1
Chapter 1. Molecular Markers Associated with Plant Disease Resistance**
Molecular markers have become the most rapid and popular method for
constructing detailed genetic maps. In addition, this technology can be used in various
plant breeding applications. In plant disease resistance breeding programs, molecular
markers are useful tools in the evaluation of resistant germplasm, identification and
mapping of both major gene resistance and quantitative resistance, marker-assisted
selection, and map-based cloning of resistance genes.
Plant diseases are generally caused by fungi, bacteria, viruses, and nematodes.
Plant resistance is usually divided into two types: qualitative and quantitative resistance.
Because of the many complexities of resistance, a series of terms to describe resistance
were developed and adapted by plant pathologists and plant breeders.
Detailed
descriptions of these terms is beyond the scope of this chapter. However, it is important
to describe two specific terms: major gene resistance and quantitative resistance loci
(QRLs).
Major gene resistance is race-specific resistance and controlled by genes with
major effects. The plants in segregating populations, such as F2 or BC1F1, can be divided
by their resistance into clear and natural groups with discontinuous distributions or a
binomial distribution. The synonyms of major gene resistance are qualitative resistance,
single gene resistance, monogenic resistance, race-specific resistance, true resistance, and
non-durable resistance.
[**As a part of paper in: A. N. Shi, and W. P. Qiu. 1997. Plant resistance to fungus and bacterial
diseases and molecular biology technology. in Biotechnology and Sustaining Agriculture (eds.) S. J.
Wu, X. L. Zhou, and Z. P. Yong. Shanghai Scientific Technology Press, P. R. China. (in press)
(Chinese) ]
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The other type of resistance that can be considered opposite to major gene
resistance can be called quantitative resistance, polygenic resistance, race-nonspecific
resistance, field resistance, durable resistance, slow resistance, partial resistance, or minor
gene resistance. This type of resistance varies continuously, without falling into clearly
defined groups and possess a true continuous distribution.
Quantitative resistance loci (QRLs) is a new term and it has been developed for
QTL analysis of molecular markers associated with resistance genes (Young, 1996). QR
was used for quantitative resistance by Geiger and Heun (1989), and is a subset of other
quantitative traits, such as grain weight, number of ears, and yield. However, QRLs are
considered separately because their expression is coupled to the expression of pathogen
genes. QR can be controlled by major genes, minor genes, or both.
QR varies
continuously in expression of resistance, and may have a normal distribution, or a skewed
distribution. QR can be either race-specific or race-nonspecific resistance. QRLs involve
normal quantitative resistance, and some qualitative resistance which varies continuously
with a skewed distribution, or it can be divided by resistance expression into groups with
a discontinuous distribution. Often the segregation ratio will not fit the expected ratios
for simple genes in the segregating populations.
QR is usually analyzed by use of
quantitative genetics methods, including estimating effective gene numbers, heritability,
and genetic components. Now, QRLs can be mapped on chromosomes by the use of
molecular markers and QTL statistical analysis, as is done for major genes.
1. Types of Molecular Markers
Molecular markers are one kind of genetic marker. Three types of genetic
markers, morphological, protein based, and DNA based markers, have been used in plant
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disease resistance research. There are many kinds of DNA markers, for example, RFLP,
RAPD, AFLP, SSR, STS, SCAR, SSCP, and VNTR (Liu 1997).
RFLP and RAPD
markers have been widely used in tagging disease resistance genes in plants (Michelmore,
1995, Young, 1996), and AFLP and SSR are suggested to have utility for tagging disease
resistance genes.
1.1 RFLP
RFLPs (Restriction Fragment Length Polymorphisms) are genetic markers that are
obtained by using restriction endonucleases to cleave a genomic DNA fragment
containing a particular gene sequence (Botstein et al. 1980). RFLPs have been widely
used in genetic mapping and QTL mapping. In tagging disease resistance genes, RFLPs
have a specific advantage. They can map major resistance genes and QRLs by the use of
available RFLP genetic maps.
RFLP markers provide the maximum amount of
information possible because they are usually co-dominant. However, compared with
PCR-based methods, RFLP assays require relatively large amounts of pure, high
molecular weight genomic DNA. They are relatively costly to develop because of the
labor involved with screening, characterizing, and cloning informative probes. The
process of preparing probes must be maintained in libraries of bacterial cultures. Batch
automation is difficult to achieve due primarily to the large amount of hands on
manipulation and the individuality of each probe.
