Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Nat Genet. 2010 Apr;42(4):348-54. doi: 10.1038/ng.548. Epub 2010 Mar 7.
Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.
虽然全基因组关联研究 (GWAS) 已经确定了许多与复杂性状相关的基因座，但研究样本中遗传相关性的不精确建模可能导致检验统计量的大量膨胀，并可能产生虚假关联。方差成分方法，如有效混合模型关联 (EMMA)，可以通过显式考虑个体之间的成对相关性，使用高密度标记来模拟表型分布，从而纠正广泛的样本结构；但这种方法在计算上是不切实际的。我们在这里报告了一种方差成分方法，该方法在可公开获取的软件 EMMA eXpedited (EMMAX) 中实现，该方法将分析大型 GWAS 数据集的计算时间从数年缩短到数小时。我们将该方法应用于两个人类 GWAS 数据集，对来自芬兰北部出生队列的十个定量性状和来自威康信托基金会病例对照联合会的七个常见疾病进行关联分析。我们发现，EMMAX 在纠正样本结构方面优于主成分分析和基因组控制。
