Chapter 7: Marker Assisted Selection and Genomic Selection

Thomas Lübberstedt; William Beavis; and Walter Suza

Marker-assisted selection (MAS) was applied as early as the 1980s when Tanksley and Rick (1980) used isozymes as markers for introgression of an exotic trait into adapted tomato cultivars. The premise behind use of markers is that selection on genotype rather than phenotype may increase speed and efficiency of selection. As you learned in the previous lesson, marker-assisted backcrossing (MABC) involves the use of markers to help recover the genome of the donor parent during a backcrossing program. In contrast, marker-assisted selection (MAS) aims to develop improved novel genotypes that are likely quite different from parental genotypes, based on markers that represent quantitative trait loci (QTL) alone (marker-based selection, MBS), or in combination with phenotypic selection, which was the original definition of marker-assisted selection (MAS) by Lande and Thompson (1990). MAS is sometimes used as summary term for application of markers in connection with selection procedures.

MAS and MBS are used to generate new lines or populations, whereas MABC is used to improve existing lines by adding one or few genes. During MAS and MBS, a breeder intermates combinations of complementary elite lines to identify transgressive segregants for multiple genes/alleles. Marker information is usually based on preceding QTL mapping experiments. This can be critical, if QTL/marker information is based on different genotypes in the mapping experiment compared to the breeding program. As different combinations of QTL segregate in different populations, the transferability of information across populations is limited. Markers would ideally be diagnostic for the presence of beneficial QTL alleles and thus valid across numerous crosses.

MAS has been shown to be more efficient than conventional phenotypic selection for traits with low heritability, and some of the MAS strategies have been successfully implemented in breeding programs of Monsanto® and other companies for different species. The following sections will discuss MAS strategies, efficiency, and factors that influence MAS and alternative approaches to MAS that can be applied in a breeding program.

Learning Objectives
  • Understand difference between marker-assisted backcrossing (MABC) and marker-assisted selection (MAS).
  • Develop an awareness of the relative efficiency of MAS versus phenotypic selection.
  • Understand factors that influence efficiency and limitations of MAS.
  • Understand MAS strategies.
  • Develop an awareness of the alternative approaches to MAS.
  • Understand differences between Marker-Assisted Selection (MAS) and GS
  • Understand principles of Genomic Selection (GS).

Limitations in QTL Mapping

QTL Dependencies

As discussed in the module on Cluster Analysis, Association & QTL Mapping, the goal of QTL mapping is to identify one or more genomic region(s) called quantitative trait locus (QTL) controlling a particular trait. However, the statistical power for detecting QTL depends on population size, leading to overestimation of QTL effects in small populations (Beavis, 1994), the Beavis effect. For this reason, QTL studies depend on very large sample sizes, and are only capable of detecting differences that are captured between the parents used to form a mapping population. Thus, within a given population, if the same parents were used to map QTL and to establish a breeding population, all QTL are of interest. Some QTL might not be relevant when they are transferred to other populations (if there is no segregation for that QTL). Another issue is, QTL determined at per se level might not be relevant for the testcross level in hybrid species like maize. Therefore, the way phenotyping is done affects the detection and consistency of QTL.

For all strategies presented in this lesson, it is crucial to understand that the reason for limited success of MAS (compared to GS) is their dependence on QTL mapping. QTL mapping has been shown to only find a fraction of QTL affecting a quantitative trait, and to overestimate genetic effects for detected QTL. Thus, during MAS, relevant regions in the genome are missed, whereas other regions likely get too much weight, so that expected findings likely differ from actual ones, leading to limited gain in selection.

Impact Graph

Fig. 1 Impact of population size (N) and trait heritability on power of QTL detection. Adapted from Utz and Melchinger, 1994.

MAS Strategies

MAS Strategies – F2 Enrichment

A. F2 Enrichment

The objective of the F2 enrichment strategy is to develop superior Recombinant Inbred Lines (RILs). MAS is particularly useful for F2 individuals or F2-derived lines, or other early generations (DHs, BC1-derived populations), because LD between marker loci and the trait of interest are at a maximum in a segregating population. A good example of the application of F2-enrichment comes from a wheat breeding program at CSIRO Plant Industry in Canberra, Australia (Bonnett et al., 2004). 