1.2 RAPD and SCAR
RAPD (Random Applified Polymorphic DNA) is a Polymerase Chain Reaction
(PCR) technique (Williams et al, 1990), but utilizing short nonspecific primers under
conditions of moderate stringency. Typically primers are 9 or 10-mer oligonucleotides
4
that are random in sequence but often biased in nucleotide content. DNA manipulations
are readily automatable, and analyses can be automated. Another similar technique is
Arbitrarily Primed PCR (AP-PCR), in which the primers are longer, but low annealing
stringencies are used for the first few rounds of amplification (Welsh and McClelland,
1990).
RAPD phenotypes are inherited in a dominant fashion and therefore do not allow
direct estimates of heterozygosity. Absence of phenotypes (bands) may arise due to
insertion/ deletion events at the primer site(s), sufficient base pair mismatch due to point
mutations at the primer site(s), complete absence of corresponding loci (or at least one or
both of the primer sites), and biased synthesis of alternate loci in the same reaction.
The advantages of RAPDs are their simplicity and speed. A disadvantage of
RAPDs is that they are very sensitive to the reaction conditions, DNA quality and PCR
temperature profiles. Because of its low reproducibility, a new PCR marker, SCAR
(Sequence Characterized Amplified Region), was developed (Paren and Michelmore,
1994).
It is a modification that allows a RAPD polymorphism to be made more robust.
A RAPD DNA fragment is cloned and sequenced, permitting the investigator to develop
new, longer primers that allow a much simpler and specific PCR fingerprint to be
generated. This is especially valuable if there is a nonsegregating band of very similar
size that makes analysis difficult. If a single product in just one of the parental lines
results, a SCAR can be used with colorimetric gel-free assays. SCAR is similar to STS
(Sequence Tagged Sites), which have been used in a genetic map of humans (Olson et al,
1989).
1.3 AFLP
5
AFLPs (Amplified Fragment Length Polymorphisms) are a recently developed
molecular marker (Zabeau and Vos, 1992, Vos et al., 1995).
AFLP is a DNA
fingerprinting technique that combines both classical, hybridization-based fingerprinting
techniques (e.g., RFLP) and PCR-based fingerprinting techniques (e.g., RAPD).
In
AFLP, genomic DNA is digested by restriction endonucleases and ligated to adapter
sequences. The amplified DNA fragments are separated by denaturing polyacrylamide
gel electrophoresis to reveal polymorphisms.
The AFLP technique can be used for DNAs of any organ or complexity.
Fingerprints are produced without prior sequence knowledge using a limited set of
genetic primers. The number of fragments detected in a single reaction can be tuned by
selection of a specific primer set. The AFLP technique is robust and reliable because
stringent reaction conditions are used for primer annealing: the reliability of the RFLP
technique is combined with the power of the PCR technique.
The AFLP bands are usually scored as dominant markers, but can be scored as a
codominant marker based on the intensities of the bands by use of a computerized
program. AFLP can produce 10-30 polymorphisms per PCR reaction, depending upon
the genomes being assayed, making AFLPs a very cost effective marker system. But,
AFLP technology has an additional template preparation step relative to other PCR-based
assays. Slightly more genomic DNA is required, and it must be of sufficient quality to
allow restriction endonuclease digestion and ligation of adaptor oligonucleotides.
1. 4 SSR
SSRs (Simple Sequence Repeats) consist of mono to tetranucleotide sequence
motifs that are tandemly repeated and display high levels of genetic polymorphism
6
resulting from the variation in the number of repeat units (Jacob et al. 1991). SSRs are
called microsatellites (Litt and Luty 1989) also. SSR is based on the PCR-amplification
of a genomic region containing a simple repeated sequences (Morgante et al., 1994). The
length of these repeated sequences often varies, and the common forms of the repeats are
simple dinucleotide repeats, such as CA and GT in mammals, and AT in plants.
markers are codominant.
The
SSR technology could provide a standardized and highly
accurate set of descriptors once relatively high development costs have been met.
However, SSR markers require considerable effort for development.
2. Major Gene Resistance
2.1 Screening of Molecular Markers
Near-isogenic lines (NILs) and bulked segregant analysis (BSA) have been widely
used for screening molecular markers for major gene resistance. The development of a
set of NILs involves selection of recurrent parent that is crossed with a series of lines with
major genes for resistance to a specific disease. Six to eight backcross generations are
routine before selfing and isolation of each NIL homozygous for a different resistance
gene. The series of derived lines each with a single major resistance gene are known as
near-isogenic lines. The NILs are similar for all traits except the major resistance genes.
