Prediction of progeny variance based on parental genetic similarity estimates in hard red spring wheat
by Rebecca Lee Burkhamer
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in
Agronomy
Montana State University
© Copyright by Rebecca Lee Burkhamer (1996)
Abstract:
The ability to predict progeny variance prior to making a cross between two varieties would enhance
the efficiency of any breeding program. The probability of recovering superior progeny from a cross is
greater if both parents are superior in agronomic performance, rather than one parent being inferior for
one or more agronomic traits. However, genetic diversity between parents of a cross is necessary to
derive transgressive segregants. Genetic diversity between parents has been associated with F1
heterosis. However, hexaploid wheat varieties are usually released as inbred lines. A study was
conducted to determine if genetic diversity among parents is useful in predicting variance among
progeny lines. Ten hard red spring wheat cultivars currently grown in the Northern Great Plains were
assayed for genetic similarity using molecular markers and coefficient of parentage estimates. One
hundred and twenty-one of the 211 primers examined (57%) were polymorphic. Genetic similarity,
estimated using the Dice coefficient, ranged from 0.390 to 0.837. Coefficient of parentage estimates
ranged from 0.096 to 0.673. Twelve crosses were made among the 10 cultivars, and 50 progeny lines
from each cross were evaluated for nine agronomic traits in three environments over two growing
seasons. -Progeny performance was compared with both parental similarity estimators to determine the
association between parental genetic similarity and variance among progeny lines. Ninety percent of
the correlations computed between the genetic similarity estimators and progeny variance estimates
were negative, suggesting the greater the genetic divergence of the parents, then the greater the
variance observed in the progeny. Twenty percent of the correlations were significant. Eighty-three
percent of the correlations computed between total genetic variance and both genetic similarity
estimators were significantly negative, indicating that genetic variance summed over all traits may be
more useful when trying to estimate progeny variance based on parental genetic similarities. PREDICTION OF PROGENY VARIANCE BASED ON PARENTAL GENETIC
SIMILARITY ESTIMATES IN HARD RED SPRING WHEAT
by
Rebecca Lee Burkhamer
A thesis submitted in partial fulfillment
o f the requirements for the degree
of
Master o f Science
in
Agronomy
MONTANA STATE UNIVERSITY
Bozeman, Montana
December 1996
A/yur
e / m i
a
APPROVAL
o f a thesis submitted by
Rebecca Lee Burkhamer
This thesis has been read by each member o f the thesis committee and has been
found to be satisfactory regarding content, English usage, format, citations, bibliographic
style, and consistency, and is ready for submission to the College o f Graduate Studies.
Dr. Luther Talbert
Signature
Date
Approved for the Department o f Plant, Soil,
and Environmental Sciences
nli2-hc.
Dr. Jeff Jacobsen
Signature
Date
Approved for the College o f Graduate Studies
Dr Robert Brown
Signature
Date
STATEMENT OF PERMISSION TO USE
In presenting this thesis in partial fulfillment o f the requirements for a master’s
degree at Montana State University-B ozeman, I agree that the Library shall make it
available to borrowers under rules o f the Library.
•
I f I have indicated my intention to copyright this thesis by including a copyright
notice page, copying is allowable only for scholarly purposes, consistent with the “fair
use” as prescribed in the U.S. Copyright Law. Requests for permission for extended
quotations from or reproduction o f this thesis in whole or in parts may be granted only by
the copyright holder.
Signature
Date
13~! /< 2 -/%
ACKNOWLEDGMENTS
I would like to express my thanks and sincere appreciation to the following:
To my major advisor Dr. Luther Talbert and the other members o f my committee; Dr. Jack
Martin and Dr. Phil Bruckner, for their advice, patience and encouragement throughout my
graduate studies at Montana State University.
To my lab companions, especially Laura Smith and Nancy Blake, for their friendship,
technical assistance, and professional discussions. Without them the lab would not function.
To Susan Banning, for her assistance and support in every aspect o f tackling such an
enormous field trial.
To every undergraduate who helped me in data collection, especially Aaron Beard, Amber
Hemphill, Ty Jones, Gail Sharp, David Sogge, and Jack Van Dort. W ithout them I ’d still
be in the field or in the fieldhouse, buried under bags o f grain.
To Luis Camargo, for suggesting and inspiring me to continue my education.
To my family, for their encouragement and support in taking my education this far.
V
TABLE OF CONTENTS
>
Page
A CK N O W LED G M EN TS............................................../ ............. ...................................
iv
LIST OF TABLES ........... ' ...........................................................................
vii
................
LIST OF F IG U R E S ................................................................................................
xi
A B S T R A C T .................. ......................................................................................................
xii
/
1.
IN TR O D U C TIO N ........................................................................
I
2.
LITERATURE REVIEW ....................................................
4
3.
MATERIALS AND M E T H O D S ...........................
. Molecular M arker E v alu atio n ..................................................................
Plant M a te ria l................................................................................
DNA E x tra c tio n ...........................
PCR Reaction C o n d itio n s...........................................................
PCR P rim e rs..................................................................................
P C R Product A nalysis...............................
Genetic Similarity Determination . ..............................................
Genetic Similarity Analysis .......................
Field Trial E v alu atio n ................................................................................
Plant M a te ria l...........................
Field Location and C onditions....................................................
Experimental D e sig n ....................................................................
Morphological E v alu atio n ...................................................... r .
Statistical Analysis .........................................., ...........................
. Parental Genetic Similarity and Agronomic Trait Correlation
A n a ly sis......................................................................................................
12
12
12
12
13
14
14
14
17
18
18
18
19
20
21
22
Vl
Page
4.
5.
R E S U L T S ............................................. .............................................................
Molecular M arker E v alu atio n ......................................................
Genetic Similarity Evaluation ....................................................
Comparisons Between Polymorphic Scoring Methods
Dice versus Jaccard’s similarity C oefficient..............................
Coefficient o f Parentage versus the Dice Coefficient . ' .........
Field Trial E v alu atio n ................................................................................
Agronomic Trait Evaluation ....................
Correlations Among Measurements o f Progeny Variance . . .
Correlations Between Parental Genetic Similarity and Variance
o f Agronomic T r a its .............................
Correlations Between Parental Genetic Similarity, and Total
Genetic Variance ........................ . . ■......................................................
43
■ D IS C U S S IO N .................................................... : . . . . . . ....................................
45
23
23
23
23
24
24
30
30
34
38
LITERATURE C IT E D .................................................................
50
APPENDICES ...........................................................................................................
57
APPENDIX A ....................
58
Agronomic Trait Data for 1995, 1996 Dryland, 1996 Irrigated
Field Trials, and for the Three Environments Combined ...................................
58
APPENDIX B ................................................................‘............................................67
Analysis o f Variance Tables for the Three Environments Combined . . . . . . .
67
Vll
LIST OF TABLES
Table
L
- ‘
2.
3.
4.
5.
6.
Page
Chromosomal locations o f sequenced-tagged sites polymerase
chain reaction primers and microsatellites used to assay the .
genetic similarity o f ten hard red spring wheat cultivars.
currently under production in M ontana and North Dakota.,
The primer sets in bold are polym orphic..............................................
25
A comparison o f molecular marker genetic similarity estimates
between 12 crosses among 10 HRSW cultivars using
polymorphic-scoring method I (505 entries) and method 2
(226 entries). The Dice coefficient was the genetic similarity
coefficient used ..................................................................
27
<
A comparison o f molecular marker genetic similarity estimates
between 12 crosses among 10 HRSW cultivars using
polymorphic-scoring method I (505 entries) and method 2
(226 entries). Jaccard's coefficient was the genetic similarity
coefficient u s e d ......................................................
28
■ Comparisons between coefficient o f parentage (COP) and the
Dice coefficient genetic simlarity estimates between 12 crosses
among 10 hard red spring wheat cultivars ..............................................
29
Average agronomic performance o f 10 hard red spring wheat
cultivars o f nine agronomic tratis in the 1995', 1996 dryland,
and 1996 irrigated field trials and for the three environments
c o m b in e d ..................................................................................................
31
Average agronomic performance o f progeny from 12 crosses
among 10 hard red spring wheat, cultivars o f nine agronomic
traits in the 1995, 1996 dryland, 1996 irrigated field trials and
for the three environments combined .......
32
V lll
Table
7.
8.
9.
10.
11 •
12. .
13.
Page
Correlations between parental mean ranges and the number of
transgressive segregants o f nine agronomic traits in the 1995,
1996 dryland, 1996 irrigated field trials and for the three
environments combined ..................................
36
Correlations between progeny mean ranges and the number of
transgressive segregants o f nine agronomic traits in the 1995,
1996 dryland, 1996 irrigated field" trials and for the three
environments combined .........................................................................
36
Correlations between progeny mean ranges and genetic variance
o f nine agronomic traits in the 1995, 1996 dryland, 1996
irrigated field trials and for the three environments combined .........
37
Correlations between the number o f transgressive segregants
and genetic variance o f nine agronomic traits in the 1995, 1996
dryland, 1996 irrigated field trials and for the three environments
combined ..............................................
37
Correlations between genetic similarity estimators, coefficient
o f parentage (COP) and molecular marker data (GS), and
progeny mean ranges o f nine agronomic traits in the 1995, 1996
dryland, 1996 irrigated field trials and for the three environments
c o m b in e d ..................................................................................................
40
Correlations between genetic similarity estimators, coeffient
o f parentage (COP) and molecular marker data (GS), and
the number o f transgressive segregates o f nine agronomic traits
in the 1995, 1996 dryland, 1996 irrigated field trials and for the
three environments combined ..............................................
41
Correlations between genetic similarity estimators, coefficient
o f parentage (COP) and molecular marker data (GS), and
genetic variance o f nine agronomic traits in the 1995, 1996
dryland, 1996 irrigated field trials and for the three environments
combined ..................................................................................................
42
IX
Table
14.
15.
16.
17.
18.
19.
20.
21.
22.
_
Page
Correlations between genetic similarity estimators, coefficient
o f parentage (COP) and molecular marker data (OS), and total
genetic variance o f nine agronomic traits in the 1995, 1996
dryland, 1996 irrigated field trials and for the three environments
c o m b in e d ....................................................................................................
44
Mean values o f parental cultivars and progeny lines o f nine
agronomic traits in the 1995 field trial. Progeny variation for each
. cross is assessed by examining the number o f transgressive
segregants and the genetic variance for each trait ................................
59
Mean values o f parental cultivars and progeny lines o f nine
agronomic traits in the 1996 dryland field trial. Progeny variation
for each cross is assessed by examining the number o f transgressive
segregants and the genetic variance for each trait ................................
61
. Mean values o f parental cultivars and progeny lines o f nine
agronomic traits in the 1996 irrigated field trial. Progeny variation
for each cross is assessed by examining the number o f transgressive
segregants and the genetic variance for each trait . . . ..........................
63
Mean values o f parental cultivars and progeny lines o f nine
agronomic traits for the three environments combined. Progeny
variation for each cross is assessed by examining the number of
transgressive segregants and the genetic variance for each t r a i t .........
65
Analysis o f variance o f nine agronomic traits for the Amidon x
Newana cross for the three environments combined ............................
68
Analysis o f variance o f nine agronomic traits for the Fortuna x
Hi-Line cross for the three environments com bined.......................
69
Analysis o f variance o f nine agronomic traits for the Fortuna x
Lew cross for the three environments co m b in ed ......................... .. . . :
70
Analysis o f variance o f nine agronomic traits for the Glenman x
Amidon cross for the three environments combined . . . . ' . ................
71
I I
X
Table
Z
23.
24. .
25.
26.
27. -
28.
29. .
30.
J
Analysis o f variance o f nine agronomic traits for the Glenman x
Lew cross for the three environments c o m b in e d ........... ......................
72
Analysis o f variance o f nine agronomic traits for the Glenman x
Marberg cross for the three environments combined . .t .......................
73
Analysis o f variance o f nine agronomic traits for the Grandin x
Pondera cross for the three environments combined ...........................
74
Analysis o f variance o f nine agronomic traits for the Hi-Line x
Newana cross for the three environments combined ...........................
75
Analysis o f variance o f nine agronomic traits for the Hi-Line x
Pondera cross for the three environments combined ...........................
76
Analysis o f variance o f nine agronomic traits for the Lew x
Amidon cross for the three environments combined ...........................
77
Analysis o f variance o f nine agronomic traits for the Len x
Glenman cross for the three environments combined .'.........................
78
Analysis o f variance o f nine agronomic traits for the Len x
Newana cross for the three environments combined ...........................
79
xi
LIST OF FIGURES
Figure
I•
2.
Page
Schematic drawing o f a gel photograph containing 10 HRSW
cultivars generated from PCR products amplified with primer
A and digested with enzyme A ........................... ....................................
16
An example o f a scoring data matrix. A matrix comprised of
all scorable polymorphisms would include three entries. A
data matrix containing one “polymorphic pattern” per primer
would include only the shaded e n tr y ........... ..........................................
16
Xll
- ABSTRACT
The ability to predict progeny variance prior to making a cross between two varieties
would enhance the efficiency o f any breeding program. The probability o f recovering superior
progeny from a cross is greater if both parents are superior .in agronomic performance, rather
than one parent being inferior for one or more agronomic traits. However, genetic diversity
between parents o f a cross is necessary to derive transgressive segregants. Genetic,diversity
between parents has been associated with F1 heterosis. However, hexaploid wheat varieties
are usually released as inbred lines. A study was conducted to determine if genetic diversity
among parents is useful in predicting variance among progeny lines. Ten hard red spring
wheat cultivars currently grown in the Northern Great Plains were assayed for genetic
similarity using molecular markers and coefficient o f parentage estimates. One hundred and
twenty-one o f the 211 primers examined (57%) were polymorphic. Genetic similarity,
estimated using the Dice coefficient, ranged from 0.390 to 0.837. Coefficient o f parentage
estimates ranged from 0.096 to 0.673. Twelve crosses were made among.the 10 cultivars, and
50 progeny lines from each cross were evaluated for nine, agronomic traits in three
environments over two growing seasons. -Progeny performance was compared with both
parental similarity estimators to determine the association between parental genetic similarity
and variance among progeny lines. Ninety percent o f the correlations computed between the
genetic similarity estimators and progeny variance estimates were negative, suggesting the
greater the genetic divergence o f the parents, then the greater the variance observed in the
progeny. Twenty percent o f the correlations were significant. Eighty-three percent o f the
correlations computed between total genetic variance and both genetic similarity estimators ■
were significantly negative, indicating that genetic variance summed over all traits may be
more useful when trying to estimate progeny variance based on parental genetic similarities.
■I
CHAPTER I
INTRODUCTION
/
W heat (Tfiticum aestivum L. Em Thell.) is the most important crop worldwide
(Briggle and Curtis, 1987; Poehlman, 1987d). Wheat has a variety o f uses ranging from
leavened bread, unleavened bread, pasta and pastries, which make it the most abundantly
consumed grain by humans. These uses combined with its ease o f storage and excellent
nutritive value have made wheat a staple food for more that one-third o f the world’s
population (Poehlman, 1987d; Johnson, 1986).
Wheat and wheat products are M ontana’s leading export, accounting fo r'86% o f
the State’s agricultural exports. In 1995, wheat and wheat products accounted for over
5 billion dollars in exports (Montana Agricultural Statistics 1996). W heat is grown on
'
6.6 million acres in the state, and spring wheat comprised 63% o f the seeded acres
devoted to wheat in M ontana in 1996 (Montana Agricultural Statistics, 1996). Montana
ranks second in the nation in terms o f bushels o f spring wheat produced, which accounts
for nearly 25% of the nations total (Montana Agricultural Statistics 1996).
M odern plant breeding has accelerated the improvement o f yield and quality traits
in crop plants. There are four main objectives o f a plant breeder. First they must
recognize traits, that are important for adaptation, yield, and quality o f the crop, and
second, design techniques to evaluate the genetic potential o f these important traits.
Third, they must find sources o f genes for the desired traits and utilize them in a breeding
2
program, and fourth; they need to devise a means for combining the genetic potential of
these traits into an improved variety or cultivar (Poehlman, 1987a). This fourth strategy
is the challenge plant breeders face as they are continually evaluating populations o f
breeding material to identify individual plants or breeding lines with superior
performance (Poehlman, 1987a).
Breeders o f cross-pollinated species focus on populations and hybrids, while
breeders o f self-pollinated crops focus on homozygosity and individual plants (Poehlman,.
1987c). Breeders of self-fertilized species use hybridizations to create new, segregating
populations (Poehlman, 1987c), which are subsequently self-pollinated to form new
inbreds. The heterozygosity o f the plants is reduced by one-half with each consecutive
self-fertilization event. Varieties are generally inbred lines, therefore the focus is on
combining favorable alleles into a single pureline or cultivar (Bailey and Comstock,
1976).
Breeders usually restrict the use o f landraces and wild relatives when making
crosses to circumstances when they are not able to find the genetic diversity they need
within improved cultivars (Porceddu et al., 1988). Wide hybridizations with non-elite
germplasm may lead to an overall negative result in yield or quality characteristics. The
desired trait is often accompanied by undesirable, linked genes, a phenomenon referred to
as linkage drag (deVicente and Tanksley, 1993) Therefore, wheat breeders may be
hesitant o f these wide crosses. Due to the constraint o f adhering to quality standards set
by the industry (Slaughter et al., 1992), wheat breeders typically make crosses within a
germplasm collection that consists o f high yielding or disease resistant lines
3
(Autrique et al., 1996). Breeders prefer to make crosses among elite germplasm because
theoretical (Bailey and Comstock, 1976) and empirical (Busch et al., 1974) evidence
suggests there is a greater probability o f recovering superior progeny if the starting
material being crossed is also superior. Ifth e germplasm pool is narrow, then elite lines
may be superior due to the same genes. However, genetic diversity between parents is
necessary in plant breeding to derive transgressive segregates. Genetic variances in
progeny lines is dependent on the amount o f diversity present between the parents, and
the mean o f the progeny population is usually associated with the parental means (Moser
et al., 1994).
The first step in a breeding program is to select parental lines to be crossed. M ost
breeding programs have limited resources, therefore the breeder must make a
compromise between the number o f recombinants desired and the number that can be
evaluated efficiently. The ability to choose lines to cross that increase the chances of
obtaining useful and beneficial recombinants to evaluate would enhance the efficiency o f
any breeding program. Many studies have been conducted to determine the genetic
relationship among lines within an existing germplasm pool. Breeders would like to use
this information to choose parental combinations with the greatest breeding potential. A
study was conducted to determine the usefulness o f knowledge about diversity o f spring
wheat parental lines as it relates to progeny variance.
4
CHAPTER 2
LITERATURE REVIEW
Plant breeding is the art and science o f the genetic improvement o f plants (Fehr,
1987). It has been around since humans began visually selecting desirable plants and
saving seed instead o f randomly taking what nature provided (Poehlman, 1987a; Fehr,
1987; Duvick, 1996). These early selections, no matter how primitive, were chosen for a
variety o f reasons, and have contributed to today’s cultivated crops (Poehlman, 1987a).