The F2 enrichment procedure involves:

  1. QTL identification
  2. Culling of undesirable genotypes to increase frequency of desirable alleles/genotypes
  3. Identification of RILs with all favorable QTL alleles fixed

[latex]y = \mu _{\mathit{0}} + \sum_{\mathit{i=1}}^{N}a_{i}x_{i}+\varepsilon[/latex]


= the expected phenotypic value of an individual
μ0 = the model mean
ai = additive effect of the marker.
ε = random environmental factor
xi= indicator variable (with values 1,0 and -1 for marker genotypes MM, mm, and mm)
 = the number of markers

Obtaining Marker Scores

B. Use of Marker Scores in Selection

Theoretically, selection of individuals is most efficient when based on additive gene effects. QTL analysis identifies chromosome segments affecting traits of interest, and enables us to estimate gene effects (additive, dominance, and epistasis) for each QTL. If summarized across all detected QTL, the expected performance of an individual can be predicted based on the QTL information. QTL analysis depends on the precise mapping of each QTL along with marker-trait regression analysis to estimate the genetic effects of QTL. Marker-trait regression uses the following equation:

[latex]y = \mu _{\mathit{0}} + \sum_{\mathit{i=1}}^{N}a_{i}x_{i}+\varepsilon[/latex]


= the expected phenotypic value of an individual
μ0 = the model mean
ai = additive effect of the marker.
ε = random environmental factor
xi = indicator variable (with values 1,0 and -1 for marker genotypes MM, mm, and mm)
 = the number of markers

Thus, markers (representing QTL) with significant effects on the trait of interest can be used to obtain marker scores (also referred to as molecular score) for each individual. The following expression is used to estimate marker scores (MS):

[latex]MS = \sum_{\mathit{\mathit{i=1}}}^{N}a_{i}x_{i}[/latex]


 = the number of markers selected
ai = additive effect of the marker i
xi = indicator variable with values 1, 0 and -1 for marker genotypes MM, Mm and mm)

Derivation of a Selection Index

C. Derivation of a Selection Index for MAS

Lande and Thompson (1990) demonstrated that MAS is most effective when breeding values are predicted by an index of QTL genotypic values and phenotypic values. Index weights are estimated that maximize the correlation between the index and a candidate’s breeding value (Atotal). Atotal is the sum of individual’s breeding value for the marked QTL (AQTL) and the breeding value for all other genes (Arest), not explained by QTL. Thus, Atotal = AQTL + Arest.

  1. Estimating marker scores

A marker weight coefficient (bMS) is estimated as follows:

[latex]b_{MS} = \frac{1-h_{2}}{1-\Theta h_{2}}[/latex]



MS = marker score
h2 = narrow sense heritability
θ = proportion of genetic variance explained by a marker score

  1.  Estimating index weight of marker score relative to phenotype 

An individual’s phenotype (P) is weighed using the following formula (Bernardo, 2009):

[latex]b_{p} = \frac {h^2(1 - \Theta )}{1-\Theta h^{2}}[/latex]


Thus, MS is weighted more heavily than P.

If h2 is 1, and θ ranges from 0.1 to 0.75, then the numerator of the marker weight coefficient bMS will be close to 0, and thus, almost no weight will be assigned to markers. Thus, the higher the heritability of a trait, the lower the marker score. On the other hand, if h2 is low, and if θ is if high (>0.5), more weight will be assigned to markers.

  1. Interpretation of the marker score

Assume a candidate’s breeding value (Atotal) is the sum of its breeding value for the marked QTL (AQTL) and its breeding value for all other genes (Arest), not explained by QTL:

Atotal = AQTL + Arest


  • The marker score gives an estimate of AQTL
  • Phenotype can be used to estimate an individual’s total breeding value, Atotal

Therefore, possible selection strategies could be based on MS and phenotype as described by the following steps:

  • Select on marker score alone: this ignores the information that is provided by phenotype on all the other genes that affect particular traits
  • Independent culling level selection: that is, based on (a) selection on marker score, (b) selection on phenotype. Some individuals with desirable genes for non-marked QTL may be eliminated in (a)
  • Index selection: develop index of marker score and phenotype (I = bMS MS + bp P). In general, expected response to selection index > independent culling > MS alone.