DNA polymorphisms between different NILs are likely to be associated with the different
resistance genes.
Screening molecular markers by use of NILs is simple and easy. But the NILs for
most disease resistance traits are unavailable, and they are tedious to produce. Another
disadvantage is NILs can be used only for screening markers associated with major genes.
Another method, bulked segregant analysis (BSA), was suggested by Michelmore
7
et al. (1992) and it has been widely used as a tool to target disease resistance genes in
segregating populations. A segregating population, usually an F2, is developed from a
cross between a resistant and susceptible parents. The individuals in this F2 population
are tested for their resistance. Equal quantities of DNA from each homozygous resistant
individual is mixed as an R group, and the same amount DNA in each homozygous
susceptible individual is mixed as an S group. In that way, the R group has the same
genetic background as that in the S group except for the resistance alleles. In theory the
difference between the two bulked DNA samples will only be present at the resistance
loci.
The disadvantage of BSA for screening dominant markers, such as RAPD
markers, is the need to test the homozygous individuals in the segregating population
(F2). For example, there are three genotypes for a resistance allele A in a F2 population:
AA, Aa, and aa, but there are only two phenotypes, resistant and susceptible. If the
resistance gene is dominant, the AA and Aa are resistant and aa is susceptible. So in the
F2 generation, we don't know which individuals are heterzygotes. Thus F2:3 lines need to
be developed and tested for reaction to the disease.
This is time-consuming and
expensive in terms of labor and supplies.
However, there is no need to conduct resistant test in F2:3 lines for codominant
markers and for coupling dominant markers. For a segregating population (F2 or BC1F1),
the DNA from resistant individuals is composited into the R group, and the DNA from
susceptible individuals is composited into the S group, regardless of whether the
individual is homozygous or heterozygous.
Besides NILs and BSA, heterogeneous inbred or backcross lines can be used in a
8
manner similar to near-isogenic lines for marker screening (Haley et al. 1994).
Individuals developed from backcrossing or selfing, should have similar genetic
backgrounds. Two sister lines, one resistant and the other susceptible are similar to two
near-isogenic lines, and can be used as a pair of NILs for screening markers associated
with resistance alleles. The advantage of this method is that one doesn't need to select a
series of near-isogenic lines. Thus, the marker-based screening and conventional breeding
population development are compatible.
Lastly, marker screening can be conducted with translocation or substitution lines.
This method has been used for identifying markers associated with resistance genes in
wheat, where many translocation and substitution lines have been developed.
2.2 Estimates of Linkage
The Maximum Likelihood Estimator (MLE) is the most widely used to estimate
the recombination frequency (r). This method requires the solving of the equation:
K
dL(r)/dr =  nidlog(ei)/dr = 0,
i
Where, K is number of phenotypes in F2 or BC1F1, ni is the observed value for each
phenotype, ei is expected frequency, and i = 1, 2, 3, ......, K.
2.2.1. Dominant marker and resistance gene
For a BC1F1 population and a resistance gene with two alleles, A and a, and a
marker with two alleles, M1 and m1, there are four possible genotypes AaM1m1, Aam1m1,
aaM1m1, and aam1m1. The corresponding four phenotypes are: RM, R-, SM, and S-. RM
is a resistant individual with the marker, in which R- is a resistant individual without the
marker, SM is a susceptible individual with the marker, and S- is a susceptible individual
without the marker. The four genotypes, phenotypes, and their expected frequencies, are
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as follows:
Phenotype
RM
R-
SM
S-
Genotype for dominant gene AaM1m1
Aam1m1
aaM1m1
aam1m1
Genotype for recessive gene aaM1m1
aam1m1
AaM1m1
Aam1m1
Observed value
n1
n2
n3
n4
Expected frequency
(1-r)/2
r/2
r/2
(1-r)/2
So, the recombination frequency, r = (n1 + n2) / N, (N = n1 + n2 + n3 +n4),
and the standard deviation Sr = [r(1-r)]/N.
