putting all
these conditions and considerations into perspective, an ideal stage is set for
the application of molecular markers to detect and locate major genomic
regions responsible for Cephalosporium stripe resistance that can be targeted
for the development of a marker-aided selection procedure for
Cephalosporium stripe resistance in wheat.
54
Thesis Objectives
•
Estimate potential grain yield loss caused by Cephalosporium stripe in
a set of adapted PNW cultivars.
•
Estimate the level of resistance required to attain minimal yield loss.
•
Evaluate and describe associations between disease, grain yield, test
weight, and kernel characteristics under Cephalosporium stripe
infection.
•
Investigate the genetics and inheritance of resistance to
Cephalosporium gramineum in winter wheat.
•
Conduct QTL analyses using a mapping population (Coda x Brundage)
to associate molecular markers with regions controlling resistance to
Cephalosporium gramineum.
55
Quantitative Trait Loci Analysis for Resistance to Cephalosporium stripe,
a Vascular Wilt Disease of Wheat
Abstract
Cephalosporium stripe, caused by Cephalosporium gramineum, can cause
severe loss of wheat (Triticum aestivum L.) yield and grain quality in some
localities and can be an important factor limiting adoption of conservation
tillage practices. Improving disease resistance is problematic, however, as
infection and disease response are environmentally dependent, and little is
known about the inheritance of resistance. A population of 268 recombinant
inbred lines (RILs) derived from a cross between two wheat cultivars was
characterized using field screening and molecular markers to investigate the
inheritance of resistance to Cephalosporium stripe. Whiteheads (sterile heads
caused by pathogen infection) and kernel quality traits were measured on
each RIL in three field environments under artificially inoculated conditions. A
linkage map for this population was created based on 204 SSR and DArT
markers. A total of 36 linkage groups were resolved, representing portions of
all chromosomes except for chromosome 1D. Quantitative trait locus (QTL)
analysis identified seven regions associated with resistance to
Cephalosporium stripe, with approximately equal effects. Four QTL derived
from the more susceptible parent (Brundage) and three came from the more
resistant parent (Coda). Two resistance QTL were found to be associated with
genes that affect head morphology. One QTL was located on the same
chromosome (5B) as toxin insensitivity genes for other wheat pathogens.
56
Additivity of QTL effects was confirmed through regression analysis and
demonstrates the advantage of accumulating multiple QTL alleles to achieve
high levels of resistance. Resistance to Cephalosporium stripe was positively
correlated with uniformity in kernel size. Combining molecular markers with
diligent field screening is probably the best approach for wheat breeding
programs to select germplasm with increased resistance to Cephalosporium
stripe.
Introduction
Molecular markers can be used to increase the efficiency with which
qualitative and quantitative disease resistance genes are manipulated in
breeding programs (Horvath et al., 1995). Marker-assisted selection offers the
potential to assemble target traits in the same genotype more precisely, with
less unintentional losses of favorable traits and in fewer selection cycles, than
with conventional breeding (Edwards and McCouch, 2007; Gupta et al., 1999;
Xu and Crouch, 2008). Gene discovery through QTL analysis is a basic step
preceding the implementation of molecular marker-assisted selection.
Cephalosporium stripe of wheat is caused by the soil-borne fungal pathogen
Cephalosporium gramineum Nisikado and Ikata (syn. Hymenula cerealis Ellis
& Everh.) (Bruehl, 1956; Ellis and Everhart, 1894; Nisikado et al., 1934). In
North America, it is widespread throughout the Pacific Northwest, where it is a
chronic yield-reducing disease, and in western Provinces of Canada (Bruehl,
57
1957; Mundt, 2002; Murray, 2006; Wiese, 1987). The disease is economically
important only in production of winter wheat. Wheat growers in erosion-prone
areas are particularly affected when early plantings and reduced or no tillage
are practiced. The fungus survives and overwinters between host crops
saprophytically as mycelium and conidia in association with host residues on
or near the soil surface (Lai and Bruehl, 1966). Infested crop residue is the
primary source of inoculum, but low rates of seed transmission have also been
reported (Murray, 2006). Conidia enter wheat roots through wounds caused by
freeze injury, root feeding insects (wireworm and nematodes), or other
mechanical injury (Bailey et al., 1982; Douhan and Murray, 2001; Slope and
Bardner, 1965). Active penetration of host tissue has been reported as well
(Douhan and Murray, 2001). Once inside the roots, the fungus has the
potential to colonize the entire plant. Successful establishment of C.
gramineum inside the host is enhanced by the production of toxic metabolites
that block the vascular system, thus preventing normal movement of water
and nutrients. (Bruehl, 1957; Spalding et al., 1961; Wiese, 1987).
Disease progress is highly dependent on environmental conditions (Specht
and Murray, 1990). Temperature, moisture, soil pH, and root wounding can
have great impact on the development of Cephalosporium stripe (Bruehl and
Lai, 1968; Martin et al., 1989; Pool and Sharp, 1969). The disease is most
severe in cool, wet soils with low pH (Blank and Murray, 1998). Severe
infections on winter wheat result in yield and quality loss. In areas conducive
58
to Cephalosporium stripe, up to 80% yield reduction from a generalized
infection on a susceptible cultivar can occur. Loss in grain yield is the result of
a reduction in the number of fertile florets per spike and smaller seed size
(Bockus et al., 1994; Johnston and Mathre, 1972; Mathre et al., 1977; Morton
and Mathre, 1980b; Richardson and Rennie, 1970).
Management of Cephalosporium stripe has relied on reducing inoculum in the
soil via cultural controls such as crop rotation, management of crop residues,
altering soil pH with lime applications, and fertilizer management (Bockus et
al., 1983; Latin et al., 1982; Martin et al., 1989; Mathre and Johnston, 1975b;
Murray et al., 1992; Pool and Sharp, 1969; Raymond and Bockus, 1984).
However, these practices are only partially effective in reducing the incidence
and severity of disease (Li et al., 2008), and often are practically or
economically unfeasible (Murray et al., 1992; Raymond and Bockus, 1984).
Additionally, Cephalosporium stripe cannot be controlled with fungicides.
Host resistance currently offers the best hope for control of Cephalosporium
stripe. Expression of resistance to Cephalosporium stripe is usually incomplete
and environmentally dependent (Martin et al., 1986). Two types of resistance
have been observed: exclusion of the pathogen, expressed as a reduction in
the percentage of diseased plants; and restriction of spread of the pathogen
after successful colonization of the host, expressed as a reduction in the
percentage of diseased tillers per infected plant and also as a reduced rate
59
and severity of symptom development. The latter is caused by restricted
pathogen movement in the plant. Both types of resistance were expressed
independently, leading the authors to conclude that maximum resistance
would be attained if both types of resistance were incorporated into a single
genotype (Morton and Mathre, 1980a).
Although variation in the degree of resistance among cultivars has been
confirmed, complete resistance to C. gramineum has not been found in the
common wheat gene pool (Bruehl et al., 1986; Martin et al., 1983; Mathre et
al., 1977; Morton and Mathre, 1980a). Screening germplasm for resistance to
Cephalosporium stripe is difficult. While expression of the disease is
inconsistent and environmentally dependent, it also requires artificial
inoculation to obtain uniform disease pressure. Only a limited number of
advanced lines can be screened for Cephalosporium stripe resistance in field
trials every year. Therefore, incorporating genetic resistance into new wheat
cultivars through conventional breeding remains difficult and challenging.
Previous work shows that inheritance of the resistance to Cephalosporium
stripe is quantitative (Martin et al., 1983; Mathre et al., 1985; Morton and
Mathre, 1980a).
At present, no molecular breeding approaches have been attempted to tackle
Cephalosporium stripe. The objective of this study was to characterize the
inheritance of resistance to Cephalosporium stripe and to explore the
60
application of molecular markers to detect and locate major genomic regions
responsible for resistance to the disease that can be targeted for the
development of a marker-aided selection for Cephalosporium stripe resistance
in wheat.
Materials and Methods
Plant Material
A population of 268 F6-derived recombinant inbred lines (RILs) was developed
at the University of Idaho from a cross between the soft white winter club
wheat cultivar ‘Coda’ (PI 594372) (Allan et al., 2000) and the soft white winter
common wheat cultivar ‘Brundage’ (PI 599193) (Zemetra et al., 1998). The
pedigree for Coda is ‘Tres’//’Madsen’/’Tres’ and the pedigree for Brundage is
‘Stephens’/’Geneva’. The initial cross was done in 1999 and the resulting F1
seed was grown in the greenhouse and allowed to self. Single seed descent
was then used to arrive at the F6:7 generation. This population was previously
used to develop an improved molecular marker for the Pch1 gene for
resistance to strawbreaker foot rot or eyespot (Leonard et al., 2008), incited by
Oculimacula yallundae and O. acuformis, and to map the compactum locus for
club head type in wheat (Johnson et al., 2008). The parents of the population
were included in each trial, together with six check cultivars (Stephens,
Madsen, Tubbs, Rossini, OR9800924 and WA7437) of known response to
Cephalosporium stripe.
61
Phenotyping
Field experiments were conducted at the Columbia Basin Agricultural
Research Center field stations near Pendleton, OR in 2006-2007 (2007) and
2007-2008 (2008) and in Moro, OR, in 2006-07 (2007). Both locations are in
semi-arid wheat producing areas of the Columbia Plateau, with mean annual
precipitation of 279 mm in Moro and 406 mm in Pendleton. Field evaluations
for Cephalosporium stripe were carried out using a randomized complete
block design. There were two replications at each site in 2007 and three
replications in 2008. Both parents and checks were replicated four times in
each block. To ensure high and uniform disease pressure, disease inoculum
was added to the seed envelopes before planting. This was done using
autoclaved oat kernels that were infested with C. gramineum and then dried,
as described by Mathre and Johnston (1975a). Infested oat kernels were
added at a dose equal in weight to the wheat seed.
Plots were seeded into stubble mulch on 12 September 2006 in Moro and
Pendleton, and on 11 September 2007 in Pendleton. Sowing dates in early
September greatly increase severity of Cephalosporium stripe at these sites.
Each plot was two rows x 2.5 m long. A Hege 500 series plot drill (H&N
Manufacturing, Colwich, KS) with deep furrow openers was used to reach soil
moisture. Fertilization and weed control practices were appropriate to
commercial winter wheat production at the two sites. A spring application of
fungicide (Bumper 41.8EC, propiconazole) was applied to avoid infection by
62
O. yallundae and O. acuformis (eyespot) which can mask symptoms of
Cephalosporium stripe. Plots were mowed to 1.8 m in length post-heading and
prior to collecting phenotypic data.
Cephalosporium stripe incidence was recorded on a plot basis by visually
estimating the percentage of tillers that were ripening prematurely, and which
usually expressed complete or partial reduction of grain-fill (whiteheads)
(Mathre and Johnston, 1975a; Morton and Mathre, 1980a). Evaluation of
known check varieties and random examination of lower stems and roots
provided confidence that whiteheads were caused predominately by
Cephalosporium stripe. Disease notes were taken at each location
approximately 2-3 wk after heading, and over two consecutive days owing to
the large number of entries. Developmental stage of the entries ranged from
early milk to early dough at this time. Additional agronomic traits were also
taken to study possible association with Cephalosporium stripe resistance
(club and common head type and presence of awns in all environments;
physiological maturity and plant height at maturity for Pendleton 2007 only).
At maturity, each plot was sampled to obtain sufficient seed for kernel
evaluations. All heads in a 0.30-m long row section, representative of the
entire plot, were hand-harvested and bulked. Samples were later carefully
threshed using a stationary head thresher, where forced air flow settings were
63
at a minimum to maximize the retention of seeds. Samples were then recleaned by hand prior to seed analyses.
A sub-sample of 300 seeds was analyzed for kernel weight (mg) and diameter
(mm), using a Single Kernel Characterization System (SKCS) model 4100
(Perten Instruments, Springfield, IL). For each sample, the SKCS integrated
computer software (Perten Instruments, Springfield, IL) provided the means
and standard deviations from the 300 individual kernel determinations.
Statistical analyses
Whitehead (WH) percentages were transformed using the square-root to meet
the assumptions of normality and homogeneity of variance. Tests of
significance of RIL, replication, environment, and RIL x environment effects
were performed with Type III F statistics estimated by PROC GLM of the
Statistical Analysis System (SAS) (SAS v9.1, SAS Institute Inc., Cary, NC,
USA). For analyses over environments, replications were considered nested
within environment. The presence of significant transgressive segregants was
verified in SAS by least squares means comparison tests adjusted by the
Dunnett-Hsu method to control the error rate for multiple comparisons. The
parents of the RIL population were used as the controls for this test.
Narrow-sense heritability (h2) for each trait was calculated on a RIL mean
basis for single environments and across environments, using an approach
64
similar to that of Fernandez et al. (2008) and Tang et al. (2006). The REML
method of SAS PROC VARCOMP was used to estimate variance
components. For single-year data, h2 = σ2G/(σ2G + σ2E/r), where r is the
number of repetitions, σ2G is the genotypic variance and σ2E is the residual
variance. For combined environments, h2 = σ2G/(σ2G + σ2GL/l + σ2E/lr), where l
is number of locations, r is the number of repetitions per location, σ2G is the
genotypic variance, σ2GL is the variance due to genotype ×location interaction,
and σ2E is the residual variance. Exact 90% confidence intervals were
calculated for single-environment h2 estimates following the method described
by Knapp et al. (1985). Pearson correlation coefficients among traits were
estimated from genotype least square means using the PROC CORR
procedure in SAS.
Genotyping
‘Coda’ and ‘Brundage’ were evaluated at the Western Regional Genotyping
Center (Pullman, Washington), Oregon State University, and the University of
Idaho, using PCR to identify polymorphic markers. The RILs were scored with
158 polymorphic SSR markers. In addition, the Coda x Brundage population
was sent for DArT analysis (Diversity Array Technologies, Triticarte Pty. Ltd.
Canberra, Australia). DNA extraction was done from fresh young leaf tissue
using the DNeasy 96 Plant DNA extraction Kit (QIAGEN), and following their
protocol. While 233 DArT markers were identified, at first only 180 were used
65
to develop an initial linkage map. The remaining DArT markers, of lower
quality, were progressively incorporated into the map.
Linkage map construction was performed using JoinMap 4.0 (Van Ooijen,
2006). Markers with high segregation distortion or with more than 50% missing
values were excluded from the map. Exclusion of other markers from the map
was based on their goodness-of-fit chi-square contribution. Map distances are
given in centiMorgan (Kosambi function). The linkage map used for QTL
analysis was adjusted to obtain evenly spaced markers throughout the
genome and clusters of markers were reduced to only one marker per locus.