Marker-Assisted Recurrent Selection

D. Marker-Assisted Recurrent Selection

Marker-assisted recurrent selection (MARS) is used to enrich favorable alleles for QTL of interest over multiple generations. Indirect selection during winter generations can be combined with phenotypic selection or selection indices in rapid breeding cycles (Fig. 2).

Two arrows pointing in a circle illustrating the seasons from summer to winter. Winter nursery is under Winter and Field evaluation is under Summer.
Fig. 2 A general scheme for marker-assisted selection in plant breeding. Adapted from Eathington et al., 2007.

MARS involves:

  1. QTL identification, similar to F2 enrichment.
  2. Identification of best individuals based on marker score (Table 1) within population.
  3. Recombination of best individuals followed by identification of best individuals as described in (2).
  4. Click here to learn more about the application of MARS: MARS information

MAS Strategies Comparison

Similarities and Differences of MAS Strategies

F2 enrichment MARS
Involves QTL identification Involves QTL identification
QTL are given equal weights QTL are weighed according to the additive effect
Culling of undesirable genotypes Identification of best individuals based on marker scores
Identification of RIL with favorable fixed alleles Recombination of best individuals

Efficiency of MAS

Selection Index

To understand, what determines efficiency of MAS, we must understand how it is estimated. First, we estimate the accuracy of selection based on the selection index theory.

The selection index (I) is used to account for the relative superiority or inferiority of individuals for all the traits represented by the index.

[latex]I = b_1X_1 + b_2X_2 + … + b_nX_n = Σb_iX_i[/latex]


bi is the weight for trait i, and Xi is the phenotype value for trait i. The value of I is calculated for every individual or family in a population.

The selection index can also be denoted as I = bMM + bpP.

[latex]b_P = \frac{V_A - V_M}{V_P - V_M}[/latex]

[latex]b_M = \frac{V_P - V_A}{V_P - V_M}[/latex]

bM and bp are weights, M (or MS) is the marker score, and P is the phenotypic value.

The following equations can be used to estimate bP and bM:

[latex]b_P = \frac{V_A - V_M}{V_P - V_M}[/latex]

[latex]b_M = \frac{V_P - V_A}{V_P - V_M}[/latex]

VA = additive genetic variance
VM = the additive variance explained by the marker
VP = phenotypic variance

Estimating Relative Efficiency of MAS

Assuming that the selection intensity and generation interval are similar, the relative efficiency (RE) of MAS over phenotypic selection is obtained by comparing response from MAS to response from phenotypic selection (Bernardo, 2002).

  1. RE of marker-based selection (REMBS:PS)

[latex]RE_{MBS:PS} = \frac{\sqrt{\frac{V_M}{V_A}}}{h}[/latex]

  1. RE of marker-assisted selection (REMAS:PS)

[latex]RE_{MAS:PS} = \frac{\frac{V_M}{V_A}}{h^2} + \frac{{(1-(\frac{V_M}{V_A}))}^2}{1-h^2(\frac{V_M}{V_A})}[/latex]

Comparison to Phenotypic Selection

As Figure 3 and Table 1 show, MAS is more efficient than phenotypic selection for traits with low heritability, but MAS may not be economically justifiable for traits with higher heritability, and that are easier to score phenotypically. The reason is that when heritability (h) is high, gain from phenotypic selection nears the maximum possible given the genetic variance, leaving a small window for additional improvement by the use of markers.

Two line charts, the top showing marker-based selection and the bottom showing marker-assisted selection. Marker-based selection starts from 0 and rises to the same results as marker-assisted selection, which starts at 1.0.
Fig. 3 Efficiency of marker-assisted and marker-based selection relative to phenotypic selection. VM = variance due to marker score; VA = additive genetic variance; h = heritability. Adapted from Bernardo, 2002.