For an F2 population, a resistance gene with two alleles, A and a, a marker locus
with two alleles, M1 and m1, and a polymorphism identified as band present (+) or absent
(-), there are four groups of phenotypes. The four phenotypes, corresponding genotypes,
and their expected frequencies are as following:
Phenotype for dominant
gene in coupling
Phenotype for dominant
gene in repulsion
Phenotype for recessive
gene in coupling
Phenotype for recessive
gene in repulsion
F2 genotype
Observed value (ni)
Expected frequency (ei)
for r in coupling
Expected frequency (ei)
for r in repulsion
RM
R-
SM
S-
R+
RM
S+
SM
SM
S+
RM
R+
S-
SM
R-
RM
A_M1_
n1
A_m1 m1
n2
aaM1_
n3
(3-2r+r2)/4
(2r-r2)/4
(2r-r2)/4
(1-r)2/4
(2+r2)/4
(1-r2)/4
(1-r2)/4
r2/4
aa m1m1
n4
The phenotype RM is a resistant individual with the marker, R+ is a resistant
individual with the polymorphic band, but without the marker, R- is a resistant individual
without the band, SM is a susceptible individual with the marker, S+ is a susceptible
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individual with the band, and S- is a susceptible individual without the band and marker.
The recombination frequency r can be estimated by the ELM method and the formula
for solving r is:
K
 nid(ei)/dr = n1d(3-2r+r2)/dr + n2d(2r-r2)/dr + n3d(2r-r2)/dr + n4d(1-r)2/dr = 0 in
i
coupling phase,
n1d(2+r2)/dr + n2d(1-r2)/dr + n3d(1-r2)/dr + n4d(r2)/dr = 0 in repulsion phase.
It is difficult to solve the two equations,
so the estimate of recombination
frequency (r) can be solved by a transformed formula with a middle varable  based on F2
generation data (Weber and Wricke 1994). Suppose  = (1-r)2 in coupling phase, and  =
r2 in repulsion phase. The expected frequencies are (2+)/4, (1-)/4, (1-)/4, and /4 for
, corresponding the four genotypes A_M1_, A_m1m1, aaM1_, and aam1m1 in the F2
population. In that way,
 = [K +  (K2 + 8Nn4)] / (2N), (N = n1 + n2 + n3 + n4, K= n1-2n2-2n3-n4),
S2 = [2(2+)(1-)]/[N(1+2)],
then the recombination frequency,
r = 1-   in coupling phase, and r =  in repulsion phase.
The standard deviation Sr = S /2.
The recombination frequency (r) can also been estimated by using SAS software
(SAS institute Inc., 1990). The following SAS software program can been used to
estimate r, kindly provided by Dr. Ben-Hui Liu, Department of Forestry, North Carolina
State University, Raleigh, NC 27695-8008, USA (unpublished).
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DATA A; ID=1;
INPUT n1 n2 n3 n4;
DO r=0.000 to 0.499 by 0.001;
L=n1*log(1-r)+n2*log(r)+n3*log(r)+n4*log(1-r);
OUTPUT;
END;
CARDS;
n1 n2 n3 n4
;
PROC MEANS NOPRINT;
VAR L;
OUTPUT OUT=B MAX=MAX;
DATA B; SET B; ID=1;
DATA AB; MERGE A B; BY ID;
RL=L/MAX;
RPOC PRINT;
RUN;
The linear equation above is adapted only for the estimate of r based on a BC1F1
population. For a gene and a marker in coupling phase in an F2 population, the linear
equation is:
L=n1*log(3-2*r+r*r)+n2*log(2*r-r*r)+n3*log(2*r-r*r)+n4*log(1-2*r+r*r);.
For a gene and a marker in repulsion phase in an F2 population, the linear equation is:
L=n1*log(2+r*r)+n2*log(1-r*r)+n3*log(1-r*r)+n4*log(r*r);.
The n1, n2, n3, and n4 are four observed values in the BC1F1 or F2 generations.
The 0.001 in the program step DO r=0.000 to 0.499 by 0.001 can be changed into 0.01,
0.001, or 0.0001 etc. according as the precision desired.
Several examples are given for explaining how to estimate the frequency of
recombination (r) follow:
Example 1 (Shi et al. 1997a). In the NK-Coker 9803*2/ NC96BGTA5 BC1F1
population, there were 32 individuals which were resistant to wheat powdery mildew and
showed RAPD marker OPAG04950, six individuals were resistant and
without the
marker, two individuals were susceptible and showed the marker, and 31 individuals were
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susceptible and without the marker. So in this BC1F1 population, the observed values for
four phenotypes RM, R-, SM, and S- are n1=32, n2=6, n3=2, and n4=31, respectively. The
total observed number N = n1+n2+n3+n4=71. Therefore, the frequency of recombination,
r = (n2+n3)/N = (6+2)/71 = 11.3%.
