Mapping binary trait loci in the F(2:3) design

In the inheritance analysis of quantitative trait with low heritability, the precision is relatively low. In this situation, an F(2:3) design, which is genotyped in F(2) plants and phenotyped in the F(2:3) progeny, is applied to increase the precision in the detection of quantitative trait loci (QTL). This is because that residual variance on the basis of family-mean-based observations has been significantly decreased by increasing the number of F(2:3) progeny. Our previous results showed that the mixture distribution for the F(2:3) family of heterozygous F(2) plant can significantly increase the power of QTL detection relative to the classical F(2) design. In this article, we extended our previous method from continuous traits to binary traits in the F(2:3) design. The method here also takes full advantage of the mixture distribution. However, the method presented here differs from our previous method in 2 aspects. One is that the penetrance model is integrated with the liability model for mapping binary trait loci (BTL), and another is that the phenotypic data used in the analysis are the sum of phenotypic values of F(2:3) progeny derived from each F(2) plant rather than the average of F(2:3) progeny due to the fact that the distribution of the sum follows binomial distribution. In addition, the threshold in the liability model could also be estimated. Therefore, a new framework of mapping BTL on the basis of a single BTL model was set up and implemented via the Expectation-Maximization algorithm. Results of simulated studies showed that the proposed method provides accurate estimates for both the effects and the locations of BTL, with high statistical power even under the low heritability. With the new method, we are ready to map BTL, as we can do for quantitative traits under the F(2:3) design. The computer program performing the analysis of the simulated data is available to users for real data analysis.