Factors Affecting Efficiency

Table 1 Relative efficiency of marker-based selection compared with phenotypic selection in maize. Data from Bernardo, 2002.
Trait VM/VA h2 Relative Efficiency
Yield 0.51 0.63 1.09
Grain moisture 0.55 0.94 1.00
Stalk lodging 0.62 0.39 1.33
Root lodging 0.62 0.39 1.33
Plant height 0.58 0.89 1.01

Therefore, many factors affect the efficiency of MAS, including the size of the QTL mapping population, the phenotype to be scored, experimental design and analysis, the number of markers available, the degree of association between available markers and the QTL, the proportion of additive effect described by the marker, and the selection method. Also, the crop to be improved and the marker development pipeline have a bearing on the efficiency of MAS. While MAS may provide greater relative efficiency than phenotypic selection, MAS programs also require higher economic efficiency to justify their application in a breeding program. As seen in Table 2, MAS is less economical for traits such as seedling emergence. Such traits may be easier to score visually and would thus not justify the use of MAS for their evaluation. On the other hand, biochemical traits such as sucrose concentration justify the application of MAS because they are difficult to score.

Table 2 Estimates of the average evaluation costs (US $) for the selected traits using phenotypic selection (PS) and marker-assisted (MAS). Data from Yousef and Juvik, 2001.
C1 [latex]\ddagger[/latex] C2 [latex]\S[/latex] C3 [latex]\S[/latex]
Emergence 56 103 42 78 37 70
Sucrose 178 164 134 109 119 90
Tenderness 158 154 119 104 105 87
Hedonic rating 370 260 278 157 247 122
[latex]\dagger[/latex] Average costs of selecting and evaluating one family for each trait in the first cycle (C1) and in subsequent cycles (C2 to C3).
[latex]\ddagger[/latex]Estimated costs based on actual responses.
[latex]\S[/latex]Projected costs based on costs associated with PS and MAS in the first cycle of selection.

Examples of Application in Crop Breeding

Example 1: Implementing MAS in Australian Wheat Breeding

The programs use DNA markers for selection for traits of high economic importance, which are controlled by single genes, and difficult to score reliably by non-marker assays (Eagles et al. 2001). In these programs, markers are also used for introgression of multiple genes controlling single traits (Lessons 4 and 5). Table 3 lists DNA markers used in wheat breeding in Australia.

Resistance to cereal cyst nematode (CCN) and tolerance to boron toxicity are difficult to phenotype. As shown in Table 3, two QTL have been identified for each of the two traits. The CCN markers are tightly linked to the resistance genes, and derived from germplasm sources outside the Australian wheat gene pool. Thus, markers for resistance to CCN have had greater success in stacking resistance in susceptible cultivars of wheat (Eagles et al. 2001). In contrast, the boron tolerance genes are present within Australian gene pool. For this reason, marker alleles for tolerance to boron are also observed in many susceptible lines in wheat breeding programs (Eagles et al., 2001), limiting their success as diagnostic tools for boron tolerance.

Example 2: Use of MAS in Breeding for Resistance to Soybean Cyst Nemotode

Soybean cyst nematodes (SCN) cause major economically important yield losses. The North American soybean germplasm pool lacks genes for resistance to SCN. The source of resistance to SCN is the center of soybean diversity in Asia. Resistance to SCN is controlled by one major gene, rhg1 and additional minor alleles (Cregan et al. 1999). Resistance to SCN is difficult to score reliably, warranting the use of MAS in selection for the trait. Novel marker alleles linked to SCN resistance genes have strong linkage disequilibrium to resistance genes. Also, the markers are reproducible consistently across multiple breeding populations. Since resistant progeny lines developed from resistant parents will also have the same marker alleles as their resistant parents, the markers can be used as diagnostic tools for resistance to SCN. Therefore, the markers will be useful in most future populations made by crossing resistant lines to susceptible lines.

The two examples underscore the importance of identifying markers that are tightly linked to target genes. Such markers are ideally developed from causal gene sequence to ensure that they are specific to the resistance allele, for example, the Cre genes for SCN resistance (Table 4). Marker alleles from different gene pools have a higher chance to be distinct and thus, diagnostic.

