Contribution Of Quantitative Trait Loci Research Articles

Comparison of Phenotypic Methods and Yield Contributions of Quantitative Trait Loci for Partial Resistance to Phytophthora sojae in Soybean

ABSTRACTIn several host–pathogen systems, the detection of an individual quantitative trait locus (QTL) differed depending on the specific pathogen isolate or phenotypic assay that was used. Moreover, the contribution of a QTL toward yield is an important factor for selecting specific QTL candidates for resistance breeding. The first experiment in this study compared the QTL identified previously in a ‘Conrad’ × ‘Sloan’ F4:6 soybean [Glycine max (L.) Merr.] population challenged with Phytophthora sojae Kaufmann and Gerdemann isolate 1.S.1.1 to the QTL identified when this same population was challenged with isolates PT2004C2.S1 and OH25 using a tray test assay. In the second experiment, QTL were mapped in the same population with P. sojae isolate 1.S.1.1 using a different phenotypic assay, the layer test. In the third experiment, the impact of these QTL on soybean yield was evaluated using 20 selected recombinant inbred lines (RILs) grown in a field infested with P. sojae. Resistance QTL with smaller effects, especially those from the susceptible parent, were not consistently detected with the three isolates or the two phenotypic assays. Composite interval mapping identified a major Conrad QTL on chromosome 18 and one on 19 that were detected with all three isolates and both phenotypic assays. Recombinant inbred lines with resistance alleles at these QTL had significantly higher yields (∼1000 kg ha−1 more) than RILs with susceptible alleles. These results indicate the important role these QTL play in conferring partial resistance to P. sojae.

Genetic dissection of Lactuca saligna nonhost resistance to downy mildew at various lettuce developmental stages

This study used the pathosystem of lettuce (Lactuca spp.) and downy mildew (Bremia lactucae) as a model to investigate the inheritance of nonhost resistance, and focused on the contribution of quantitative trait loci (QTLs) to nonhost resistance at various developmental stages in the lettuce life cycle. A set of 28 backcross inbred lines (BILs) of L. saligna CGN05271 (nonhost) introgressions in a L. sativa cv. Olof (host) background identified 16 introgressions that contributed to resistance at various plant developmental stages: seedlings, young plants, adult plants in the greenhouse and adult plants in the field. This paper provisionally considered these introgressions to be 16 QTLs. Of these 16 QTLs, seven were identified previously and nine were new. For 15 QTLs (Rbq1, Rbq2, rbq3–7 and Rbq8–15), the resistance alleles were derived from the nonhost L. saligna; the resistance allele of the other QTL (Rbq16) was from the susceptible L. sativa cv. Olof. Of the 15 QTLs in L. saligna, only two, rbq5 and rbq7, were found to be effective at every plant developmental stage; the other 13 QTLs were only effective at certain developmental stages. Experiments with seven B. lactucae races did not provide evidence that any QTL was race‐specific. The data suggest that nonhost resistance in L. saligna is the result of cumulative effects of many resistance QTLs operating at various developmental stages.

A CENTENNIAL CELEBRATION FOR QUANTITATIVE GENETICS

Quantitative genetics is at or is fast approaching its centennial. In this perspective I consider five current issues pertinent to the application of quantitative genetics to evolutionary theory. First, I discuss the utility of a quantitative genetic perspective in describing genetic variation at two very different levels of resolution, (1) in natural, free-ranging populations and (2) to describe variation at the level of DNA transcription. Whereas quantitative genetics can serve as a very useful descriptor of genetic variation, its greater usefulness is in predicting evolutionary change, particularly when used in the first instance (wild populations). Second, I review the contributions of Quantitative trait loci (QLT) analysis in determining the number of loci and distribution of their genetic effects, the possible importance of identifying specific genes, and the ability of the multivariate breeder's equation to predict the results of bivariate selection experiments. QLT analyses appear to indicate that genetic effects are skewed, that at least 20 loci are generally involved, with an unknown number of alleles, and that a few loci have major effects. However, epistatic effects are common, which means that such loci might not have population-wide major effects: this question waits upon (QTL) analyses conducted on more than a few inbred lines. Third, I examine the importance of research into the action of specific genes on traits. Although great progress has been made in identifying specific genes contributing to trait variation, the high level of gene interactions underlying quantitative traits makes it unlikely that in the near future we will have mechanistic models for such traits, or that these would have greater predictive power than quantitative genetic models. In the fourth section I present evidence that the results of bivariate selection experiments when selection is antagonistic to the genetic covariance are frequently not well predicted by the multivariate breeder's equation. Bivariate experiments that combine both selection and functional analyses are urgently needed. Finally, I discuss the importance of gaining more insight, both theoretical and empirical, on the evolution of the G and P matrices.