Prior to 1910 genetic recombinations arose by chance mutations and natural outcrossings,
as little attention was paid to planned hybridizations (Gizlice et al., 1994). Once plant
breeders assumed control o f planned genetic recombinations, traditional plant breeding
was conducted much the same as it is today, where crosses were made among chosen
lines to improve a desired trait, the progeny were tested and evaluated before making
selections. The successful plant breeders combined their skills o f art and experience to
develop new varieties (Duvick, 1996).
Plant breeders have been extremely successful in their endeavors. In the 50 year
period between 1931-1980, corn yields increased 325%, wheat yields increased 146%,
and rice yields increased 111% (Poehlman, 1987a).
A difference between plant breeding in the early 1900's and today is the
development o f molecular biology, which has come to the forefront o f research in the last
5
two decades. It compliments traditional plant breeding by providing a solid scientific
basis that can explain the genetic and biochemical basis for changes plant breeders have
made over the years (Duvick, 1996). Molecular biology gives plant breeders the ability
to make quantitative changes in some traits by design rather than by chance (Duvick,
1996).
Molecular biology has provided plant breeders with DNA markers, which assist
in locating important chromosomal regions affecting a given trait. Molecular markers
allow researchers to examine genetic variation o f crop plants at the DNA level without
environmental influences (Miller and Tanksley, 1990). Molecular markers help plant .
breeders track and manipulate genes, prove ownership o f genetic stocks, trace genetic
relationships throughout evolution, and assess the level o f genetic diversity in
agricultural crop plants.
Breeders have proposed many methods to quantify genetic relations among lines
within a species. The three most common estimates are based on coefficient o f parentage
(COP), the multivariate analysis o f quantitative trait variation, and the analysis of
molecular markers (Moser and Lee, 1994).
Restriction fragment length polymorphisms (RFLP), first proposed by Botstein et
al. (1980), have been the most commonly used molecular markers. Genetic diversity
measures based on RFLP’s has been examined in many crops, including maize (Zea mays
L.) (Lee et al., 1989; Melchinger et al., 1990), tomato (Lycopersicon esculentum L.)
(Miller and Tanksley, 1990), soybean {Glycine max L.) (Keim et al., 1992), barley
{Hordeum vulgare L.) (Zhang et al., 1993), rice {Oryza sativa) (Zhang et al., 1992),
.6
durum wheat (Triticum turgidum L. var. durum) (Autrique et al., 1996), oats (Avem
sativa L.) (M oser and Lee, 1994), alfalfa (Medicago sativa L.) (Kidwell et al., 1994), and
wild oats (Avena sterilis L.) (Beer et al., 1993). Other genetic markers used to study
genetic diversity include randomly amplified polymorphic DNA (RAPD), microsatellites,
and sequenced-tagged-site (STS) polymerase chain reaction (PCR) primers.
Randomly amplified polymorphic DNA (Williams et al., 1990), microsatellites
(Weber and May, 1989; Litt and Luty, 1989), and STS-PCR (Olson et al., 1989) are
genetic markers that exploit PCR technology. PCR-based techniques are advantageous
over RFLPs due to increased safety, reduced cost and time o f the procedure
(Tragoonrung et al., 1992; Martin et al., 1995) as well as the relatively small amount of
lower quality DNA acceptable for use in a PCR reaction (Erpelding et al., 1996). The
STS-PCR technique is a combination o f RFLP and PCR technology (Martin et al., 1995).
An STS is a short, unique sequence amplified during a PCR reaction which identifies a
known location on a chromosome (Olson et al., 1989). ,The ends o f RFLP clones are
sequenced and PCR primers are designed to amplify the contained region. STS
technology was first proposed by Olson et al. (1989) for use in the human genome
mapping project, and has recently been extended to crop plants including wheat (Talbert
et al., 1994), barley (Tragoonrung et al., 1992), and rice (Inoue et al., 1994; Williams et
al., 1991). In wheat, STS-PCR technology has been advantageous in identifying
molecular polymorphisms due to the low level detected by RFLPs (Kam-Morgan et al.,
1989; Chao et al., 1989) and RAPD’s. Talbert et al. (1994) reported 9 o f 16 STS-PCR
amplified products were polymorphic when digested with restriction enzymes. Similarly,
.
7
Chen et al. (1994) detected polymorphisms among wheat cultivars currently under
production in Montana and North Dakota. In wheat, the RFLP location predicts the STS
location 69% o f the time, while sometimes amplification o f non-homologous sequences
occurs (Erpelding et al., 1996). It is not surprising that the STS-PCR.primers amplify
other regions o f the genome than where the RFLP mapped. Restriction fragment length
polymorphisms used to create the STS-PCR primers used in this study were, on the
average, 1-2 kb, and the primers created from these RFLPs were typically 20 bases long.
Assessing genetic similarity is a relative measurement between species, therefore,
amplification o f non-homologous sequences should not be a problem.
A second method to quantify genetic relations among lines within a species is the
use o f coefficient o f parentage (COP) information. The coefficient o f parentage between
two individuals is the probability that two alleles chosen at random from each individual
are identical by descent (Kempthorne, 1969). This technique o f measuring genetic
diversity was used prior to the discovery o f molecular markers, and is still used when
pedigree information is available (Cox et al., 1985; Gizlice et al., 1996; Knauft and
Gorbert, 1989; Martin et al., 1991). Coefficient o f parentage estimates have also been
recently used in combination with molecular markers to compare genetic diversity
estimates (Autrique et al., 1996; Martin et al., 1995).
The maintenance o f genetic diversity in' domesticated crops is essential in
decreasing vulnerability to pests and abiotic stresses (Martin et al., 1991). The Southern
I.
corn leafblight outbreak in 1970 was due primarily to 90% o f the U.S. corn hybrids
containing the Texas male-sterile cytoplasm which was susceptible to an attack by
8
Bipolaris mayBis(N iski) Shoemaker, Race T (Smith, 1988). This outbreak caused a
heightened interest in the U.S. to measure and increase germplasm diversity (Smith,
1988). Although researchers realize the importance o f maintaining genetic diversity,
surveys conducted in the mid-1980's among corn breeders concluded there was little
immediate interest for increasing genetic diversity in hybrid maize production (Duvick,
1984; Goodman, 1985)., Rodgers et al. (1983) reported a narrow germplasm base for oat
cultivars released from 1941-1951 due to the majority o f them being derived almost
entirely from three cultivars. However, Souza and Sorrells (1989) reported the oat gene
pool is expanding over time. Similarly, Dilday (1990) reported rice as having a narrow
germplasm, with two current rice cultivars sharing as much as 90% o f the same genes.
Genetic diversity is essential to assure continued genetic improvement o f
agricultural crops (Martin et al., 1991), but in the process o f selecting for desirable traits
and quality measures plant breeders tend to decrease the level o f diversity. Often due to
specific maturity groups, as with soybeans (Gizlice et al., 1994), or strict quality
standards, as with malting barley (Martin et al., 1991) or wheat (Chen et al., 1994),
hybridizations may be restricted to utilizing parents that possess the same characteristics.
Thus, the same genetic background may be present in many cultivars, resulting in a
narrow germplasm base. Gizlice et al. (1994) found 17 ancestors contributed 94% o f the
genes in southern soybean cultivars, and nearly half o f those were from two lines. The
result is two-thirds o f the genetic base o f southern soybean cultivars are defined by five
ancestors. In northern soybean varieties, 10 ancestors accounted for 80% o f the genes.
Martin et al. (1991) found five ancestors contributed 62% o f the germplasm base in
9
recent two-rowed barley cultivars and 44% in the recent six-rowed barley cultivars.
Similarly, Cox (1991) reported the hard red spring wheat germplasm pool is based
largely on one cultivar introduced from Canada in 1912. Chen et al. (1994) determined
the average genetic similarity among hard red spring wheat cultivars from the North
American Great Plains was 0.88, versus 0.78 for a broader based germplasm collection.
In addition to decreasing vulnerability to epidemics and assuring genetic
improvement, evaluation o f genetic diversity in progenitor species o f a crop allows the
utilization o f a huge gene pool o f useful new genes, including pest resistance genes
(Lubbers et al., 1991). Disease resistance genes are often easy to identify in wild
germplasm through disease screening methods, however there may be other important
genes in wild species that are not so easy to identify, yet useful. Yield, for example, is a
trait all plant breeders are interested in, yet it is not a character easily measured in wild
germplasm. Even though a wild species may not display the character o f interest, it is
probable it contains alleles that can improve the character (deVicente and Tanksley,
1993). Therefore knowledge about genetic information o f wild species may benefit
improvement o f crop plants.
It is equally important to possess information about the genetic variance within a
narrow pool o f elite breeding material. Knowledge o f readily available genes might
allow direct accumulation o f favorable alleles into breeding lines. Directly assembling
favorable alleles may speed up the selection process by decreasing the amount o f material
which needs to be screened in the field (Plaschke et al., 1995).
Additionally, knowledge about genetic diversity can facilitate more intelligent
10
selection o f parents to be crossed for superior gene combinations involved in cultivar
development (Dilday, 1990; Patterson et al, 1991; Talbert, 1993; and Martin et al.,
1995). Estimating the genetic variation o f progeny lines ahead o f time without having to
make the crosses and evaluate the progeny would help to accelerate progress in a
breeding program (Moser and-Lee, 1994).
Estimating progeny variance and performance has been an area o f research that
has received a lot o f attention in the last decade. Heterosis in the F 1 progeny has been .
advocated as a measure o f genetic diversity between the parents. Smith et al. (1990)
found a positive correlation between genetic distance estimates, based on RFLP and
COP, and grain yield heterosis in maize. Melchinger et al. (1990) found a positive
correlation between RFLP and grain yield heterosis in maize, but stated it was too small
to be o f any predictive value. Kidwell et al. (1994) found a positive correlation between
genetic dissimilarities based on RFLP’s and yield in tetraploid alfalfa, but no significant
association between diversity and yield in diploid alfalfa. Cowen and Frey (1987a,b)
were unable to predict yield heterosis in oats based on COP and three other diversity
measurements. Cox and Murphy (1990) were unable to predict heterosis in winter wheat
using COP as a measure o f genetic diversity. M oser and Lee (1994) found limited
significant positive correlations between genetic distance based on RFLP’s and six
agronomic traits measured in oats, and Souza and Sorrells (1991) stated COP estimates
were the best predictors o f variance among F4 families in oats. M artin et al. (1995) found
diversity measurements based on COP and STS-PCR primers were not useful in
predicting F1 performance in wheat. Results have been inconsistent among crops and
11
within crops in attempting to use diversity measurements to predict hybrid performance.
Wheat cultivars are almost always released as inbreds and rarely as hybrids. It
would perhaps be more advantageous to predict progeny variance and performance on
later generations than F1 heterosis in wheat. The objective o f this study was to evaluate
the genetic similarity among hard red spring wheat (HRSW) cultivars using molecular
markers, and measure the morphological characteristics o f lines produced from twelve
crosses among the ten cultivars. By combining coefficient o f parentage information with
molecular marker data obtained from using STS-PCR primers, we will determine whether
greater parental diversity in spring wheat leads to greater phenotypic and genetic
variance in progeny lines grown in a field setting.
12
CHAPTER 3
MATERIALS AND METHODS
Molecular Marker Evaluation
Plant material
Ten hard red spring wheat cultivars, currently grown in the Northern Great Plains,
were planted in the greenhouse. The cultivars Amidon, Fortuna, Glenman, Grandin, HiLine, Len, Lew, Marberg, Newana, and Pondera were evaluated based on their genetic
similarities using sequence-tagged-site PCR primers.
DNA Extraction
Young leaves from each cultivar were harvested, and total genomic DNA was
extracted using the procedure o f Dellaporta et al. (1983). Fifteen milliliters (ml) of
extraction buffer (100 mM Tris pH 8.0, 50 mM EDTA pH 8.0, 100 mM NaCl, 1% SDS,
and IOmM 2-mercapto ethanol) was added to approximately one gram o f leaf tissue,
which was ground using a mortar and pestle. The ground tissue was transferred to an
Oakridge tube and incubated in a 65°C waterbath for 10 minutes. Five ml o f SM
potassium acetate was added to the tubes followed by a 20 minute incubation on ice. The
tubes were spun at 25,000 X g for 20 minutes, and the remaining supernatant was poured
through miracloth into a clean Oakridge tube containing 10 ml o f cold isopropanol and
13
I ml 5 M ammonium acetate. These tubes were gently mixed and incubated at -20°C for
20 minutes. To pellet the DNA3 the tubes were spun at 20,000 X g for 15 minutes. After
discarding the supernatant, the pellet was air-dried and resuspended in 0.7 ml TE Buffer
( I OmM Tris-Cl, Im M EDTA pH 8.0). The resuspended DNA was separated from
remaining polysaccharides during a final wash, using 75 pi 3 M sodium acetate, pH 7:0
and 0.5 mis cold isopropanol. A thirty second spin in a microfuge (15,000 rpms) pelleted
the DNA, which was air-dried and resuspended in 0.1 to 0.3 ml o f TE buffer. DNAs were
quantified on a 0.7% agarose gel by comparing sample intensities to 2 pi aliquots o f .
known DNA standards. DNA concentrations were adjusted to approximately 100 ng/pl
for use in PCR reactions.
P C R reaction conditions
PCR amplifications were performed in 100 pi reactions that consisted o f 10X
reaction buffer (Promega, Madison, WI) (50 mM KCl, 10 mM Tris-HCl, 0.1% Triton x100), 50 pM o f each o f the four dNTP’s, 1.5 mM MgCl2, 400 nM o f each o f the left and
right primers, 0.8 units of Taq polymerase, and 100 ng o f genomic DNA. Reactions
were contained in 0.5 ml microfuge tubes and overlayed with approximately 100 pi o f
mineral oil. PCR was performed in a model 50 Coy Tempcycler (Coy Laboratory
Products Inc., Grasslake, MI) using the following conditions; an initial denaturation at
94°C for 4 minutes, followed by 30 cycles o f 94°C for I minute, 45°C or 5O0C for I
minute, and 72°C for 1.2 minutes, and a final extension at 72°C for 7 minutes followed by
a hold at 4°C .
14
PCR primers
A total o f 2 1 1 primers were used to evaluate the genetic similarity among the 10
HRSW cultivars. Six o f the primers evaluated were microsatellites (Roder et al., 1995),'
and the other 205 were STS-PCR primers developed at Montana State University
(Tragoonrung et al,. 1992; Talbert et al., 1994).
PCR product analysis
The PCR products were digested with 1.9 units o f ZMeI, HhaI, Hinfi and Rsdi
(New England Biolabs, Beverly, MA) and incubated at 37°C for one hour. The products
were separated on a 0.7% polyacrylamide gel with a 0.5% Tris-borate EDTA running
buffer (22 mM Tris-HCl, 22 mM boric acid, and 0.5 mM EDTA). The gels were stained
with ethidium bromide and the DNA was visualized with UV light and photographed.
Genetic similarity determination
To examine the genetic similarity between 10 HRSW cultivars based on
molecular markers, gel photographs were scored visually for polymorphic primerenzyme combinations. A polymorphism was defined as the absence o f a band in one or
more cultivars. A data matrix o f the polymorphic primers was created by scoring bands
as present or absent. A score o f I indicated the presence o f a band, while a score o f 0
represented the absence o f a band.
Two polymorphic primer data matrices were constructed to compare methods o f
15
scoring DNA polymorphisms. The first data matrix contained 505 primer-enzyme
combination polymorphisms from the 21 1 primers evaluated. This data matrix was
comprised o f all scoreable polymorphisms observed in the gel photos. The second data
matrix contained 226 primer-enzyme combination polymorphisms from the 211 primers
evaluated. This matrix contained “polymorphic patterns”, which were only represented
once per primer in the data matrix. For example, PCR products resulting from
amplification with primer A and digested with enzyme A are visualized on a gel, and
reveal a presence o f a band in cultivars A, C, and J. An additional polymorphic band
from primer A digested with the same enzyme reveals a presence o f a band in cultivars B,
D, E, F, G, H, and I, and an absence o f a band in cultivars A, C, and J (Figure I). In the
first data matrix, the three entries for this primer-enzyme combination would be included
(Figure 2). However, in the second data matrix we considered the three polymorphisms
scored to contribute the same information, thus perhaps unfairly weighting the
association between cultivars A, C, and J. Therefore, we entered the “polymorphicpattern” into the data matrix once (Figure 2). This same procedure holds true for scoring
“like” polymorphisms with different enzymes o f the same primer. The same
“polymorphic pattern” was only entered once per primer.
Pearson correlations were computed to determine the relationship between the
two polymorphism scoring methods.
Genetic similarity estimates based on coefficient o f parentage were determined
using pedigree information. A data matrix o f COP values was constructed where values
o f 0 indicate the-cultivars are completely unrelated and have no alleles in common, and a
Figure I. Schematic drawing of a gel photograph containing 10 HRSW cultivars generated from PCR products amplified
with primer A and digested with enzyme A.
Cultivar
A
B
C
D
E
F
G
H
I
J
Figure 2. An example o f a scoring data matrix. A matrix comprised o f all scorable polymorphisms would include three
entries. A data matrix containing one "polymorphic pattern" per primer would include only the shaded entry.
Cultivar
Primer
Polymorhic
Enzyme
Band #_____ A
1
2
3
B
D
0
I
I
0
I
I
0
0
E
0
I
I
F
V
A
A
I
I
I
V
U
I
0 5* 0 0
o
o
17
value o f I indicates two cultivars have all alleles in common (Martin et al., 1.991).
Coefficient o f parentage estimates assume that (i) a cultivar received half o f its genes
from each parent, (ii) parents used in the cross were homozygous and homogenous, (iii)
ancestors for which no pedigree information was available were unrelated, and (iv) the
COP value between a cultivar and a selection from that cultivar is 0.75 (Martin et al.,
1991).
Genetic similarity analysis
Genetic similarity estimates based on molecular marker data were calculated
using two different similarity coefficients. The Dice coefficient according to Nei and Li
(1979) defines genetic similarity (GS) as
G S y = ^ N ti,
M + N j)
where Ny is the number o f bands in common between lines i and j. N i is the total number
o f bands in i, and Nj is the total number o f bands in j. Gsij reflects the proportion of
bands in common between two inbred lines.
Jaccard's coefficient formula (Jaccard, 1901) defines genetic similarity (GS*) as
GS** = ___ Ny____
(Ny + N i +Nj)
where N i is the number of bands present in line i and absent in line j, Nj is the number o f
bands present in line j and absent in line i, and N ij is the number o f bands in lines i and j.
The genetic similarity coefficients o f Dice and Jaccard were computed using the
appropriate procedures o f the computer package NTSYS-pc (Rohlf, 1993). Genetic
18
similarities were computed from each o f the two data matrices described in the previous
section.
