Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies.
Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies.
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DOI：
10.1038/s41588-018-0184-y
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发表时间：
2018-09
期刊：
影响因子：
30.8
通讯作者：
Lee S
中科院分区：
文献类型：
--
作者：
Zhou W;Nielsen JB;Fritsche LG;Dey R;Gabrielsen ME;Wolford BN;LeFaive J;VandeHaar P;Gagliano SA;Gifford A;Bastarache LA;Wei WQ;Denny JC;Lin M;Hveem K;Kang HM;Abecasis GR;Willer CJ;Lee S
在针对大型生物库中数千种表型的全基因组关联研究（GWAS）中，大多数二元性状的病例数明显少于对照组。线性混合模型和最近提出的逻辑混合模型这两种广泛使用的方法在分析不平衡的病例对照表型时表现不佳，产生较大的 I 类错误率。在这里，我们提出了一种可扩展且准确的广义混合模型关联测试，该测试使用鞍点近似来校准分数测试统计量的分布。即使病例对照比极其不平衡，这种方法（SAIGE）也能提供准确的 p 值。它利用最先进的优化策略来降低计算成本，因此适用于大型生物库的数千种表型的 GWAS。通过对英国生物银行 408,961 个英国欧洲血统白人样本的超过 1400 个二元表型的分析，我们表明 SAIGE 可以有效地分析大样本数据，控制不平衡的病例对照比和样本相关性。
In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, linear mixed model and the recently proposed logistic mixed model, perform poorly - producing large type I error rates - in the analysis of unbalanced case-control phenotypes. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation to calibrate the distribution of score test statistics. This method, SAIGE, provides accurate p-values even when case-control ratios are extremely unbalanced. It utilizes state-of-art optimization strategies to reduce computational cost, and hence is applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 white British European-ancestry samples for >1400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.
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影响因子：
30.8
作者：
Zhou, Xiang;Stephens, Matthew
通讯作者：
Stephens, Matthew
影响因子：
46.9
作者：
通讯作者：
--
影响因子：
30.8
作者：
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.
通讯作者：
Neale, Benjamin M.
影响因子：
30.8
作者：
Loh, Po-Ru;Tucker, George;Bulik-Sullivan, Brendan K.;Vilhjalmsson, Bjarni J.;Finucane, Hilary K.;Salem, Rany M.;Chasman, Daniel I.;Ridker, Paul M.;Neale, Benjamin M.;Berger, Bonnie;Patterson, Nick;Price, Alkes L.
通讯作者：
Price, Alkes L.
影响因子：
2.7
作者：
Kuonen, D
通讯作者：
Kuonen, D