QTL analysis
QTL analysis was performed using the composite interval mapping (CIM)
procedure (Zeng, 1994) implemented in WinQTL Cartographer v.2.5 (Wang et
al., 2007). CIM analyses were performed on least square means for RILs
estimated by SAS PROC GLM for each environment independently and for the
combined data across locations. Model 6 of Win QTL Cartographer was used
and up to 10 cofactors for CIM were chosen using a stepwise forwardbackward regression method, with a significance threshold of 0.05. Walk
speed was set to 2 cM and the scan window to 10 cM beyond the markers
flanking the interval tested. Significance likelihood ratio test (LR) thresholds for
QTL identification were established by 1000 permutations at α = 0.05. The
additive effects and coefficients of determination for individual QTL were
66
estimated by CIM. Results from CIM with the combined data were used to
estimate possible epistatic effects between significant QTL. The multiple
interval mapping (MIM) procedure (Kao et al., 1999) implemented in Win QTL
Cartographer was used for this purpose. In order to verify the additivity of QTL
effects, RILs were classified according to the number of resistance alleles
present at each of the QTL detected with combined data, and regression
analysis (PROC GLM) was used to estimate the change in disease incidence
caused by an increasing number of resistance alleles.
Results
Phenotypic evaluation and statistical analysis
Cephalosporium stripe pressure was moderately severe in all field trials, with
the percentage of whiteheads on the susceptible check Stephens averaging
34, 58, and 31% in Moro 2007, Pendleton 2007, and Pendleton 2008,
respectively. Brundage, the susceptible parent of the RIL population,
possesses some resistance to the disease, as its mean whitehead percentage
(Fig. 2.1) was always less than that of Stephens. A wide range in disease
ratings for the RILs was observed (Fig. 2.1). Disease reactions transformed by
square root appeared to be normally distributed in all three environments.
Combined analysis of variance of disease response, kernel weight and kernel
weight standard deviation (SD) showed significant influence of environments,
RILs and RIL by environment interaction for all three traits (P<0.01) (Table
67
2.1). Mean squares for the interaction terms were relatively small (<30%)
relative to the main effect of RIL, thus combined analyses over environments
were considered to be informative. The parents differed for average
whiteheads, kernel weight and kernel weight standard deviation (Table 2.2).
The RIL population means for whiteheads, kernel weight, and kernel weight
SD were intermediate between the two parents (Table 2.2).
Analyses of variance for each environment are presented in Table 2.1.
Significant differences among RILs were observed for whiteheads, kernel
weight and kernel weight SD for each environment (P<0.01) (Table 2.1).
Coefficients of variation for whiteheads were always larger than for the kernel
traits (Table 2.1). Disease responses of the parents were consistent between
years at Pendleton. Coda had significantly lower disease scores and kernel
weight than Brundage, as well as lower variability for kernel weight as
measured by the standard deviation (Fig. 2.1-2.3 and Table 5.1 in the
appendix). At Moro in 2007, disease ratings of Coda and Brundage were not
significantly different (P=0.695). The parents did differ, however, in kernel
traits (Fig. 2.1-2.3 and Table 5.1 in the appendix). Means and ranges of
whiteheads and kernel traits for RILS were relatively similar at each
environment (Figs.2.1-2.3 and Table 5.1 in the appendix)
Histograms of RIL means at each environment suggest that disease scores
and kernel traits were normally distributed (Fig. 2.1-2.3). RIL values were
68
observed beyond the limits of the parents for all variables and at each
environment, suggesting the occurrence of transgressive segregation. Onetailed Dunnett tests for RILs compared with Coda and Brundage confirmed the
presence of transgressive segregants. Averaged across environments, two
lines had significantly lower average whitehead than Coda (P<0.01), while two
lines had significantly higher mean whiteheads than Brundage (P<0.05).
These findings suggest that the more susceptible parent may carry genes that
contribute to resistance. Several lines had significantly lower or higher average
kernel weight than the parents. For kernel weight SD, only one RIL was
significantly lower than Coda and two were significantly higher than Brundage.
Pearson correlation coefficients were calculated among kernel weight, kernel
weight SD and whitehead least square means. Whiteheads were positively
correlated with kernel weight (r=0.35, P<0.001) and kernel weight SD (r=0.54,
P<0.001) when values for RILs were averaged over environments. The
correlation between kernel traits was 0.65 (P<0.001). Correlation coefficients
among variables for each environment and the combined data are presented
in Table 5.2 in the appendix. Cephalosporium stripe response in Pendleton
2007 was found to be correlated with plant height (r=0.15, P<0.001) and
maturity (r=-0.17, P<0.001).
Heritability and 90% exact confidence intervals were calculated for each
variable based on RILs at each location and over locations (Table 2.1).
69
Whiteheads heritability estimates were moderately high with a maximum of
0.79% at Pendleton 2007 and a minimum of 0.59% at Moro 2007. Heritability
estimates for kernel traits were similar, with slightly higher values for kernel
weight than for its standard deviation.
Construction of linkage map
A total of 220 SSR markers were found to be polymorphic between Coda and
Brundage. Of these, 158 were successfully used to genotype and differentiate
individual RILs. A total of 233 polymorphic markers were identified using DArT
marker technology. An initial genetic map was constructed based on 313 total
markers. A total of 36 linkage groups were created, representing areas from all
chromosomes except for chromosome 1D. The total map length was 1879.3
cM with an average interval between markers of 6.0 cM. Significant
segregation distortion was observed for 18 markers, all in favor of Coda alleles
and all mapped to the 1B chromosome.
For QTL analysis, the number of markers in the linkage map was reduced by
eliminating co-segregating markers, leaving only one per cluster. Markers
which showed high chi-square contribution were also discarded from the map,
leaving 204 well distributed markers, with a total coverage of 1910.2 cM, and a
mean distance of 9.4 cM between markers.
QTL identification
70
Composite interval mapping from the combined experiments identified QTL in
seven chromosomal regions that were significantly associated with resistance
to Cephalosporium stripe. LOD scores at the peak for these QTL ranged from
3.5 to 10.9, and all had similar effects on disease response. Three QTL were
contributed by Coda, while four were contributed by Brundage, the more
susceptible parent. The analyses identified seven regions associated with
variation in kernel weight, and also seven regions associated with kernel
weight SD (Table 2.3, Fig. 2.4).
QTL for whiteheads were grouped into two categories. Primary QTL are those
considered to be coincident with QTL for kernel traits. Secondary QTL are
single QTL, or those that are found to share the position with only one other
trait. Primary QTL were detected on chromosomes 2B, 2D (two QTL) and 4B,
accounting for 8.1, 10.7, 3.9 and 5.4% of the phenotypic variance respectively.
The first QTL on 2D was consistently associated with a region on chromosome
2D where the Compactum (C) locus has been mapped, which controls spike
compactness. The second QTL on 2D was located about 70 cM downstream
of the first (Fig. 2.4). The first resistance allele was contributed by Coda and
the second by Brundage. The QTL on chromosome 4B, contributed by
Brundage, also was consistent across environments and traits. Approximate
position of the QTL on 2B varied between environments and the combined
analysis, although the resistance could be attributed to Coda.
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Secondary QTL include two located on different linkage groups on 5A and one
located on 5B. The two QTL for resistance on 5A (Fig. 2.4), both derived from
Brundage, accounted for 12.6% of the phenotypic variation. One of these 5A
QTL was found co-located with the phenotypic marker for awns, which is
regulated by the B1 gene. A QTL for kernel weight was also associated with
this region. The QTL for disease response mapped to chromosome 5B was
revealed by the combined analysis only. Although LOD scans of this
chromosome for Moro and Pendleton 2007 suggested a peak, it was below
the threshold for statistical significance. QCs.orp-5B had a LOD score at the
peak of 4.7 and accounted for the highest proportion of the phenotypic
variance for whiteheads (12.4%). The allele conferring resistance at this QTL,
derived from Coda, contributing the highest additive effect (-0.31) for the trait
(whiteheads square root-transformed). The total amount of phenotypic
variation explained jointly by all QTL found for Cephalosporium stripe was on
average 50.4%, varying conditional on the background markers used as
cofactors (Table 2.3).
The region around the Compactum locus on chromosome 2D explained
around 40% of the phenotypic variance in kernel traits and was associated
with the largest additive effect (Table 2.3, Fig. 2.4). Coda alleles contributed
decreased kernel weight and higher kernel size uniformity. No other QTL for
any kernel trait accounted for more than 7% of the observed variation.
72
Results of the multiple interval mapping analysis to account for possible
interactions among identified QTL across environments revealed only nonsignificant QTL x QTL interactions (data not shown), therefore these were not
considered.
Effect of number of resistance alleles
The regression of disease response on number of resistance alleles at
significant QTL showed a significant (P<0.01) slope of -0.41 and a coefficient
of determination (R2) of 0.38 (Fig. 2.5). Even though associations were found
between disease response, presence of awns and head compactness, results
provide evidence that the effect of pyramiding resistance QTL was not heavily
affected by head morphology, since similar trends were observed for the four
head type classes (Fig. 2.5). In addition, RILs within each level of number of
resistance alleles showed higher levels of variability than can be explained by
head morphology traits alone.
Discussion
The Coda x Brundage RIL population was initially developed for mapping
genetic loci for resistance to Puccinia striiformis (stripe rust) and O. yallundae
and O. acuformis (eyespot), as well as various agronomic traits. This
population was a good choice for mapping resistance to Cephalosporium
stripe, as both parents possess some resistance, but have different genetic
backgrounds. Field experiments showed a wide range of variability for
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Cephalosporium stripe response in the Coda x Brundage RIL population.
Despite the high number of RILs observed with mean trait values higher or
lower than Coda or Brundage, few transgressive segregants were statistically
confirmed. Nevertheless, it appears that both parents carry genes for
resistance against Cephalosporium stripe. Disease response was found to be
associated statistically with plant height and maturity in the Pendleton 2007
trial, yet coefficients of correlation were small. In addition, graphical
examination of the data showed high levels of variation for disease response
at all maturity levels and along the plant height range (data not shown).
Biologically, these observations indicate that neither plant height nor plant
maturity should be expected to interfere with selection for higher resistance
levels in a breeding program.
Moderately high and consistent heritability estimates obtained in this study
were similar to previous estimates from segregating breeding populations
(Chapter 5). Such levels of heritability for resistance are common for other
soil-borne pathogens (Li et al., 2009; Schneider et al., 2001), and reinforce the
likelihood of success of breeding for resistance. A relatively large population
size, significant levels of phenotypic variation and narrow sense heritability,
and a moderately dense linkage map are necessary to identify QTL associated
with resistance to Cephalosporium stripe and kernel weight traits with high
resolution.
74
Quantitatively-expressed disease resistance is often considered to be
inherited polygenically. Reviews of published studies, however, suggest a
median QTL number of only three (Young, 1996) or four (Kover and Caicedo,
2001), often with one or two QTL accounting for a large proportion of the total
variance in disease response. These results are consistent with previous
studies using traditional quantitative genetic approaches (Geiger and Heun,
1989). However, limitations in population size or phenotyping (Vales et al.,
2005) may bias estimates of QTL number downwards (Kover and Caicedo,
2001; Young, 1996). Nonetheless, our study fits more with the exceptions, as
we identified seven putative QTL associated with resistance to
Cephalosporium stripe and no single QTL accounted for a large proportion of
the total variation. Some researchers had hypothesized that resistance to soilborne pathogens would be genetically similar to that of foliar pathogens e.g.,
(Ellingboe, 1983), while others suggested that it may be more complex e.g.,
(Bruehl, 1983). Recent QTL studies of resistance to soil-borne pathogenic
fungi have identified only 1-4 QTL for resistance (Hernandez-Delgado et al.,
2009; Kim et al., 2008; Lein et al., 2008; Li et al., 2009; Rygulla et al., 2008;
Taguchi et al., 2009), though there are exceptions (Wang et al., 2008).
In this study, seven putative QTL were identified and associated with
resistance to Cephalosporium stripe. No single QTL accounted for a large
proportion of the total variation. Of the seven QTL for Cephalosporium stripe
identified, two were associated with head morphology traits. The first region on
75
chromosome 2D (QCs.orp-2D.1) was centered on the Compactum gene (C
locus), which defines ear compaction, among other spike morphology traits
(Gul and Allan, 1972; Zwer et al., 1995). This locus was mapped by Johnson
et al. (2008) to the centromeric region of chromosome 2D. Results suggest
that the allele derived from Coda at the C locus, responsible for the club head
type, is associated with locus QCs.orp-2D.1 that confers increased resistance
to Cephalosporium stripe. Thus, the resistance could be controlled by closely
linked resistance gene(s), rather than due to pleiotropic effects of the
Compactum gene. Bruehl (1983) proposed that resistance to non-specialized
pathogens (i.e. soil-borne fungi) frequently involved changes in fundamental
physiological processes within the host. This is in contrast to highly specialized
pathogens where, in most cases, the resistance genes have no known role in
the basic physiology of the plant.
Influences of the Compactum gene at various developmental stages were
determined with co-isogenic lines by Tsunewaki and Koba (1979). This gene
was associated with increased node diameter and number of spikelets per ear
and spike density, but decreased lengths of ear rachis and grains, and
decreased grain index (grain length/thickness). Mathre and Morton (1990),
suggested that the superior level of resistance to C. gramineum found in
related wheat species (Thinopyron intermedium and Elytrigia elongata), was
due to restricted movement of the pathogen through the roots, and particularly
through crown tissues. The transition zone from the roots to culm tissue was
76
suggested as the morphological structure presenting a potential barrier to the
pathogen’s movement. An indirect effect of the Compactum gene, as
suggested by Tsunewaki and Koba (1979), could be an increased complexity
of the root-crown transition zone. This may decrease the pathogen’s ability to
enter the above-ground portion of the plant and limit its access to the vascular
system for movement and colonization. Morton and Mathre (1980a) defined
limited spread of C. gramineum within the plant as one possible type of
resistance. However, causality of the C locus on increased complexity of the
root-crown transition zone needs further investigation. If this relationship holds,
it would mean that club wheat germplasm would have an inherent resistance
and relative advantage over common winter wheat. However, the compactum
gene alone is not sufficient to confer adequate levels of resistance in absence
of other QTL.
A decrease in kernel weight and kernel weight SD was related to the Coda
allele at the C locus as well. The effects of club head type on kernel size are
well established (Zwer et al., 1995). This locus alone was found responsible
for 41 and 39% of the phenotypic variation observed for kernel weight and
kernel weight SD, respectively, and represents the most important QTL found
in this study for grain weight and its variability.
