Xiong Momiao, Zhao Jinying, Boerwinkle Eric
Human Genetics Center, University of Texas-Houston, 77225, USA.
Am J Hum Genet. 2002 May;70(5):1257-68. doi: 10.1086/340392. Epub 2002 Mar 29.
Recent progress in the development of single-nucleotide polymorphism (SNP) maps within genes and across the genome provides a valuable tool for fine-mapping and has led to the suggestion of genomewide association studies to search for susceptibility loci for complex traits. Test statistics for genome association studies that consider a single marker at a time, ignoring the linkage disequilibrium between markers, are inefficient. In this study, we present a generalized T2 statistic for association studies of complex traits, which can utilize multiple SNP markers simultaneously and considers the effects of multiple disease-susceptibility loci. This generalized T2 statistic is a corollary to that originally developed for multivariate analysis and has a close relationship to discriminant analysis and common measure of genetic distance. We evaluate the power of the generalized T2 statistic and show that power to be greater than or equal to those of the traditional chi2 test of association and a similar haplotype-test statistic. Finally, examples are given to evaluate the performance of the proposed T2 statistic for association studies using simulated and real data.
基因内和全基因组单核苷酸多态性（SNP）图谱开发方面的最新进展为精细定位提供了一种有价值的工具，并促使人们提出进行全基因组关联研究以寻找复杂性状的易感位点。一次仅考虑一个标记而忽略标记间连锁不平衡的全基因组关联研究检验统计量效率低下。在本研究中，我们提出了一种用于复杂性状关联研究的广义T2统计量，它可以同时利用多个SNP标记，并考虑多个疾病易感位点的效应。这种广义T2统计量是最初为多变量分析而开发的统计量的一个推论，并且与判别分析和遗传距离的常用度量密切相关。我们评估了广义T2统计量的效能，并表明其效能大于或等于传统的关联χ2检验以及类似的单倍型检验统计量的效能。最后，给出了使用模拟数据和真实数据评估所提出的T2统计量用于关联研究的性能的示例。