Estimation of the contribution of quantitative trait loci (QTL) to the variance of a quantitative trait by means of genetic markers

The estimation of the contribution of an individual quantitative trait locus (QTL) to the variance of a quantitative trait is considered in the framework of an analysis of variance (ANOVA). ANOVA mean squares expectations which are appropriate to the specific case of QTL mapping experiments are derived. These expectations allow the specificities associated with the limited number of genotypes at a given locus to be taken into account. Discrepancies with classical expectations are particularly important for two-class experiments (backcross, recombinant inbred lines, doubled haploid populations) and F2 populations. The result allows us firstly to reconsider the power of experiments (i.e. the probability of detecting a QTL with a given contribution to the variance of the trait). It illustrates that the use of classical formulae for mean squares expectations leads to a strong underestimation of the power of the experiments. Secondly, from the observed mean squares it is possible to estimate directly the variance associated with a locus and the fraction of the total variance associated to this locus (r l (2) ). When compared to other methods, the values estimated using this method are unbiased. Considering unbiased estimators increases in importance when (1) the experimental size is limited; (2) the number of genotypes at the locus of interest is large; and (3) the fraction of the variation associated with this locus is small. Finally, specific mean squares expectations allows us to propose a simple analytical method by which to estimate the confidence interval of r l (2) . This point is particularly important since results indicate that 95% confidence intervals for r l (2) can be rather wide:2-23% for a 10% estimate and 8-34% for a 20% estimate if 100 individuals are considered.

Estimation of the contribution of quantitative trait loci (QTL) to the variance of a quantitative trait by means of genetic markers

Estimation of the contribution of quantitative trait loci (QTL) to the variance of a quantitative trait by means of genetic markers

The contribution of quantitative trait loci and neutral marker loci to the genetic variances and covariances among quantitative traits in random mating populations.

Using Cockerham's approach of orthogonal scales, we develop genetic models for the effect of an arbitrary number of multiallelic quantitative trait loci (QTLs) or neutral marker loci (NMLs) upon any number of quantitative traits. These models allow the unbiased estimation of the contributions of a set of marker loci to the additive and dominance variances and covariances among traits in a random mating population. The method has been applied to an analysis of allozyme and quantitative data from the European oyster. The contribution of a set marker loci may either be real, when the markers are actually QTLs, or apparent, when they are NMLs that are in linkage disequilibrium with hidden QTLs. Our results show that the additive and dominance variances contributed by a set of NMLs are always minimum estimates of the corresponding variances contributed by the associated QTLs. In contrast, the apparent contribution of the NMLs to the additive and dominance covariances between two traits may be larger than, equal to or lower than the actual contributions of the QTLs. We also derive an expression for the expected variance explained by the correlation between a quantitative trait and multilocus heterozygosity. This correlation explains only a part of the genetic variance contributed by the markers, i.e., in general, a combination of additive and dominance variances and, thus, provides only very limited information relative to the method supplied here.

Effect of genetic architecture on the power of human linkage studies to resolve the contribution of quantitative trait loci

The effect of genetic architecture (linkage relationships, dominance and two forms of non-allelic interaction) on the power of marker studies to detect, locate and analyse the contributions of specific quantitative trait loci (QTLs) to continuous human traits is considered for randomly mating populations in linkage equilibrium under a two-locus model. The expected regression of the within-sibling-pair mean-square on number of alleles identical by descent (IBD) at two marker loci is explored for every possible pair of markers over a region of the genome containing two QTLs linked loosely (50 CM) or more tightly (20 CM). For the cases examined, it is shown that epistasis between the pair of QTLs reduces considerably the total amount of information available for the location and analysis of the QTL effects. The overall effects of epistasis are more marked when there are duplicate gene interactions (i.e. genes operate in parallel) than when there are complementary interactions (i.e. genes operate in series). However, when there are complementary interactions, the regression approach is almost certain to fail to detect any evidence of epistasis. The numerical analysis suggests that methods of QTL analysis based on IBD in humans are unlikely to offer the resolving power that is desirable if QTLs are to be located precisely unless inheritance is very simple or prohibitively large numbers of highly selected individuals are available.