Journal of Heredity Advance Access originally published online on July 10, 2007
Journal of Heredity 2007 98(4):337-344; doi:10.1093/jhered/esm041
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Mapping Binary Trait Loci in the F2:3 Design
From the Section on Statistical Genomics, State Key Laboratory of Crop Genetics and Germplasm Enhancement/National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing 210095, China
Address correspondence to Dr Y.-M. Zhang to the address above, or e-mail: soyzhang{at}njau.edu.cn.
In the inheritance analysis of quantitative trait with low heritability, the precision is relatively low. In this situation, an F2:3 design, which is genotyped in F2 plants and phenotyped in the F2:3 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 F2:3 progeny. Our previous results showed that the mixture distribution for the F2:3 family of heterozygous F2 plant can significantly increase the power of QTL detection relative to the classical F2 design. In this article, we extended our previous method from continuous traits to binary traits in the F2:3 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 F2:3 progeny derived from each F2 plant rather than the average of F2:3 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 F2:3 design. The computer program performing the analysis of the simulated data is available to users for real data analysis.
Corresponding Editor: Reid Palmer
Received December 1, 2006
Accepted March 8, 2007