Pearson correlations were computed to determine the relationship between the
Dice and Jaccard genetic similarity coefficients.
Field Trial Evaluation
Plant material
Twelve crosses were made among the 10 elite hard red spring wheat cultivars.
The crosses were chosen based on coefficient o f parentage, preliminary genetic distance
(I-G S) estimates, and information from previous field trials. Crosses included four
hollow x hollow-stemmed varieties, four solid x hollow-stemmed varieties, and four solid
x solid-stemmed varieties. M ore than 50 F3-derived F5 lines per cross were increased in
1994.
Field location and conditions
Field trials were conducted at the Arthur H. Post Field Research Farm near
Bozeman, MT in 1995 and 1996. The soil type is an Amsterdam silt loam. The elevation
is 1,439 m (4,772 f t) ..
In the 1995 field trial the average temperature during the growing season was
12.6°C (54.8°F), with 35.92 cm (14.17 in.) o f precipitation during the growing season.
There were 72.6 kg/ha (160 Ib/A) o f stored available N and 27.2 kg/ha (60 Ib/A) was
19
added in the form o f urea.
In the 1996 field trial the average temperature during the growing season was
14.6°C (58.2°F), with 17.0 cm (6.7 in.) o f precipitation during the growing season. There
were 79.4 kg/ha (175 Ib/A) o f stored available N and 22.6 kg/ha (50 Ib/A) was added in
the form o f urea.
Experimental Design
In 1995, fifty randomly chosen F3-derived F5 lines per cross were planted in a
randomized block split plot design. The twelve crosses were the whole plots. The 50
random lines per cross plus the two appropriate parents were the sub-plots, for a total o f
624 sub-plots. Each sub-plot was a single row, 3 m in length with 30 cm between the
rows.
In 1995, there were three replications in an unirrigated field. One replication
from 1995 was chosen to supply the seed for the 1996 field trial. The 1996 field
experiment contained the F6 generation o f the same 624 lines evaluated in 1995 and was
also a randomized block split plot design. However instead o f three unirrigated
'
replications, there were two replications in an unirrigated field and tw o replications in an
irrigated field.
In 1995, plantings were delayed due to a wet spring and the experiment was
planted on May 19. It was harvested September 13-15.
In 1996, the dryland replications were planted on May 2 and harvested August
20-21. The irrigated replications were planted on May I and harvested August 29-30.
20
These plots received approximately 8.9 centimeters (3.5 inches) o f additional moisture
on June 24-25, and approximately 8.9 centimeters (3.5 inches) o f moisture on July 9-10.
Morphological Evaluation
Each sub-plot was evaluated for nine morphological characteristics in both
growing seasons.
Heading Date - recorded as the number o f days from January I when 50% o f the
heads in a row were completely emerged from the flag leaf sheath. It was reported as the
heading date in Julian days - planting date in Julian days.
Tillers/ft. - a 3 0 cm ruler was placed on the soil surface and the number o f tiller's
within this area were counted.
Stem Solidness - two stems per row, cut at random, were rated for stem solidness
A cross section was cut through the center o f two intemodes, starting at the bottom
internode and a rating o f 1-5 was given. A rating o f I indicates complete hollowness and
a rating o f 5 indicates complete solidness (McNeal, 1956). The ratings were summed to
give a single score, with a maximum possible score o f 20. '
Plant Height - the average plant height o f the row was measured in centimeters
from the soil surface to the top o f the spike,’excluding awns, o f 3-5 main tillers.
Physiological Maturity - recorded as the number o f days from January I when a
complete loss o f green color from the glumes was observed in 75% o f the row (Hanft and
Wych, 1982). It was reported as the physiological maturity date in Julian days - planting
date in Julian days.
21
Grain Fill - calculated by subtracting the heading date expressed in Julian days
from physiological maturity expressed in Julian days.
Yield- grain from individual lines (rows) was weighed in grams and yield was
expressed as M g ha"1.
Test Weight- test weight was measured on a Seedburo test weight scale and
expressed as kg m"3.
Protein - the percent protein was measured on whole grain samples using, an
Infratec (Tecator, Hdganas, Sweden) whole kernel analyzer in the cereal quality
laboratory at M ontana State University, Bozeman.
Statistical Analysis
An analysis o f variance, mean trait values and variance components were
computed for the nine traits measured for 12 crosses in the 1995, 1996 dryland, 1996
irrigated field trials, and for the three environments combined using the Statistical
Analysis Systems (SAS) package version 6.11 (SAS Institute, 1988).
Variation among progeny lines was examined by looking at genetic variance, the
range between progeny means (maximum - minimum) and the number o f transgressive
segregants for each cross and each trait. Mean values for the nine agronomic traits o f the
50 progeny lines plus the two appropriate parents were ranked from low to high for each
cross and each environment. Additionally, the three environments were combined and
the means were ranked for each trait. The number o f transgressive segregants for each
cross and trait was determined from the ranked order o f means. Transgressive segregants
22
are defined as progeny that are more extreme and fall beyond the range o f the parents for
traits inherited in a quantitative manner (Poehlman, 1987b; deVicente and Tanksley,
1993). We determined the number o f transgressive segregants for each cross and trait to
be those individuals that were one least significant difference (LSD) above the high
parent mean and/or below the low parent mean.
Pearson correlations were computed between progeny mean range and the
number o f transgressive segregants; progeny mean range and genetic variance; and the
number o f transgressive segregants and genetic variance, using NPCOR in MSUSTAT
version 5.2 (Lund, 1993). In addition, parental mean range (low-high) and the number of
transgressive segregants were correlated.
P a ren tal G enetic Sim ilarity and A gronom ic T rait C orrelation'A nalysis
Genetic similarity estimators based on coefficient o f parentage and molecular
, marker data (GS), were correlated with progeny mean ranges, number o f transgressive
segregants, and genetic variances for nine agronomic traits using the NPCOR procedure
in MSUSTAT version 5.2 (Lund, 1993).
Correlations between parental genetic similarity estimators and total genetic
variance were examined. Total genetic variance for each cross was computed by first
standardizing the data for each trait to a mean o f zero and a standard deviation o f one.
Genetic variance components for each trait-cross combination were computed as before.
These genetic variance components were then summed over the nine traits within a cross
to give a measure o f overall genetic variance.
23
CHAPTER 4
RESULTS Molecular Marker Evaluation
Genetic similarity evaluation
The level o f genetic similarity between 10 HRSW cultivars was determined using
restriction digested products from 205 STS-PCR primers and six microsatellites. One
hundred and twenty-one primers o f the 2 1 1 primers examined (57%) were polymorphic.
The number o f primers represented on each chromosome group ranges from 14 to 3 1,
with 72 primers remaining unmapped (Table I).
Comparisons between polymorphic-scoring methods
Genetic similarities between 10 HRSW cultivars based on molecular markers
were determined using two polymorphism-scoring methods, and tw o genetic similarity
coefficients. .We examined the genetic similarity for 12 o f the 45 possible combinations
between the 10 cultivars. Polymorphic-scoring method one considers all scorable
polymorphisms observed in gel photographs, while polymorphisrm-scoring method two
examines unique “polymorphic patterns” once per primer.
When using the Dice coefficient to determine genetic similarity between two
cultivars, the coefficients for polymorphism-scoring method one and tw o ranged from.
24
0.4084 to 0.8531, and 0.3902 to 0.8370, respectively (Table 2). The correlation between
polymorphism-scoring methods one and two for the Dice coefficient is 0.973 (P < 0.01).
When using Jaccard's genetic similarity coefficient, the genetic similarities for
polymorphism-scoring methods one and two, ranged from 0.2566 to 0.7439, and 0.2424
to 0.7196, respectively (Table 3). The correlation between polymorphism-scoring
methods one and two for Jaccard's coefficient is 0.978 (P < 0.01). Due to the highly .
significant correlation between the two polymorphic scoring methods, only genetic
similarities computed by method two were used in subsequent analyses.
Dice versus Jaccard’s similarity coefficient
The correlation between Dice and Jaccard's genetic similarity coefficients was
0.995 (P < 0.01) for both polymorphism-scoring method one and two (data not shown).
This is in agreement with the findings o f Thorman et al. (1994). Therefore, due to the
highly significant correlation between genetic similarity coefficients only the Dice
coefficient was used in subsequent analyses.
Coefficient of parentage versus the Dice coefficient
Genetic similarity estimates based on COP range from 0.096 to 0.673 (Table 4).
The correlation between COP estimates and the Dice coefficient is 0.577 (P < 0.05)
(Table 4), and the correlation between COP estimates and Jaccard's coefficient is 0.589
. (P < 0.05) (data not shown).
Table I. Chromosomal locations o f sequence-tagged sites polymerase chain reaction primers and microsatellites used to
assay the genetic similarity of ten hard red spring wheat cultivars currently under production in Montana and
North Dakota. The primer sets in bold are polymorphic.
Chromosome
Group
Number of
Primer Sets
I
14
2
31
Primer Set
D 14, ES, E l l a , E19, G2, M 148, A B C 152, A B C 160, A B G 059. A B G 452, C D Q 464, C D 0 5 4 5 , M S T 101(K V 1,2),
M S T 102(K V 1,9)
OS, D18, D 22, E 16, F2a, F l I, F15, F36, F 41, G 5, G 49, HS, H 9, M 149, ABA005(PST327), A B C 156, A B C 160,
A B C 252, A B C 306, A B C 311, ABC45I, A B C 454, ABG058, ABG317, ABG356, ABG602, BCD175, C D O 370,
M S T 126(S T 7,8), M S T 510(T B 33,34), W G 541
F34, G13, G 36, G 53, G59, HS, H15, A B C 156, ABC 156.2, A B C 160, ABC 166, ABG070, A B G 377, ABG396, A BG 459,
BCD269, C D 0 4 7 4 , WG110(KV25,26), W G l78
3
19
4
28
5
16
6
23
D l, D12, D17, E 14, F19, F37, C S, G43, G48, Hl I, A B G 065, ABG072, A B G 378, ABG458, ABG466, ABG471,
ABG602, B C D 402, C D G 213, M S T 109(K V 14,T B 14), M S T 212(N A R 7L 1), W G 232, W G 669
7
28
A B G 366, ABG396, A B G 460, A B G 603, A B G 701, C D G 213, M S T 107(K V 12,13), M S T l08(K V 12,24),
B 5, C2, D21, E6, E9, F8, G10, A B A 0 03(T B 19,20), A B C 252, ABC303, ABC451, A B C 455, ABC468, ABG020,
ABG054, A B G 366, A B G 394, ABG466, ABG472, A B G 484 A B G 498, ABG602, B A R G 10, B TA 2, C D 0 4 7 5 , WG181,
WG464, W G 9 4 0
AB9, D 16, G 12, G 14, G44, HS, 126, A B A 0 0 1 (M 1 7 ,1 9 ), A B C 717, ABG466. B C D 828, CD0673, M S T 103(K V 9,10),
P S T 319, P S T 337, W G 541
A l , A5, D 2, D7, D 9. D15, F 48a. G 12, C 39, HS, A B C 152. A B C 156, A B C 253. A B C 255, A B C 465, A B G 320,
M S T 126(S T 7,8), M S T 211(N A R 1), WG686, WG996
Table I continued
Chromosome
Group
Number o f
Primer Sets
unmapped
72
Primer Set
ABA004(TB21,22), ABC254, ABC320, A B C 322, A B G 002, ABC003, ABGO14, A B G 316, A B G 318, ABG319,
ABG358, ABG398, ABG468, A B G 495, A B G 499, ABG601, A B G 616, A B G 619, A B G 704, B C D 304, BCD327,
BTAl, CDO036, CDO063, CDG395, CDO506, CD0541, CDG588, CDG662, CDG749, MST104(KV3,4),
MSTI05(KV5,6), MST106(KV7,8), MST110(KV16,17), MST111(KV22,23), MST112(KV29,30),
M S T 1 2 1 (S 1 ,S 2 ), M S T 122(S 2,S 3), M S T 123(S 2,S 4), M S T 124(S T 1,2), M S T 125(S T 4,6), M S T 127(S T 9,10),
M S T 1 2 8 (S T 1 2 ,1 3 ), M S T 129(S T 14,16), MST202(DHN1L2R), MST501(TB1,2), MST503(TB4,5),
M S T 5 0 6 (T B 1 0 ,1 1 ), MST508(TB15,16), M S T 509(T B 17,18), M S T 511(T B 35,36), MST512(TB36,37),
M S T 5 1 3 (T B 3 8 ,3 9 ), MST516(TB67,68), M S T 5 1 7 (T B 6 9 ,7 0 ), M W G 060, PST073(KV27,28), PST316, PST321,
W G 2 4 1 , W G 564, W G 622, W G 719, W G 983, W G 1026, W T A l, W M S -2 , W M S -1 8 , W M S -2 4 , W M S -3 0 , W M S -44,
W M S -4 6
27
Table 2. A comparison o f molecular marker genetic similarity estimates between 12
crosses among 10 HRSW cultivars using polymorphic-scoring method I
(505 entries) and method 2 (226 entries). The Dice coefficient was the
genetic similarity coefficient used.
Cross
Parentl
Amidon
Fortuna
Fortuna
G rlen m an
G rlen m an
G le n m a n
Grandin
Hi-Line
HB-Line
Lew
Len
Len
Grenetic Similarity Coefficients
Parent2
Newana
Hi-Line
Lew
Amidon
Lew
Marberg
Pondera
Newana
Pondera
Amidon
Glenman
Newana
M ethod I (5 05entries)
M ethod 2 (226 entries)
0.5133
0.4206
0.8531
0.4234
0.6547
0.4594
0.4272
0.6195
0.4875
0.4084
0.4458
0.5331
0.5116
0.4141
0.8370
0.4246 0.6000
■0.4574
0.4433
0.5279
0.4681
0.3902.
0.4695
' 0.5314
* correlation between method I and 2 = 0.973 (P < 0.01)
I
28
T able 3. A comparison o f molecular marker genetic similarity estimates between 12
crosses among 10 HRSW cultivars using polymorphic-scoring method I
(505 entries) and method 2 (226 entries). Jaccardls coefficient was the
genetic similarity coefficient used.
Cross
Genetic Similarity Coefficients
Parentl
. Parent2
M ethod I (505entries)
M ethod 2 (226 entries)
Amidon
Fortuna
Fortuna
Glenman
Glehman
Glenman
Grandin
H -L ine
H -L ine
Lew
Len
Len
Newana
H -L ine
Lew
Amidoh
Lew
Marberg
Pondera
Newana
Pondera
Amidon
Glenman
Newana
0.3453
0.2663
0.7439
0.2685
0.4867
0.2982
0.2716
0.4488
0.3223
.0.2566
0.2869
0.3634
0.3438
0.2611
0.7196
0.2695
' 0.4286
0.2966
0.2848
0.3586
0.3056
0.2424 .
0.3067
0.3618
* correlation between method I and 2 = 0.978 (P
< 0.01)
y
29 _
Table 4. Comparisons between coefficient o f parentage (COP) and the Dice
coefficient genetic similarity estimates between 12 crosses among
10 hard red spring wheat cultivars.*
Cross
Parent I
Amidon
Fortuna
Fortuna
Glenman
Glenman
Glenman
Grandin
H -L ine
H-Lirie
Lew
Len
Len
Genetic Similarity
Parent 2
Newana
H -L ine
Lew
Amidon
Lew .
Marberg
Pondera
Newana
Pondera
Amidon
Glenman
Newana
COP
0.198
0.108
0.673
0.264
0.399
0.338
0.147
0.636
0.344
0.506
0.096
, 0.225
Molecular M arker
0.512
0.414
0.837
0.425
0.600
0.457
0.443
0.528
0.468
0.390
0.470
0.531
* correlation between COP and the Dice coefficient = 0.577 (P < 0.05)
30
Field Trial Evaluation
Agronomic trait evaluation
Nine agronomic traits were measured on 50 progeny lines and the two parents o f
12 crosses in the 1995, 1996 dryland and 1996 irrigated field trials. The average
agronomic performance o f the 10 parental HRSW cultivars in each environment and over
the combined environments are listed in Table 5. In the 1996 irrigated field trial every
cultivar headed later, tillered more, matured later, and yielded more than in the 1995 and
1996 dryland field trials.
The average agronomic performance o f the 50 progeny lines for each cross, for
each environment and over .the combined environments are listed in Table 6. In the 1996
irrigated field trial each cross headed later, matured later, and yielded more than in the
1995 and 1996 dryland field trials.
31
T able 5. Average agronomic performance o f 10 hard red spring wheat cultivars o f nine
agronomic traits in the 1995, 1996 dryland, 1996 irrigated field trials and for
the three combined environments.
Plant
G rain
Test
G rain
H eading
H eight
Stem
Phys.
Grain Y ield W eight Protein
D ate Tillers/ft. (cm) Solidness M aturity Fill (M g/haj (kg/m3) (%)
Cultivar
E n v .'
Amidon
1995
1996-Dry
■1996-Irr.
Comb.
57.67
64.00
66.00
61.86
40.33
44.50
49.83
44.24
93.00
83.50
95.17
90.91
15.78
16.67
13.67
15.43
106.56
103.00
112.33
107:19
48.89
39.00
46.33
45.33
5.16
3.93
5.58
4.93
734.45
780.56
748.93
751.76
15.97
15.05
15.00
15.40
Fortuna
1995
1996-Dry
1996-Irr.
Comb.
56.33
63.50
65.75
61.07
29.83
48.75
49.50
40.86
94.50
85.00
95.00
91.93
17.33
18.25
13.00
16.36
102.17
98.50
110.75
103.57
45.83
35.00
45.00
42.50 .
3.14
3.10
4.91
163
754.08
787.90
779.50
771.00
16.47
15.00
15.18
'15.68
G lenm an
1995
1996-Dry
1996-Irr.
Comb,.
58.17
64.88
66.38
62.43
39.50
43.38
54.88
45.00
84.33
71.75
80.82
14.17
15.00
14.00
14.36
106.92
98.75
111.00
■ 105.75
48.75
33.88
44.63
43.32 '
5.47
3.74
6:43
5.25 .
766.56
778.71
766.22
769.93
14.37
13.81 ’
13.00
13.82
-1995
1996-Dry
1996-Irr.
Comb.
58.00
63.50
64.50
61.43.
28.33
35.50
55.50
38.14
81.00
77.00
86.00
81.29
6.00.
7.50
7.50 '
6.86
108.33
97.50
113.50
106.71
50.33
34.00
49.00
45.29
3.21
3.57
6.11
4.14
764.78
791.76
772.54
16.17
15.30
15.35
15.69
1995
55.44
62.83
1996-Dry
1996-Irr. . 64.17
60.05
Comb.
33.22
43.83
45.50
5.56
9.33
7.50
7.19
105.11
99.33
110.83
105.10
49.67
3 9 .7 6
75.00
74.33
76.00
75.10
4.89
3.22
5.90
4.70 .
