Department of Epidemiology and Public Health, Yale University, New Haven, Connecticut 06510, USA.
Genet Epidemiol. 2011 Jan;35(1):70-9. doi: 10.1002/gepi.20555.
Genome-wide association studies typically test large numbers of genetic variants in association with trait values. It is well known that linkage disequilibrium (LD) between nearby markers tends to introduce correlation among association tests. Failure to properly adjust for multiple comparisons can lead to false-positive results or missing true-positive signals. The Bonferroni correction is generally conservative in the presence of LD. The permutation procedure, although has been widely employed to adjust for correlated tests, is not applicable when related individuals are included in case-control samples. With related individuals, the dependence among relatives' genotypes can also contribute to the correlation between tests. We present a new method P(norm) to correct for multiple hypothesis testing in case-control association studies in which some individuals are related. The adjustment with P(norm) simultaneously accounts for two sources of correlations of the test statistics: (1) LD among genetic markers (2) dependence among genotypes across related individuals. Using simulated data based on the International HapMap Project, we demonstrate that it has better control of type I error and is more powerful than some of the recently developed methods. We apply the method to a genome-wide association study of alcoholism in the GAW 14 COGA data set and detect genome-wide significant association.
全基因组关联研究通常测试大量的遗传变异与性状值的关联。众所周知，附近标记之间的连锁不平衡（LD）往往会导致关联检验之间的相关性。如果不能正确调整多重比较，可能会导致假阳性结果或丢失真正的阳性信号。在存在 LD 的情况下，Bonferroni 校正通常是保守的。尽管置换程序已被广泛用于调整相关测试，但在包含相关个体的病例对照样本中不适用。对于相关个体，亲属基因型之间的相关性也可能导致测试之间的相关性。我们提出了一种新的方法 P(norm)，用于校正病例对照关联研究中存在一些个体相关时的多重假设检验。P(norm)的调整同时考虑了两个来源的测试统计数据的相关性：（1）遗传标记之间的 LD；（2）相关个体之间基因型的依赖性。使用基于国际 HapMap 项目的模拟数据，我们证明它具有更好的控制 I 型错误的能力，并且比一些最近开发的方法更强大。我们将该方法应用于 GAW 14 COGA 数据集的酒精中毒全基因组关联研究中，并检测到全基因组显著关联。
