Bulik-Sullivan Brendan K, Loh Po-Ru, Finucane Hilary K, Ripke Stephan, Yang Jian, Patterson Nick, Daly Mark J, Price Alkes L, Neale Benjamin M
1] Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. [2] Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA. [3] Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
1] Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. [2] Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Nat Genet. 2015 Mar;47(3):291-5. doi: 10.1038/ng.3211. Epub 2015 Feb 2.
Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
多基因性（许多微小的遗传效应）和混杂偏倚，如隐秘相关性和群体分层，都可能在全基因组关联研究（GWAS）中导致检验统计量的分布膨胀。然而，目前的方法无法区分真正的多基因信号导致的膨胀和偏倚。我们开发了一种方法，即连锁不平衡分数回归（LD Score regression），通过检查检验统计量与连锁不平衡（LD）之间的关系来量化各自的贡献。与基因组对照相比，LD Score回归截距可用于估计一个更强大、更准确的校正因子。我们发现有力证据表明，在许多大样本量的GWAS中，多基因性是检验统计量膨胀的主要原因。
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