The QTL for resistance identified on chromosome 5A was found associated
with the region that determines presence or absence of awns. In fact, the
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closest locus to QCs.orp-5A.2 was the phenotypic marker for presence of
awns. This QTL explained 5% of the phenotypic variance for WHSQRT, which
is similar to the proportion explained by the other QTL. Resistance was
associated with the awnless condition coming from Brundage. This trait is
regulated by the B1 gene, located on the distal part of the long arm of
chromosome 5A (Kato et al., 1998). No evidence is available to determine if
B1 has a pleiotropic effect on Cephalosporium stripe tolerance, or if the
observed effect is due to strong linkage between the morphological trait and a
resistance factor. B1 is an awn development inhibitor, but also affects grain
index and kernel thickness (Tsunewaki and Koba, 1979). A variety of
resistance genes and QTL have been mapped to chromosome 5A (stripe rust,
Fusarium head blight, powdery mildew, leaf and glume blotch, hessian fly, and
cereal cyst nematode), as well as heading date and lodging resistance (Gupta
et al., 2008; Lillemo et al., 2008). The awned condition from presence of the
recessive b1 allele, derived from Coda, also was associated with increased
kernel weight, as indicated by the presence of QGw.orp-5A co-located with the
phenotypic marker for awns.
CIM analysis with the whitehead trait combined over locations identified a
particularly interesting QTL on chromosome 5B, attributed to Coda. QCs.orp5B had the highest additive effect (0.31) and explained the highest proportion
of the phenotypic variability for disease response (12.4%). Numerous mapping
studies have placed disease resistance genes on chromosome 5B, e.g., Lr52
78
for leaf rust and Pm30 for powdery mildew, as well as resistance QTL for
stripe rust and Fusarium head blight (Gupta et al., 2008; Lillemo et al., 2008).
For this study, the most relevant trait found to be controlled by chromosome
5B is tolerance to a specific host-selective toxin (HST) produced by
Pyrenophora tritici-repentis (tan spot) and Stagonospora nodorum (S.
nodorum blotch) known as ToxA and SnToxA (Faris et al., 1996; Friesen et al.,
2006; Tomas et al., 1990). P. tritici-repentis acquired the ability to produce the
toxin through a recent horizontal gene transfer event from S. nodorum
(Friesen et al., 2006). Tsn1 is the gene which governs sensitivity to both
toxins, and therefore serves as a major determinant for susceptibility to both S.
nodorum blotch and tan spot (Liu et al., 2006). However, Gonzalez-Hernandez
et al. (2009), reported a QTL proximal to Tsn1 in durum wheat and indicated
that resistance to each disease was controlled by different but linked loci.
Graminin A is a toxic compound produced by C. gramineum which has been
implicated in the proliferation and development of the fungus in wheat
(Creatura et al., 1981; Kobayashi and Ui, 1979; Rahman et al., 2001; Van
Wert et al., 1984). This relationship opens the possibility that the region on 5B
might have evolved some kind of specialization to resist the infection of fungal
pathogens that rely on toxins or toxin-like compounds to enhance
pathogenesis.
Four QTL for disease resistance were mapped to chromosomes 2B, 2D, 4B
and 5A. Those on 2D and 5A were unlinked to the other resistance QTL on the
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same chromosomes. These explained between 3.9 and 8.1% of the
whiteheads variance. Resistance genes and QTL for many wheat diseases
and pests have been mapped to these chromosomes, including leaf and stripe
rust, Fusarium head blight, powdery mildew, septoria tritici blotch, and even
wheat streak mosaic virus, Hessian fly and cereal cyst nematode (Gupta et al.,
2008; Lillemo et al., 2008; Marshall et al., 2001). Agronomic characters like
heading date (Ppd-B1, Eps), plant height, spike length and compactness,
grain weight, or coleoptile growth were reported to be controlled, at least in
part, by factors located on 2B (Gupta et al., 2008; Kumar et al., 2006; Marshall
et al., 2001; Marza et al., 2006; Rebetzke et al., 2001; Sourdille et al., 2000).
Resistance to lodging and stem strength QTL have been located on
chromosome 2D (Gupta et al., 2008). Different authors have associated
chromosome 4B with plant height (Rht-B1), coleoptile, internode and peduncle
length, and grain size (Gupta et al., 2008; Marshall et al., 2001; Rebetzke et
al., 2001). It is currently unclear if any of these agronomic traits are directly
associated with resistance to Cephalosporium stripe.
Of the seven resistance QTL identified by CIM, three were assigned to Coda
and four to Brundage. Yet the cumulative effect of the QTL from Coda, based
on additive effects, is higher than the cumulative effect of the QTL from
Brundage. The relationship between disease response and number of
resistance alleles at different QTL was shown to be significant. Regression
analyses suggest that superior levels of resistance can be attained through
80
accumulation of resistance QTL. The relative importance of individual QTL
was estimated by obtaining additive effects through CIM. These differences
were not considered in the regression analysis. Class mean comparisons
(Fisher’s Protected least significant difference, P<0.05) suggest that there are
three distinct levels of resistance. Susceptible types possess 0, 1 or 2 resistant
alleles, with class means approximately 4.0 or higher, and are not statistically
different from each other. The intermediate types, with mean whitehead scores
in the range of 3.0 to 3.5, possess 3 or 4 resistance alleles and are not
significantly different from each other. The resistant types, with 5, 6 or 7
resistance alleles, showed the lowest levels of disease with means between
2.0 and 2.5. There was no difference in disease response among those with 5,
6, or 7 alleles. Two transgressive segregants with whitehead scores
significantly lower than Coda had 6 and 7 resistance alleles. Head type
morphology did not have a substantial influence on the observed regression
between QTL number and disease response, however, and there was
substantial variability among RILs within each class.
Associations between kernel traits and response to Cephalosporium stripe
were significant, but weak. Though whitehead showed stronger correlations
with kernel weight SD than with mean kernel weight, only a small proportion of
the variation in either kernel trait could be explained by variation in disease
response. Previous reports have shown strong influence of Cephalosporium
stripe on reducing yield and related changes in yield components (Bockus et
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al., 1994; Johnston and Mathre, 1972; Morton and Mathre, 1980b; Richardson
and Rennie, 1970). In general, Cephalosporium stripe decreases grain yield,
kernel number and kernel weight. However, most of these studies are based
on per-plant results and not whole plots, which reduces the chance of potential
compensation effects on yield and kernel traits. In a related yield loss study on
12 cultivars and two levels of Cephalosporium stripe, disease response
measured as the difference in percent whiteheads between inoculated and
non-inoculated plots on individual genotypes, was strongly associated with
loss in grain yield, kernel weight, and increases in kernel weight SD (Chapter
3).
This study confirms that genetic variability for response to C. gramineum
exists among adapted PNW winter wheat germplasm, and that breeding and
selection for enhanced tolerance to C. gramineum is possible. The
methodology used for field screening proved to provide excellent results with
high levels of consistency and reliability. We were able to detect several
promising genomic regions associated with resistance to Cephalosporium
stripe. QCs.orp-5B and its potential relation with sensitivity to host selective
toxins was particularly notable. Several QTL were found related to variation in
kernel weight and kernel weight standard deviation. These are interpreted as
related to disease resistance QTL, but also may be associated with genes
defining kernel traits. The QTL for resistance associated with the Compactum
locus is a good example of possible pleiotropic effect that may not be related
82
to disease expression. Additional research is needed to determine
associations between Cephalosporium stripe and kernel traits, and whether
these are valuable, complementary measurements to visual disease ratings of
breeding lines for an accurate assessment of Cephalosporium stripe
response.
This study indicates that an acceptable level of resistance to Cephalosporium
stripe requires combining at least three to four resistance QTL, followed by
evaluation in infested fields to confirm performance, as it was shown that high
levels of variability are present within each class of number of accumulated
resistance alleles. Validation of QTL identified in this study is essential (Collard
et al., 2005; Melchinger et al., 2004) prior to implementing a marker assisted
selection program, however, since these results are based on one population
only and are not necessarily applicable to other situations. It will be important
to confirm that QTL and genomic regions identified for Cephalosporium stripe
resistance perform similarly in other genetic backgrounds to assure broad
applicability over a diverse range of germplasm. New mapping populations are
being phenotyped in similar field experiments and extensive genotyping is in
progress to address these issues.
83
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Table 2.1. Analyses of variance, coefficients of variation (CV), and heritability
estimates (h2) for percentage whiteheads, kernel weight, and kernel weight
standard deviation (SD) for a population of 268 recombinant inbred wheat
lines (RIL) exposed to Cephalosporium stripe disease in three environments
(Moro and Pendleton 2007 and Pendleton 2008)
Environment
Source of variation
DF
Whiteheads
a
(%)
Mean squares
Kernel weight
(mg)
Kernel weight
SD
b
Combined
Rep(Env)
Environment
RIL
RIL x Env
Error
CV (%)
2
h
Moro 2007
Rep
RIL
Error
CV (%)
2
h (90% CI)
Pendleton 2007
Rep
RIL
Error
CV (%)
2
h (90% CI)
Pendleton 2008
Rep
RIL
Error
CV (%)
2
h (90% CI)
4
2
267
534
c
1066
3.25**
337.16**
5.06**
1.48**
0.72
26.17
0.71
443.55**
3703.31*
58.49**
9.83**
6.54
8.29
0.83
6.55**
477.35**
7.38**
1.35**
1.00
13.26
0.81
1
267
267
0.43
2.26**
0.92
25.14
0.59 (0.50-0.67)
21.58*
22.11**
5.02
7.11
0.77 (0.72-0.81)
1.17
2.64**
0.88
11.71
0.67 (0.59-0.73)
1
267
266
1.75
3.62**
0.77
23.85
0.79 (0.74-0.83)
94.62**
27.86**
7.07
7.96
0.75 (0.69-0.79)
17.56**
4.61**
1.24
13.43
0.73 (0.67-0.78)
2
267
532
5.41**
2.00**
0.58
30.09
0.71 (0.66-0.76)
829.01**
28.45**
7.04
9.24
0.75 (0.71-0.79)
3.74*
2.50**
0.93
14.37
0.63 (0.56-0.69)
* Significant at the 0.05 probability level
** Significant at the 0.01 probability level
a
Whitehead percentages were square root-transformed prior to analysis
b
Sources of variation were tested for significance with appropriate error terms
c
Due to 1 missing value Whiteheads had only 1065 degrees of freedom
90
Table 2.2. Means and standard errors (SE) for percentage whiteheads (square
root-transformed), kernel weight, and kernel weight standard deviation (SD) for
parents (Coda and Brundage) and a population of 268 recombinant inbred
lines (RIL) of wheat exposed to Cephalosporium stripe disease in three
environments
Means ± SE
Coda
Whiteheads
Kernel weight (mg)
Kernel weight SD
Brundage
2.73 ± 0.15
4.39 ± 0.15
29.89 ± 0.49
34.69 ± 0.49
6.92 ± 0.21
8.87 ± 0.21
RIL
3.232 ± 0.020
RIL range
0.788 - 5.962
30.859 ± 0.059 23.089 - 39.444
7.535 ± 0.023
5.327 - 10.658
Table 2.3. Results of composite interval mapping (CIM) of Cephalosporium stripe response (square root of
percentage whiteheads), kernel weight, and kernel weight SD combined over three environments
Trait
QTL name
Linkage
a
Group
Whiteheads
SQRT
QCs.orp-2B
2B.1
QCs.orp-2D.1
2e
SD: 2.8
d
LOD 3.02
Total R : 50.4
QTL peak
position (cM)
1.0-LOD support
interval (cM)
Closest
marker
8.0
1.5-16.0
wmc453-2B
2D
23.7
22.9-25.8
C
QCs.orp-2D.2
2D
94.0
90.4-96.3
QCs.orp-4B
4B.1
33.9
QCs.orp-5A.1
5A.2
QCs.orp-5A.2
5A.3
QCs.orp-5B
5B
LOD
Additive
b
effect
R
2c
4.5
-0.26
8.1
10.9
-0.29
10.7
barc206
3.5
0.18
3.9
22.6-43.5
wpt-3908
5.3
0.21
5.4
22.0
15.5-23.9
wpt-3563-5A
6.3
0.25
7.7
24.6
22.5-26.3
B1
4.5
0.20
4.9
100.4
90.7-107.4
gwm639-5B
4.7
-0.31
12.4
a
Linkage groups correspond to chromosomes and Arabic numbers denote a specific linkage group within a
chromosome. Highlighted in bold letters are consistent QTL
b
Additive effects indicate an additive main effect of the parent contributing the higher value allele: positive values
indicate that higher value alleles are from Coda and negative values indicate that higher value alleles are from
Brundage
c
Proportion of the phenotypic variance explained by the QTL after accounting for co-factors
d
Threshold level to declare a significant QTL, based on 1000 permutations
e
Total phenotypic variance explained by all QTL after accounting for co-factors, and standard deviation
91
Table 2.3. (Continued)
QTL name
Linkage
a
Group
Kernel weight
QGw.orp-1B
1B.1
33.6
23.5-40.1
barc137-1B
4.5
-0.67
5.0
QGw.orp-2B
2B.1
50.4
46.7-53.6
gwm120
3.6
-0.48
2.6
Total R : 66.8
2e
QGw.orp-2D.1
2D
23.7
23.4-25.2
C
45.9
-1.94
40.7
SD: 0.9
QGw.orp-2D.2
2D
88.0
84.0-91.9
wpt-8713-2D
7.8
0.68
5.0
QGw.orp-4B
4B.1
33.9
22.3-44.9
wpt-3908
7.9
0.70
5.3
QGw.orp-5A
5A.3
24.6
22.8-26.3
B1
6.5
0.64
4.5
QGw.orp-7D
7D.2
57.5
53.6-58.5
wmc273
7.3
0.66
4.9
d
LOD 3.02
QTL peak
position (cM)
1.0-LOD support
interval (cM)
Closest
marker
LOD
Additive
b
effect
R
2c
Trait
a
Linkage groups correspond to chromosomes and Arabic numbers denote a specific linkage group within a
chromosome. Highlighted in bold letters are consistent QTL
b
Additive effects indicate an additive main effect of the parent contributing the higher value allele: positive values
indicate that higher value alleles are from Coda and negative values indicate that higher value alleles are from
Brundage
c
Proportion of the phenotypic variance explained by the QTL after accounting for co-factors
d
Threshold level to declare a significant QTL, based on 1000 permutations
e
Total phenotypic variance explained by all QTL after accounting for co-factors, and standard deviation
92
Table 2.3. (Continued)
Trait
QTL name
Linkage
a
Group
Kernel weight
SD
QGwsd.orp-2B
2B.1
27.3
22.5-31.9
gwm410
QGwsd.orp-2D.1
2D
23.7
23.5-24.4
C
2e
QGwsd.orp-2D.2
2D
90.0
81.9-94.6
SD: 0.6
QGwsd.orp-3A
3A.1
17.1
QGwsd.orp-4B
4B.1
QGwsd.orp-6B
6B
QGwsd.orp-7A
7A.2
d
LOD 3.04
Total R : 64.7
QTL peak
position (cM)
1.0-LOD support
interval (cM)
Closest
marker
LOD
Additive
b
effect
R
2c
3.7
-0.19
3.1
41.8
-0.66
38.5
wpt-8713-2D
4.2
0.19
3.3
14.9-17.7
barc019
3.2
0.15
2.0
33.9
27.8-40.0
wpt-3908
9.2
0.28
6.6
66.3
61.0-75.2
wpt-2726-6B
6.3
-0.22
4.5
132.5
118.6-132.5
wpt-1259-7A
4.1
-0.18
2.7
a
Linkage groups correspond to chromosomes and Arabic numbers denote a specific linkage group within a
chromosome. Highlighted in bold letters are consistent QTL
b
Additive effects indicate an additive main effect of the parent contributing the higher value allele: positive values
indicate that higher value alleles are from Coda and negative values indicate that higher value alleles are from
Brundage
c
Proportion of the phenotypic variance explained by the QTL after accounting for co-factors
d
Threshold level to declare a significant QTL, based on 1000 permutations
e
Total phenotypic variance explained by all QTL after accounting for co-factors, and standard deviation
93
94
Number
of lines
Moro 2007
100
75
50
25
0
Brundage: 3.7
Coda: 3.6
Stephens: 5.8
0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8
Whiteheads
Number
of lines
Pendleton 2007
100
75
50
25
0
Brundage: 5.1
Coda: 2.1
Stephens: 7.6
0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8
Whiteheads
Number
of lines
Pendleton 2008
125
100
75
50
25
0
Brundage: 4.4
Coda: 2.4
Stephens: 5.5
0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8
Whiteheads
Figure 2.1. Frequency distributions of Cephalosporium stripe response
(square root of percentage whiteheads) for 268 recombinant inbred lines of
wheat derived from a cross between the cultivars Coda and Brundage, in three
environments; the cultivar Stephens was a susceptible check.
