Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA.
Nat Rev Genet. 2010 Jul;11(7):459-63. doi: 10.1038/nrg2813.
Genome-wide association (GWA) studies are an effective approach for identifying genetic variants associated with disease risk. GWA studies can be confounded by population stratification--systematic ancestry differences between cases and controls--which has previously been addressed by methods that infer genetic ancestry. Those methods perform well in data sets in which population structure is the only kind of structure present but are inadequate in data sets that also contain family structure or cryptic relatedness. Here, we review recent progress on methods that correct for stratification while accounting for these additional complexities.
全基因组关联（GWA）研究是一种有效的方法，可以识别与疾病风险相关的遗传变异。GWA 研究可能受到群体分层的影响——病例和对照之间存在系统的祖先差异——这以前是通过推断遗传祖先的方法来解决的。这些方法在数据集中表现良好，其中群体结构是唯一存在的结构，但在也包含家庭结构或隐性相关性的数据集中是不够的。在这里，我们回顾了最近在纠正分层的同时考虑这些额外复杂性的方法方面的进展。
