Ainong Shi Site

Chapter 1. Molecular Markers Associated with Plant Disease Resistance

1. Types of molecular markers
• RFLP
• RAPD and SCAR
• AFLP
• SSR
2. Major gene resistance
• Screening of molecular markers
• Estimates of linkage
• Dominant marker and resistance gene
• Co-dominant markers and resistance gene
• Examples of identified major resistance genes
3. Mapping of QRLs by use of QTL methods
• Screening marker
• QTL mapping
• Examples
4. Marker-assisted selection
5. Map-based gene cloning
6. Reference

Plant diseases are generally caused by fungi, bacteria, viruses, and nematodes. Plant resistance is often 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.; Major gene resistance and quantitative resistance loci (QRLs) will be described in this chapter.

Major gene resistance is race-specific resistance and controlled by genes with major effects. Plants in segregating populations, such as F₂ or BC₁F₁, can be divided by their resistance into clear and natural groups with a discontinuous distribution or a binomial distribution. Synonyms of major gene resistance are qualitative resistance, vertical resistance, single gene resistance, monogenic resistance, race-specific resistance, true resistance, and non-durable resistance.

The other type of resistance, which can be considered the opposite of major gene resistance, can be called quantitative resistance, horizontal resistance, polygenic resistance, race-nonspecific resistance, field resistance, durable resistance, slow resistance, partial resistance, or minor gene resistance. This type of resistance varies distribution in the segregating populations.

Quantitative resistance loci (QRLs) are a new term and it has been developed for QTL (Quantitative Trait Loci) 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 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 avirulent genes in pathogen. Major genes, minor genes, or both can control QR. 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 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 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 may 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. RFLPs are often preferred, because they can be used to tagging 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 Amplified Polymorphic DNA) is a Polymerase Chain Reaction (PCR) technique (Williams et al, 1990), that utilizes short, nonspecific primers. Typically primers are 9 or 10-mer oligonucleotides 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 at 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 RAPD’s 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

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. With AFLP the reliability of the RFLP technique is combined with the power of the PCR technique.

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. An AFLP reaction can produce 10-30 polymorphisms, depending upon the genomes being assayed, making AFLPs a very cost effective marker system. However, AFLP technology has an additional template preparation step relative to other PCR-based assays. Slightly more genomic DNA is required for an AFLP reaction, 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 resulting from the variation in the number of repeat units (Jacob et al. 1991). SSRs are also called microsatellites (Litt and Luty 1989). SSR technology is based on the PCR-amplification of a genomic region containing 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. The markers are codominant. 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 (NILs). 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 NILs for most disease resistance traits are unavailable, and are tedious to produce. Another disadvantage is that NILs can be used only for screening markers associated with major genes.

Bulked segregant analysis (BSA), was suggested by Michelmore 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 resistant and susceptible parents. The individuals in this F₂ population are tested for resistance. Equal quantities of DNA from each homozygous resistant individual are mixed as a resistant ( R ) group, and the same amount of DNA in each homozygous susceptible individual is mixed as a susceptible ( S ) group. In that way, the R group has the same genetic background as in the S group except for the resistance alleles. In theory the difference between the two bulked DNA samples will be only 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 F_2:3 lines are needed to test for reaction to the disease. This is time-consuming and expensive in terms of labor and supplies. However, there is no need to conduct resistance test in F_2:3 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 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 (Qi et al. 1996, Shi et al. chapter 6), 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 = n_idlog(e_i)/dr = 0,
i

Where, K is number of phenotypes in F₂ or BC₁F₁, n_i is the observed count for each phenotype, e_i is expected frequency, and i = 1, 2, 3, ......, K.