778.72
15.83
778.14 • 15.60
776.17
15.07
777.83
15.55
Len
1995
1996-Dry
1996-Irr.
Comb.
57.50
64.75
66.25
62.07
41.00
48.50
50.00
45.71
84.67
74.00
84.50
81.57
8.00
9.25
8.50 .
8.50
107.33
100.25
113.75
107.14
Lew
1995
1996-Dry
1996-Irr.
Comb.
59.44
65.50
67.67
63.52
■47.33
46.67
52.33
48.57
98.56
84.00
97.83
94.19
15.11
17.17
15.33
15.76
106.00
100.00
110.00
105.43
46.56
34.50
42.33
.41.91
M arberg
1995
1996-Dry
1996-Irr.
Comb.
53.33
62.00
63.00
58.57
43.00
47.00
75.00
69.50
81.00
75.14
6.67
9.50
8.00
7.86
105.67
97.00
111.50
104.86
1995
1996-Dry
1996-Irr.
Comb.
59.11
65.50
35.22
50.50
58.33
46.19
76.8 9
29.17
41.75
46.00
37.57
G randin
H i-Line
N ew ana
Pondera
1995
1996-Dry
1996-Irr.
Comb.
68.50
63.62
55.33
63.5 0
65.25
60.50
69.50
51.71
. 84.63
3 6 .5 0
46.67
45.05
49.83
4.77
35.50 ^ 3.53
47.50
6.51
45.07
4.91
764.97
756.95
779.50
751.13
761.73
15.28
15.10
14.85
15.11
4.72
3.36
5.57
4-57.
782.83
790.40
784.65
785.52
15.24
14.35 .
14.18
14.69
52.33
35.00
48.50
46.29
4.64
3.51
6.75
4.92
765.12
788.13
770.42
773.21
15.27
14.75
14.35
14.86
69.83
80.33
75.86
6.00 ■ . 109.33 ' 50.22
8.00
104.33
38.83
7.33
110.50 42.00
6.95
108.24 44.62
4.80
3.65
5.23
4.60
15.19
14.90
746.36 ■.13.88
760.83
14.73
76.00
73.00
80.25
76.36
6.50
10.00
8.25
8.00
4.54
3.35
6.52
4.76
107.17
97.25
111.75
105.64
51.83
33.75
46.50
45.14
761.98.
773.60
' 772.09
788.35
774.28
777.36
15.45
15.08
14.18
14.98
Table 6. Average agronomic performance o f progeny from 12 crosses among 10 hard red spring wheat cultivars o f nine
agronomic traits in the 1995, 1996 dryland, 1996 irrigated field trials and for the three environments combined.
Plant
Height
Heading
C ross
A m id o n x N ew a n a
F o rtu n a x H i-L in e
F o rtu n a x L ew
G len m an x A m id o n
G len m an x L ew
.
G lem n an x M a rb e rg
E n v ir.
D ate
T illers/ft.
(cm )
1995
• 1 9 9 6 -D ry
1996-Irr.
C om b.
5 9 .6 8
65.35
67.42
63.51
27.89
44.89
, 4 8 .1 3
85.44
80.79
87.45
'84.69
1995
I 9 9 6 -D ry
1996-Irr.
C om b.
55.32
62.42
64.35
59.93
34.57
43.54
53.70
42.60
86.64
76.57
90.64
1995
1 9 9 6 -D ry
1996-Irr.
C om b.
57.96
64.87
66.47
62.37
40.97
45.87
99.53
84.96
1995 .
1 9 9 6 -D ry
1996-Irr.
C om b.
57.84
64.40
6 5 .9 8
62.04
3 8 .5 3
5 3 .8 8
9 6 .4 8
46.06
94.50
36.11
42.54
55.06
43.36
1995
1 9 9 6 -D ry '
1996-Irr.
Comb.-
67.74
63.47
40.55
48.40
48.32
45.01
1995
1 9 9 6 -D ry
1996-Irr.
C om b.
56.94
64.15
66.09
61.61
37.41
43.14
53.30
43.59
59.40
6 5 .3 2
8 4 .9 1
91.97
Stem
Phys.
Solidness M a tu rity
8 .2 9
9.04
8.54
8 .5 8
.
109.70
104.46
• .110.56
■ 108.42
Grain
Grain
Yield
Test
Weight
Grain
Protein
Fill
(M g/ha)
(kg/m 3)
(% )
50.13
39.11
43.14
44.87
3.70
3.76
4.74
4.02
727.70
761.84
• 745.44
742.52
15.94
15.10
14.94
15.41
11.39
13.09
10.18
11.53
103.05'
97.63
109.22 .
103.27
47.73 ~
35.21
44.87
43.34 •
3.73
2.93
5.28
3.95
763.27
782.19
778.33
772.98
16.52
15.90
15.53
16.06
15.30
16.50
14.09
15.30
105.34
100.14
47.38
35.27
45.41
43.36
3.78
3.37
5.33
4.11
771.47
795.54
783.55
781.80
15.56
14.84
14.41
15.02
4.36
3.55
6.24
4.67
749.09 •
782.52 '
759.70
761.67
15.76
14.82
14.64
15.17
770.38
785.29
767.72
773.88
14.94
14.33
14.01
14.50
7 6 5 .9 9
' 14.81
14.75
13.83
14.51
1 1 1 .8 8
105.72
94.78
89.42
16.05
16.37
13.45
15.40
1 0 6 .1 8
48.07
35.86
46.54
44.14
88.93
78.01
91.34
86.50
15.53
17.85
15.33
16.14
106.55
100.25
111.23
106.09
47.15
34.93
43.49
42.61
4.59
3.67
5.56
4.60
80.49
69.57
84.12
78.41
. 10.37
12.40
• 10.87
11.09
105.87 .
99.37
111.29
48.93
35.22
45.20
43.95
4.87
3.43
6.35
8 0 .2 5
105.91
100.26
112.52
1 0 5 .5 6
*
4 .8 8
'
780.44
759.20
768.18
'
'
Table 6 continued
Plant
Height
Heading
C ross
Steni
Phys.
Grain
Grain
Yield
Test
Grain
Weight . Protein
Fill
(M g/ha)
(kg/m 3)
(% )
4.18
3.33
5.75
4.39
769.74
780.28
768.73
772.46
16.09
15.83
15.51
15.85
E n v ir.
D a te
T illers/ft.
(cm )
1995
1 9 9 6 -D ry
1996-Irr.
. C om b.
55.52
63.00
64.36
60.18
3 3 .6 9
8 9 .9 9
41.35
48.72
40.17
84.04
. 93.59
89.32
7.11
8.04
7.66
7.53
105.26
98.40
112.81
105.46
1995
1 9 9 6 -D ry
1996-Irr.
C om b.
57.79
64.47
66.59
62.21
34.24
45.27
41.76
76.71
66.86
78.63
74.45
6.24
7.59
7.67
7.03
■ 107.06
100.24
111.15
106.28
49.27
• 35.77
- 44.56
. 44.07
4.41
3.40
5.41
4.04
758.66
775.58
752.19
761.65
15.51
15.33
■ 14.42
15.15
1995
1 9 9 6 -D ry
1996-Irr.
C om b.
55.75
62.38
64.17
60.05
35.58
41.60
47.70
40.76 .
83.45
75.40
83.43
81.14
6.39
8.36
7.77
7.35
104.70
48.95
36.35
47.15
44.83
4.43
3.30
5.55
4.42
769.22
778.30
762.77
769.97
15.67
15.63
15.07
15.48
1995
1 9 9 6 -D ry
1996-Irr.
C om b.
5 8 .9 6
3 6 .9 5
64.72
66.67
62.81
42.72
51.56
42.77
95.73
84.25
95.88
92.49
15.58
16.47
. 15.16
15.7,1
106.36 ■
100.80
111.82
106.33
47.43
36.08
45.15
43.52
4.12
3.51
5.44
4.32
' 752.47
791.04
763.92
766.76
15.74
14:74
14.79
15.18
L e n x G lenm an
1995
1 9 9 6 -D ry
1 9 9 6 -Irr
C om b.
59.42
64.64
66.73
63.00
34.70
43.24
50.10
41.54
89.04
79.78
92.84
87.48
11.57
11.84
10.29
• 11.28
107.71
99.14
112.44
106.60
48.37
34.50
45.71
43.61
4.15
3:50
5:41
4.32
747.21
776.30
750.89
756.57
14.82
14.44
14.37
14.59
L e n x N ew a n a
1995 .
57.45'
64.52
66.92
62.17
34.06
46.67
50.58'
42.38
106.01
103.55
114.14
107.63
48.59
39.03
47.22
45.46
3.71
3.42
.- 5.35
4.10
759.51
783.47
.760.59
766.66
16.00
15.72
15.40
15.75
G fa n d in x P o n d era
H i-L in e x N e w a n a
H i-L in e x P o n d e ra
L e w x A m idon
1 9 9 6 -D ry
1996-Irr.
C om b.
4 9 .5 2
.
91.21 •
80.95
93.37
88.90
Solidness M a tu rity
49.74
35.40 ■■
48.45
45.27
9 8 .7 3
111.32
104.89
.
7.6 6 . .
8.33
8.52
8.10
'
34
Correlations among measurements of progeny variance
Variation among the progeny lines for nine agronomic traits was examined by
looking at progeny mean range, the number o f transgressive segregants and genetic
variance for each cross. Pairwise correlations among these three measures o f genetic
variability estimates were made to determine if there is an association between the
measurements. In addition, parental mean range and the number o f transgressive
segregants were correlated.
Correlations between parental mean range and the number o f transgressive
segregants were generally negative (Table I). The closer the mean o f the parents are the
greater chance o f detecting transgressive segregants. Significant correlations were
detected for every trait in at least one of the environments. Twenty o f the 36 possible
correlations were significantly negative.
Correlations between progeny mean range and the number o f transgressive
segregants were in general, positive (Table 8). There was a greater chance o f detecting
transgressive segregants when the progeny mean values spanned a greater range.
Fourteen.of 36 possible correlations were significantly positive.
Correlations between progeny mean ranges and genetic variance were positive for
every trait in each environment, and generally highly significant (P < 0.01) (Table 9).
The greater genetic variance observed in the progeny results in a greater spread o f the
progeny means for a given trait. Thirty-three o f the possible 36 correlations were
significantly positive. This overwhelming number o f highly significant positive
correlations indicates progeny mean range is a good indicator o f progeny variance.
35
Correlations between the number o f transgressive segregants and genetic variance r
were generally positive for eight o f the nine agronomic traits measured over all three
environments (Table 10). The greater genetic variance observed in the progeny results in
a greater chance o f detecting transgressive segregants. Overall, 13 o f the 36 possible
correlations were significantly positive between the number o f transgressive segregants
and genetic variance. Stem solidness was the only trait with negative correlations
between genetic variance and the number o f transgressive segregants observed. There
were four hollow by hollow-stemmed crosses and four solid by solid-stemmed crosses in
this study. These eight crosses had a lower genetic variance than the four hollow by
solid-stemmed crosses, in every environment and when the environments were combined
(Appendix A, Tables 15-18). However, there were not many transgressive segregants
observed for stem solidness in any cross, in any environment or when the environments
were combined. Ifth e cross was between hollow by hollow-stemmed varieties, then all
the progeny tended to be hollow and there wasn’t a great range in means. The same is
true for solid by solid-stemmed crosses. However, in the hollow by solid-stemmed
varieties the wide range in the parental means was too great to allow the progeny to fall
beyond the range o f the parents. If the parent, was solid and received an average rating o f ■
four or five, it is difficult for progeny to get any “solider” and fall beyond this parental
mean. Thus, the higher genetic variances, and few transgressive segregants observed for
these four crosses helped contribute to the negative correlation.
36
Table 7. Correlations between parental mean ranges and the number of transgressive
segregants of nine agronomic traits in the 1995, 1996 dryland, 1996 irrigated
field trials and for the three environments combined.
P lant
H eading
Envir.
1995
. D ate
H eight
Tillers/ft.
(cm)
Stem
Phys.
Grain
Solidness M aturity
Fill
G rain
Test
Grain
Yield
W eight
Protein
(M g/ha) (kg/m3)
(%)
-0.637**
-0.316
-0.377
-0.511*
-0.367
-0.450
1 9 9 6 -D ry
-0.447
0.282
-0.473
-0.257
-0.458
-0.578** -0.582** -0.365
1996 - L r.
-0.697*** -0.423
-0.522*
-0.607** -0.078 ■ -0.612** -0.527*
Combined
-0.850*** -0.692*** -0.331
-0.480
- 0 . 628 **
-0.597** -0.623**
-0.545*
-0.351
-0.521*
-0.456
-0.831***
-0.622** -0.576** -0.690***
* Significant at the 10% level (P < 0.10)
** Significant at the 5% level (P < 0.05)
. *** Significant at the 1% level (P < 0.01)
\__
x
T able 8. Correlations between progeny mean ranges and the number o f transgressive
segregants .of nine agronomic traits in the 1995, 1996 dryland, 1996 irrigated
field trials and for the three environments combined.
P lant
H eading
D ate
1995
0.636**
Tillers/ft.
0.367
(cm)
Stem
P h y s .'
Solidness M aturity
0.897** -,0.319
0.291
Test .
Y ield
W eight
Fill
(M g/ha)
Grain
Protein.
(%)
0.852*** 0.495*
0.600**
0.274
0.343
0.614**
0.018
0.671**
0.449
0.336 '
0.298
0.595**
0.347
0.040
1996 - D ry
0.530*
0.459
0.637** 0.094
1996 - Irr.
0.349
0.204
0:427
Combined
0.576**
0.134
0.666** 0.067
0.193
-0.126
G rain
Grain
I
Envir.
Height
0.154
0.418
■ 0.653**
0.515*
•
* Significant at the 10% level (P < 0,10)
** Significant at the 5% level (P < 0.05)
*** Significant at the 1% level (P < 0.01)
37
Table 9. Correlations between progeny mean ranges and genetic variance of nine
agronomic traits in the 1995, 1996 dryland, 1996 irrigated field trials and
for the three environments combined.
■•
Plant
H eading
Envir.
1995
D ate
H eight
Tillers/ft.
0.901*** 0.288
(cm)
Stem
0.933*** 0.873***
1996 - Irr.
0.823***
0.695*** 0.880*** 0.766***
Combined
0.885*** 0.506*
O
P
S
0.012
0.913***
Fill
G rain
Y ield
W eight
Protein
(M g/ha) (kg/m3)
(%)
0.797*** 0,918*** 0.907*** 0.912*** 0.902***
0.826***, 0.327
0.841*** 0.7,85*** 0.831***
0.958*** 0.930*** 0.858*** 0.895***
O
I
0.724*** 0.649**
O
0.745***
Grain
Test
I
1996 - D ry
0.934***
Phys.
Solidness M aturity
G rain
0:729*** 0.922*** 0:874***
r
* Significant at the 10% level (P < 0.10)
** Significant at the 5% level (P < 0.05)
*** Significant at the 1% level (P < 0.01)
T able 10. Correlations between the number o f transgressive segregants and genetic
variance o f nine agronomic traits in the 1995, 1996 dryland, 1996. irrigated
field trials and for the three environments combined.
P lant
H eading
Envir.
1995 '
Height
D ate
Tillers/ft..
(cm)
0.727***
Stem
Phys.
Solidness M aturity
0.707***
0.555*
1996 - D ry
0.790***
0.592** 0.408
-0.075
1 9 9 6 '-Irr.
0.327
0.381
0.229
-0.073
Combined
0.528*
0.442
0.497*
-0.272
0.347
-0.395
0.339
Grain
Fill
G rain
Test
Grain
Y ield
W eight
Protein
(M g/ha) (kg/m3)
(%)
0.803***
0.517*
0.397
0.444
0.098
0.307 .
0.228
0.665**
0.600**
0.320
0.343
0.479
0.008
0.450
0.476
-0.141
0.588**
0.653**
* Significant at the 10% level (P < 0.10)
** Significant at the 5% level (P < 0.05)
*** Significant at the 1% level (P < 0.01)
38
Correlations Between Parental Genetic Similarity and Variance of Agronomic Traits
Coefficient o f parentage and molecular marker data were the two measurements ■
used to estimate genetic similarity between the 10 parental hard red spring wheat
cultivars. The three types o f progeny variation discussed in the previous section were
correlated with the two genetic similarity estimators. Correlations between genetic
similarity estimates and the measures o f variability were generally negative
• .
(Table 11-13). That is, the more genetically similar the parents o f a cross were the less
variation was observed in the progeny.
Correlations between progeny mean range and genetic similarity estimators, COP
and GS, were generally negative for the nine agronomic traits measured over all three
environments (Table 11). Non-significant positive correlations between progeny mean
range and both COP and GS, were observed for tillers/ft. in the 1995 field trial and grain
fill in the 1996 dryland field trial. Also in the 1996 dryland field trial, non-significant
positive correlations between progeny mean range and COP were observed for
physiological maturity, and non-significant positive correlations between progeny mean
range and GS were observed for tillers/ft. Progeny mean range for heading date had
highly significant correlations (P < 0.01) with GS in each environment and when the
three environments were combined, and non-significant negative correlations were
observed between progeny mean range and COP estimates. Overall, six o f the 36
possible correlations were significantly negative for progeny mean range and COP
estimates, while 11 o f the 36 possible correlations were significantly negative for GS
estimates.
39
Correlations between the number o f transgressive segregants and genetic
similarity estimators, COP and OS, were generally negative for the nine agronomic traits
measured over all three environments (Table 12). Heading date, tillers/ft., and grain
protein were the only traits exhibiting significant correlations with either genetic
similarity estimator. Seven o f the 36 possible correlations were significantly negative
between the number o f transgressive segregants and COP estimates, while two of the 36
possible correlations were significantly negative for GS estimates.
Correlations between genetic variance and genetic similarity estimators, COP and
OS, were generally negative for the nine agronomic traits measured over all three
environments (Table 13). Non-significant positive correlations between genetic variance
and COP estimates were observed for physiological maturity, grain fill, and grain yield in
the 1996 dryland field trial, and for test weight in the 1996 irrigated field trial. Nine of
the 36 possible correlations were significantly negative between genetic variance and
COP estimates, while eight o f the 36 possible correlations were significantly negative for
GS estimates.
40
Table 11. Correlations between genetic similarity estimators, coefficient of parentage
(COP) and molecular marker data (CS), and progeny mean ranges of nine
agronomic traits in the 1995, 1996 dryland, 1996 irrigated field trials and for
the three environments combined.
P lant
H eading
Envir.
D ate
H eight
Tillers/ft.
Stem
Phys.
(cm) ' Solidness M aturity
Grain
G rain
Test
G rain
' Yield
W eight
Protein
(kg/m3)
. (%)
Fill ' (M g/ha)
1995.