Example 3: Use of MAS In Introgression of Yield QTL Alleles in Soybean

Reyna and Sneller (2001) observed insignificant marker effects for yield QTL when a superior northern soybean cultivar was tested in southern environments. Therefore, MAS may not be useful in transferring superior genetic value of a cultivar to populations of environments in which the superior cultivar is not adapted. Such negative results from MAS are not always reported, resulting in publication bias for research that generates positive value of MAS in cultivar development.

Reasons for Varying Successes of MAS

In the case of polygenic traits such as yield MAS has produced mixed results. The reasons for less success of MAS in selection of polygenic traits include:

  • Accurate estimation of location and effects of underlying QTL is difficult.
  • Different QTL may be important in different populations.
  • Phenotypic selection is already efficient for moderate to high heritability traits, making MAS less economical.
  • QTL mapping methods require integration into efficient breeding procedures.

The above limitations to the success of MAS contribute to the “catch-22 of MAS” which means that if phenotypic data are poor indicators of genotypes, QTLs cannot be adequately mapped to implement MAS. On the other hand, if phenotypic data are good, MAS is not needed.

The Catch-22 of MAS can be avoided if a small number of QTL explain most of the genetic variation. In that case, high heritability in the QTL mapping phase is optimal to identify QTL markers. Then, markers can be implemented more economically than phenotyping in future selection cycles. Nonetheless, yield variation is not likely to be explained by few QTL, because underlying QTL will vary across populations.

Alternative Approaches to MAS

A. Mapping As You Go (MAYG)

The MAYG strategy re-estimates the value of QTL alleles as new germplasm is developed over breeding cycles (Podlich et al., 2004). In general, MAYG involves the following steps:

  1. Estimation of QTL effects in progeny of an initial set of crosses.
  2. Construction of marker alleles based on information from step 1 for MAS on germplasm.
  3. Creation of new set of crosses among selected lines.
  4. Update of the estimates of the QTL effects for use in the next selection cycle.
  5. Continuation of the process (1-5) using new estimates of QTL effects (Fig. 4).
An arrow showing cycles of the breeding program over time, with markers just after each program for re-estimating QTL allele effects.
Fig. 4 A schematic illustration of the MAYG strategy to marker-assisted selection. Adapted from Podlich et al., 2004.

B. Breeding By Design

Markers are useful in development of haplotype maps (see the eModule on Markers and Sequencing). Breeding by design requires information about chromosome haplotypes. Figure 5 below is an example of a haplotype map. Breeding by design describes the use of chromosome haplotypes to aid selection of F2 or BC individuals to develop superior elite line genotype.

Breeding by design describes the use of chromosome haplotypes to aid selection of F2 or BC individuals to develop superior elite line genotype.

Chromosome haplotypes
Fig. 5 Chromosome haplotypes. Adapted from Peleman and van der Voort, 2003.

The Principle of Breeding by Design

In Fig. 6, three chromosomes, A, B and C, of five parental lines, 1-5 are indicated side by side. Selection of specific recombination points on chromosomes A and B are done and chromosome C is selected from parental line 1. Dotted lines delineate marker positions used to select for the desired recombinants. The genome composition of the ideal line with respect to the three chromosomes is indicated.


Fig. 6 The Principle of Breeding by Design. Adapted from Peleman and van der Voort, 2003.

Genomic Selection

Advantage of Using GS

As discussed in previous sections, selection based on the genotype rather than the phenotype may result in faster and more efficient ways to conduct selection. However, the paradox of MAS makes detection of quantitative traits with low heritability less reliable because the power of detecting quantitative trait loci (QTL) depends on size of the mapping population and heritability of the trait. Also, application of MAS in small populations may lead to bias in magnitude of QTL effects and estimation of location of QTL. In contrast, Genomic Selection (GS) is a form of MAS involving estimation of the breeding values of lines in a population by evaluating their phenotypes and scores of markers that span the entire genome. The incorporation of all marker information in the GS prediction models helps avoid biased estimate of marker effects allowing the capturing of variation caused by small-effect QTL.

GS Principles

QTL studies detect in most cases only the “tip of the iceberg”, a limited number of QTL representing a small subset of all QTL affecting the trait(s) of interest. As QTL mapping employs a significance test, most true QTL are not detected (below significance threshold) (Fig. 7). Locus effect estimates of QTL that are detected are generally inflated (Fig. 7; “Beavis effect”).