95
Figure 2.2. Frequency distributions of kernel weight for 268 recombinant
inbred lines derived from a cross between the wheat cultivars Coda and
Brundage in the presence of Cephalosporium stripe in three environments.
96
Figure 2.3. Frequency distributions of kernel weight standard deviation (SD)
for 268 recombinant inbred lines derived from the cross of the wheat cultivars
Coda and Brundage evaluated in the presence of Cephalosporium stripe in
three environments.
97
Figure 2.4. Partial linkage map of the Coda x Brundage recombinant inbred
line population (n=268), with graphical presentation of significant QTL
revealed by CIM on combined data of disease response (square root of %
whiteheads, black bars), kernel weight (cross hatched bars) and kernel weight
standard deviation (white bars) over three environments.
Figure 2.4.
1B
2B
cfd44-2D
wPt-8713-2D
96.3
barc206*
wPt-6340-5A
20.8
gwm443
37.3
gwm154-5A
56.6
62.1
69.9
70.0
barc180.1-5A
barc056-5A
wPt-0270-5A
wPt-2329-5A
gwm291-5A
Awns-5A
gwm666
8.8
wPt-1036-3A
16.5
wPt-4545-3A
wPt-9504-5B
wPt-6880-5B
31.3
barc232-5B
45.9
49.1
wPt-7665-5B
wPt-5168-5B
64.7
wPt-9894-5B
79.0
86.3
90.3
92.4
wPt-9454-5B
wPt-3661-5B
wPt-1250-5B
gwm639-5B
118.4
barc004-5B
wPt-3908-4B
69.4
barc125.1
7A
wPt-3774-6B
22.3
wPt-8814-6B
35.6
39.4
barc136-6B
wPt-5333-6B
60.3
64.3
68.7
wPt-3060-6B
wPt-2726-6B
wPt-4924-6B
78.7
barc178-6B
97.6
gwm219-6B
0.0
2.7
9.7
7D
wPt-8789-7A
wPt-1510-7A
wPt-6668-7A
0.0
barc052-7D
9.2
wmc506-7D
19.3
barc184-7D
0.0
barc219.1-7A
13.4
16.9
wPt-1928-7A
wPt-3883-7A
0.0
55.0
58.0
wPt-3393-7A
wPt-4796-7A
45.9
48.4
49.4
49.7
53.5
58.5
91.4
96.5
wPt-6013-7A
wPt-4553-7A
132.6
wPt-1259-7A
gwm044
wPt-5945-7D
wPt-1628-7D
PCH1-7D
cfd175-7D
XUST-7D
wmc273-7D
QGw.orp-7D
20.6
26.3
0.0
98
QGwsd.orp-7A
wPt-5096-5A
QGw.orp-5A
0.0
QCs.orp-5A.2
wPt-3563-5A
QCs.orp-5A.1
23.9
14.4
20.6
0.0
QCs.orp-5B
barc151.1-5A
wPt-0836-3A
gwm369-3A
6B
barc059.1-5B
QGwsd.orp-6B
10.4
0.0
QGwsd.orp-5B
wPt-1165-5A
55.1
57.4
33.9
QGwsd.orp-4B
85.9
88.0
barc012-3A
barc057-3A
wPt-4352-3A
wPt-7992-3A
wmc11-3B
wPt-8756-4B
QGw.orp-4B
wPt-0638-2D
5B
0.0
0.0
gwm484.2-2D
75.2
wPt-2397-2B
106.2
5A
67.9
barc019
barc067
26.1
32.0
34.8
36.5
39.7
0.0
QCs.orp-4B
wPt-5242-2B
wPt-9257-2B
13.2
17.1
QGwsd.orp-3A
74.2
75.8
QGw.orp-2D.2
wPt-3132-2B
wPt-4144-2D
wPt-0330-2D
QGwsd.orp-2D.2
62.7
47.1
48.4
QGw.orp-2D.1
wPt-9032-1B
barc018
wPt-0335-2B
barc091-2B
gwm120
wPt-5044-2B
4B
wPt-2910-3A
0.0
QGwsd.orp-2D.1
82.9
38.3
43.0
44.4
52.1
54.8
wPt-6003-2D
barc297.1-2D
cfd168-2D
cfd56-2D
wmc144-2D
Compactum-2D
wmc630b-2D
wmc112-2D
QCs.orp-2D.2
barc081
wmc453-2B
wPt-8492-2B
wPt-2314-2B
gwm410
3A
0.0
6.6
10.9
13.9
21.3
23.7
26.5
31.2
QCs.orp-2D.1
75.1
11.9
17.7
21.3
25.2
QGw.orp-2B
barc137-1B
wPt-3561-2B
QGwsd.orp-2B
43.4
2D
0.0
QCs.orp-2B
barc032-1B
barc152
wPt-2762-1B
wPt-0328-1B
wPt-0240-1B
barc119.1
gwm413
QGw.orp-1B
0.0
8.1
11.1
12.1
18.9
21.2
27.6
36
Common-Awned
Common-Awnless
Club-Awned
Club-Awnless
25
B
Whiteheads (%)
16
9
C
4
1
0
0
Whiteheads (sq root) =
-0.41x + 4.67
R² = 0.382
1
2
3
4
5
Number of resistance alleles
6
7
Figure 2.5. Regression of least squares means of % whiteheads caused by Cephalosporium stripe on number of
resistance alleles present at 7 QTL for a population of 268 recombinant inbred lines derived from a cross between the
wheat cultivars Coda (C) and Brundage (B).
99
100
RELATIONSHIP BETWEEN INCIDENCE OF CEPHALOSPORIUM STRIPE
AND YIELD LOSS IN WINTER WHEAT
Abstract
Cephalosporium stripe (caused by Cephalosporium gramineum) is an
important disease of winter cereals, and it is widespread throughout the Pacific
Northwest of the USA. The disease can cause severe loss of grain yield and
quality in winter wheat (Triticum aestivum L.), especially when early plantings
and reduced or no tillage are practiced to control soil erosion. Yield loss and
impact on kernel characteristics were examined using 12 varieties in field plots
inoculated and not inoculated with the pathogen and by estimating the
percentage of whiteheads (sterile heads caused by pathogen infection). Grain
yield of the susceptible cultivar Stephens was reduced by 1.7 t ha-1 in
Pendleton and test weight was reduced by 5.4 kg hl-1 with the addition of
inoculum. The most resistant and the most susceptible varieties performed
similarly in the two environments studied, while varieties with intermediate
levels of resistance sometimes interacted with environment. Regression
analyses suggest that some yield and test weight loss occurs in the absence
of whitehead symptoms. The regression analyses also reveals a linear
relationship between yield and % whiteheads in one environment and a
curvilinear relation in the other. Percentage of whiteheads caused by
Cephalosporium stripe was positively correlated with uniformity in kernel size,
possibly as a function of reduced number of seeds per spike. Results suggest
that breeding programs should strive to produce cultivars with less than 5%
101
whiteheads for environments in which Cephalosporium stripe is severe.
Introduction
Cephalosporium stripe of wheat is caused by the soil-borne fungal pathogen
Cephalosporium gramineum Nisikado and Ikata (syn. Hymenula cerealis Ellis
& Everh.) (Bruehl, 1956; Ellis and Everhart, 1894; Nisikado et al., 1934). The
fungus has a wide range of hosts, mainly among winter cereals (Bruehl, 1957;
Willis and Shively, 1974). Cephalosporium stripe is of economic importance
only in winter wheat, however. The disease is an important, limiting factor in
many winter wheat production areas (Gray and Noble, 1960; Hawksworth and
Waller, 1976; Kobayashi and Ui, 1979; Slope and Bardner, 1965). It is
widespread throughout the Pacific Northwest, where wheat growers in
erosion-prone areas are particularly affected when early plantings and
reduced or no tillage are practiced (Bruehl, 1957; Mundt, 2002; Murray, 2006;
Wiese, 1987).
C. gramineum survives between host crops saprophytically as mycelium and
conidia in association with host residues on or near the soil surface (Lai and
Bruehl, 1966). Infested crop residue is the primary source of inoculum. Conidia
produced in the top layer of soil on crop stubble and released during cool and
moist weather conditions during fall and winter are washed down into the root
zone to infect the next crop (Mathre and Johnston, 1975a; Wiese and
Ravenscroft, 1973). Once inside the roots, the fungus invades the vascular
102
system and has the potential to colonize the entire plant. Successful
establishment of C. gramineum inside the host is enhanced by the production
of toxic metabolites that block the vascular system, thus preventing normal
movement of water and nutrients (Bruehl, 1957; Spalding et al., 1961; Wiese,
1987).
The most typical and recognizable symptom, chlorotic leaf striping, is apparent
on the younger, upper leaves during jointing and heading. Severely infected
stems are stunted and prematurely ripen, producing a white and usually sterile
head, containing sometimes just a few shriveled seeds. It is at this level of
infection in which the greatest amount of yield loss is observed (Johnston and
Mathre, 1972; Mathre and Johnston, 1975b; Morton et al., 1980; Nisikado et
al., 1934; Wiese, 1987). In areas conducive to Cephalosporium stripe (i.e.
Kansas and Montana in the USA, and Scotland), up to 80% yield reduction
from a generalized infection on a susceptible cultivar can occur (Bockus et al.,
1994; Johnston and Mathre, 1972; Mathre et al., 1977; Morton and Mathre,
1980a; Richardson and Rennie, 1970). Precise information on the impact of
Cephalosporium stripe on grain yield for Pacific Northwest conditions is not
available. Yield losses caused by this fungus appear to be the product of a
combination of reduced seed number and reduced seed weight (Johnston and
Mathre, 1972; Morton and Mathre, 1980a). Impacts on grain protein, test
weight, and end-product quality also may occur (Johnston and Mathre, 1972;
Mathre et al., 1977; Morton and Mathre, 1980a).
103
Reducing incidence of Cephalosporium stripe has generally been
accomplished by reducing inoculum in the soil via cultural controls such as
crop rotation, management of crop residues, altering soil pH with lime
applications, and fertilizer management (Bockus et al., 1983; Latin et al., 1982;
Martin et al., 1989; Mathre and Johnston, 1975a; Murray et al., 1992; Pool and
Sharp, 1969; Raymond and Bockus, 1984). However, these practices are only
partially effective in reducing the incidence and severity of the disease (Li et
al., 2008), and often are practically or economically unfeasible. Additionally,
Cephalosporium stripe cannot be controlled with fungicides. Although variation
in the degree of resistance among cultivars has been confirmed, genotypes
with complete resistance to C. gramineum have not been found (Bruehl et al.,
1986; Martin et al., 1983; Mathre et al., 1977; Morton and Mathre, 1980b).
However, repeated planting of moderately resistant cultivars has been
reported to reduce both the incidence and severity of Cephalosporium stripe
over years (Shefelbine and Bockus, 1989).
The goals of this study were to estimate the magnitude of potential grain yield
loss caused by Cephalosporium stripe under Oregon production conditions, its
association with changes in test weight and kernel characteristics, and to
estimate the level of host plant resistance required to attain minimal yield loss
under mild to extreme infection levels.
104
Materials and methods
Field trials were conducted at the Columbia Basin Agricultural Research
Center field station near Pendleton, OR, during three winter wheat seasons
(2005-06, 2006-07 and 2007-08), and one trial at the field station in Moro, OR,
for the 2006-07 crop season. Both locations are in semi-arid wheat-producing
areas of the Columbia Plateau, with mean annual precipitation of 406 mm in
Pendleton and 279 mm in Moro. These sites are representative of eastern
Oregon winter wheat production areas where Cephalosporium stripe is
frequent. Two trials at Pendleton (2006-07 and 2007-08) were excluded from
analyses due to failure to establish a sufficiently large disease differential
between inoculated and non-inoculated plots. A randomized complete block
design with four replications was used at each location. Treatments consisted
of a factorial of two levels of disease (inoculated and non-inoculated), with 10
varieties in Pendleton and 12 in Moro; 10 varieties were common to both trials.
Differential disease levels were obtained by adding autoclaved oat kernels that
were previously infested with C. gramineum to the inoculated plots.
Autoclaved oat kernels not infested with the fungus were added to the noninoculated plots. Inoculum was produced following the description by Mathre
and Johnston (1975b), and was added to the seed envelopes before planting,
at a dose equal in weight to the wheat seed.