COP
GS
-0.408
0.359
-0.700*** 0:357
-0.424
-0.376
-0.514*
-0.420
-0.368
-0.174
-0.443
1 9 9 6 -D ry
COP
GS
-0.209
-0.206
-0.723*** 0.259
-0.315
-0.278
-0.486
-0.484
0.104
-0.197
0.050
COP
GS
-0.380
-0.314
-0.723*** -0.253
-0.432
-0.349
-0.415
-0.356
-0.161
-0.017
-0.738*** -0.393
C om bined
. COP
GS
-0.434
-0.331
-0.768*** -0.096
-0.445
-0.345
-0.439
-0.454
-0.434
-0.489
- 0 .5 2 6 *
0.308
-0.592**
-0.249
-0.509*
-0.367
-0.524*
-0.042
-0.148
-0.339
-0.480
-0.573**
-0.666**
-0.431
-0.400
-0.077
-0.423
-0.556*
-0.650**
-0.221
-0.478
-0.458
-0.689***
- 0.298
1996-Irr.
-0.376 -0.673**
-0.530* -0.379
* Significant at the 10% level (P < 0.10)
** Significant at the 5% level (P < 0.05)
*** Significant at the 1% level (P < 0.01)
41
Table 12. Correlations between genetic similarity estimators, coefficient o f parentage
(COP) and molecular marker data (GS), and the number o f transgressive
segregants o f nine agronomic traits, in the 1995, 1996 dryland, 1996 irrigated
field trials and for the three environments combined.
P lant
H eading
Envir.
H eight
G rain
Stem
' Phys.
Test
G rain
Grain
Y ield
W eight
Protein
Fill
(M g/ha)
(kg/m3)
(%)
-0.386
-0.332
-0.279
-0.138
-0:340
-0.051
-0.255
-0.156
-0.624**
-0.456
0.397
-0.329
0.158
0.001
0.121
-0.038
-0.062
0.278
-0.344
-0.070
-0.580**
-0.288
-0.278
0.149
-0.071
-0.156
-0.093
-0.217
-0.165
0.056
-0.084
-0.380
•,0.238
-0.425
-0.073
-0.430
-0.234
0.180
0:158 .
-0.335
- 0.2 6 4
-0.394
0.025
-0.008
-0.229
-0.475
-0.211
-0.607**
-0.484
D ate
Tillers/ft.
(cm)
COP
CS
-0.526*
-0.464
. -0.471
-0.134
-0.287
-0.315
0.103
-0.073
1 9 9 6 -D ry
COP
GS
-0.529*
-0.395
-0.360
-0.204
-0.436
-0.264
1996-Irr.
COP
GS
-0.445
-0.517*
- 0.3 6 9
-0.049
-0.707*** -0.510*
-0.648** -0.146
Solidness M aturity
1995
C om bined
COP .
■ GS ’
- 0.286
* Significant at the 10% level (P < 0.10)
** Significant at the 5% level (P < 0.05)
*** Significant at the 1% level (P < 0.01)
42
Table 13. Correlations between genetic similarity estimators, coefficient o f parentage
(COP) and molecular marker data (GS), and genetic variance o f nine
agronomic traits in the 1995, 1996 dryland, 1996 irrigated field trials and for
the three environments combined.
Plant
H eading
Height
D ate Tillers/ft. (cm)
Stem
Phys.
Solidness M aturity
-0.208
-0.465
-0.210
-0.264
-0.276
-0.291
-0.545*
-0.343
-0.381
-0.354
-0.419
-0.339
-0.232
-0.401
-0.182
-0.261
-0.285
-0.250
- 0.3 9 7
-0.416
0.113
-0.490
0.205
0.134
-0.354 . -0.052
COP
GS
-0.336
-0.530*
-0.532*
-0.299
-0.434
-0.315
-0.559*
-0.375
-.0.156
-0.161
-0.648** -0.480
C om bined
COP
GS
-0.380
-0.541*
-0.358
-0.288
-0.377
-0.297
-0.522*
-0.373
-0.266
-0.554*
Envir.
Grain
Fill
G rain
Yield
(MgZha)
Test
W eight
(kg/m3)
Grain
Protein
(%)
1995
COP
GS
-0.689*** -0.144
-0.331'
-0.508*
-0.572**
-0.474
1 9 9 6 -D ry
COP
GS
-0.268
-0.327
-0.472 .
-0.396
-0.227
-0.396
0.172
-0.252
-0.590**
-0.532*
-0.514*
-0.456 .
-0.062
-0.414
-0.546* '
-0.511*
1996-Irr.
-0.407
-0.544*
. * Significant at the 10% level (P < 0.10)
** Significant at the 5% level (P < 0.05)
*** Significant at the 1% level (P < 0.01)
/
43
Correlations Between Parental Genetic Similarity and Total Genetic Variance
Total genetic variance o f all nine agronomic traits for each cross was computed
and correlated with the two genetic similarity estimators (Table 14). Correlations
between total genetic variance and COP were significantly negative in the 1995 field trial
(P < 0.05), 1996 irrigated field trial (P < 0.10), and when the three environments were
combined (P < 0.05). Significant negative correlations between total genetic variance
and GS were observed in the 1995 field trial, the 1996 dryland field trial, the 1996
irrigated field trial, and when the three environments were combined
(P < 0 .0 5 ).
44
Table 14. Correlations between genetic similarity estimators, coefficient o f
parentage (COP) and molecular marker data (GS), and total genetic
variance o f nine agronomic traits in the 1995, 1996 dryland, 1996
irrigated field trials and for the three environments combined.
Environment
Genetic Similarity
Estimator
Total Genetic
Variance
COP
GS
-0.613**
-0.586**
COP
GS
-0.368
-0.624**
COP
GS
-0.555*
-0.623** ■
COP
GS
-0.603**
-0.605**
1995
1996-Dry
1996-lrr.
Combined
* Significant at the 10% level (P < 0.10)
** Significant at the 5% level (P < 0.05)
45
CHAPTER 5
DISCUSSION
PCR-based molecular markers are an attractive choice to assess genetic diversity
among hard red spring wheat cultivars. Knowledge o f genetic similarity among potential
parental lines would be useful in choosing lines that perform equally agronomically, yet
have different genetic make-ups. The ability to make crosses that are likely to produce
transgressive segregants would greatly enhance the efficiency o f a breeding program.
Genetic similarity based on COP and STS-PCR primers (GS) were significantly
correlated (r = 0.58). Martin et 'al. (1995) found COP and genetic similarity based on 27
STS-PCR primers in hard red spring wheat to be highly correlated (r = 0.68). Autrique et
al. (1996) found a low correlation (r = 0.23) between COP and genetic distance based on
RFLPs in durum wheat. Keim et al. (1992) found a moderate correlation (r = 0.54)
between genetic distance based on RFLPs and pedigree information for 18 ancestral and
20 adapted soybean lines. M oser and Lee (1994) found genetic distance based on RFLPs
significantly correlated with COP (r = 0.63) in oats. Hahn et al. (1995) found moderate
correlations between the similarity measurements o f RFLPs, RAPDs and COP in maize
inbreds, while Smith et al. (1990) found genetic similarity based on RFLPs to be highly
correlated (r = 0.81) with COP estimates among 37 inbred maize lines. Thorman et al.
(1994) found a high correlation (r = 0.97) between genetic similarity estimates based on
46
RAPDs and genomic RFLPs in crucifer species.
The moderate correlation observed between molecular marker similarity estimates
and COP in this study could be explained in several ways. Although 211 primers were
used to assess the level o f similarity among the 10 HRSW cultivars, and since wheat is a
allopolyploid containing three genomes, perhaps the number o f primers mapped to each
chromosome was not sufficient. For example chromosome groups one and five have 14
and 16 primers mapped, respectively (Table I). This equates to an average o f 4.6 and 5.3
primers per genome for each chromosome, respectively, which may not provide adequate
genome coverage. Seventy-two primers remain unmapped. Future determination o f their
map location, may further saturate the existing chromosomes and provide better
coverage.
The moderate correlation between molecular marker similarity estimates and COP
could also be attributed to poor COP estimates. Coefficient o f parentage becomes more
precise the closer the lines are related (Smith et al., 1990). This is due to the assumption
that ancestors that have no pedigree information are assumed to be unrelated (Martin et
al., 1991). Smith et al. (1990) noted that as the distance between maize inbred lines
approached 0.65-0.70, the measure o f relatedness was based on very imprecise pedigree
information. In the present experiment, there are several estimates o f similarity based on
COP that fall below 0.20 (distance measure o f 0.80 or above) (Table 4). These numbers
may be based on unreliable pedigree information, thus contributing to a lower correlation
between genetic similarity estimates based on molecular markers.
Correlations among the three progeny variation measurements were generally
47
positive (Tables 8-10), indicating variation observed with one method implies similar
levels o f variation using another method.
Highly significant negative correlations were observed between progeny mean
range for heading date and GS for all three environments and when the three
environments were combined (Table 11), however only non-significant to slightly
significant negative correlations were observed between heading date genetic variances
and GS (Table 13). Grain protein follows the same trend o f having significant
correlations between GS and progeny mean range (Table 11), and yet has only non­
significant to slightly significant correlations between GS and genetic variance (Table
13). Progeny mean range utilizes the extreme progeny lines in computing the variance
estimate for each trait, while the genetic variance estimates are computed over all 50
progeny lines. This may account for the greater significant correlations between genetic
similarity estimators and progeny mean range.
The majority o f correlations between genetic similarity estimators and progeny
variation are negative, and significant correlations ranged from slightly significant
(P < 0.10),to highly significant (P < 0.01) (Tables 11-13). Overall, 22 o f the 108 possible
correlations were significantly negative for progeny variation estimates and COP
estimates. Twenty-one of the 108 possible correlations were significantly negative for
progeny variation estimates and GS estimates, indicating the two similarity
measurements are similar in their predictive value (Tables 11-13). Although the number
o f significant correlations are few (20%), the negative sign suggests the greater the
genetic divergence in the parents, then the greater variance observed in the progeny. This
48
study examined the degree OfF5 and F6 progeny variance based on the genetic relatedness
o f the parents. Souza and Sorrells (1991) state the prediction o f progeny performance in
the F4 generation is more difficult to determine than in the F1 due to the nature o f the
parameters being measured. In the F1 generation, the means are predicted, while in the F4
generation, the parameter being predicted is variance. There is a greater error associated
with the prediction o f variance compared to the prediction o f means. Therefore, due to
the greater error associated with variances and the fact that 90% o f the correlations
computed between genetic similarity estimators and progeny variance are negative is
encouraging.
Correlations between total genetic variance and both genetic similarity estimators
were significant in every environment but one (Table 14). Combining genetic variances
o f a cross may be more useful when trying to estimate progeny variance based on
parental genetic similarities. Recent empirical studies indicate that accurately predicting
progeny variance based on molecular marker-based distance estimates does not seem
likely (Moser and Lee, 1994; Souza and Sorrells, 1991). In both o f these studies, and the
present one, progeny genetic variance for individual traits were correlated separately with
genome wide similarity measurements, when perhaps prediction o f total genetic variance
is more feasible. Molecular markers assay the whole genome, however an arbitrarily
selected set o f markers that covers the whole genome may not accurately predict
heterosis or progeny performance if the loci controlling important traits are not marked
by the molecular markers (Charcosset et al., 1991). The markers used to assess genetic
similarity among the 10 HRSW cultivars in this study, may or may not be marking the
49
loci that control these nine agronomic traits among the 12 crosses. It is likely these traits
are controlled by many genes and are probably inter-related among each other, therefore
combining the variance estimates may include more o f the loci controlling these traits
and provide a better estimate. In fact it seems the more traits included in the total
variance estimate the better the correlation would be, due to an increased chance of
having marked those loci by molecular markers.
Coefficient o f parentage and GS appear to be better estimators o f progeny
variance when total genetic variance is examined rather than individual traits one at a
time. Although more than nine traits control the phenotype o f a plant, plant breeders may
be able to examine parental similarity estimates to predict total genetic variance of a few
traits they deem most important.
50
LITERATURE CITED
)
51
Autrique, E., M.M. Nachit, P. Monneveux, S.D. Tanksley, and M.E. Sorrells. ■1996.
Genetic diversity in durum wheat based on RFLPs, morphophysiological traits,
and coefficient o f parentage. Crop Sci. 36:735-742.
Bailey, T B ., Jr., and R E . Comstock. 1976. Linkage and the synthesis o f better
genotypes in self-fertilizing species. Crop Sci. 16:363-370.
Beer, S.C., J. Goffieda, T.D. Phillips, J.P. Murphy, and M.E. Sorrells. 1993. Assessment
o f genetic variation 'm Avena sterilis using morphological traits, isozymes, and
RFLPs. Crop Sci. 33:1386-1393.
Botstein, D., R.L. White, M. Skolnick, and R.W. Davis. 1980. Construction o f a genetic
linkage map in man using restriction fragment length polymorphisms. Am. J.
Hum. Genet. 32:314-331.
Briggle L.W., and B.C. Curtis. 1987. Wheat worldwide, p.1-32. In E.G. Heyne (ed.)
Wheat and wheat improvement. 2nd ed. Agron. M onogr. 13. ASA-CSSASSSA, Madison, WL
Busch, R H , J.C. Janke, and R.C. Frohberg. 1974. Evaluation o f crosses among high
and low yielding crosses o f spring wheat (Triticum aestivum L.) and bulk
prediction o f line performance. Crop Sci. 14:47-50.
Chao, S., P.J. Sharp, A.J. Worland, E.J. Warham, R M D . Koebner, and M D . Gale.
1989. RFLP-based genetic maps o f wheat homoeologous group 7 chromosomes.
Theor. Appl. Genet. 78:495-504.
Charcosset A., M. Lefort-Buson, and A. Gallais. 1991. Relationship between heterosis
and heterozygosity at marker loci: a theoretical computation. Theor. Appl. Genet.
81:571-575.
Chen, H.B., JM . Martin, M. Lavin, and L.E. Talbert. 1994. Genetic diversity in hard
red spring wheat based on sequence-tagged-site PCR markers. Crop Sci.
34:1628-1632.
Cowen, N.M., and K J. Frey. 1987a. Relationship between three measures of genetic '
distance and breeding behavior in oats (Avena sativa L.) Genome 29:97-106.
Cowen, N.M., and K J. Frey. 1987b. Relationship between genealogical distance and
breeding behavior in oats (Avena sativa L.) Euphytica 36:413-424.
52
Cox, T.S. 1991. The contribution o f introduced germplasm to the development ofU .S.
wheat cultivars. p. 25-47. In H.L. Shands and T E. Wiesner (ed.) Use o f plant
introductions in cultivar development. CSSA Spec. Publ. 17. CSSA, Madison,
WL
Cox, T.S., Y.T. Kiang, M.B. Gorman, and D M. Rodgers. 1985. Relationship between
coefficient o f parentage and genetic similarity indices in the soybean. Crop Sci.
25:529-532.
Cox, T.S., and J.P. Murphy. 1990. The effect o f parental divergence on F2 heterosis in
winter wheat crosses. Theor. Appl. Genet. 79:241-250.
Dellaporta, S.L., J. Wood, and TB. Hicks. 1983. A plant DNA minipreparation: version
II. Plant Mol. Biol. Rep. 1:19-21.
deVicente, M.C., and S.D. Tanksley. 1993. QTL analysis o f transgressive segregation in
an interspecific tomato cross. Genetics 134:585-596.
Dilday, R H . 1990. Contribution o f ancestral lines in the development o f new cultivars of
rice. Crop Sci. 30: 905-911.
Duvick, D.N. .1984. Genetic diversity in major farm crops on the farm and in reserve.
Econ. Bot. 38:157-174.
Duvick, D.N. 1996. Plant breeding, an evolutionary concept. Crop Sci. 36:539-548.
Erpelding, TE., N.K. Blake, T.K. Blake, and L.E. Talbert. 1996. Transfer o f sequence
tagged site PCR markers between wheat and barley. Genome 39:802-810.
Fehr W.R. 1987. Role of plant breeding in agriculture, p.1-10. In Principles o f cultivar
development. Macmillan Publ. Co., New York, NY.
Gizlice, Z., T.E. Carter, Ir., and J.W. Burton. 1994. Genetic base for N orth American
public soybean Cultivars released between 1947 and 1988. ■Crop Sci. 34:11431151.
Gizlice, Z., T E. Carter, Ir., T M . Gerig, and J.W. Burton. 1996. Genetic diversity
patterns in North American public soybean cultivars based on coefficient of
parentage. Crop Sci. 36:753-765.
Goodman, M.M. 1985. Exotic maize germplasm: Status, prospects, and remedies. Iowa
State T Res. 59:497-527.
53
Hahn, V., K. Blankenhorn, M. Schwall, and A.E. Melchinger. 1995. Relationships
among early European maize inbreds: HL Genetic diversity revealed with RAPD
markers and comparison with RFLP and pedigree data. Maydica 40:299-310.
Hanft, J.M., and R.D. Wych. 1982. Visual indicators o f physiological maturity o f hard
red spring wheat. Crop Sci. 22:584-588.
Inoue, T., H S . Zhong, A. Miyao, I. Ashikawa, L. Monna, S. Fukuoka, N. Miyadera, Y.
Nagamura, N. Kurata, T. Sasaki, and Y. Minobe. 1994. Sequence-tagged sites
(STSs) as standard landmarkers in the rice genome. Theor. Appl. Genet. 89:728
734.
Jaccard, P. 1901. Etude comparative dans la distribution florale dans une portion des
Alpes et des Jura. Bull. Soc. Vaudoise SciN at. 37:547-579.
Johnson, V. A. 1986. World wheat production, p.1-5. /77 E.L. Smith (ed.) Genetic
Improvement in Yield o f Wheat. CSSA special publication number 13. CSSAAS A, Madison, WL
Kam-Morgan, L.N.W., B.S. Gill, and S. Muthukrishnan. 1989. DNA restriction
fragment length polymorphism: a strategy for genetic mapping the D genome o f
wheat. Genome 32:724-732.
Keim, P., W. Beavis, J. Schupp, and R. Freestone. 1992. Evaluation o f soybean RFLP
marker diversity in adapted germ plasm. Theor. Appl. Genet. 85:205-212.
Kempthorne, O. 1969. An introduction to genetic statistics. Iow a State University
Press, Ames, IA.
Kidwell, K.K., D R Woodfield, E.T. Bingham, and T C. Osborn. 1994. Molecular
marker diversity and yield o f isogenic 2x and 4x single-crosses o f alfalfa. Crop
Sci. 34:784-788.
Knauft, D. A. and D.W. Gorbet. 1989. Genetic diversity among peanut cultivars. Crop
Sci. 29:1417-1422.
Lee, M., E B . Godshalk, K R. Lamkey, and W.W. Woodman. 1989. Association of
. restriction fragment length polymorphisms among maize inbreds with agronomic
performance o f their crosses. Crop Sci. 29:1067-1071.
Litt, M., and J.A. Luty. 1989. A hypervariable microsatellite revealed by in vitro
amplification o f a dinucleotide repeat within the cardiac muscle actin gene. Am.
