Wang Xuexia, Zhang Shuanglin, Li Yun, Li Mingyao, Sha Qiuying
Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, United States of America.
Genet Epidemiol. 2015 May;39(4):294-305. doi: 10.1002/gepi.21894. Epub 2015 Mar 10.
Population stratification has long been recognized as an issue in genetic association studies because unrecognized population stratification can lead to both false-positive and false-negative findings and can obscure true association signals if not appropriately corrected. This issue can be even worse in rare variant association analyses because rare variants often demonstrate stronger and potentially different patterns of stratification than common variants. To correct for population stratification in genetic association studies, we proposed a novel method to Test the effect of an Optimally Weighted combination of variants in Admixed populations (TOWA) in which the analytically derived optimal weights can be calculated from existing phenotype and genotype data. TOWA up weights rare variants and those variants that have strong associations with the phenotype. Additionally, it can adjust for the direction of the association, and allows for local ancestry difference among study subjects. Extensive simulations show that the type I error rate of TOWA is under control in the presence of population stratification and it is more powerful than existing methods. We have also applied TOWA to a real sequencing data. Our simulation studies as well as real data analysis results indicate that TOWA is a useful tool for rare variant association analyses in admixed populations.
群体分层长期以来一直被认为是基因关联研究中的一个问题，因为未被识别的群体分层可能导致假阳性和假阴性结果，并且如果不进行适当校正，可能会掩盖真正的关联信号。在罕见变异关联分析中，这个问题可能会更严重，因为罕见变异通常表现出比常见变异更强且可能不同的分层模式。为了校正基因关联研究中的群体分层，我们提出了一种新方法，即测试混合群体中变异的最优加权组合的效应（TOWA），其中可以从现有的表型和基因型数据计算出分析得出的最优权重。TOWA对罕见变异以及与表型有强关联的变异进行加权。此外，它可以调整关联方向，并考虑研究对象之间的局部祖先差异。广泛的模拟表明，在存在群体分层的情况下，TOWA的I型错误率得到了控制，并且它比现有方法更有效。我们还将TOWA应用于实际测序数据。我们的模拟研究以及实际数据分析结果表明，TOWA是混合群体中罕见变异关联分析的一个有用工具。