Simple distribution graph of effect estimate. The center is labeled "true effect." The edge is marked as the average detected effect estimate, and the distance from the center to the edge is labeled "bias."
Fig. 7 Bias in effect estimation using traditional MAS approaches.

Application of GS

The application of GS in plant breeding was first introduced in the early 2000 (Meuwissen et al., 2001) and is based on the following principles:

  1. Dense marker maps covering all chromosomes allow accurate estimation of breeding values of individuals that have no phenotypic record and no progeny.
  2. Estimation of breeding value requires large number of marker haplotype effects.
  3. Methods that are based on prior distribution of variance associated with each chromosome segment provide more accurate prediction of breeding values.
  4. Selection based on genomic estimated breeding value (GEBV) has potential to increase the rate of genetic gain (Fig. 8) when combined with reproductive techniques, for example, doubled-haploids.
Fig. 8 Genomic selection shortens the breeding cycle by eliminating phenotypic evaluation of lines prior to selection as breeding materials for the subsequent cycles. Adapted from Heffner et al., 2009.

Important Factors

In applying GS it is also important that:

  1. All markers contribute to prediction, i.e. there is no distinction between “significant” and “non-significant” effects (Fig. 7). Thus, there is no arbitrary exclusion or inclusion of markers. The value of analyzing all loci is illustrated by Fig. 9.
  2. More effects are estimated than there are phenotypic observations.
  3. Smaller QTL effects are captured.
  4. Genetic relationships are captured.
  5. Multiple low cost markers are available.
Fig. 9 Correlations (Corr) from random crosses between simulated (Sm) and several accuracy estmators (sFull = full Bayesian treatment; sAll = all marker posterior average treatment; sSel = selected marker posterior average treatment; µAll = all marker cross mean treatment; µSel = selected marker cross mean treatment; µPhen = phenotypic selection). Adapted from Zhong and Jannink, 2007.

Two Population Types

In GS, two types of populations are considered:

  1. Training population – Both genotypic and phenotypic data should be available allowing fitting of a large number of markers as random effects in a linear model to estimate all marker effects simultaneously. The aim is to capture all of the additive genetic variance caused by alleles with both large and minor effects
  2. Breeding population – Only genotypic data are required to allow estimates of marker effects for prediction of breeding values, and selection of lines with GEBV.

GS Methods

The Basic Model

Statistical methods used for GS include, stepwise regression, ridge regression best linear unbiased prediction (RR-BLUP), and Bayesian estimations (Heffner et al., 2009). The basic model (Habier et al., 2007) underlying these methods can be written as:

[latex]y = \mu + \sum_{k}^{} X_{h}\beta _{k}\delta _{k} + e[/latex]

= a vector of tarit phenotypes
μ = the overall mean
xk =a column vector of marker genotypes at locus k
βk = the marker effect
δk = a 0/1 – indicator variable
= a vector of random residual effects

Table 3 Characteristics and trends of performance for BLUP and GS methods. Data from Heffner et al., 2009.
Performance with increased
Method Marker effect; variance assumption Proportion of markers fitted in model Marker density QTL [latex]\dagger[/latex]  density Large-effect QTL Small-effect QTL Inbreeding depression; loss of diversity
Traditional BLUP N/A N/A N/A N/A Captured only by phenotype Captured only by phenotype Yes
Stepwise regression Fixed Subset Reduced Reduced Overestimated Excluded Marginally Reduced
RR-BLUP[latex]\ddagger[/latex] Random; Equal All Reduced [latex]\S[/latex] Increased Underestimated Captured Reduced
BayesA Random; Unique All>0 All ? Reduced More accurately estimated Captured Reduced
BayesB Random; Unique Some=0 All Insensitive [latex]\S[/latex] Reduced More accurately estimated Captured Reduced
[latex]\dagger[/latex] QTL, quantitative trait locus.
[latex]\ddagger[/latex] RR, ridge regression
[latex]\S[/latex] Source: Fernando (2007).

