Mapping quantitative trait loci underlying triploid endosperm traits

Abstract

Endosperm, which is derived from two polar nuclei fusing with one sperm, is a triploid tissue in cereals. Endosperm tissue determines the grain quality of cereals. Improving grain quality is one of the important breeding objectives in cereals. However, current statistical methods for mapping quantitative trait loci (QTL) under diploid genetic control have not been effective for dealing with endosperm traits because of the complexity of their triploid inheritance. In this paper, we derive for the first time the conditional probabilities of F 3 endosperm QTL genotypes given different flanking marker genotypes in F 2 plants. Using these probabilities, we develop a multiple linear regression method implemented via the iteratively reweighted least-squares (IRWLS) algorithm and a maximum likelihood method (ML) implemented via the expectation-maximization (EM) algorithm to map QTL underlying endosperm traits. We use the mean value of endosperm traits of F 3 seeds as the dependent variable and the expectations of genotypic indicators for additive and dominance effect of a putative QTL flanked by a pair of markers as independent variables for IRWLS mapping. However, if an endosperm trait is measured quantitatively using a single endosperm sample, the ML mapping method can be used to separate the two dominance effects. Efficiency of the methods is verified through extensive Monte Carlo simulation studies. Results of simulation show that the proposed methods provide accurate estimates of both the QTL effects and locations with very high statistical power. With these methods, we are now ready to map endosperm traits, as we can for regular quantitative trait under diploid control.

Mapping epistatic quantitative trait loci underlying endosperm traits using all markers on the entire genome in a random hybridization design

“…The method described here, however, offers advantages over triploid-based methods. As in Kao (2004) method, it allows for a model that includes all main and pair-wise epistatic effects, in contrast to other methods in which only a single ETL genetic model is considered (Wu et al, 2002a, b;Xu et al, 2003;Hu and Xu, 2005;Wen and Wu, 2006). In our new model, biased estimates will not occur if there are linked or eETL.…”

“…Endosperm is a main part of grain seed and many endosperm traits are directly related to grain quality, so endosperm traits are of great importance. To uncover their genetic architecture, several methods of mapping ETL have been proposed (Wu et al, 2002a, b;Xu et al, 2003;Kao, 2004;Hu and Xu, 2005;Wen and Wu, 2006). These triploid-based methods are all superior to diploid methods for ETL mapping.…”

“…Theoretically, this can lead to an increase in genetic information extraction from both the maternal plant and its offspring embryo genomes, and in resolution for ETL mapping, compared with a single segregation generation (one-stage) design. Xu et al (2003) expressed the mean value of endosperm traits of F 2:3 seeds as a dependent variable and the expectations of genotypic indicators for additive and dominant effects of a putative ETL as independent variables for iteratively reweighted least-squares mapping. Recently, Hu and Xu (2005) postulated that genetic expression of an endosperm trait may be controlled simultaneously by triploid endosperm and diploid maternal genotypes, and proposed a statistical method for ETL mapping that included maternal genetic effects.…”

Mixed linear model approach for mapping quantitative trait loci underlying crop seed traits

“…Abbreviations: Chr., the number of simulated chromosome; Est., the estimate of parameter; Par., the true parameter value in simulations; Pos., position (the distance (cM) between the QTL and the first marker on the same chromosome); Power, the percentage of the QTL that is detected correctly to be significant at 0.05 and/or within the 95% confidence interval; QTL, quantitative trait locus. Unlike the previous methods (Wu et al, 2002;Xu et al, 2003), most of which are based on one QTL model and thus may have a potential bias in parameter estimation when two or more linked QTLs exist in genome, the proposed method uses a QTL full model to estimate all possible effects of QTLs. False positive QTL(s) could be eliminated by model selection, while higher power and more accurate estimation of QTL effects could be achieved, benefiting from smaller and more accurate estimation of the residual effects because of background control.…”

Mapping Quantitative Trait Loci Using the Experimental Designs of Recombinant Inbred Populations

“…With the ability to consider the changes in population structures of different populations, the proposed method can serve as an effective tool to map for QTL in specific designs and evaluate the efficiency of QTL mapping among different experimental designs under the system of RI populations. Other important issues of QTL mapping by using the (F u /F v , u # v) designs include the consideration of endosperm traits (Wu et al 2002;Xu et al 2003;Kao 2004) and the extension of the methods from the system of RI populations to the system of IRI populations. The IRI populations, which are derived by randomly mating for some generations after F 2 and then followed by cycles of selfing, have the advantages of producing more recombinants as compared to the RI populations, and they can benefit the analysis of quantitative traits (Liu et al 1996;Winkler et al 2003).…”