The standard deviation,
Sr =  r(1-r)/N = [(0.113)*(1-0.113)/75] = 0.0376 = 3.76%.
In using the SAS software program, the four observed values, 32, 6, 2, and 31,
followed the CARDS step. In the SAS output results, r = 0.113 was obtained in the RL
= 1.000 row.
Example 2 (Chapter 3).
In the NK-Coker 68-15/CP4 (NK-Coker 68-
15*6//CI13836/8*Cc) F2 population, 73 resistant individuals showed RAPD marker
OPU17730, zero individuals were resistant and without the marker, 21 susceptible
individuals were without the marker, and two susceptible individuals showed the marker.
The four observed values n1=73, n2=0, n3=2, and n4=21 for corresponding as four
phenotypes RM, R-, SM, and S-.
Here, N = n1+n2+n3+n4 = 73+0+2+21 = 96, K = n1-2n2-2n3-n4 = 73-0-2*2-21 =
48, so  = [k +(K2+8Nn4)]/(2N) = [48 + (48**2 + 8*96*21)]/(2*96) = 0.9571. S2 =
2(1-)(2+)/[N(1+2)]
=
2*0.9571*(1-0.9571)(2+0.9571)/[96*(1+2*0.9571)]
=
0.0008679, and S = 0.02946,
Therefore, the r = 1- = 1-  0.9571 = 0.022, and Sr = S/2 = 0.0147.
In the SAS program, the linear equation
L=n1*log(3-2*r+r*r)+n2*log(2*r-r*r)+n3*log(2*r-r*r)+n4*log(1-2*r+r*r)
was
used
because segregation of reactions for resistance to powdery mildew in the F2 population fit
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a 3R:1S expected ratio for one dominant gene with the marker and the gene in coupling
phase. An r = 0.022 was obtained.
2.2.2 Codominant markers and resistance gene
The method for estimating r between codominant markers and a resistance gene is
the same as described for a dominant marker.
For an F2 population, a resistance gene with two alleles, A and a, and three
genotypes, AA, Aa, and aa, can be grouped as two types: A_ and aa. If the resistance
gene is dominant, the A_ is resistant and aa is susceptible. If the resistance gene is
recessive, the A_ is susceptible and aa is resistant.
Suppose the marker is M, the codominant marker has three genotypes M11, M12,
and M22, so in a F2 population, there are six phenotypes RM11, RM12, RM22, SM11, SM12,
and SM22. The phenotypes, genotypes, and their expected frequencies are as following:
Phenotype for dominant gene RM11
RM12
RM22
SM11
SM12
SM22
Phenotype for recessive gene SM11
SM12
S M22
RM11
RM12
RM22
F2 genotype
A_M11
A_M12
A_M22
aa M11
aaM12
aa M22
n1
n2
n3
n4
n5
n6
r2/4
r(1-r)/2
(1-r)2/4
Observed value (ni)
Expected frequency (ei)
(1-r2)/4 (1-r+r2)/2 (2r-r2)/4
The recombination frequency can be estimated by the use of MLE, and solved by
the formula:
nidlog(ei)/dr = n1log(1-r2)/dr + n2log2(1-r+r2)/dr + n3logr(2-r)/dr + n4log(r2) /dr +
n5log2r(1-r)/dr + n6log(1-r)2/dr = 0.
Nevertheless, use of the SAS program is recommended: (a) n1 n2 n3 n4 n5 n6
are substituted for n1 n2 n3 n4 in the INPUT step; (b) the six observed values of n1 n2
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n3 n4 n5 n6, are in the CARDS step; and (c) the linear equation is:
L=n1*log(1-r*r)+n2*log(2-2*r+r*r)+n3*log(2*r-r*r)+n4*log(r*r)+n5*log(2*r-2*r*r)+n6*
log(1-2*r-r*r).
In the following example, 152 resistant plants and 48 susceptible plants are
observed in an F2 population.
The segregation for disease resistance fits a 3R:1S
expected ratio for one dominant gene. The six phenotypes observed are n1=49, n2=101,
n3=2, n4=1, n5=1, and n6=46 corresponding as RM11, RM12, RM22, SM11, SM12, and
SM22. The estimate of r is 0.025 by use of the SAS program.