105
Trials were sown into stubble mulch on 12 September 2005 in Pendleton and
on 12 September 2006 in Moro. Early September sowing dates increase
severity of Cephalosporium stripe at these sites. Each plot was four rows (1.5
m) x 6.1 m long. Border plots were included around each trial. A Hege 500
series plot drill (H&N Manufacturing, Colwich, KS) with deep furrow openers
was used to place seed into moist soil. Fertilization and weed control practices
were appropriate to commercial winter wheat production at the two sites.
Hand-weeding was necessary in Pendleton at post-anthesis to keep weeds
pressure at a low level. A spring application of fungicide (Bumper 41.8EC,
propiconazole) was applied to avoid infection by O. yallundae and O.
acuformis, which can mask symptoms of Cephalosporium stripe. Plots were
mowed to approximately 4.5 m in length post-heading and prior to collecting
disease data. Plot length was recorded before harvest to adjust yield
estimations. Trials were harvested during July at maturity and after an
adequate level of grain moisture was reached. Entire plots were harvested
with a plot combine, adjusted to maximize the retention of diseased and
shriveled kernels.
Cephalosporium stripe incidence was recorded on a plot basis through visual
estimation of the percentage of tillers that were ripening prematurely, and
which usually expressed complete or partial reduction of grain-fill (whiteheads)
(Mathre and Johnston, 1975b; Morton and Mathre, 1980b). Evaluation of
known check varieties and random examination of lower stems and roots
106
provided confidence that whiteheads were caused predominately by
Cephalosporium stripe. Disease notes were taken at each location about 3
weeks after heading. Developmental stage of the entries ranged from early
milk to early dough at this time. Plant height and physiological maturity were
also recorded.
Harvested grain was carefully cleaned using airflow to remove non-grain
contamination. Grain weight per plot was measured after cleaning. A one
kilogram sample was taken from each bag to determine test weight (hectoliter
weight), grain protein concentration (%), and grain moisture content (%). Test
weight and grain moisture were measured with a Grain Analysis Computer
(GAC) model 2100b (Dickey-john® Corporation, Auburn, IL). Protein content
was measured with an Infratec 1241 Grain Analyzer (Foss, Eden Prairie, MN)
with appropriate settings for soft white or hard red winter wheat varieties.
A 300 seed sub-sample was randomly taken from each bulk and analyzed for
kernel weight (mg) and diameter (mm), using a Single Kernel Characterization
System (SKCS) model 4100 (Perten Instruments, Springfield, IL). For each
sample, the SKCS integrated computer software (Perten Instruments,
Springfield, IL) provided the means and standard deviations of the 300
individual kernel determinations.
Plant Material
107
Varieties and germplasm were included in the experiments based on
commercial importance, performance in previous Cephalosporium stripe
screening nurseries, and their range in disease response. Ten varieties were
evaluated in the Pendleton trials. Stephens (CI 017596), Madsen (PI 511673)
and Tubbs (PI 629114) are major cultivars grown in the region. The European
variety Rossini and two derived breeding lines (OR9800919 and OR9800924,
both with the pedigree Rossini/Ysatis//Oracle) were included, as these were
previously shown to have moderate-to-high levels of disease resistance. Three
experimental lines with varying levels of resistance were included. These
originated from crosses between the Rossini-derived lines and adapted
Oregon material [OR02F-B-46 (Tubbs//OR9800924/Weatherford), OR02F-C169 (Tubbs//OR9800924 /OR9900553) and OR02F-D-27
(OR9800924/Weatherford)]. A highly resistant club winter wheat (WA 7437, PI
561033) was included as a resistant check. At Moro, two new releases were
added to the previous list of varieties to verify their performance to the disease
(Skiles and ORSS-1757). Skiles had previously shown moderate-to-high levels
of resistance, while ORSS-1757 (PVP 200500336) was considered
moderately susceptible to the disease.
Statistical analyses
Statistical analyses were performed with the Statistical Analysis System (SAS)
(SAS v9.1, SAS Institute Inc., Cary, NC, USA). Analyses of variance (ANOVA)
for disease response, yield, test weight, and kernel related traits were
108
conducted with PROC GLM to determine the level of variation between blocks,
and to test the significance of both treatment factors (disease and varieties)
and their interaction. Type III F statistics were used to test the significance of
variance sources. For ANOVA, whitehead percentages were square-root
transformed to meet the assumptions of normality and homogeneity of
variance. The significance of the disease treatment on individual varieties was
determined with the SLICE option in the LSMEANS statement.
Yield loss for genotypes was estimated as the reduction in grain yield between
non-inoculated and inoculated plots expressed as percentage relative to the
yield in non-inoculated plots. Similar calculations were done to estimate loss or
change in test weight and kernel traits due to disease.
Pearson correlation coefficients among traits were estimated from genotype
least square means using the PROC CORR procedure in SAS, pooling means
from inoculated and non-inoculated treatments together.
Linear regressions were fitted to estimate the relationship of grain yield loss
and test weight loss to the susceptibility of genotypes to Cephalosporium
stripe. The level of susceptibility was measured as the difference in
whiteheads between inoculated and non-inoculated plots. Grain yield loss was
calculated for each cultivar as: (grain yield non-inoculated - grain yield
inoculated)*100/ grain yield non-inoculated. Test weight loss was estimated as
109
well, and was calculated similarly. Linear and quadratic regressions of yield
and test weight loss on disease response differential were fitted using the
PROC REG procedure in SAS.
Results
Analyses of variance for each trial and all measured variables are presented in
Table 3.1. Main effects were significant (P=0.05) for all variables in both
experiments, the exception being grain protein, which showed no significant
effect of genotypes in Pendleton and inoculation in Moro. However, all
variables showed a significant interaction (P=0.05) between the inoculation
treatment and genotype in Pendleton. For the Moro trial the interaction term
was also significant (P=0.05) for most of the variables, with the exceptions of
test weight, kernel weight and kernel diameter.
Cephalosporium stripe occurred in non-inoculated plots of both trials, but more
so at Moro than at Pendleton (Tables 3.2 and 3.4). Nonetheless, the mean
disease scores (whiteheads) for genotypes differed significantly (P<0.01)
under inoculated versus non-inoculated conditions for all varieties, with the
exception of WA 7437 and OR9800924 in Pendleton (Table 3.2) and WA 7437
in Moro (Table 3.4). Varieties responded similarly with increased whiteheads
and reduced grain yield under inoculated conditions, with yield reductions
ranging from 14.2 to 32.0%. For OR02F-D-27 increased whiteheads rating
was not related to loss in grain yield in Pendleton. Stephens, a highly
110
susceptible cultivar, showed in both trials the biggest difference in percentage
whiteheads between inoculated and non-inoculated plots (41.3 and 42.5% for
Pendleton and Moro, respectively) and the greatest yield loss (32.0 and
41.2%, respectively).
Genotypes with significant grain yield loss also had significant reductions in
test weight in Pendleton. The reduction in test weight ranged from 0.23 to 5.43
kg hl-1. Standard deviation of kernel weight and kernel diameter also were
affected by increased disease pressure. The same eight genotypes that
showed an increase in whitehead scores were observed to have a significant
increase in kernel weight SD, and six of these genotypes also showed an
increase in kernel diameter SD, reflecting an increase in kernel size variability
due to higher disease incidence (Table 3.3 and Table 3.5). Differences in test
weight among varieties at Moro were small and non-significant for many
genotypes. None of the other variables studied at this location presented
consistent changes among inoculated and non-inoculated treatments.
Significant differences were observed but were always genotype-dependent.
There was a general negative correlation of disease scores with grain yield,
with r-values of -0.62 at Pendleton and -0.81 at Moro (Table 3.6). Similar
correlations, but lower in magnitude, were observed between disease and test
weight, with r=-0.52 at Pendleton and r=-0.44 at Moro. Overall, grain yield was
independent of test weight with a non-significant correlation (P>0.05). In
111
Pendleton 2006, Cephalosporium stripe response was positively correlated
(P<0.001) with kernel weight SD and kernel diameter SD, as would be
expected from differences observed among genotypes at contrasting disease
levels. These correlations were not significant at Moro. There was no
significant correlation between disease and mean kernel weight or mean
kernel diameter at either location.
Regressions were fitted to estimate % grain yield loss as a function of
increasing susceptibility to Cephalosporium stripe. Changes in test weight
were calculated similarly. The response of grain yield to whiteheads difference
between inoculated and non-inoculated plots in Pendleton followed a
polynomial regression that included a quadratic term (Fig. 3.1). The regression
model was highly significant (P<0.001) with a coefficient of determination (r2)
of 0.76. The intercept was estimated to be 6.44%, but was not significant
(P=0.11). In Moro, the data were best represented with a simple linear
regression with coefficient of determination (r2) of 0.74 (Fig. 3.1). The intercept
(7.49%) was significant at P=0.06.
The relationship between whiteheads and reductions in test weight was nonlinear for both locations. The best fit was a polynomial regression with
significant quadratic terms (P<0.05) (Fig. 3.2). The intercept was significant for
Moro (P<0.05), however, it was not statistically different from zero at
Pendleton (P=0.19). The linear coefficients were highly significant (P<0.01) for
112
Pendleton but not for the Moro trial (P=0.10). Coefficients of determination
were 0.80 and 0.53 for Pendleton and Moro, respectively.
Discussion
The general objective of yield loss studies is to provide quantitative estimates
regarding effect of disease on its host crop (Savary et al., 2006). In many hostpathogen systems the assessment of crop damage is done using comparisons
with a fungicide protected control (Griffey et al., 1993; Herrera-Foessel et al.,
2006). Trials often include artificial inoculation of the pathogen to ensure high
and uniform disease pressure (Bockus et al., 1994; Gutteridge et al., 2003;
Johnston and Mathre, 1972; Morton and Mathre, 1980a). For Cephalosporium
stripe, such studies are limited to treatments with artificial inoculation only as
no fungicides are yet available for the control of C. gramineum. Because low
levels of whiteheads occurred in non-inoculated plots in Pendleton, and even
higher levels occurred in Moro, we evaluated yield loss based on differences
in whiteheads between inoculated and non-inoculated plots.
Despite major differences in rainfall, soil fertility, and environmental stress,
susceptible and resistant varieties performed similarly at both Pendleton and
Moro. The range of differences in whiteheads between inoculated and noninoculated plots was also similar among locations, with a maximum increase of
40%. Lines with intermediate levels of Cephalosporium stripe showed more
variability between locations, however. OR9800924 performed similarly to WA
113
7437 (resistant check) in Pendleton. In Moro, however, the same variety had a
mean 22.0% increase in whiteheads and 23.2% grain yield loss under
inoculation. Madsen, which is considered to have acceptable levels of field
resistance, showed 12.8% whiteheads difference at Pendleton with a mean
24.8% yield loss. This is close to the level of yield reduction exhibited by the
susceptible cultivars Stephens and Tubbs. In Moro, however, with similar
whiteheads difference (11.5%), yield loss was only 13.0% and was similar to
the reduction observed for the resistant check (WA 7437). All Rossini-derived
genotypes, selected in Pendleton, showed fewer whiteheads under inoculation
than their resistant parent in the Pendleton trial, with yield losses ranging from
1.7% to 28.1%. In Moro, however, these lines had higher whiteheads
differences than Rossini and yield losses between 21.0 and 36.0%. Thus,
selection for resistance to Cephalosporium stripe should be performed under
inoculation in both environments for best results. Significant year x treatment
interactions have been reported for Cephalosporium stripe response and
associated yield losses by Bockus et al. (1994). Roberts and Allan (1990)
found no significant genotypic differences for response to C. gramineum
among 20 varieties in one trial, but important differences among the same set
of varieties were found in two other experiments.
Regressions of grain yield loss on whitehead change were similar for both
environments, although the fitted models were not the same. At Pendleton, a
quadratic polynomial provided the best fit, while at Moro the relationship was
114
linear. Intercepts of the two models indicated similar yield loss at zero
whitehead change (6.44 and 7.49% at Pendleton and Moro, respectively),
though statistical support for the intercepts was not high in either case (P =
0.06 and P = 0.11 at Pendleton and Moro, respectively). Both intercepts were
positive, indicating that even for highly resistant varieties some level of yield
loss may occur under high disease pressure. As an example, the highly
resistant selection WA 7437 showed around 10% grain yield loss in both
environments, yet not significant, with 5% whiteheads or less. Thus, infection
by C. graminium probably induces sufficient damage to cause yield loss even
in absence of whiteheads. In fact, leaf symptoms are commonly seen on
infected plants in absence of whiteheads. Another possibility is that resistance
mechanisms induced by the pathogen result in physiological “costs” to the
host (Brown, 2002; Purrington, 2000). “Cost of resistance” has been observed
for resistance genes in several host-pathogen systems (Bergelsen and
Purrington, 1996; Herrera-Foessel et al., 2006). When a plant is attacked by a
pathogen it induces defense mechanisms that are energy-demanding,
implying an extra cost for the plant. However, there are no reports as to
whether resistance to Cephalosporium stripe involves such a defense
mechanism.
At low to intermediate disease levels, the relationship between disease and
yield loss was linear for both environments. Regression coefficients for
Pendleton (0.6) and Moro (0.7) suggest that for each additional unit increase
115
in disease pressure, there is a loss of 0.6 to 0.7% in grain yield (Fig. 1).
Maximum yield losses estimated by the regressions were around 30 to 35%
for Pendleton and Moro, though higher yield losses are certainly possible
under more severe disease pressure. Bockus et al. (1994), reported yield loss
to Cephalosporium stripe ranged from 26 to 65% on a single susceptible
cultivar, depending on the year. Earlier, Richardson and Rennie (1970) and
Johnston and Mathre (1972), had reported estimates of potential yield loss on
individual plants of up to 70 and 78% respectively.
Analysis of test weight is often included in yield-loss studies to evaluate the
effect of disease on grain quality, which can be an important component of the
monetary value of the crop. The inclusion of a quadratic effect increased the
overall fit of the regressions. However, shapes of the curves differed between
the two sites. For Pendleton the function was parabolic while, for Moro, there
was a hyperbolic relationship between loss of test weight and whitehead
increase. Maximum reduction in test weight was recorded for Stephens and
OR02F-C-169 at Pendleton, and was 5.43 kg hl-1.
Test weight loss increased linearly at a rate of 0.32 kg hl-1 for each unit
increase in whiteheads in Pendleton. As whiteheads increased to 15 to 20%,
the slope deceased, meaning the rate of change in test weight was less at
higher disease levels. In contrast, results from Moro indicated that test weight
losses were not substantial until more than 25% whiteheads change was
116
observed. The maximum loss observed at Moro was about half of that
observed in Pendleton. In studies on take-all, which is another soil bornepathogen that affects wheat and also produces whiteheads, test weight was
usually inversely related to disease severity and responded to take-all intensity
similarly to grain yield (Gutteridge et al., 2003).
