Zhu Chengsong, Huang Ju, Zhang Yuan-Ming
Section on Statistical Genomics, State Key Laboratory of Crop Genetics and Germplasm Enhancement/National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing 210095, China.
J Hered. 2007 Jul-Aug;98(4):337-44. doi: 10.1093/jhered/esm041. Epub 2007 Jul 10.
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.
在低遗传力数量性状的遗传分析中，精度相对较低。在这种情况下，一种在F(2)植株中进行基因分型并在F(2:3)后代中进行表型分析的F(2:3)设计被用于提高数量性状基因座（QTL）检测的精度。这是因为基于家系均值观测的剩余方差通过增加F(2:3)后代的数量而显著降低。我们之前的结果表明，相对于经典的F(2)设计，杂合F(2)植株的F(2:3)家系的混合分布可以显著提高QTL检测的功效。在本文中，我们将之前的方法从F(2:3)设计中的连续性状扩展到了二元性状。这里的方法也充分利用了混合分布。然而，这里提出的方法与我们之前的方法在两个方面有所不同。一是将外显率模型与易患性模型相结合来定位二元性状基因座（BTL），另一个是分析中使用的表型数据是每个F(2)植株衍生的F(2:3)后代的表型值之和，而不是F(2:3)后代的平均值，因为和的分布遵循二项分布。此外，易患性模型中的阈值也可以估计。因此，基于单个BTL模型建立并通过期望最大化算法实现了一个新的BTL定位框架。模拟研究结果表明，所提出的方法能够准确估计BTL的效应和位置，即使在低遗传力情况下也具有较高的统计功效。有了这种新方法，我们就可以像在F(2:3)设计下对数量性状进行定位一样对BTL进行定位。执行模拟数据分析的计算机程序可供用户进行实际数据分析。