The recombination frequency can be estimated directly by use of computer
software programs, such as MAPMAKER/QTL (Lander et al. 1987), PGRI (Liu, 1997).
2.2.3 Examples of identified major resistance genes
Molecular markers linked to major genes for disease resistance have be widely
identified (Michelmore, 1995), including genes for resistance to fungal, bacterial, virus,
and nematode diseases (Table 1).
3. Mapping of QRLs by Use of QTL Methods
3.1 Screening markers
The BSA for major genes also can be used for the mapping of QRL. QTL
analysis is a little different from that for major genes. Usually, in a segregating
population, 5-10% of the most resistant individuals are selected and composited to form
the R group, and 5-10% of the most susceptible individuals are selected and composited
to form the S group. The R and S groups are used to screen markers for mapping QRLs.
QRLs include many resistance loci, and usually more than one locus is linked on a
chromosome. The linkage analysis using a segregating population (F2 or backcross) can’t
15
distinguish the closely linked alleles. The location of identified resistance loci usually
spans a segment of the chromosome. Progenies in a segregating population can’t be
reproduced.
However, RILs can be used to solve this problems. RILs are obtained by use of
single seed descent (SSD). The advantage of RILs is their reproducibility. However, the
disadvantage of RILs is cost. Development of set of RILs takes approximately seven or
more growing seasons.
In order to overcome this disadvantage of RILs, doubled haploid lines (DHLs)
can be developed in some species. A doubled haploid is derived from a haploid plant by
doubling its chromosome number. Anther culture of pollen from an F1 of a resistant by
susceptible cross can provide a source of haploid plants segregating for the resistant
allele(s). A set of DHL’s can be produced in one half the time of RIL’s.
3.2 QTL mapping
There are many statistical methods and theories for QTL mapping (Liu, 1997).
Several computer software programs are available, such as MAPMARKER/QTL,
QTLSTAT, QTL Cartographer, MAPQTL, QGENE, Map Manager QT, and PGRI (Liu,
1997).
3.3. Examples
Many QRLs for resistance to important diseases in plants have been mapped by
QTL methods (Table 2).
4. Marker-assisted Selection
For disease resistance, marker-assisted selection (MAS) can be used as a
complementary method for selecting linked resistance genes, and gene pyramiding.
16
Identification of disease resistance is conducted in the field, greenhouse, and laboratory.
Some disease screens are difficult to conduct in the field because of variability in
aggressiveness or availability of the pathogen, or sensitivity of the disease reaction to
environmental conditions.
Some disease screens are time-consuming or
can be
conducted only at particular locations, times of year, or stages of plant development.
However, it is not necessary to test the resistance reaction by inoculating with a pathogen
or evaluating the resistance reaction in marker assisted selection. MAS is used for the
pyramiding of major genes, the combination of major genes and minor genes, not only for
one disease, but for multiple diseases.
Selection of disease resistance by MAS is very efficient. For example, Shi et al.
(unpublished) identified a RAPD marker OPU17750 linked to the Pm1 gene for wheat
powdery mildew resistance (r = 2.2  1.07 %).
The effectiveness of the marker was
determined in other wheat lines with different resistance genes, and the results showed all
wheat lines, which contain the Pm1 gene, exhibited the marker OPU17750.
The pyramiding of resistance genes may be an efficient method of control of
plant diseases and provide more durable resistance for plant breeding. Gene pyramiding
results in resistant cultivars which contain more than one gene for resistance to a disease,
such as in the wheat cultivar Normandie, which contains three genes, Pm1, Pm2, and
pm9. Molecular markers tightly linked to resistance genes can be used for marker-assisted
selection and facilitate stabilization of genetic resistance through gene pyramiding
(Stuber, 1992; Michelmore, 1995).
VI. Map-based Gene Cloning
Map-based Cloning has been used to cloned plant resistance genes (Martin et al.
17
1993). It is also called chromosome walking (Keen et al. 1993), or positional cloning
(Cai, et al. 1997). Major steps involved in map-based cloning are: Step 1, development
of a high-density RFLP, RAPD, AFLP and/or SSR genetic map. Step 2, screening of a
genomic library to identify overlapping clones covering the region of interest. Step 3,
identifying clones that harbor the target genes by transformation and complementation.