I. Hum. Genet. 44:397-401.
54
Lubbers, E.L., K.S. Gill, T.S. Cox, and B.S. Gill. 1991. Variation o f molecular markers
among geographically diverse accessions o f Triticum tauschii. Genome 34:354361.
Lund, R. 1993. MSUSTAT Statistical analysis package. Version 5.20. Montana State
University, Bozeman, MT.
Martin, J.M., T.K. Blake, and E. A. Hockett. 1991. Diversity among N orth American ■
spring barley cultivars based on coefficients o f parentage. Crop Sci. 31:11311137.
Martin, TM., L.E. Talbert, S.P. Lanning, and N.K. Blake. 1995. Hybrid performance in
wheat as related to parental diversity. Crop Sci. 35:104-108. .
McNeal, F.H. 1956. Inheritance of stem solidness and spikelet number in a Thatcher x
Rescue wheat cross. USDA Tech. Bull. 1125. U S . Gov. Print Office,
Washington, D C.
Melchinger, A.E., M. Lee, K R. Lamkey, A R. Hallauer, and W.L. Woodman. 1990.
Genetic diversity for restriction fragment length polymorphisms and heterosis for
two diallel sets o f maize inbreds. Theor. Appl. Genet. 80:488-496.
Miller, TC. and S.D. Tanksley. 1990. RFLP analysis o f phylogenetic relationships and
genetic variation in the genus Lycopersicon. Thebr. Appl. Genet. 80:437-448.
Montana Agricultural Statistics. 1996. Volume XXXIII. M ontana Agricultural
Statistics Service. Helena, MT.
M oser H., and M. Lee. 1994. RFLP variation and genealogical distance, multivariate
distance, heterosis, and genetic variance in oats. Theor. Appl. Genet. 87:947-956.
Nei, M., and W. Li. 1979. Mathematical model for studying genetic variation in terms o f
restriction endonucleases. Proc. Nat. Acad. Sci. USA. 76:5269-5273.
Olson, M., L. Hood, C. Cantor, and D. Botstein. 1989. A common language for physical
mapping o f the human genome. S cience 245:1434-1435.
Patterson, A.H., S.D. Tanksley, and M.E. Sorrells. 1991. DNA markers in plant
improvement. Adv. Agron. 46:39-90.
Plaschke, I., M W . Ganal, and M.S, Roder. 1995. Detection o f genetic diversity in
closely related bread wheat using micro satellite markers. Theor. Appl. Genet.
91:1001-1007.
55'
Poehlman5IM . 1987a. Plant breeders and their work, p.1-15. /77 Breeding field crops.
3rd ed. Avi Publishing Co., Inc., W estport5 CT.
Poehlman5IM . 1987b. Quantitative inheritance in plant breeding, p. 64-86. In
Breeding field crops. 3rd ed. Avi Publishing Co. Inc., W estport5 CT.
Poehlman5 IM . 1987c. Breeding cross-pollinated and clonally propagated crops, p.
214-236. Th Breeding field crops. 3rd ed. Avi Publishing Co. Inc., Westport,
CT.
Poehlman5 IM . 1987d. Breeding wheat and triticale. p. 290-339. In Breeding field
crops.. 3rd ed. Avi Publishing Co. Inc., Westport5 CT.
Porceddu5E., C. Ceoloni5D. Lafiandra5 (LA. Tanzarella5 and G.T. Scarascia-Mugnozza.
1988. Genetic resources and plant breeding: Problems and prospects, p.7-21. In
Seventh In t Wfieat Genetics Symp., Vol. I. Cambridge5England. 13-19 July
1988. Bath Press5Bath5 Avon.
<Rbder5M.S., J. Plaschke5-S.U. Kbnig5 A. Bbrner5 M.E. Sorrells, S.D. Tanksley5 and M.W.
Ganal. 1995. Abundance, variability and chromosomal location o f
microsatellites in wheat. Mol. Gen. Genet. 246:327-333.
Rodgers5D M ., J.P. Murphy5 and K I Frey. 1983. Impact o f plant breeding on the grain
' yield and genetic diversity o f spring oats. Crop Sci. 23:737-740.
Rohlf5F .I 1993. NTSYS-pc: Numerical taxonomy and multivariate analysis system.
Version 1.80. State University o f New York5 Stony Brook5NY.
SAS Institute. 1988. SAS user’s guide. Release 6.11 ed. SAS Institute5 Inc., Cary5N.C.
Slaughter, D C., K U Norris5 and W.R. Hruschka. 1992. Quality and classification of
hard red wheat. Cereal Chem. 69:428-432.
'
/
Smith5 J.S.C. 1988. Diversity o f United States hybrid maize germplasm; isozymic and
chromatographic evidence. Crop Sci. 28:63-69.
Smith5 O S., J.S.C. Smith5 S E. Bowen5R A. Tenborg5 and S.J. Wall. 1990. Similarities
among a group o f elite maize inbreds as measured by pedigree, F 1 grain yield,
grain yield, heterosis, and RFLPs. Theor. Appl. Genet. 80:833-840.
Souza5E. and M.E. Sorrells. 1989. Pedigree analysis o f North American oat cultivars
released from 1951 to 1985. Crop Sci. 29:595-601.
56
Souza, E., and M.E. Sorrells. 1991. Prediction o f progeny variation in oat from parental
genetic relationships. Theor. Appl. Genet. 82:233-241.
Talbert, L.E. 1993. Molecular biology and wheat improvement, p. 235-250. In J.
Janick (ed.) Plant Breeding Reviews Vol' 11. John Wiley and Sons, Inc., New
York, NY.
Talbert, L.E., N.K. Blake, P.W. Chee, T.K. Blake, and G.M. Magyar. 1994. Evaluation
o f sequence-tagged-site PCR products as molecular markers in wheat. Theor.
Appl. Genet. 87:789-794.
Thorman, C.E., M.E. Ferreira, L.E.A. Camargo, J.G Tivang, and T C. Osborn. 1994.
Comparison o f RFLP and RAPD markers to estimating genetic relationships
within and among cruciferous species. Theor. Appl. Genet. 88:973-980.
Tragoonrung, S., V. Kanazin, P M. Hayes, and T.K. Blake. 1992. Sequence-tagged-sitefacilitated PCR for barley genome mapping. Theor. Appl. Genet. 84:1002-1008.
Weber, J.L., and P.E. May. 1989. Abundant class o f human DNA polymorphisms which
can be typed using the polymerase chain reaction. Am. J. Hum. Genet. 44:388396.
Williams, J.G.K., A R. Kubelik, J.A. Rafalski,- and S.V. Tingey. 1990. DNA
polymorphisms amplified by arbitrary primers are useful as genetic markers.
Nucleic Acids Res. 18:5631-6535.
Williams, M.N.V., N. Pande, S’. Nair, M. Mohen, and J. Bennett. 1991. Restriction
fragment length polymorphism analysis o f polymerase chain reaction products
amplified from mapped loci o f rice (Oryza sativa L.) genomic DNA. Theor.
Appl. Genet. 82:489-498.
Zhang, Q., M.A. Saghai Maroof, T.Y. Lu, and B.Z. Shen. 1992. Genetic diversity and
differentiation o f indica and japonicd rice detected by RFLP analysis. Theor.
Appl. Genet. 83:495-499'
Zhang, Q., M.A. Saghai Maroof, and A. Kleinhofs. 1993. Comparative diversity
analysis o f RFLPs and isozymes within and among populations o f Hordeum
vulgare ssp. spontaneum. Genetics 134:909-916.
'
57
APPENDICES
58
Ap p e n d i x a
AGRONOMIC TRAIT DATA FOR 1995, 1996 DRYLAND, 1996 IRRIGATED FIELD
TRIALS, AND FOR THE THREE ENVIRONMENTS COMBINED
J.
59
Table 15. Mean values of parental cultivars and progeny lines of nine agronomic
traits in the 1995 field'trial. Progeny variation for each cross is assessed
by examining the number of transgressive segregants and the genetic
variance for each trait.
H eading
D ate Tillers/ft.
AM IDON
NEW ANA
PROGENY
Trans. Seg.
V ariance
.
FORTUNA
HI-LINE
PROGENY
Trans. Seg.
V ariance
. Stem.
Phys.
Grain
Solidness M aturity Fill
G rain
Y ield
(M g/ha)
T e s t, Grain
W eight Protein
(kg/m?)
(%)
57.7
59.1
59.7
40.3
35.2
27.9
93.0
7.6.9
85.4
15.8
6.0
8.3
106.6
109.3
109.7
48.9
50.2
50.1
5.2
4.8
3.7
734.4
762.0
727.7
16.0
15.2
15.9
24
2.28
10
23.9
8
94.31
0
6.5
18
6.04
13
4.19
33
0.66
.8
216.81
17
0.81
56.3
55.4
55.3
. 2 9 .8
33.2
34.6
94.5
75.0
86.6
17.3
5.6
11.4
. 102.2
105.1
103.1
45.8
49.7 ■
47.7
3.1
4.9
3.7
754.1
' 778.7
763.3
16.5
15.8
16.5
3
13.41
1 3 8 .3 4
0
2.42
I
0.49
. 0
140.79
31
1.19
' 45.8
46.6
47.4
3.1
4.7
3 .8
754.1
782.8
771.5
16.5
15.2
15.6
30
6.07
FORTUNA
LEW
PROGENY
P lant
H eight
(cm)
56.3
59.4
' 58.0
2 9 .8
47.3
41.0
10
o'
. 11.65
3
4.75
94.5
98.6
99.5
17.3
15.1
15.3
.1 0 2 ,2
106.0
105.3
Trans. Seg.
V ariance
' "0 •
0.62
9 .8 3
I
4.31
0'
0.77
0
0.63
0
0.36
I
0.19
0
20.62
0
0.10
GLENMAN
AMDON
PROGENY
58.2
57.7
57.8
39.5
40.3
36.1
84.3
93.0
92.0
14.2
15.8
16.1
106.9
106.6
105.9
48.8
48.9
48.1
5.5
5.2.
4.4
766.6
734.4
749.1
14.4
16.0
15.8
Trans. Seg.
V ariance
17
2.33
0
-0.16
11
58.57
I
2
2 .6 5
2 .0 9
3
1.10
2
0.18
0
13
102.20 . 0.62
GLENM AN
LEW
PROGENY
58.2
59.4
59.4
39.5
47.3
40.6
8 8 .9
14.2
15.1
15.5
106.9
106.0
106.6
4 8 .8
986
46.6
47.2
5.5'
4.7
4.6
766.6
782.8
77,0.4 .
14.4
15.2
14.9
Trans. Seg.
V ariance
8
2.25
0
-4.76
5
62.49
4
0.25
■3
3.27
0
0.99
8
0.11
3
192.90
17
0.73
58.2
53.3
84.3
75.0
80.5
14.2
6.7
10.4
106.9
105.7
105.9
48.8
52.3
48.9
5.5
4.6
4.9
766.6
7 6 5 .1
5 6 .9
39.5
43.0
37.4
766.0
14.4
15.3
■ 14.8
4
3.08
2
13.34
I
16.92
4.99
3
1.38
3
2.22
3
0.34
2
115.87
11
0.51
GLENM AN
M ARBERG
PROGENY
Trans. Seg.
V ariance
'
0
84.3
o •
60
Table 15 continued
P lant
Height
(cm)
H eading
D ate Tillers/ft.
GRANDIN
PONDERA
PROGENY
Trans. Seg.
V ariance
HI-LINE
NEW ANA
PROGENY
Trans. Seg.
V ariance
HI-LINE
PONDERA
PROGENY
Trans. Seg.
V ariance
LEW
AMLDON
PROGENY
Trans. Seg.
V ariance .
■
58.0
55.3
55.5
33.7
76.0
90.0
6.0
6.5
7.1
11
3.78
0
. 3.78
33
94.01
55.4
• 59.1
57.8
33.2
35.2
105.3
3.2
. 4.5'
4.2
0
-0.02
26
3.23
12
3.1
3 4 .2
75.0
76.9
76.7
5.6
6.0
6.2
105.1
109.3
107.1
10
5.15
0
-0.35
26
177.02
I
0.05
■55.4
55.3 '
55.8
33.2
29.2
35.6
75.0
76.0
83.4
.
8 1 .0
Test
Grain
W eight Protein
(kg/im )
(%)
764.8
772.1
'769.7
16.2
15.5
16.1
3.00
0.43
4
123.58
21
0.43
49.7
50.2
49.3
4.9
4.8
4.4
778.7
758.7
15.8
15.2
15.5
5
4.46
7
3.06
2
0.1
6
100.57
10
0.43
5.6
6.5
6.4
105.1
107.2
104.7
49.7
51.8
48.9
4.9
4.5
4.4
778.7
772.1
769.2
15.8
15.5
15.7
9
0.93
1 0 7 .2
.
7 6 2 .0
36
I
29
668
1 2 .6 7
188.02
8
0.61
9
3.71
8
4.58
3
0.2
3
112.98
59.4
57.7
59.0
47.3
40.3
36.9
9 8 .6
15.1
15.6
46.6
48.9
47.4.
4.7
5.2
4,1
1 5 .2
1 5 .8
106.0
106.6
106.4
7 8 2 .8
93.0
95.7
734.4
752.5
16.0
15.7
9
0
25.86
Tl
20.66
4
1.83
13
2.3
I
1.12
11
0.29
3
365.65
15
0.33
84.7
84.3
89.0
8.0
14.2
11.6
107.3
106.9
107.7
49.8
48.8
48.4
4.8
5.5
4.2
7 5 6 .9
766.6
747.2
15.3
14.4
.14.8
23
116.47
0
4.06
6
3.21
I
2.4
15
0.57
10
215.11
11
0.54
8.0
6.0
7.7
107.3
109.3
106.0
49.8
50.2
48.6
4.8
4.8
3.7
756.9 '
762.0
759.5
15.3
15.2
16.0
7'
15
2.39
15
3.91
0.21
3
90.35
33
0.43
2 .2 8
5 8 .2
59.4
41.0
39.5
34.7
Trans. Seg.
V ariance
13
2.03
5
13.26
57.5
59.1
57.4
41.0
35.2
34.1
17
3.66
0
6.77
Trans. Seg.
V ariance
.
108.3
Grain
Yield
(MgZha)
50.3
51.8
49.7
2 8 .3
2 9 .2
LEN
•
GLENM AN
PROGENY
LEN
NEW ANA
PROGENY
Stem
Phys.
Grain
Solidness M aturity Fill
57.5
84.7
, 76.9
91.2
32
126.38
1 .9 5
'2 2
,
'
61
Table 16. Mean values of parental cultivars and progeny lines of nine agronomic traits
in the 1996 dryland field trial. Progeny variation for each cross is assessed
by examining the number of transgressive segregants and the genetic
variance for each trait.
H eading
D ate Tillers/ft.
AM IDON
NEW ANA
PROGENY
Plant
Height
(cm)
Stem
Phys.
Grain
Solidness M aturity Fill
64.0
65.5
65.4
44.5
. 8 3 .5
50.5
69.8
44.9 '
80.8
16.7
8.0
9.0 ■
10
1.65
0
10.32
9
85.91
63.5
62.4
48.8
43.8
43.5
27
2.39
.63.5
65.5
64.9
Trans. Seg.
V ariance
G rain
Y ield ,
(M g/ha)
103.0
104.3
104.5
3 8 .8
3.9
3.7-
39.1
3 .8
0
3.54
2
1.4
12
1.95
0
0.05
85.0
74.3
76.6
18.3
9.3
13.1
98.5
3.1
3.2
97.6
35.0
36.5
35.2
0
5.34
4
81.54
0
7.48
10
3.33
4 8 .8
46.7
45.9
85.0
84.0
85.0
18.3
17.2 •
16.5
O
0.07
0
10.33
2
4
GLENMAN
AM IDON
PROGENY
64.9
64.0
64.4
43.4
44:5
42.5
7 1 .8
Trans. Seg.
V ariance
2
• 0.52 .
GLENM AN
LEW
PROGENY
Trans. Seg.
V ariance '
39.0 .
Test
Grain
W eight Protein
(kg/im )
(%)
15.1
14.9
15.1
780 6
773.6
761.8
2 1 6 .2 3
35
0.64
2 .9
787.9
778.1
782.2
15.0
15.6
15.9
4
1.02
I
0.56
6
26
1 3 9 .9 6
0.86
98.5
100.0
100.1
35.0
34.5
35.3
3.1
3.4
3.4
787.9
790.4
795.5
15.0
14.4
14.8
0
0.55
6
1.31
2
1.21
3 ■
0.35
2
15.78
■ 3
0.21
15.0
16.7
16.4
9 8 .8
83.5
80.3
103.0
100.3
33.9
39.0
35.9
3.7
3.9
. 3.6
778.7
780.6
782.5
13.8
15.1
14.8
0
9.26
I
45.01
0
2.97
I
2.49
0
1.85
0
0.02
6
144.86
12
0.35
64.9
65.5
65.3
43.4
46.7
48.4
71.8
84.0
78.0
15.0
17.2
17.9
98.8
100.0
100.3
33.9
34.5
.34.9
3.7
3.4
3.7
778.7
790.4
785.3
13.8
14.4
14.3
Trans. Seg.
V ariance
7
0.98
0
-6.33
4
54.49
0
0
5
1.43
I
0.63
2
0.05
11
188.01
8
0.22
GLENM AN '
M ARBERG
PROGENY
64.9
62.0
65.3
43.4
47.0
48.4
7 1 .8
15.0
9.5
17.9
9 8 .8
3.7
3.5
3.4
1 3 .8
97.0
100.3
33.9
35.0
34.9
7 7 8 .7
69.5
78.0
7 8 8 .1
14.8
14.7
Trans. Seg.
V ariance
I
0.71
9 .9 2
5
11.61
I
4.13
19
1.84
5
0.94
2
0.04
9
84.5
FORTUNA
HI-LINE
PROGENY
6 2 .8
Trans. Seg.
V ariance
FORTUNA
LEW
PROGENY
'
0
9 9 .3
16
785.3
'
8
0.33
62
Table 16 continued
H eading
D ate Tillers/ft.
GRANDIN
PONDERA
PROGENY
Trans. Seg.
V ariance
HI-LINE
NEW ANA
PROGENY
Trans. Seg.
V ariance
HI-LINE
PONDERA
PROGENY
Trans. Seg.
V ariance
LEW
AM IDON
PROGENY
Trans. Seg.
V ariance
LEN
GLENM AN
PROGENY
Trans. Seg.
Variance.
LEN
NEW ANA
PROGENY
Trans. Seg.
V ariance
Plant
Height
(cm)
Stem
Phys.
Grain
Solidness M aturity Fill
63.5
63.5
63.0
35.5
41.8
41.4
77.0
73.0
84.0
7.5
10.0
17
1.60
I
13.18
29
6 0 .9 8
6 2 .8
65.5
64.5
43.8
50.5
45.3
8
2.14
G rain
Yield
(M g/ha)
Test
Grain
W eight Protein
(kg/nu)
(%)
8 .0
97.5
97.3
98.4
34.0.