In Pendleton trial, Cephalosporium stripe not only affected grain yield and test
weight, but also had a significant impact on uniformity of kernel size and
weight. Morton and Mathre (1980a), investigating the physiological effects of
C. gramineum on winter wheat, determined that pathogenesis was most
damaging after anthesis during the grain filling period. The disease had little
impact on the number of seeds per spike, but had a large impact on kernel
weight. Richardson and Rennie (1970) also attributed grain yield losses to the
effects of the pathogen that occur later in the life of plants (grain filling).
Johnston and Mathre (1972) reported that decreased yield of infected plants
was related to both decreased weight and number of seeds formed per head.
In the Moro trial, although test weight decreased, kernel attributes did not
change in relation to increasing disease. Perhaps the disease contributed to
subtle changes in kernel conformation, or shape, unrelated to weight or size,
that impacted test weight. The significant yield losses at Moro, without
corresponding changes in kernel weight, suggest the disease either reduced
tillering or kernel number, as was suggested by Johnston and Mathre (1972).
117
The role and impact of plant pathogens are not static, but change in relation to
varieties, environments, management, and cropping systems. Understanding
potential damage, risk, and vulnerability from pathogens are important for
prioritizing breeding objectives and allocating resources to crossing, selection,
and screening of germplasm. It also has direct impact on release decisions, in
that new cultivars should have low risk of yield loss from major diseases that
occur in the target region. For producers, risk of losses from pathogens are
important considerations in many management decisions, including choice of
tillage practices, planting date, crop rotations, and choice of varieties. For
example, potential yield gains from early fall seeding dates can be far
outweighed by increased risk of damage from soil diseases.
Cephalosporium stripe is known to cause significant damage to wheat grown
in the Pacific Northwest. Economic damage has been erratic, often
inconsistent within fields, and generally reduced by avoiding early plantings.
Cephalosporium stripe often is lumped into the category of ‘chronic diseases’,
for which modest resources have been allocated for prevention and breeding
for resistance. In this study, there was evidence for yield reduction in presence
of the disease before whitehead symptoms were significant. Yield losses of
nearly 50% were found in the most susceptible varieties. Intermediate levels of
resistance were shown to be valuable in reducing economic damage from the
pathogen. Varieties with intermediate resistance should be sufficient for most
production situations, especially as the disease is generally not highly
118
aggressive. However, higher levels of resistance, as observed in WA 7437,
are needed to avoid losses under high inoculum levels and favorable
environmental conditions.
119
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Table 3.1. Analysis of variance for % whiteheads (square root-transformed) and grain parameters for wheat
genotypes grown in plots inoculated or not inoculated with Cephalosporium gramineum in Pendleton 2006 and Moro
2007
Environment
Source of
variation
DF
Whiteheads
Yield
Test
weight
Kernel weight
Kernel diameter
Protein
avg
SD
avg
0.714
9.40**
0.677
0.030**
0.0002
77.15**
42.506**
0.143**
0.0590**
139.08**
20.803**
0.332**
0.0277**
SD
Pendleton 2006
Block
3
0.31
3.12**
8.88**
Inoculation
1
113.34**
21.65**
212.23**
Genotype
9
15.08**
1.21**
36.27**
0.546
Inoculation x
Genotype
9
4.33**
0.63**
6.61**
0.678*
4.52**
0.781*
0.010**
0.0017**
0.20
0.20
1.34
0.320
1.61
0.330
0.003
0.0006
22.0
9.3
1.6
5.3
3.6
5.9
2.3
4.9
Error
57
CV (%)
5.274**
Moro 2007
Block
3
0.11
0.83**
10.63**
0.371
25.50**
0.659
0.056**
0.0011
Inoculation
1
198.27**
25.06**
33.36**
0.004
12.80*
5.880**
0.028*
0.0093**
Genotype
11
7.25**
1.37**
30.81**
2.318**
96.50**
8.978**
0.160**
0.0109**
Inoculation x
Genotype
11
2.37**
0.35**
1.34
1.179*
3.33
1.209**
0.006
0.0022**
Error
69
0.46
0.11
0.81
0.520
2.53
0.250
0.005
0.0005
CV (%)
16.4
9.4
* Significant at the 0.05 probability level
** Significant at the 0.01 probability level
1.2
8.4
4.7
5.7
2.9
4.5
122
Table 3.2. Percent whiteheads, grain yield, test weight and grain protein of 10 wheat genotypes non-inoculated (U)
and inoculated (I) with Cephalosporium gramineum in field plots in Pendleton, Oregon, 2006
-1
Whiteheads (%)
U
I
-1
Grain yield (t ha )
a
Change
U
I
Loss (%)
Test weight (kg hl )
b
U
I
Loss
Grain protein (%)
U
I
Change
Stephens
5.8
47.0
41.3**
5.35
3.64
32.0**
77.15
71.73
5.43**
10.31
11.46
1.15**
Madsen
0.5
13.3
12.8**
5.58
4.20
24.8**
78.05
73.68
4.38**
10.40
10.97
0.57
Tubbs
2.9
35.8
32.9**
5.66
4.03
28.9**
76.95
72.10
4.85**
10.17
10.92
0.75
OR9800919
0.6
9.0
8.4**
5.77
4.95
14.2*
74.28
71.10
3.18**
10.47
10.88
0.42
OR9800924
0.2
1.1
0.9
4.90
4.82
1.7
75.80
74.38
1.43
10.74
10.92
0.18
Rossini
1.1
17.5
16.4**
5.91
4.50
23.8**
77.63
73.93
3.70**
10.02
10.63
0.61
OR02F-B-46
0.4
4.8
4.3**
5.71
4.54
20.6**
72.83
70.30
2.53**
10.31
10.69
0.38
OR02F-C-169
0.5
11.8
11.2**
4.70
3.38
28.1**
78.30
72.88
5.43**
10.29
11.59
1.31**
OR02F-D-27
0.1
5.0
4.9**
5.01
4.59
8.3
71.78
70.33
1.45
9.86
10.47
0.62
WA 7437
0.0
0.0
0.0
4.72
4.27
9.5
78.08
77.85
0.23
11.32
10.49
-0.83*
LSD (0.05)
3.7
3.7
0.63
0.63
1.64
* Significant at the 0.05 probability level; ** Significant at the 0.01 probability level
a
Significance is based on percent whiteheads square root-transformed
b
Grain yield loss (%) = (non-inoculated – inoculated)/non-inoculated * 100
1.64
0.80
0.80
123
Table 3.3. Kernel parameters of 10 wheat genotypes grown non-inoculated (U) and inoculated (I) with
Cephalosporium gramineum in field plots in Pendleton, Oregon, 2006
Kernel weight avg (mg)
U
I
Stephens
40.59
37.42
Madsen
33.18
Tubbs
Kernel weight SD
a
Change
U
I
-3.17**
10.93
13.41
30.96
-2.22*
8.62
37.88
36.36
-1.51
OR9800919
38.67
36.63
OR9800924
37.28
Rossini
Change
Kernel diameter avg (mm)
U
I
2.48**
2.784
2.650
9.94
1.32**
2.437
10.28
12.49
2.21**
-2.05*
8.89
9.97
35.65
-1.63
8.77
9.52
44.08
42.74
-1.34
9.74
OR02F-B-46
34.04
31.38
-2.66**
OR02F-C-169
35.39
30.34
OR02F-D-27
30.64
WA 7437
30.62
Change
Kernel diameter SD
U
I
Change
-0.135**
0.582
0.667
0.085**
2.355
-0.082
0.444
0.509
0.065**
2.673
2.594
-0.078
0.525
0.619
0.094**
1.08**
2.791
2.704
-0.087*
0.530
0.560
0.031
0.75
2.704
2.641
-0.063
0.496
0.516
0.020
11.28
1.54**
2.991
2.970
-0.021
0.517
0.588
0.071**
8.90
10.61
1.71**
2.523
2.390
-0.134**
0.474
0.514
0.040*
-5.05**
9.51
11.18
1.67**
2.576
2.337
-0.239**
0.485
0.572
0.088**
31.07
0.42
7.59
8.99
1.40**
2.382
2.388
0.006
0.460
0.487
0.027
30.19
-0.43
6.21
6.61
0.40
2.359
2.346
-0.013
0.405
0.426
0.022
LSD (0.05)
1.80
1.80
0.81
0.81
0.078
* Significant at the 0.05 probability level; ** Significant at the 0.01 probability level
a
Change = (inoculated – non-inoculated)
0.078
0.035
0.035
124
Table 3.4. Percent whiteheads, grain yield, test weight and grain protein of 12 wheat genotypes non-inoculated (U)
and inoculated (I) with Cephalosporium gramineum in field plots in Moro, Oregon, 2007
-1
Whiteheads (%)
I
42.5**
4.26
2.51
41.2**
77.62
74.81
6.8
18.3
11.5**
4.10
3.57
13.1*
78.17
11.8
45.0
33.3**
3.82
2.88
24.6**
OR9800919
2.8
31.8
29.0**
5.01
3.51
OR9800924
4.3
26.3
22.0**
4.44
Rossini
1.5
14.5
13.0**
22.5
44.3
OR02F-C-169
7.8
OR02F-D-27
Loss
Grain protein (%)
U
I
Change
2.81**
9.03
8.70
-0.33
77.33
0.84
9.88
8.25
-1.63**
76.03
74.16
1.87**
9.00
8.75
-0.25
30.0**
74.19
72.45
1.74**
7.43
8.15
0.73
3.41
23.2**
74.87
74.68
0.19
7.80
9.15
1.35**
4.21
3.58
14.8*
77.94
76.07
1.87**
8.83
8.18
-0.65
21.8**
3.41
2.71
20.7**
72.71
72.77
-0.06
8.80
9.23
0.42
42.5
34.8**
3.80
2.43
36.0**
77.97
76.87
1.10
9.85
9.30
-0.55
14.0
34.3
20.3**
4.12
2.81
31.6**
73.58
73.19
0.39
7.68
8.15
0.48
WA 7437
7.5
12.8
5.3
3.15
2.76
12.3
76.94
76.00
0.94
8.53
8.50
-0.03
Skiles
6.8
35.0
28.3**
4.65
3.41
26.6**
79.43
78.00
1.42*
8.38
8.80
0.43
ORSS-1757
5.0
37.5
32.5**
4.07
3.10
23.6**
77.91
76.87
1.03
7.93
7.80
-0.13
LSD (0.05)
8.6
8.6
0.47
0.47
1.27
* Significant at the 0.05 probability level; ** Significant at the 0.01 probability level
a
Significance is based on percent whiteheads square root-transformed
b
Grain yield loss (%) = (non-inoculated – inoculated)/non-inoculated * 100
1.27
1.02
1.02
OR02F-B-46
52.5
Loss (%)
b
I
Tubbs
10.0
Change
Test weight (kg hl )
U
Madsen
I
a
U
Stephens
U
-1
Grain yield (t ha )
125
Table 3.5. Kernel parameters of 12 wheat genotypes grown non-inoculated (U) and inoculated (I) with
Cephalosporium gramineum in field plots in Moro, Oregon, 2007
Kernel weight avg (mg)
Kernel weight SD
U
I
Change
U
I
Stephens
37.34
35.22
-2.12
8.42
10.77
Madsen
31.26
31.31
0.05
7.63
Tubbs
33.75
32.76
-0.99
OR9800919
37.06
33.30
OR9800924
34.61
Rossini
Change
Kernel diameter avg (mm)
U
I
2.35**
2.628
2.534
8.17
0.53
2.396
8.94
9.90
0.96**
-3.75**
7.80
8.07
33.77
-0.84
8.69
8.10
41.39
41.15
-0.24
10.67
11.68
OR02F-B-46
30.55
32.01
1.46
8.05
OR02F-C-169
33.17
33.04
-0.13
OR02F-D-27
32.43
32.49
WA 7437
27.36
Skiles
ORSS-1757
Change
Kernel diameter SD
U
I
Change
-0.094
0.473
0.563
0.090**
2.441
0.045
0.420
0.468
0.048**
2.471
2.413
-0.058
0.463
0.500
0.037*
0.27
2.610
2.458
-0.152**
0.475
0.473
-0.002
-0.58
2.555
2.508
-0.047
0.501
0.474
-0.027
1.00**
2.811
2.784
-0.027
0.558
0.582
0.024
8.86
0.80*
2.360
2.397
0.037
0.437
0.468
0.032*
8.69
9.14
0.46
2.477
2.465
-0.012
0.466
0.496
0.030
0.06
8.40
8.53
0.13
2.425
2.429
0.004
0.471
0.497
0.026
26.97
-0.39
7.12
6.66
-0.46
2.207
2.173
-0.034
0.450
0.422
-0.028
37.43
36.08
-1.35
9.15
9.04
-0.11
2.538
2.482
-0.056
0.526
0.513
-0.013
34.99
34.48
-0.52
8.62
9.19
0.58
2.517
2.499
-0.018
0.483
0.503
0.019
LSD (0.05)
2.24
2.24
0.71
0.71
0.100
* Significant at the 0.05 probability level; ** Significant at the 0.01 probability level
a
Change = (inoculated – non-inoculated)
0.100
0.032
0.032
126
Table 3.6. Pearson correlation coefficients among Cephalosporium stripe rating, grain yield, test weight, and several
kernel traits of 10 and 12 cultivars tested in Pendleton 2006 (below diagonal) and Moro 2007 (above diagonal) under
inoculated and non-inoculated conditions
Whiteheads
Whiteheads SQRT
Whiteheads
Whiteheads
SQRT
Grain
yield
Test
weight
Grain
protein
Kernel
weight
(avg)
Kernel
weight
(SD)
Kernel
diameter
(avg)
Kernel
diameter
(SD)
----
0.986
-0.809
-0.435
***
***
*
0.134
ns
-0.155
ns
0.290
ns
-0.190
ns
0.217
ns
----
-0.820
-0.447
***
*
0.142
ns
-0.217
ns
0.228
ns
-0.249
ns
0.145
ns
----
0.304
ns
-0.271
ns
0.461
0.464
*
-0.096
ns
*
-0.011
ns
----
0.273
ns
0.263
ns
0.093
ns
0.210
ns
0.140
ns
----
-0.161
ns
-0.001
ns
-0.141
ns
-0.235
ns
0.747
0.971
0.794
***
***
***
----
0.730
0.910
***
***
----
0.751
0.952
***
Grain yield
Test weight
Grain protein
Kernel weight (avg)
Kernel weight (SD)
Kernel diam. (avg)
Kernel diam. (SD)
-0.626
-0.618
**
**
-0.433
+
-0.517
*
0.389
+
0.540
0.526
-0.749
*
*
***
-0.262
ns
0.176
ns
0.211
ns
0.419
+
0.229
ns
-0.266
ns
----
0.793
0.881
***
-0.413
+
0.332
ns
0.442
***
-0.378
ns
0.123
ns
0.161
ns
0.428
+
0.183
ns
-0.262
ns
0.988
0.812
0.879
*
***
0.382
+
----
127
-0.376
-0.429
0.367
0.520
0.946
0.486
ns
+
ns
***
***
*
***
*
+ Significant at the 0.10 probability level; * Significant at the 0.05 probability level; ** Significant at the 0.01 probability level;
*** Significant at the 0.001 probability level
***
45
40
Pendleton 2006
y = -0.0253x2 + 1.6145x + 6.4405
R² = 0.7649
Yield Loss (%)
35
30
25
20
Moro 2007
y = 0.7069x + 7.4906
R² = 0.7375
15
10
Moro 2007
Pendleton 2006
5
0
0
10
20
30
40
50
% Whiteheads inoculated - % whiteheads non-inoculated
Figure 3.1. Yield loss caused by inoculation of wheat genotypes with Cephalosporium gramineum in Pendleton 2006
(10 wheat genotypes) and Moro 2007 (12 wheat genotypes)
128
6
Test weight loss (kg hl-1)
5
Pendleton 2006
y = -0.0054x2 + 0.3223x + 0.8351
R² = 0.8032
4
3
2
Moro 2007
y = 0.0037x2 - 0.1352x + 1.9012
R² = 0.5265
1
Moro 2007
Pendleton 2006
0
0
-1
10
20
30
40
50
% Whiteheads inoculated - % whiteheads non-inoculated
Figure 3.2. Reduction of grain test weight caused by inoculation of wheat genotypes with Cephalosporium gramineum
in Pendleton 2006 (10 wheat genotypes) and Moro 2007 (12 wheat genotypes)
129
130
CHARACTERIZATION OF FIELD RESISTANCE TO CEPHALOSPORIUM
STRIPE IN FOUR RELATED WHEAT RIL POPULATIONS.