The first step in this strategy is to identify genetically polymorphic DNA markers that are
closely linked to the disease resistance allele. A high-density genetic map of the
resistance gene region is constructed, and the marker DNA sequences are located at both
sides of the target gene. These linked sequences are used as starting points for the cloning
of the chromosomal region where the target gene is located. RFLP or RAPD markers are
the most useful flanking sequences for map-based cloning, since saturated maps of the
target region using these markers can be easily obtained. The second step is to isolate
DNA clones covering the entire region between the makers by a process known as
chromosome walking. A physical mapping is constructed. Then gene identification can
begin in earnest, complementary DNAs (cDNAs) encoded by the PFGE-band or YAC
cloned DNA represent obvious candidate genes. The last, often most difficult, step in
map-based cloning is pinpointing the clone harboring the target gene among all the
overlapping clones identified during chromosome walking. It will be necessary to
transform this DNA into a susceptible plant and inoculate with a pathogen to prove that
the targeted gene has the capacity to suppress the pathogen. Obviously, the more tightly
linked the flanking markers to the target gene, the lower the number of clones that have to
be screened.
Six genes have been isolated by use of map-based cloning (MBC) for resistance to
18
fungi, bacteria, virus, and nematode in plants (Table 3).
With the development and
construction of high-density genetic maps in plants, it is no doubt that more genes will be
isolated by use of map-based cloning.
19
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28
Table 1. Examples of previously identified molecular markers linked to major disease resistance genes
_________________________________________________________________________________________________
Gene
Host
Pathogen
Type of markers
Method
Reference
_________________________________________________________________________________________________
Xa-1
Rice
Xanthomonas oryzae pv. oryzae RAPD
NILs
Yoshimura et al. 1995
xa-13
RAPD, RFLP
BSA
Zhang et al. 1996
Xa-21
RAPD, RFLP
NILs
Ronald et al. 1992
STS
NILs
Williams et al. 1996
RFLP
GMBA
RFLP
NILs
Yu et al. 1996
Pi-4(t)
RFLP
NILs
Yu et al. 1991
Pi-2(t)
STS
NILs
Hittalmani et al. 1995
Pi-5(t), 7(t)
RFLP
RILs
Wang et al. 1994
Pi-10(t)
RAPD, SCAR
RILs
Naqvi & Chattoo 1996
RFLP
NILs
Ma et al. 1994
Pm1
RAPD
BSA
Hu et al. 1997
Pm1, 3
RFLP
GMBA
Pm1,3,18
RAPD, RFLP
NILs
Hartl et al. 1993, 1995
Pm2
AS-PCR
NILs
Mohler & Jahoor, 1996
Pm12
RFLP
GMBA
Jia et al. 1996
Pm13
RFLP
GMBA
Donini et al. 1995
Pm21
RAPD
TL
Xa-22(t)
Pi-1(t), 2(t)
Pm1, 2, 3, 4
Pyricularia grisea
Wheat
Blumeria graminis f. sp. tritici
Lin et al. 1996
Nelson et al. 1995
Qi et al. 1996
29
_____________________________________________________________________________________________________
30
Table 1 Continue
__________________________________________________________________________________________________
Pm3 locus
RAPD
NILs
Shi et al. 1995
Pm12,25
RAPD
PP & BSA
Shi et al. 1997a,b
NILs
Feuillet et al. 1995
Lr1
Puccinia recondita f. sp. tritici RFLP, RAPD, STS
Lr9
RAPD
NILs
Schachermayr et al. 1994
Lr24
RAPD, RFLP
NILs
Schachermayr et al. 1995
RAPD, SCAR
NILs
Dedryver et al. 1996
XM
RFLP
GMBA
Bonhomme et al. 1995
Lr9,19,24,32
RFLP
GMBA
Autrique et al. 1995
Bt-10
Tilletia tritici
RAPD
NILs
Demeke et al. 1996
Wsml
Streak Mosaic virus
STS, RAPD
PP
Talbert et al. 1996
Sr22
P. graminis f. sp. tritici
RFLP
NILs
Paull et al. 1994
Puccinia hordei
RAPD
BSA
Poulsen et al. 1995
P. graminis
RAPD, STS
NILs
Kilian et al. 1994,
RphQ
Barley
Ppg1
Horvath et al. 1995
Rh
Rhynchosporium secalis
Rh2
Ml(La)
E. graminis f. sp. hordei
Mlt, Mlf,,Mlj
Pg3
Oat
P. graminis f.sp. avenae
RFLP, STS
DHLs
Graner et al. 1996
RFLP
DHLs
Schweizer et al. 1995
RFLP
DHLs
Giese et al. 1993
RFLP
F2 seg.
