Huang B E, Lin D Y
Department of Biostatistics, University of North Carolina, Chapel Hill 27599-7420, USA.
Am J Hum Genet. 2007 Mar;80(3):567-76. doi: 10.1086/512727. Epub 2007 Jan 30.
Selective genotyping (i.e., genotyping only those individuals with extreme phenotypes) can greatly improve the power to detect and map quantitative trait loci in genetic association studies. Because selection depends on the phenotype, the resulting data cannot be properly analyzed by standard statistical methods. We provide appropriate likelihoods for assessing the effects of genotypes and haplotypes on quantitative traits under selective-genotyping designs. We demonstrate that the likelihood-based methods are highly effective in identifying causal variants and are substantially more powerful than existing methods.
选择性基因分型（即仅对那些具有极端表型的个体进行基因分型）能够极大地提高在基因关联研究中检测和定位数量性状位点的能力。由于选择取决于表型，因此所得数据无法通过标准统计方法进行恰当分析。我们提供了合适的似然函数，用于评估在选择性基因分型设计下基因型和单倍型对数量性状的影响。我们证明，基于似然函数的方法在识别因果变异方面非常有效，并且比现有方法更具效力。