Abstract
Cephalosporium stripe, caused by Cephalosporium gramineum, can cause
severe loss of yield and quality in wheat (Triticum aestivum L.). The disease is
an important factor limiting adoption of conservation tillage practices in the
Pacific Northwest. There are no fungicides that provide effective control of the
pathogen. Development of resistant cultivars offers the best hope for disease
control, as cultural control methods conflict with soil conservations goals.
However, disease response of current varieties ranges from moderate to
highly susceptible. Improving disease resistance is problematic as infection
and disease response is environmentally dependent. There is little known
about inheritance of resistance. A total of 276 lines derived from four crosses
between parents differing in disease response, were evaluated in inoculated
field trials over three years. Disease was scored as percent of whiteheads in
each plot. Association of disease response with agronomic traits and kernel
quality was examined. Disease response over populations was distributed as
expected for a quantitatively-inherited trait. White head ratings ranged from
0.5% to 70% in 2005, and from 0.5 to 47% in 2006. Disease ratings for
Stephens, the susceptible check cultivar, averaged 44% and 36% in 2005 and
2006, respectively. There was some suggestion of a bimodal distribution
within populations, which suggests the inheritance may not be highly complex.
The single-cross population showed a distribution skewed towards resistance.
131
Results indicate potential to select genotypes with higher levels of resistance
to Cephalosporium stripe than is present in currently available cultivars.
Marker-based studies are currently underway to determine the inheritance of
resistance and to evaluate the potential for marker-assisted selection.
Introduction
Cephalosporium stripe, caused by Cephalosporium gramineum, can lead to
severe loss of grain yield and quality in wheat (Triticum aestivum L.) and is a
limiting factor to adoption of conservation tillage practices for many growers in
winter wheat-fallow areas of the Pacific Northwest (PNW) (Bruehl, 1957;
Mundt, 2002; Murray, 2006; Wiese, 1987). The development of resistant
cultivars offers the best hope for disease control, as cultural methods conflict
with soil conservation goals and no effective fungicides are available (Bockus
et al., 1983; Latin et al., 1982; Martin et al., 1989; Mathre and Johnston, 1975;
Murray et al., 1992; Pool and Sharp, 1969; Raymond and Bockus, 1984).
Selection of resistant genotypes remains problematic and superior levels of
disease resistance are needed. Expression of resistance is incomplete and
environmentally dependent and no soft white winter wheat cultivar shows
complete resistance to Cephalosporium stripe (Bruehl et al., 1986; Martin et
al., 1983; Mathre et al., 1977; Morton and Mathre, 1980). This work is part of
the longer term goal to produce winter wheat cultivars that can be sown under
conservation tillage conditions in early September without experiencing
significant economic loss to disease.
132
Objectives
1) Estimate heritability and number of genes that may be involved in
resistance from crosses with European germplasm that have previously shown
resistance to Cephalosporium stripe resistance.
2) Examine association of disease resistance with physical kernel quality
parameters.
Materials and methods
Four populations of F3 derived lines were developed from single and threeway crosses between OSU varieties ‘Tubbs’ and ‘Weatherford’ as susceptible
parents and ‘Rossini’ derivatives as resistance donor source (Table 4.1).
Field evaluation for response to Cephalosporium stripe was conducted in
inoculated field trials at the Columbia Basin Agricultural Research Center in
Pendleton, OR, on previously infested fields. There were two and three
replications in the 2004-05 and 2005-06 crop years, respectively. Plot size was
two rows by 1.8 m and planting was accomplished in early September to
optimize conditions for pathogen development. A Hege 500 series plot drill
(H&N Manufacturing, Colwich, KS) with deep furrow openers was used to
reach soil moisture. Fertilization and weed control practices were appropriate
133
to commercial winter wheat production at the two sites. A spring application of
fungicide (Bumper 41.8EC, propiconazole) was applied to control eyespot
disease which can mask symptoms of Cephalosporium stripe. To ensure high
and uniform disease pressure, wheat seeds were mixed before planting with
equal weight of sterilized oat kernels colonized with the pathogen. Inoculum
was prepared following the method described by Mathre and Johnston
(1975).In each population, four cultivars and the two resistance donor sources
were included as checks.
Disease response was visually assessed as percentage of whiteheads in the
entire plot, and accounting for stunted and prematurely ripen tillers. The
reading was done approximately two weeks after heading, when symptoms
were considered optimal. Evaluation of known check varieties and random
examination of lower stems and roots provided confidence that whiteheads
were caused predominately by Cephalosporium stripe. All tillers in a 0.30-m
long row section, representative of the entire plot, were hand-harvested and
bulked. Samples were later carefully threshed using a stationary head
thresher, where forced air flow settings were at a minimum to maximize the
retention of seeds. Samples were then re-cleaned by hand prior to seed
analyses. A sub-sample of 300 seeds from the first two replications of the
2006 trials was evaluated for physical kernel characteristics, including kernel
134
weight and diameter, with a Single Kernel Characterization System (SKCS
4100, Perten Instruments).
Results and discussion
Significant differences in disease response were found within populations in
each year (Table 4.2). Figures 4.1 a-d show frequency distributions of progeny
for the two seasons of evaluation. Whitehead ratings ranged from less than
1.0% to 70% in 2005 and from less than 1% to 47% in 2006. The susceptible
check Stephens ranged from 41 to 51% and from 26 to 43% in 2005 and 2006
respectively, while the resistance sources (OR9800924 and OR9800919)
ranged from less than 1 % to 5 % whiteheads. Frequency distributions suggest
the presence of transgressive segregating progeny for each population in both
environments, indicating that the susceptible parent involved in the cross is
carrying resistance genes for Cephalosporium stripe as well. The populations
were found to segregate for resistance as expected for a quantitativelyinherited trait. However, there often appeared to be a bimodal distribution,
which suggests that the inheritance may not be highly complex.
Broad sense heritability for whitehead response was consistently around 90%
(Table 4.3). This confirms the opportunity of successfully selecting lines with
high levels of resistance.
135
Cephalosporium stripe had a significant impact on kernel properties, with
kernel diameter being the most affected. Pearson’s correlation coefficients
between the disease scores and kernel quality traits are presented in Table
4.4 (a and b) for each population. Moderately to high positive correlations were
observed between whiteheads and the standard deviation for kernel weight
and kernel diameter. With the exception of the correlation with kernel weight
SD in population C, the values are above 0.5. Consistently higher correlations
were obtained for kernel diameter SD.
Conclusions
Reponse to Cephalosporium stripe varied widely among progeny of the four
populations. Frequency distributions of whiteheads differed among the four
crosses and results were consistent between years. Progeny from Population
D, the single cross not involving 'Tubbs' showed the most resistance, with 50%
of the progeny showing less than 10% whiteheads. This population also had
the lowest standard deviations for kernel weight and kernel diameter,
indicating less variability in physical kernel quality.
Resistance to Cephalosporium stripe is inherited quantitatively, with the trait
being controlled possibly by one gene with large effect and two to four
additional minor genes.
136
Consistency of field screenings and high heritability estimates obtained in this
study indicate the likely success in selecting breeding material with high levels
of resistance to Cephalosporium stripe required for the adaptation of new
cultivars to conservation tillage practices in erosion-prone areas in the Pacific
Northwest.
137
References
Bockus W.W., Oconnor J.P., Raymond P.J. (1983) Effect of residue
management method on incidence of Cephalosporium stripe under
continuous winter-wheat production. Plant Disease 67:1323-1324.
Bruehl G.W. (1957) Cephalosporium stripe disease of wheat. Phytopathology
47:641-649.
Bruehl G.W., Murray T.D., Allan R.E. (1986) Resistance of winter wheats to
Cephalosporium stripe in the field. Plant Disease 70:314-316.
Latin R.X., Harder R.W., Wiese M.V. (1982) Incidence of Cephalosporium
stripe as influenced by winter-wheat management-practices. Plant
Disease 66:229-230.
Martin J.M., Mathre D.E., Johnston R.H. (1983) Genetic-variation for reaction
to Cephalosporium-gramineum in 4 crosses of winter-wheat. Canadian
Journal of Plant Science 63:623-630.
Martin J.M., Johnston R.H., Mathre D.E. (1989) Factors affecting the severity
of Cephalosporium stripe of winter-wheat. Canadian Journal of Plant
Pathology-Revue Canadienne De Phytopathologie 11:361-367.
Mathre D.E., Johnston R.H. (1975) Cephalosporium stripe of winter-wheat:
infection processes and host response. Phytopathology 65:1244-1249.
Mathre D.E., Johnston R.H., McGuire C.F. (1977) Cephalosporium stripe of
winter-wheat - pathogen virulence, sources of resistance, and effect on
grain quality. Phytopathology 67:1142-1148.
Morton J.B., Mathre D.E. (1980) Identification of resistance to Cephalosporium
stripe in winter-wheat. Phytopathology 70:812-817.
Mundt C.C. (2002) Performance of wheat cultivars and cultivar mixtures in the
presence of Cephalosporium stripe. Crop Protection 21:93-99.
Murray T.D. (2006) Seed transmission of Cephalosporium gramineum in
winter wheat. Plant Disease 90:803-806.
Murray T.D., Walter C.C., Anderegg J.C. (1992) Control of Cephalosporium
stripe of winter-wheat by liming. Plant Disease 76:282-286.
Pool R.A.F., Sharp E.L. (1969) Some environmental and cultural factors
affecting Cephalosporium stripe of winter wheat. Plant Disease
Reporter 53:898-902.
Raymond P.J., Bockus W.W. (1984) Effect of seeding date of winter-wheat on
incidence, severity, and yield loss caused by Cephalosporium stripe in
kansas. Plant Disease 68:665-667.
Wiese M.V. (1987) Compendium of wheat diseases. 2nd ed. APS Press, St.
Paul, Minn.
138
Table 4.1. Pedigree and number of lines per population used in the study.
Population
Pedigree
Nr. of
progeny
A
Tubbs//OR951431/(Rossini/Ysatis//Oracle)
67
B
Tubbs//(Rossini/Ysatis//Oracle)/Weatherford
51
C
Tubbs//(Rossini/Ysatis//Oracle)/OR9900553
78
D
(Rossini/Ysatis//Oracle)/Weatherford
40
Table 4.2. Mean square values from analyses of variance for percentage
whiteheads, kernel weight, and kernel diameter of progeny from four wheat
crosses exposed to Cephalosporium stripe disease in Pendleton, Oregon, in
two years. Kernel characteristics were measured in 2006 only.
Trait
Whiteheads
Population
a
a
Kernel Weight
Avg
SD
Kernel Diameter
2005
2006
A
1.83 **
2.86 **
18.83 **
3.62 **
0.045 **
0.0078 **
B
1.46 **
1.83 **
24.15 **
2.25
0.062 **
0.0048 **
C
1.01 **
1.59 **
25.32 **
2.33
0.056 **
0.0047 *
D
2.72 **
3.04 **
25.67 **
4.23 **
0.060 **
0.0065 **
Results for disease are presented on the log transformed scale
*, ** F value significant at the 0.05 and 0.01 levels, respectively.
Avg
SD
139
Table 4.3. Broad sense heritability (h2) estimates for disease response
(whiteheads) at Pendleton 2005 and 2006, in four related RIL populations.
2
h (Whiteheads
a
a)
Population
2005
2006
A
0.86
0.93
B
0.87
0.86
C
0.92
0.83
D
0.89
0.85
Heritability estimates for disease response were based on the log transformed scale
Table 4.4. Pearson’s coefficients of correlation between disease scores and kernel quality traits at Pendleton 2006,
and significance
a) Above diagonal population A, below diagonal population B
Whiteheads
Whiteheads
---
Whiteheads
(log)
0.879
***
Kernel
Weight
-0.235
Kernel Weight
SD
0.677
***
Kernel
Diameter
-0.186
Kernel
Diameter SD
0.751
***
-0.147
0.583
***
-0.074
0.708
***
-0.024
0.879
***
-0.050
-0.059
0.919
***
Whiteheads (log)
0.923
***
---
Kernel Weight
0.016
-0.023
Kernel Weight SD
0.412
**
0.428
**
0.234
Kernel Diameter
0.119
0.100
0.890
***
Kernel Diameter
SD
---
--0.099
0.484
0.547
0.145
0.826
***
***
***
* Significant at the 0.05 probability level; ** Significant at the 0.01 probability level;
*** Significant at the 0.001 probability level
--0.211
0.038
---
140
b) Above diagonal population C, below diagonal population D.