Schonfeld et al. 1996
RAPD
NILs
Penner et al. 1993a
31
Table 1. Continue
____________________________________________________________________________________________________________
Pg9, 13
RFLP
NILs, BSA
Donoughue et al.1996
RAPD
BSA
Penner et al. 1993b
RFLP
BDLs
Rooney et al. 1994
Bipolaris maydis
RFLP
GMBA
Zaitlin et al. 1993
Htn1
Setosphaeria turcica
RFLP
GMBA
Simcox et al. 1993
Rp3
Puccinia sorghi
RFLP
NILs
Sanz-Alferez et al. 1995
Phytophthora infestans
RFLP
F1 seg.
El-Kharbotly et al. 1994
AFLP, RFLP
BSA
Meksem et al. 1995
RFLP
BSA
Hamalainea et al. 1996
Potato virus Y
AFLP
BSA
Brigneti et al. 1996
Tobacco Mosaic virus
RFLP, RAPD
BDL
Pillen et al. 1996
Pc68
P. coronata
Pc91, 92
rhm
R1, 2
Maize
Potato
R1
Rysto
Tm-2a
Tomato
NILs = near-isogenic lines, BSA = bulked segregant analysis, BDLs = backcross derived lines, PP = parents, TL = translocation line,
SL = substitution line, GMBA = genetic map-based analysis, and PGA = pedigree analysis.
32
Table 2. Examples of dissection of quantitative trait loci determining QRLs
____________________________________________________________________________________________________________
Host
Pathogen
no. QTL no. markers
Method
Reference
____________________________________________________________________________________________________________
Rice
Barley
Maize
Potato
Soybean
Pyricularia oryzae
10
127 RFLP
131/281 RIL, MMQTL
Wang et al. 1994
Rhizoctonia solani
6
113 RFLP
MMQTL
Li et al. 1995
Erysiphe graminis f. sp. hordei
2
155 RFLP
113 DHL, MMQTL
Heun 1992
Puccinia striiformis f. sp. hordei
2
78 RFLP
110 DHL, MMQTL
Chen et al. 1994
Exserohilum turcicum
7
103 RFLP
150 F2/F3, MMQTL
Freymark et al. 1993
Cercospora zeae-maydis
9
87 RFLP
139-193 F2/F3, ANOVA
Bubeck et al.1993
5
78 RFLP
MMQTL
Saghai-Maroof et al.1996
Gibberella zea
10
95 RFLP, 10RAPD
150 F2/F3, MMQTL
Pe et al. 1993
Colletotrichum graminicola
1
113 RFLP
158 F2/F3, MMQTL
Jung et al. 1994
Cochliobolus heterostrophus
4
116FLP, SSR
179 RIL, REG
Carson et al. 1996
Phaeosphaeria maydis
3
116FLP, SSR
179 RIL, REG
Carson et al. 1996
Phytophthora infestans
13
77+68 RFLP
189 F1 , LSIM
Leonards et al. 1994
Pseudomonas solanacearum
3
67 RFLP, 12RAPD
71 F2, MMQTL
Danesh et al.1994
Globodera rostochiensis
2
107RFLP
F1
Kreike et al. 1993
Heterodera glycines
3
36 RFLP, 7RAPD
56 F2/F3, ANOVA
Concibido et al. 1994
RIL = recombinant inbred line, DHL = doubled haploid line, F2/F3 = genetic analysis of F2 plant with disease screening of F2:3 families,
MMQTL = MAPMARKER/QTL, LSIM = least squares interval mapping, Reg. = regression analysis, and ANOVA = analysis of
variance.
33
Table 3. Previously identified cloned disease resistance genes by use of map-based cloning.
____________________________________________________________________________
Gene
Host
Pathogen
Reference
____________________________________________________________________________
Cf2
Tomato
Cladosporium fulvum
Dixon et al. 1996
Pto
Tomato
Pseudomonas syringae pv. tomato
Martin et al. 1993
Xa-21
Rice
Xanthomonas oryzae pv. oryzae
Song et al. 1995
RPS2
Arabidopsis
Pseudomonas syringae pv. tomato
Bent et al. 1994,
Mindrinos et al. 1994
RPM1
Arabidopsis
Pseudomonas syringae pv. maculicola
Grant et al. 1995
Hs1prp1
Sugar Beet
Cochliobolus arbonum
Cai et al. 1997