33.8
35.4
3.6
3.3
3.3
791.8
788.4
780.3
15.3
15.1
15.8
0
-0.08
4
0.74
6
1.46
3
0.09
7
70.73
33
0.28 ■
99.3
104.3
100.2
36.5
38.8
35.8
3.2
3.7
3.4
7 7 8 .1
66.9
9.3
8.0
7.6
773.6
■ 775.6
15.6
14.9
15.3
0
7.46
9
120.44
I
0.52
10
2 .6 8
13
2.21
I.
0.12
' 10
185.14
5
0.23
62.8
63.5
62.4
43.8
41.8
41.6
74.3
73.0
75.4
9.3
10.0
8.4
9 9 .3
36.5
3 3 .8
36.4
3.2
3.3.
3.3
7 7 8 .1
97.3
98.7
788.4
778.3
15.6
15.1
15.6
12
2.08
2
19.44
18 '
135.2
I
0.03
11
2 .9 1
8
2.34
5
0.07
5
73.39
30
0:62
65.5
64.0
64.7
46.7
44.5
42.7
84.0
83.5
84.3
17.2
16.7
16.5
' 100.0
103.0
100.8
34.5
39.0
36.1
3.4
3.9
3.5
790.4
780.6
791.0
14.4
15.1
14.7
6 :
0.74
0
15.34
11
11.71
3 •
2.35
0
3.47
0
2 .6 1
0
0.03
2
117.27
• 5
0.32
64.8
64.9
64.6
48.5
43.4
43.2
74.0
71.8
79.8
9.3
15.0
11.8
100.3
98.8
3.5
3.7
3.5
779.5
778.7
776.3
1 3 .8
9 9 .1
35.5
33.9
34.5
5
0.6
2
18.05
22
91.29
1 .8 2
2
1.95
I
1.66
2
0.04
8
183.21
3
0.28
64.8
65.5
64.5
48.5
50.5
46.7
74.0
35.5
38.8
39.0
3 .5
3.7
3.4
779.5
773.6
783.5
15.1
14.9
15.7
12
0.93
0
15.01
0
1.43
6
0.06
9 8 .0 6
74.3
6 9 .8
0
'
6 9 .8
9.3
8.0
81.0
8 .3
100.3
104.3
103.6
26
82.37
I
0.44
3
1.90
11
15.1
14.4
30 .
0 .2 8
63
Table 17. Mean values of parental cultivars and progeny lines of nine agronomic traits
in the 1996 irrigated field trial. Progeny variation for each cross is assessed
by examining the number of transgressive segregants and the genetic
variance for each trait.
H eading
D ate 'rillers/ft.
AM IDON
NEW ANA
PROGENY
66.0
68.5
67.4
49.8
58.3
48.1
9
2.02
0
-5.28
65.8
64.2
64.4
P lant
H eight
(cm)
G rain
Yield
(M g/ha)
Test
Grain
W eight Protein
(kg/ms)
(%)
87.5
13.7
7.3
8:5
112.3
110.5
110.6
46.3
42.0
43.1
5.6
5.2
4.7
748.9
746.4
745.4
15.0
13.9
14.9
3
89.06
0
4.74
8
1.8
6
3.15
0
-0.03
11
275.66
15
0.64
49.5
45.5
53.7
95.0
76.0
90.6
13.0
7.5
10.2
110.8
110.8
109.2
45.0
46.7
44.9
4.9
5.9
5.3
779.5
776.2
778.3
15.2
15.1 '
15.5
9
2.79
2
10.63
3
104.49
2
3.77
3
2.37
0
0.65
6
0.62
15
109.13
25
1.27
6 5 .8
49.5
52.3
53.9
95.0
97.8
96.5
13.0
15.3
14.1
110.8
110.0
111.9
45.0
42.3
45.4
4.9
5.6
5.3
7 7 9 .5
. 67.7
66.5
784.6
783.5
15.2
14.2
14.4
Trans. Seg.
V ariance
3
0.14
0
-9.4
0
-0.23 '
0 .
0.08
0
2.91
-0.01
2
0.07
0
16.95
0.10
GLENMAN
AM IDON
PROGENY
66.4
66.0
66.0
55.1
84.6
95.2
94.8
14.0
13.7
<13.5
111.0
4 9 .8
112.3
112.5
44.6
46.3
46.5
6.4
5.6 .
6.2
766.2
748.9
759.7
13.0
15.0
14.6
Trans. Seg.
V ariance
9
0.79
0
-6.02
I
39.31
0
2.15
2
0.93
2
0.77
0.05
5
157.35
9
0.56
GLENMAN
LEW
PROGENY
66.4
67.7
67.7
54.9 .
52.3
48.3
14.0
15.3
15.3
111.0
97.8
91.3
110.0
111.2
44.6
42.3
43.5
6.4
5.6
5.6
766.2
784.6
767.7
13.0
14.2
14.0
Trans. Seg.
V ariance
7
1.51
0
1.91
•5
73.51
3
1.71
7
1.54
I
11
1.2
0.08
9
242.95
13
0.48
GLENM AN
M ARBERG
PROGENY
66.4
63.0
66.1
54.9
69.5
. 53.3
84.6
81.0
84.1
14.0
8.0
10.9
111.0
111.5
111.3
44.6
48.5
45.2
.6.4
6.7
6.4
766.2
770.4
759.2
13.0
14.4
1 3 .8
Trans. Seg.
V ariance
5
1.32
13
16.52
0
0.88
7
3.56
I
3.5
0
0.32
8
91.31
5
0.55
Trans. Seg.
V ariance
FORTUNA
HI-LINE
PROGENY
Trans. Seg.
V ariance
FORTUNA
LEW
PROGENY
54.9
0
-10.97
95.2
Stem
Phys.
Grain
Solidness M aturity Fill
8 0 .3
.
I
8 4 .6
I
11
\
64
Table 17 continued
P lant
H eight
(cm)
H eading
D ate Tillers/ft.
GRANDIN
PONDERA
PROGENY
Trans. Seg.
V ariance
HI-LINE
NEW ANA
PROGENY
Trans. Seg.
Variance.
HI-LINE
PONDERA
PROGENY
Trans. Seg.
V ariance
LEW
AM IDON
PROGENY
64.5
65.3 .
64.4
46.0
48.7
16
1.89
0
41.96
64.2
68.5
66.6
45.5
58.3
49.5
' 2
2.32
0
8.21
64.2
65.3
64.2
45.5
46.0
47.7
17
2.73
86.0
80.3
G rain
Yield
(M g/ha)
83 6
7.5
8.3
7.7
113.5
111.8
112.8
49.0
46.5
48.5
29
76.57
I
2
0.65
1 .3 6
3
1.39
10.00
' 0.35
'7.5
7.3
7.7
110.8
110.5
111.2
46.7
42.0
44.6
5,9
6
88.64
2
0.16
3
1 .9 9
0
.0.97
76.0
80.3
83.4
7.5
0
13.69
19
159.62
67.7
66.0
66.7
52.3
49.8
51.6
11
5 5 .5
76.0
. 80.3
78.6
■
.
4
118.23
17
0.54
5.4
776.2
746.4
■ 752.2
15.1
13.9
14.4
12
0.47
9
420.54
0.83
5.9
6.5
15.1
14.2
15.1
5 .2
"I
5 .5
776.2
774.3
762,8
0
0.22
5
2.18
8
2.02
10
0.33
20
144.99
16
0.83
97.8
95.2
95.9
15.3
13.7
15.2
110.0
112.3
111.8
42.3
46.3
45.2
5.6
5.6
5.4
7 8 4 .6
748.9
7 6 3 .9
14.2
15.0
14.8
2
1.58
0
3.11
0
3.04
6
0.2
2.
246.48
12
.0.32
' 47.5
44.6
45.7
6.5
6.4
5.4
751.1
766.2
1 4 .9
7 5 0 .9
13
14.4
0
1.58
8
0.35
7
242.81
4
0.36
15
21.58
LEN
GLENMAN
PROGENY
.66.3
66.4
66.7
50.0
54.9
50.1
84.5
84.6
Trans. Seg.
V ariance
9
1.26
47.9
LEN
NEW ANA
PROGENY '
66.3
8 .3
7 .8 . .
8 .5
113.8
111.0
9 2 .8
14.0
10.3
11
94.5
0
2.49
84.5
8.5
7.3
8.5
50.0
58.3
50.6
93.4
3 .
5
2.23 ■ 27.37
8 0 .4 9
6 6 .9
■15.4
14.2
15.5
46.5
47.2
0
5.88
6 8 .5
774.3
768.7
7 6 5 .0 "
4 6 .7 ■
1.67
I
'
6.1 .
6.5
5.7
Test
Grain
W eight Protein
(kg/ms)
(%)
110.8
111.8
111.3
Trans. Seg.
V ariance
Trans. Seg.
V ariance
Stem
Phys.
Grain
Solidness M aturity Fill
8 0 .3
33
2
-0.09
112.4
I
,
2.49
113.8
47.5
110.5 , 42.0
114.1
47.2
I
0.71
0
1.44
6.5
5 .2
5.4
8
0.30
\
751.1 . 14.9
746.4 . 13.9.
760.6.
15.4
21
123.44
30
0.38
65
Table 18. Mean values of parental cultivars and progeny lines of nine agronomic traits
for the three environments combined. Progeny variation for each cross is
assessed by examining the number of transgressive segregants and the
genetic variance for each trait.
P lant
H eight
(cm)
H eading
D ate Tillers/ft.
AMTOON
NEW ANA
PROGENY
61.9
63.6
63.5
44.2
' 46.2
38.5
16
1.87
Stem
Phys.
Grain
Solidness M aturity Fill
90.9
75.9
84.7
15.4
7.0
I
3.46
61.1
60.0
59.9
40.9
39.8
42.6
24
3.61
G rain
Y ield
(M g/ha)
(k g /m 3 )
(%)
Test
G rain
W eight Protein
8 .6
107.2
108.2
108.4
45.3
44.6
44.9
4.9
4.6
4.0
751.8
760.8
742.5
15.4
14.7
15.4
19
89.24 •
0
4.92
3
0.82
I
0 .3 6
6
0.1
23
208.69
14
0.62
9 1 .9
16.4
7.2
11.5
1 0 3 .6
75.1
84.9
105.1
103.3
42.5
45.0
43.3
3.6
4.7
3.9
771.0
' 777.8
773.0
15.7
15.5
16.1
2
12.4
5
100.74
0
8.01
5 ,
2.92
0
0.51
I
0.27
7 .
99.08
32
1.03
. 61.1
63.5
62.4
40.9
48.6
46.1
91.9
94.2
94.5
16.4
15.8
15.3
103.6
105.4
105.7
42.5
41.9
43.4
3.6
4.6
. 4.1
771.0
785.5
781.8
15.7
14.7
15
Trans. Seg.
V ariance
O
0.22
0
6.67
7
2 .8 8
4
0.29
I
0.26
I
0.08
2
0.08
0
12.98
0
0.08
GLENM AN
AMTOON
PROGENY
62.4
61.9
62.0
45.0
44.2
43.4
90.9
89.4
14.4
15.4
15.4
105.8
107.2
106.2
43.3
45.3
44.1
5.3
4.9
4.7
769.9
751.8
761.7
13.8
15.4
15.2
13 '
1.20
0
4.38
13
47.67
4
1.70
3 ■
0.76
0
OTO
7
0.05
5
108.52
9
0.49
GLENM AN
LEW
PROGENY
62.4
63.5
63.5
45.0
48.6
45.0
8 0 .8
14.4
94.2
86.5
1 5 .8
16.1
105.8
105.3
106.1
43.3
41.9
42.6
5.3
4.6
4.6
769.9
785.5
773.9
13.8
14.7
14.5
Trans. Seg.
V ariance
14
0
1 .6 3
- 2 .8 8
7
61.44
12
0.42
3
1.73
0
0.38
11
0.03
16
175.02
8
0.43
GLENM AN
M ARBERG
62.4
58.6
45.0
51.7
80.8
75.1 ■
14.4
7.9
105.8
104.9
43.3
46.3
5.3
4.9
769.9
773.2
13.8
14.9
PROGENY
61.6
43.6
78.4
11.1
105.6
43.9
4.9
768.2
14.5
0
-3.34 ■
7
13.43
I
3.59
11
1.49
0
1.02
13
0.16
9
6 8 .8 8
5"
0.41
Trans. Seg.
V ariance
FORTUNA
HI-LINE
PROGENY
Trans. Seg.
V ariance
FORTUNA
LEW
PROGENY
Trans. Seg.
V ariance
Trans. Seg.
V ariance
.
• '5
1.59
8 0 .8
•
'
Table IS ^continued
H eading
Date. Tillers/ft.
P lant
H eight
(cm)
GRANDIN .
PONDERA
PROGENY
61.4
60.5
60.2
38.1
37.6
40.2
81.3
76.4
89.3
6.9
8.0
7.5'
Trans. Seg.
V ariance
23
2.40
8
16.17
■ 36
78.67
0
0.05
3
1.18
2
1.08
7.2
7.0 •
7.0 .
105.1
108.2
HI-LINE
NEW ANA
PROGENY
-
60.0
63.6
Trans. Seg.
V ariance
3 9 .8
75.1
' 7 5 .9
6 2 .2
46.2
41.8
0
2.40
0
1.50
60.0
60.5
60.1
74.4
Grain
Y ield
(M g/ha)
Stem
Phys.
Grain
Solidness M aturity Fill
106.7
; 105.6
105.5
45.3
45.1
45.3
772.5
777.4
772.5
15.7
15
15.8
2.00
' 0.20
7
91.21
16
0.35
1 0 6 .3
45.0
44.6
44.1
4.7
4.6
4.4
777.8
760.8
761.6
15.5
14.7
15.1 ‘
3
1.50
0 .
0.09
9
0.13
5
145.99
3
0.29
45.0
45.1
44.8
4.7
4.8
4.4
777.8
777.4
770.0
15.5
15.0
15.5
2
1.21
0
0.61
.1 6
0.16
10
67.08
21
0.75
105.4
107.2
106.3
41.9
45.3
43.5
4.6
4.9
4.3
785.5
751.8
766.8
14.7
15.4
15.2
0
2.02
I
1.16
14
0.12
8
233.31
15
0.30
.45.1
43.3
43.6 .
4.9
5.3
4.3
. 761.7
769.9
756.6
15.1
13.8
14.6
13
134.80
9
0.35
.
4.1
48
4.4
Test
G rain .
W eight Protein
(kg/ms)
(%)
.
13
113.93
o.ii
39.8
37.6
. 40.8
75.1
76.4
81.1
7.2
8.0
7.3
19
3.28
5
8.78
27
159.7
0
0.06
63.5
61.9
62.8
48.6
44.2
42.8
90.9
92.5
Trans. Seg.
V ariance
21
1.50
0
11.45
14
15.96
LEN
GLENM AN
PROGENY
62.1
62.4
63.0
45.7
45.0
41.5
81.6
80.8
87.5
8.5
1 14.4
11.3
, 107.1
105.8
106.6
16
1.15
5'
10.09
24
97.92
9
2.93
7
1.76
3
0.95
15
■ 0.24
62.1
4 5 .7
63 6
81.6
75.9
62.2
46.2
42.4
. 8 8 .9
8.5
7.0
8.1
107.1
108.2
107.6
45.1
44.6
45.5
4.9
4.6
4.1
761.7 '
760.8
766.7
15.1
14.7
15.7
14 :
2.06
12
5.62
36
' 97.52
5
0.83
0
0.59
2
0.70
19
0.10
. 13
97.50 '
0.28
HI-LINE
PONDERA
PROGENY
Trans. 'Seg.
V ariance
LEW
AM lDON
PROGENY
Trans. Seg..
V ariance
LEN
NEW ANA
PROGENY
Trans. Seg.
V ariance
'
942
2
.
15.8
. 15.4
15.7
9
1.91 '
105.1
. 105.6
1049'
.
28
67
APPENDIX B
ANALYSIS OF VARIANCE TABLES FOR THE THREE ENVIRONMENTS
COMBINED
68
Table 19. Analysis of variance of nine agronomic traits for the Amiflon x Newana
cross for the three environments combined.
Source
Environ.
Rep. (Env.)
Lines
Env. x Lines
Error
Source
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
df
2
4
51
102
204
df
2
4
51
102
204
Heading Date
MS
F-value
2135.3
5.1
13.6
0.75
0.44
4852.3**
11.6**
30.9**
1.7**
Stem Sohdness
MS
F-value
19.0
15.5
41.1
3.4
3.1
6 .2 * *
5.0**
13.3**
1.1
Tillers/ft.
MS
F-value
14736.5
191.8**
212.2 . ' 2.8*
127.5
1.7** •
103.7 .
1.4*
76.8
Yield ( M g /h a )
Environ.
Rep. (Env.)
Lines
Env. x Lines
Error
1147.9
48.0
13.3
7.4
1.6
635.9
26.4
22.2
GrainFill
. MS
F-value
3918.5
24.6
10.7
8.1
1.8
8 .3 * *
4.6**
T est
F -v a lu e
MS
2
4
51
102
204
34.4
2.7
1.6
0.84
90.3**
7.0**
4.2**
2.2**
36171.9
10904.9
1617.2
1 6 9 .5
128.1.
,
F -v alu e
282.3**
85.1**
12.6**
1.3*
2222.5**
14.0**
6 .1**
4 .6 **
G ra in
W e ig h t ( k g /m 3 ) .
MS
57.7**
10.7**
28.6**
1.2
2 3 8 .6
711.9**
29.7**
df
0 .3 8
1283.8
Phys. Maturity
MS
F-value
G ra in
S o u rce
Plant Height (cm)
MS
F-value
.
P r o te in ( % )
MS
F -v alu e
38.3
1.1
4.5
0.26
0.05
738.1**
2L0**
86.3**
4.9**
* Significant at the 5% level (P < 0.05)
** Significant at the 1% level (P < 0.01)
69
Table 20. Analysis of variance of nine agronomic traits for the Fortuna x Hi-Line
' cross for the three environments combined.
H e a d in g D a te
T ille rs/ft.
P la n t H e ig h t (c m )
S o u rce
' df
MS
F -v a lu e
MS '
F -v alu e
MS
F -y alu e
Environ. .
Rep. (Env.)
Lines
Env. x Lines
E rror
2
4
51
102
204
2997.1
9.3
26.0
1.6
0.58
5162.3**
16.0**
44.9**
2.8**
11406.7
531.6
152.0
67.1
69.3
164.6**
7.7**2.2**
0.97
5458.1
613.7
750.6
47.3
20.2
270.8**
30.5**
37.2**
2.4**
S te m S o h d n e s s
S o u rce
Environ.
Rep. (E n v .)'
Lines
Env. x Lines
E rror
dT
2
4
51
102
204
P h y s . M a tu r ity
MS
F -v a lu e
MS
F -v alu e
238.2
4.2
65.4
6.4
5.5
43.7**.
