Chen H-S, Zhu X, Zhao H, Zhang S
Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA.
Ann Hum Genet. 2003 May;67(Pt 3):250-64. doi: 10.1046/j.1469-1809.2003.00036.x.
Recently, statistical methods have been proposed using genomic markers to control for population stratification in genetic association studies. However, these methods either have unacceptable low power when population stratification becomes strong or cannot control for population stratification well under admixture population models. In this paper, we propose a semiparametric association test to detect genetic association between a candidate marker and a qualitative trait of interest in case-control designs. The performance of the test is compared to other existing methods through simulations. The results show that our method gives correct type I error rate both under discrete population models and admixture population models, and our method is robust to the extent of the population stratification. In most of the cases we considered, our method has higher power and, in some cases, substantially higher power than that of existing methods.
最近，有人提出了利用基因组标记来控制遗传关联研究中群体分层的统计方法。然而，当群体分层变得强烈时，这些方法要么功效低得不可接受，要么在混合群体模型下无法很好地控制群体分层。在本文中，我们提出了一种半参数关联检验，以在病例对照设计中检测候选标记与感兴趣的定性性状之间的遗传关联。通过模拟将该检验的性能与其他现有方法进行了比较。结果表明，我们的方法在离散群体模型和混合群体模型下均能给出正确的I型错误率，并且我们的方法对群体分层程度具有稳健性。在我们考虑的大多数情况下，我们的方法具有更高的功效，并且在某些情况下，比现有方法的功效要高得多。