Regression Models

The ability of GS to capture information on genetic relatedness is valuable. However, information on genetic relatedness decays rapidly. Importantly, the amount of information captured is strongly related to the number of markers fitted by a model. In estimating marker effects, two components contribute to an effect, these are, marker and error. When the effect is large, chances are that the error is also large. Thus methods that shrink (regress) the effects toward the mean as of a function of relative error and factor variances are used. Regression models (e.g., Bayesian) can partition contributions of linkage disequilibrium (LD) versus genetic relatedness. Thus regression models help and increase in long-term accuracy in estimating marker effects.

Marker Difference

In GS procedures, marker effects are considered to be random, in contrast to MAS, where marker effects are considered to be fixed effects. The differences between random and fixed marker effects are listed below.

Random markers

  • Genome-wide markers
  • Each effect considered as coming from a population of marker effects with a probability distribution
  • Interested in predicting future values marker effects
  • Hypothesis testing may be done on populations, which are considered static
  • Estimation and prediction are important
  • To predict, one needs to quantify influence of error relative to “factor processes”
  • Dependence on population properties best addressed by random effect

Fixed markers

  • They are developed from candidate loci
  • Each locus is different biologically, i.e., there is no population
  • Each candidate locus is a hypothesis
  • Hypothesis testing based on effects
  • Estimation and prediction are not important
  • No particular interest in estimating effects as long as a hypothesis is tested
  • Future values of effects are also not relevant.

Simulation Studies

GS vs. MARS Comparison

Simulation studies of testcross performance of doubled haploids in maize (Fig. 10) suggest that GS is more effective than MARS for complex traits under the control of many QTL with low heritability (Table 10). However, GS is less beneficial for recurrent selection for choosing parents of breeding populations or selection of single-crosses (Bernardo and Yu, 2007).

Fig. 10 GS and MARS in maize. Cycle 0 is evaluated during the regular growing season. Cycles 1 and 2 of GS and MARS are done in a winter nursery where generations can be grown in 1 year. Adapted from Bernardo and Yu, 2007.

Responses to Different Selections

Table 4 Responses to phenotypic, marker-assisted, and genomewide selection among maize doubled haploids. Relative efficiencies of MAS and GS are highlighted. Data from Bernardo and Yu, 2007.
Number of QTL Method Number of Markers 0.20 0.50 0.80
20, 40, or 100 Phenotypic selection 0 1.60\dagger 2.26 2.61
20 MARS 32 2.50 (0.4)\ddagger 3.14 (0.4) 3.38 (0.4)
64 2.72\S (0.3) 3.42 (0.4) 3.73 (0.4)
128 2.54 (0.3) 3.47 (0.2) 3.87 (0.4)
256 2.26 (0.2) 3.19 (0.2) 3.72 (0.2)
Genomewide selection 64 2.86 3.50 3.76
128 2.98 3.67 4.02
256 3.06 3.72 3.98
512 3.05 3.68 4.10
768 3.06 3.73 4.05
RGS:MARS 113% 107% 106%
R(GS-PS):(MARS-PS)# 130% 121% 118%

MAS Compared to GS

The similarities and differences between MAS and GS are shown in Figure 11. In general, MAS involves identification of alleles for development of markers for use in pre-selection of individuals containing an allele of interest. In contrast, GS does not require identification of genes as a source of markers for pre-selection of segregants with desirable alleles. Instead, whole chromosome segments are scanned to estimate the effect of QTL on a trait of interest.

MAS and GS. Longer description in caption.
Fig. 11 A comparison of GS and MAS methods in plant breeding. Both methods contain training and breeding stages. The training stage involves the identification of QTL (by MAS approaches) to generate formulae for predicting GEBV (GS models). In the breeding stage, desirable lines are selected based on markers (MAS) or GEBV (GS). Adapted from Nakaya et al., 2012.


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How to cite this module: Lübberstedt, T., W. Beavis, and W. Suza. (2023). Marker Assisted Selection and Genomic Selection. In W. P. Suza, & K. R. Lamkey (Eds.), Molecular Plant Breeding. Iowa State University Digital Press.


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Chapter 7: Marker Assisted Selection and Genomic Selection Copyright © 2023 by Thomas Lübberstedt; William Beavis; and Walter Suza is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, except where otherwise noted.