Mapping quantitative trait loci for binary trait in the F2:3 design.

ABSTRACT

In the analysis of inheritance of quantitative traits with low heritability, an F(23) design that genotypes plants in F(2) and phenotypes plants in F(23) progeny is often used in plant genetics. Although statistical approaches for mapping quantitative trait loci (QTL) in the F(23) design have been well developed, those for binary traits of biological interest and economic importance are seldom addressed. In this study, an attempt was made to map binary trait loci (BTL) in the F(23) design. The fundamental idea was the F(2) plants were genotyped, all phenotypic values of each F(23) progeny were measured for binary trait, and these binary trait values and the marker genotype informations were used to detect BTL under the penetrance and liability models. The proposed method was verified by a series of Monte-Carlo simulation experiments. These results showed that maximum likelihood approaches under the penetrance and liability models provide accurate estimates for the effects and the locations of BTL with high statistical power, even under of low heritability. Moreover, the penetrance model is as efficient as the liability model, and the F(23) design is more efficient than classical F(2) design, even though only a single progeny is collected from each F(23) family. With the maximum likelihood approaches under the penetrance and the liability models developed in this study, we can map binary traits as we can do for quantitative trait in the F(23) design.