0.76
12.0**
1.2 •
3496.7
47.4
22.8
2.8
1.2
2955.2**
40.1**
.19.3**
2.4**
G r a in
Y ie ld ( M g /h a )
S o u rce
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
df
2
■ 4
.51
102
204
G ra in F ih
MS
F -v alu e
5072.3
34.2
7.9
3.7 '
1.4
T est
3614.8**
24.4**
5.6**
2.6**
G ra in
W e ig h t (k g /m 3 )
P r o te in ( % )
MS
F -v alu e
MS
F -v alu e
MS
F -value
149.3
2.9
2.4
0.48
.0.22
679.8**
13.0**
10.9**
2.2**
13185.6
943.4
808.1
144.3
64.5
204.4**
14.6**
12.5**
2.2**
33.7 .
0.64
7.2
0.27
0.07
486.9**
9.2**
104.7**
3.9**
** Significant at the 1% level (P < 0.01)
70
Table 21. Analysis of variance of nine agronomic traits for the Fortuna x Lew
cross for the three environments combined.
Source
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
S o u rce
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
Source
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
df
2
4
51
102
204
df
2
4
51
102
204
df
2
4
51
102
204
Heading Date
MS
F-value
2738.0
15.5
. 2.4
0.52
0.30
9134.9**
51.7**
8.0**
1.7**
Stem Sohdness
MS
F-value
149.1
. 2 8 .2
4.9
61.5**
11.6**
2.0**
1.2
Tillers/ft.
MS
F-value
5533.8
901.6
141.4
91.3
97.3
56.9**
9.3**
1.5*
0.94
Phys. Maturity
MS
F-value
3611.2
77.8
3 .8
3029.5**
65.3**
3.2**
1.8**
2.4
2.1
1.2
Grain
Yield (MgZha)
MS
F-value
Test
Weight (kg/m3)
MS
F-value
2 .9
113.8
5.5
0.81
0.25
0.19
598.7**
29.0**
4.3**
1.3
17809.6
1848.7
199.1
95.5
93.4
190.6**
19.8**
2.1**
LO
Plant Height (cm)
MS
F-value
6979.6
258.2
31.4
11.6
9.6
730.5**
27.0**
3.3**
1.2
G rainFih
MS
F-value
4876.5
28.4
3.0
2.3
1.3
3712.1**
21.6**
2.3**
1.7**
Grain
Protein (%)
MS
F-value
42.9
3.7
0.74
0.15
0.04
* Significant at the 5% level (P < 0.05)
** Significant at the 1% level (P < 0.01)
978.2**
83.3**
16.9**
3.4**
71
Table 22. Analysis of variance of nine agronomic traits for, the Glemnan x Amidon
cross for the three environments combined.
Source
Environ.
Rep. (Env.)
Lines
Env. x Lines
Error
Source
Environ.
Rep. (Env.)
Lines
Env. x Lines
Error
Source
Environ.
Rep. (Env.)
Lines
Env. x Lines
Error
df
2
4
51
102
204
df
2
4
51
102
204
df
2
4
51
102
204
Heading Date
MS
F-value
2486.8
44.2
8.9
0.77
0.41
6115.0**
108.6**
21.8**
1.9**
64.9**
8.3**
4.2**
1.5**
Grain
Yield (Mg/ha)
MS
F-value
203.0 .
2.0
0.82
0.44
0.39
11325.9
103.0
102.0
74.4
127.9**
1.2
1.2
0.84
88.6.
Stem Sohdness
MS
F-value
280.2
35.8
17.9
6.4
4.3
Tillers/ft.
MS
F-value
526.7**
5.2**
2.1**
1.2
Phys. Maturity.
MS
F-value
3911.5
95.7
9.4
4.3
1.8
2123.2**
52.0**
5.1**
2.3**
Test
Weight (kg/m3)
MS
F-value
34871.8
6748.4
929.0
197.9
149.4
233.4**
45.2**
Plant Height (cm)
MS
F-value
6287.2
395.6
353.3
17.6
14.5
434.0**
27.3**’
24.4**
1.2
G rainFih
MS
F-value
5084.2
14.1
5.1
4.4
1.9
2727.5**
7.6**
2.8**
2.4**
Grain
Protein (%)
MS
F-value
6.2**
48,2
0.61
3.6
1.3*
0.12
0.03
* Significant at the 5% level (P < 0.05)
** Significant at the 1% level (P < 0.01)
1507.1**
19.1**
112.7**
3.7**
72
Table 23. Analysis of variance of nine agronomic traits for the Glenman x Lew
cross for the three environments combined.
Source
Environ.
Rep. (Env.)
Lines
Env. x Lines
Error
df
2
4
51
102
204
Heading Date
MS
F-value
2417.2
8.6
11.8
0.77
0.76
3175.5**
11.4**
15.5**
1.0
S te m S o H d n ess
S o u rce
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
df
2
4
51
102
204
fillers/ft.
MS
F-value
2519.2
231.6
. 102.5
118.9
122.7
P h y s . M a tu rity .
365.6**
2.9*
30.0**
1.2
G ra in F iH
F -v alu e
MS
F -v alu e
MS
F w a lu e
215.5
41.7
6.4
3.0
2.8
77.4**
15.0**
2.3**
LI
3154.0
18.4
14.4
■ 2.6
1.6
2022.1**
11.8**
9.3**
1.7**
4717.1
9.1
6.2
3.2
2.0
2359.9**
4.6**
3.1**
1.6**
Y ie ld (M g Z ha)
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
5415.8
43.5
444.1
17.8
14.8
MS
G ra in
S o u rce
20.5**
1.9
0.84
0.97
Plant Height (cm)
MS
F-value
G ra in
T est
W e ig h t (k g /m 3 )
P r o te in (% )
df
MS
F -v alu e
MS
F -v alu e
MS
F -v alu e
2
4
51
102
204
96.3
1.0
0.76
0.38
0.30
316.1**
3.4**
2.5**
1.3
9433.6
5200.2
1351.2
164.9
112.1
84.1**
46.4**
12.1**
. 1.5** .
30.4
1.2
3.2
0.22
0.03
961.3**
' 36.5**
100.8**
7.0**
* Significant at the 5% level (P < 0.05)
** Significant at the 1% level (P < 0.01)
73
T a b l e 2 4 . A n a ly s is o f v a r ia n c e o f n in e a g r o n o m ic tr a i t s f o r t h e G l e n m a n x M a r b e r g
c r o s s f o r t h e t h r e e e n v ir o n m e n ts c o m b in e d .
H e a d in g D a t e
S o u rce
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
df
2
4
51
102
204
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
df
2
4
51
102
204
MS
F -v alu e
MS
F -value
3082.8
11.7
13.2
1.2
0.60
5128.5**
19.4**
22.0**
2.0**
7953.3
231.5
. 89.8
103.9
818
94.9**
2.8*
1.1
1.24
6012.1
106.8
107.3
15.7
11.5
520.7**
9.3** ■
9.3**
1.4*
G r a in F ill
F -v alu e
MS
F -v alu e
MS
F -v alu e
126.9
25.7
31.2
4.0
4.0
32.0**
6.5**
7.9**
1.0
3741.4
18.2
12.9
2.9
1.4
2602.0**
12.7**
9.0**
2.0**
6056.3
14.6
12.2
4.4
1.7
3612.9**
8.7**
7.3**
2.6**
G ra in
Y ie ld (M gZ ha)
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
P h y s . M a tu r ity
MS
■
S o u rce
P la n t H e ig h t (c m )
F -v a lu e
S te m S o lid n e s s
S o u rce
T ille rs/ft.
MS
G ra in
T est
W e ig h t (k g /m S )
P r o te in (% )
df
MS
F -v a lu e
MS
F -v alu e
MS
F -value
2
4
51
102
204 .
222.6
4.1
1.5
0.45
0.30
731.3**
13.5**
5.1**
1.5**
12290.5
2249.1
' 639.5 .
161.9
102.5
119.9**
22.0**
6.2**
1.6**
33J
2.5
3.0
0.21
0.06
565.8**
41.6**
50.9**
3.5**
* Significant at the 5% level (P < 0.05)
** Significant at the 1% level (P < 0.01)
74
Table 25. Analysis of variance of nine agronomic traits for the Grandin x Pondera
cross for the three environments combined.
H e a d in g D a te
S o u rce
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
df
2
4
51
102
204
MS
F -v a lu e
MS
3003.0
23.2
17.5
1.2
0.80 -
3777.0**
29.1**
22.1**
1.6**
7235.6
94.3
173.7
6 6 .8 '
62.1
S te m S o h d n e s s
S o u rce
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
df
2.
4
■ 51
102
204
T ille rs/ft.
MS
30.1
8.5
2.5 •
2.1
1.9
F -v alu e '
116.6**
1.5
2.8**
1.1
P h y s . M a tu r ity
P la n t H e ig h t (c m )
MS
F -v alu e
2394.0
250.9
578.3
17.3
15.7
152.0**
15.9**
36.7**
1.1
G ra in F ih
F -v a lu e
MS
F -v alu e
MS
F -v alu e
15.9**
4.5**
1.3
1.1
5425.5
200.9
11.4
3.4
1.4
3880.6**
143.7**
8.2**
2.4**
7240.5
188.1
11.4
4.3
1.8
4105.5**
106.7**
6.5**
2.4**
G ra in
Y ie ld (M gZ ha)
. T est
G ra in
W e ig h t (k g /m 3 )
P r o te in (% )
S o u rce
. df
MS
F -v a lu e
MS
F -v alu e
MS
F -v alu e
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
2
4
51
102
204
159.6
2.6
1.8
0.48
0.24
662.3**
10.9**
7.6**
2.0**
4676.6
1418.3
750.0
133.8
108.6
43.1**
13.1**
6.9**
1.2
11.2
0.25
2.7
0.20
0.04
281.2**
6.3**
67.2**
5.1**
** Significant at the 1 % level (P < 0.01)
75
Table 26. Analysis of variance of nine agronomic traits for the Hi-Linex Newana
cross for the three environments combined.
H e a d in g D a te
S o u rce
df
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
2
4
51 .
102 '
204
■
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
■
MS
F -v alu e
MS
F -value
2804.5
5.5
20.1
2.8
0.48
5859.8**
11.5**
42.0**
5.9**
8733.2
436.2
105.2
104.3
95.3 '
91.7**
4.6**
1.1
1.1
3963.0
278.9
849.3
86.6
31.2
127.2**
9.0**
27.3**
2.8**
P h y s . M a tu r ity
MS
F -v a lu e
MS
F -v alu e
MS
2
4
51
102
204
91.5
10.3
2.0
1.4
1.1
80.1**
9.0**
1.8**
1.2
3130.0
2.9
17.1
6.4
2.4
1282.1**
1.2
7.0**
2.6**
5651.1
9.9
' 9.1
7.5
2.9
Y ie ld (M gZ ha)
Environ.
Rep. (Env.)
Lines
Env. x Lines
Error
G r a in F iU
df
G ra in
S o u rce
P la n t H e ig h t (c m )
F -v a lu e
; S te m S o U d n e ss
S o u rce
T iU ers/ft.
MS
df
2
4
51
102
204
MS
107.4
3.4
' 1.4
0.49
0.40
T est
■ F -v alu e
1961.3**
3.5**
3.2**
2.6**
G ra in
W e ig h t (k g /m 3 )
P r o te in (% )
F -v a lu e
MS
F -v alu e
MS
F -value
266.9**
8.5**
3.5**
1.2
15514.8
1573.3
1227.9
229.5
90.5
171.4**
17.4**
13.6**
2.5**
39.4
2.0
2.2
0.18
0.08
513.2**
26.5**
28.9**
2.3**
** Significant at the 1% level (P < 0.01)
76
Table 27. Analysis of variance of nine agronomic traits for the Hi-Line x Pondera
cross for the three environments combined.
H e a d in g D a te
S o u rce
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
df
2
4
51
102
204
MS
2613.8
1.9
24.7.
2.6
0.46
F - v a lu e .
5652.9**
4.1**
53.5**
5.5**
S te m S o h d n e ss
Source
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
df
2
4
51
102
204
MS
145.5
14.7
3.0
2.7
1.7
F-value
85.6**
■ 8.7**
1.8**
1.6**
T illers/ft.
MS
4789.5
276.9
129.5
70.1
. 61.7
Y ie ld (M gZha)
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
df
2
4
51
102
204
F-value
MS
F-value
77.6**
4.5**
2.1**
1.1
2388.6
192.2
1117.9
33.6
20.8
114.8**
9.23**
53.7**
1.6**
P h y s. M atu rity
G r a in F ih
MS
F-value
MS
F-value
4146.7
27.6
13.6
5.4
1.3
3298.9**
22.0**
10.8**
4.3**
5419.7
23.8
11.5
7.4
1.4
3870.4**
17.0**
8.2**
5.3**
G rain
Source
P la n t H e ig h t (c m )
T e st
G rain
W e ig h t (k g /m 3 )
P r o te in (% )
MS
F-value
MS
F-value
MS
F-value
135.0
1.9
. 1.5
0.36
0.32
417.4**
5.8**
4.5**
LI
6186.8
4251.8
629.4
153.2
67.3
92.0**
63.2**
9.4**
2.3**
13.1
1.3
5.5
0.44
0.40
32.7**
3.3**
13.7**
1.1
** Significant at the 1% level (P < 0.01)
\
77
Table 28. Analysis of variance of nine agronomic traits for the Lew x Anaidon
cross for the three environments combined.
H e a d in g D a te
S o u rce
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
df
2
4
51
102
204
MS
2113.5
.1 6 .4
10.8
0.79
0.39
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
MS
5363.1**
41.6** ■
27.5**
2.0**
S te m S o h d n e ss
S o u rce
T ille rs/ft.
. F -v a lu e
6424.2
329.6 ■163.2
85.6
77.3
P h y s . M a tu r ity
MS
F -value
4896.4
351.6
122.9 •
14.8
10.0
[ 491.2**
35.3**
12.3**
1.5**
G r a in F ill
MS
F -v a lu e
MS
F -v alu e
MS
F -value
2
451
102
204
47.4
13.1
16.9
3.7
3.9
12.3**
3.4**
4.4**
0.97
3129.7
20.1
• 17.9
3.9
1.8
1705.9**
11.0**,
9.8**
2.1**
4193.2
13.2
12.4
4.4
1.9
2203.7**
7.0**
6.5**
2.3**
Y ie ld ( M g /h a )
Environ.
Rep. (Env.)
Lines
Env. x Lines
Error
83.1**
4.3**
2.1**
LI
P la n t H e ig h t (c m )
df
G ra in
S o u rce
F -v alu e
df
2
4
51
102
204
T est
G ra in
W e ig h t (k g /m 3 )
P r o te in (% )
M S.
F -v a lu e
MS
F -v alu e
MS
F -value
99.6
2.1
1.2
0.43
0.31
318.9**
6.8**
4.0**
1.4*
46179.3
9999.6
1730.6
206.7
126.5
365.2**
79.1**
13.7**
1.6**.
42.0
0.95
2.1
0.12
0.04
955.3**
21.7**
48.1**
2.7**
. * Significant at the 5% level (P < 0.05)
** Significant at the 1% level (P < 0.01)
78
Table 29. Analysis of variance of nine agronomic traits for the Len x Glenman
cross for the three environments combined.
H e a d in g D a te
S o u rce
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
df
2
4
51
102
204
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
MS
F -v alu e
MS
F -value
1887.3
13.1
8.8
1.0
0.56
3342.9**
23.2**
15.6**
1.8**
7264.5
80.5
169.8
105.4
76.1
95.4**
1.1
2.2**
1.4*
4792.2
169.8
707.2
42.4
32.3
' 148.4
5.3** .
21.9**
1.3*
df
MS
2
4
51
102
204
68.5
10.9
23.9
5.1
4.6
‘
'
P h y s. M a tu r ity MS
F -v alu e
MS
F -value
14.8**
2.3
5.2**
1.1
4771.2
83.5
15.7
3.9
2.0
2347.4**
41.1**
7.7**
1.9**
6317.3
38.2
10.5
4.3
2.0
3116.6**
18.8**
5.2**
2.1**
Y ie ld ( M g /h a )
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
df
2
4
51
102
204
MS
G ra in F ih
F -v alu e
G ra in
S o u rce
P la n t H e ig h t (c m )
F -v alu e
S te m S o h d n e s s
S o u rce
T iile rs/ft.
MS
F -v alu e
100.6 .
266.1**
7.1
18.7**
2.3
. 6.2**
0.62
1.6**
0.38
T est
G ra in
W e ig h t (k g /m 3 )
MS
28312.3
8092.0
1231.4
297.1
132.0
P r o te in (% )
F -v alu e
MS
F -v alu e
214.5** '
61.3**
9.3**
2.3**
8.1
2.0
2.6
0.27
0.07
119.1**
28.7**
37.5**
3.9**
* Significant at the 5% level (P < 0.05)
** Significant at the 1% level (P < 0.01)
79
Table 30. Analysis of variance of nine agronomic traits for the Len x Newana
cross for the three environments combined.
H e a d in g D a te
S o u rce
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
df
2
4
51
102
204
F -v a lu e
MS
F -v alu e
3195.7
16.8
15.5
1.4
0.48
6618.5**
34.8**
32.1**
2.9**'
9998.9
292.6
126.9
84.1
64.9
154.0**
4.5**
2.0**
1.3
S te m S o lid n e s s
S o u rce
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
T ille rs/ft.
MS
df
MS
2
4
51
102
204
29.4
2.1
7.9
2.2
2.0
■
P h y s . M a tu r ity
Environ.
Rep. (Env.)
Lines
Env. x Lines
E rror
2
4
51
102
204
F -v alu e
4695.5
32.3
722.8 .
29.2
20.6
227.5**
1.6
35.0**
1.4*
G r a in F ill
- MS
F -v alu e
MS
F -value
14.7**
' LI
4.0**
1.1
3246.4
■25.5
8.0
4.3
1.2
2777.9**
21.8**
6.9**
3.7**
3128.2
6.2
10.6
6.2
1.6
1896.0**
3.8**
■6.4**
3.8**
Y ie ld ( M g /h a )
df
MS
F -v alu e
G r a in
S o u rce
P la n t H e ig h t (c m )
T est
G ra in
W e ig h t ( k g /m 3 )
P r o te in (% )
MS
F -v alu e
MS
F -v alu e
MS
F -value
119.3
1.8 ■
1.1
0.37
0.16
767.6**
11.9**
7.2**
2.4**
20279.9
5247.8
776.0
121.8
126.8
160.0**
41.4**
6.1**
0.96
12.3
0.95
2.4
0.26
0.05
264.9**
20.6**
50.8**
5.6**
* Significant at the 5% level (P < 0.05)
** Significant at the 1% level (P < 0:01)
MONTANA STATE UNIVERSITY LIBRARIES
3 1762 10310234 7