2.2.1. Dominant marker and resistance gene

For a BC₁F₁ population and a resistance locus with two alleles, A and a, and a marker locus 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-, in which RM is a resistant individual with the marker, 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 as follows:

So, the recombination frequency estimator, r = (n₁ + n₂) / N, (N = n₁ + n₂ + n₃ +n₄),

and the standard deviation of the estimator ( r ) S_r = Ö( (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:

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 individual with the band; and S- is a susceptible individual without the band and marker. The recombination frequency r can be estimated by the MLE method and the formula for solving r is:

k
n_idlog(e_i)/dr = n₁dlog(3-2r+r²)/dr + n₂dlog(2r-r²)/dr + n₃dlog(2r-r²)/dr + n₄dlog(1-r)²/dr = 0,
i

n₁dlog(2+r²)/dr + n₂dlog(1-r²)/dr + n₃dlog(1-r²)/dr + n₄dlog(r²)/dr = 0 in repulsion phase.

It is difficult to obtain an analytical solution from the two equations. The estimate of recombination frequency (r) can be solved by a transformed formula with a middle variable, q, based on F₂ generation data (Weber and Wricke 1994). Suppose q = (1-r)² in coupling phase, and q = r² in repulsion phase. The expected frequencies are (2+q)/4, (1-q)/4, (1-q)/4, and q/4 for q, corresponding the four genotypes A_M₁_, A_m₁m_1, aaM₁_, and aam₁m₁ in the F₂ population. In that way,

q = [K +  Ö(K² + 8Nn₄)] / (2N), (N = n₁ + n₂ + n₃ + n₄, K= n₁-2n₂-2n₃-n₄),

S_q² = [2q(2+q)(1-q)]/[N(1+2q)],

then the recombination frequency,

r = 1- Öq in coupling phase, and r = Öq in repulsion phase.

The standard deviation S_r = S_q /2.

The recombination frequency (r) can also been estimated by using SAS software (SAS institute Inc., 1990). The following SAS software program, used to estimate r, was kindly provided by Dr. Ben-Hui Liu, Department of Forestry, North Carolina State University, Raleigh, NC 27695-8008, USA (unpublished).

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 counts 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 to the precision desired.

Several examples are given for explaining how to estimate the frequency of recombination (r) as follows:

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 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,

S_r = Ö r(1-r)/N = Ö[(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.

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 q = [k +Ö(K²+8Nn₄)]/(2N) = [48 + Ö(48**2 + 8*96*21)]/(2*96) = 0.9571. S_q² = 2q(1-q)(2+q)/[N(1+2)] = 2*0.9571*(1-0.9571)(2+0.9571)/[96*(1+2*0.9571)] = 0.0008679, and S_q = 0.02946,

Therefore, the r = 1-Öq = 1- Ö 0.9571 = 0.022, and S_r = S_q/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 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:

     The recombination frequency can be estimated by the use of MLE, and solved by the formula:

n_idlog(e_i)/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 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 using 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), and these include 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

BSA used for mapping major genes also can be used for the mapping of QRL. QTL analysis is similar to 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 usually include many loci and more than one locus may be linked on a chromosome. The linkage analysis using a segregating population (F₂ or backcross) can’t distinguish the closely linked loci. The location of identified resistance loci usually spans a segment of the chromosome. Progenies in a segregating population can’t be reproduced.

Nevertheless, recombined inbred lines (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 time and cost. Development of set of RILs takes approximately seven or more growing seasons. In order to overcome the 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 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 MAPMAKER/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. Identification of disease resistance is conducted in the field, greenhouse, and laboratory. Some disease screening procedures 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 procedures 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 also for multiple diseases.

Selection of disease resistance by MAS is very efficient. For example, Shi et al. (chapter 3) identified a RAPD marker, OPU17_750, linked to the Pm1 gene for wheat powdery mildew resistance (r = 2.2 ± 1.07 %). The utility of the marker was determined in 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 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).

4. Map-based Gene Cloning

Map-based Cloning has been used to clone plant resistance genes (Martin et al. 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: 1) development of a high-density RFLP, RAPD, AFLP and/or SSR genetic map; 2) screening of a genomic library to identify overlapping clones covering the region of interest; and 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 map is constructed. 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 transfer this DNA into a susceptible plant and inoculate it 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 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.

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Table 1. Examples of previously identified molecular markers linked to major disease resistance genes.

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.

Table 2. Examples of dissection of quantitative trait loci determining QRLs

Table 3. Previously identified cloned disease resistance genes by use of map-based cloning.