Whiteheads
Whiteheads
---
Whiteheads
(log)
0.913
***
Kernel
Weight
-0.146
Kernel Weight
SD
0.308
**
Kernel
Diameter
-0.026
Kernel
Diameter SD
0.524
***
-0.072
0.392
***
0.033
0.586
***
0.119
0.930
***
0.074
0.045
0.875
***
Whiteheads (log)
0.848
***
---
Kernel Weight
-0.386
*
-0.345
*
Kernel Weight SD
0.417
**
0.400
**
-0.056
Kernel Diameter
-0.351
*
-0.262
0.935
***
Kernel Diameter
SD
---
---0.127
0.476
0.519
-0.045
0.889
**
***
***
* Significant at the 0.05 probability level; ** Significant at the 0.01 probability level;
*** Significant at the 0.001 probability level
---0.066
0.103
---
141
142
Figure 4.1. a-d. Frequency distributions of lines for four segregating
populations (A-D) evaluated for % whiteheads caused by Cephalosporium
gramineum in artificially inoculated field trials at Pendleton in 2005 and 2006.
a) Population A
143
b) Population B
144
c) Population C
145
d) Population D
146
GENERAL CONCLUSIONS
Improved resistance to Cephalosporium stripe is needed in wheat cultivars for
the dryland production in the Pacific Northwest. Such cultivars would allow
wheat growers to more successfully implement conservation tillage practices
in erosion-prone areas.
In these studies, field trials were used to investigate yield loss and the
inheritance of resistance to Cephalosporium gramineum. Yield loss trials,
which compared performance of cultivars under inoculated and non-inoculated
conditions, showed the importance of selecting germplasm with low whitehead
ratings in disease conditions. Low levels of grain yield loss were observed
when varieties showed less than 5% whiteheads. The highest damage was
observed on the susceptible cultivar Stephens, reaching levels of up to 40%
yield loss when whitehead ratings approach 40%. Grain test weight, kernel
weight, and kernel size were affected by Cephalosporium stripe, which
contributes to overall crop damage.
Acceptable levels of resistance to Cephalosporium stripe were identified in
populations derived from Rossini parentage and in a Coda x Brundage RIL
population. This suggests it is possible, through conventional breeding and
screening using inoculated field trials, to select for germplasm with enhanced
resistance. Disease response is highly dependent on environmental
147
conditions, however, and only a limited number of progeny can be tested in
each cycle. Molecular markers for genes that contribute to resistance are
needed, such that progress can be made in absence of the pathogen.
QTL analyses of the Coda x Brundage population identified seven regions
significantly associated with resistance factors to Cephalosporium stripe.
These had approximately equal effects on disease response. Coda, the more
resistant parent contributed three QTL, while Brundage, the more susceptible
parent, contributed four resistance QTL.
Regression analysis suggest that effects of individual QTL were additive,
indicating the direct benefit of accumulating resistance QTL in one genotype.
Considerable variability in disease response could not be explained by the
seven QTL alone. Thus, further genomic regions associated with resistance to
Cephalosporium stripe are potentially present in this population.
A particularly promising resistance QTL was revealed on chromosome 5B.
This QTL is located in vicinity of toxin insensitivity genes described for other
wheat pathogens. Two resistance QTL were found to be related to head
morphology traits (head compactness and presence of awns). However,
neither of these traits is conditional to achieving higher resistance levels. In
fact, similar levels of variability in response to Cephalosporium stripe were
present in each of the four head type classes. In this study, plant height and
148
maturity were not highly correlated with disease response and, thus, would not
interfere with selection of resistance in a breeding program. Increased
susceptibility to Cephalosporium stripe was correlated with kernel weight SD,
leading to increased variability in kernel size.
A minimum of three resistance QTL were required to achieve acceptable
levels of disease resistance. However, even with multiple resistance QTL, the
resistance must be confirmed in multi-environment field trials with high disease
pressure. Furthermore, QTL described in this study need to be validated in
different genetic backgrounds before they can be extensively used in a
breeding program. If successfully validated, molecular markers linked to
Cephalosporium stripe resistance will be valuable in more rapid development
of resistant cultivars.
149
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APPENDIX
Table 5.1. Means, standard error (SE) and range for percentage whiteheads (square root-transformed), kernel weight,
and kernel weight standard deviation (SD) for a population of 268 recombinant inbred lines (RIL) of wheat and their
parents evaluated in Moro 2007, Pendleton 2007 and Pendleton 2008.
a. Disease scores (Whiteheads square root-transformed)
RIL
Environment
Moro 07
Pendleton 07
Pendleton 08
Mean ± SE
3.818 ± 0.041
3.682 ± 0.038
2.540 ± 0.027
RIL Parents
Range
1.207 - 6.802
0.354 - 7.904
0.569 - 4.903
Coda
3.623
2.150
2.413
Range
23.634 - 42.421
25.60 - 43.71
18.552 - 37.634
Coda
29.805
32.832
27.029
Brundage
3.737
5.067
4.353
b. Kernel weight (mg)
Environment
Moro 07
Pendleton 07
Pendleton 08
RIL
Mean ± SE
31.504 ± 0.097
33.41 ± 0.12
28.724 ± 0.094
RIL Parents
Brundage
34.974
38.474
30.625
c. Kernel weight SD
RIL
Environment
Moro 07
Pendleton 07
Pendleton 08
Mean ± SE
7.997 ± 0.040
8.293 ± 0.048
6.720 ± 0.034
RIL Parents
Range
5.535 - 11.322
4.728 - 13.050
4.834 - 9.596
Coda
7.754
6.941
6.060
Brundage
9.177
9.185
8.237
170
Table 5.2. Pearson correlation coefficients between disease scores for Cephalosporium stripe and kernel physical
quality traits in a 268 recombinant inbred line (RIL) population of winter wheat.
a. Above diagonal: correlations across environments, below diagonal: Moro 2007.
Whiteheads
Kernel weight
---
Whiteheads
(square root)
0.980**
0.341**
Kernel weight
SD
0.546**
Whiteheads (square root)
0.984**
---
0.349**
0.544**
Kernel weight
0.238**
0.255**
---
0.654**
0.352**
0.601**
---
Whiteheads
Kernel weight SD
0.347**
** Significant at the 0.001 probability level
b. Above diagonal: Pendleton 2007, below diagonal: Pendleton 2008
Whiteheads
Whiteheads
---
Whiteheads
(square root)
0.977**
Kernel weight
0.042
Kernel weight
SD
0.466**
Whiteheads (square root)
0.971**
---
0.080
0.497**
Kernel weight
0.277**
0.251**
---
0.405**
0.331**
0.546**
---
Kernel weight SD
0.320**
** Significant at the 0.001 probability level
171
Table 5.3. Results of composite interval mapping of Cephalosporium stripe response (percentage whiteheads), kernel
weight, and kernel weight standard deviation (SD) for Moro 2007, Pendleton 2007 and Pendleton 2008.
Trait
Environment
a
(LOD)
QTL name
Link
b
Group
2
d
QTL
1.0-LOD
Closest marker
LOD
Additive
R (%)
c
position
support
effect
(cM)
interval (cM)
#
Whiteheads
QCs.orp-2B
Moro 07
2B.1
44.4
41.3-48.7
barc091-2B
5.0
-0.238
5.0
QCs.orp-3B
(LOD=3.2)
3B.1
63.4
48.8-67.4
Wpt-9310-3B
3.7
0.235
4.7
QCs.orp-4B
4B.1
33.9
21.3-46.9
wpt-3908-4B
4.2
0.238
4.6
QCs.orp-5B
5B
110.6
106.0-118.4
barc004-5B
3.6
-0.332
9.6
QCs.orp-2D
2D
23.7
23.2-26.2
Compactum
16.3
-0.457
17.7
Pendleton 2007 QCs.orp-5A
5A.2
18.0
12.0-24.0
wpt-3563-5A
6.3
0.518
14.4
QCs.orp-2B
(LOD=3.1)
2B.1
10.0
4.0-16.0
wmc453-2B
7.4
-0.491
12.8
QCs.orp-4B
4B.1
33.9
24.7-42.7
wpt-3908-4B
3.8
0.293
4.4
QCs.orp-6B
6B
51.4
46.7-77.3
wpt-3060-6B
5.7
-0.531
15.2
QCs.orp-2D.1
2D
23.7
17.6-29.0
Compactum
4.3
-0.289
4.3
QCs.orp-2D.2
2D
90.0
84.5-95.8
wpt-8713-2D
4.2
0.312
5.1
Pendleton 2008 QCs.orp-1A
1A.1
15.7
13.4-21.9
gdm33-1A
3.3
-0.187
5.2
QCs.orp-3A
(LOD=3.0)
3A.1
8.0
2.3-16.1
barc019
3.9
0.225
7.5
QCs.orp-2B
2B.1
6.0
1.1-10.3
wmc453-2B
3.3
-0.214
6.7
QCs.orp-4B
4B.1
22.0
20.9-23.5
wpt-3908-4B
3.0
0.391
22.5
QCs.orp-7B
7B.1
12.0
4.3-19.5
wpt-4863-7B
5.2
-0.233
8.0
QCs.orp-2D.1
2D
23.7
22.6-25.9
Compactum
7.6
-0.265
9.2
#
Whitehead percentages were square root-transformed prior to analysis
a
Threshold level to declare a significant QTL, based on 1000 permutations
b
Linkage groups correspond to chromosomes and Arabic numbers denote a specific linkage group within a chromosome
c
Additive effects indicate an additive main effect of the parent contributing the higher value allele: positive values indicate that higher value
alleles are from Coda and negative values indicate that higher value alleles are from Brundage
d
Proportion of the phenotypic variance explained by the QTL after accounting for co-factors
172
Table 5.3. Continued
QTL name
Link
b
Group
QTL
position
(cM)
1.0-LOD
support
interval (cM)
Closest marker
Kernel weight
Moro 07
QGw.orp-5A
5A.3
24.6
22.2-26.6
Awns-5A
4.8
0.650
3.7
(LOD=3.0)
QGw.orp-1B
1B.1
27.2
23.0-33.8
gwm413
7.3
-0.808
5.2
QGw.orp-2B
2B.1
50.4
49.9-51-9
gwm120
3.1
-0.540
2.6
(LOD=3.2)
Pendleton 2008
(LOD=2.9)
Additive
c
effect
R (%)
d
Environment
a
(LOD)
Pendleton 2007
LOD
2
Trait
QGw.orp-4B
4B.1
37.9
21.3-47.7
wpt-3908-4B
4.3
0.822
5.9
QGw.orp-2D.1
2D
23.7
23.5-35.2
Compactum
42.0
-2.153
39.8
QGw.orp-2D.2
2D
88.0
87.0-89.1
wpt-8713-2D
3.1
0.492
2.1
QGw.orp-7D
7D.2
57.5
53.3-57.5
wmc273-7D
6.0
0.686
4.1
QGw.orp-4B
4B.1
33.9
25.0-44.5
wpt-3908-4B
6.3
0.925
5.7
QGw.orp-2D.1
2D
23.7
23.4-25.5
Compactum
33.9
-2.315
36.3
QGw.orp-2D.2
2D
88.0
79.0-91.8
wpt-8713-2D
4.5
0.741
3.8
QGw.orp-5A
5A.2
22.0
12.0-22.0
wpt-3563-5A
3.6
0.692
4.8
QGw.orp-2B
2B.1
42.3
39.1-45.5
wpt-0335-2B
5.0
-0.697
5.0
QGw.orp-4B
4B.1
33.9
22.6-46.4
wpt-3908-4B
6.3
0.851
7.1
QGw.orp-2D.1
2D
23.7
22.8-26.3
Compactum
15.5
-1.293
17.3
QGw.orp-2D.2
2D
85.9
83.4-92.6
cfd44-2D
6.9
0.890
8.0
QGw.orp-4D
4D.2
14.0
0.0-21.6
wmc720-4D
3.2
-1.358
19.4
QGw.orp-7D
7D.2
45.9
33.3-48.7
wpt-5945-7D
5.0
0.736
5.3
a
Threshold level to declare a significant QTL, based on 1000 permutations
b
Linkage groups correspond to chromosomes and Arabic numbers denote a specific linkage group within a chromosome
c
Additive effects indicate an additive main effect of the parent contributing the higher value allele: positive values indicate that higher value
alleles are from Coda and negative values indicate that higher value alleles are from Brundage
d
Proportion of the phenotypic variance explained by the QTL after accounting for co-factors
173
Table 5.3. Continued
Trait
Kernel
weight SD
Link
b
Group
QTL
position
(cM)
1.0-LOD
support
interval (cM)
Closest marker
Moro 2007
QGwsd.orp-7A
7A.2
132.5
118.4-132.5
wpt-1259-7A
3.9
-0.213
5.4
(LOD=3.0)
QGwsd.orp-4B
4B.1
41.8
22.8-51.8
wpt-3908-4B
3.8
0.366
10.1
QGwsd.orp-6B
6B
70.7
60.8-75.8
wpt-4924-6B
3.8
-0.220
3.6
QGwsd.orp-2D.1
2D
23.7
23.3-26.8
Compactum
32.9
-0.740
40.7
QGwsd.orp-2D.2
2D
90.0
79.6-93.9
wpt-8713-2D
5.2
0.274
5.4
QGwsd.orp-4B
4B.1
33.9
29.1-38.8
wpt-3908-4B
10.4
0.473
9.4
QGwsd.orp-6B
6B
55.4
48.2-62.7
wpt-3060-6B
4.4
-0.414
7.1
QGwsd.orp-2D
2D
23.7
23.4-25.8
Compactum
29.8
-0.854
30.1
QGwsd.orp-2B
2B.1
29.3
24.1-34.6
gwm410
3.8
-0.225
5.9
QGwsd.orp-4B
4B.1
22.0
19.1-26.5
wpt-3908-4B
3.9
0.396
18.2
QGwsd.orp-5B
5B
14.1
12.0-14.7
wpt-9504-5B
3.2
-0.173
3.5
QGwsd.orp-6B
6B
66.3
62.0-74.0
wpt-2726-6B
6.1
-0.240
6.7
(LOD=2.9)
Pendleton 2008
(LOD=3.1)
Additive
c
effect
R (%)
d
QTL name
Pendleton 2007
LOD
2
Environment
a
(LOD)
QGwsd.orp-2D
2D
23.7
23.1-27.3
Compactum
16.7
-0.406
19.1
Threshold level to declare a significant QTL, based on 1000 permutations
b
Linkage groups correspond to chromosomes and Arabic numbers denote a specific linkage group within a chromosome
c
Additive effects indicate an additive main effect of the parent contributing the higher value allele: positive values indicate that higher value
alleles are from Coda and negative values indicate that higher value alleles are from Brundage
d
Proportion of the phenotypic variance explained by the QTL after accounting for co-factors
a
174