Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Nat Genet. 2018 Jul;50(7):906-908. doi: 10.1038/s41588-018-0144-6.
Biobank-based genome-wide association studies are enabling exciting insights in complex trait genetics, but much uncertainty remains over best practices for optimizing statistical power and computational efficiency in GWAS while controlling confounders. Here, we introduce a much faster version of our BOLT-LMM Bayesian mixed model association method—capable of running analyses of the full UK Biobank cohort in a few days on a single compute node—and show that it produces highly powered, robust test statistics when run on all 459K European samples (retaining related individuals). When used to conduct a GWAS for height in UK Biobank, BOLT-LMM achieved power equivalent to linear regression on 650K samples—a 93% increase in effective sample size versus the common practice of analyzing unrelated British samples using linear regression (UK Biobank documentation; Bycroft et al. bioRxiv). Across a broader set of 23 highly heritable traits, the total number of independent GWAS loci detected increased from 5,839 to 10,759, an 84% increase. We recommend the use of BOLT-LMM (retaining related individuals) for biobank-scale analyses, and we have publicly released BOLT-LMM summary association statistics for the 23 traits analyzed as a resource for all researchers.
基于生物库的全基因组关联研究正在为复杂性状遗传学提供令人兴奋的见解，但在优化 GWAS 的统计功效和计算效率同时控制混杂因素方面，仍存在许多不确定性。在这里，我们介绍了我们的 BOLT-LMM 贝叶斯混合模型关联方法的一个更快版本——能够在单个计算节点上几天内运行整个英国生物库队列的分析——并表明当在所有 459K 个欧洲样本（保留相关个体）上运行时，它会产生高功效、稳健的检验统计量。当用于在英国生物库中进行身高的 GWAS 时，BOLT-LMM 实现了与在 650K 个样本上进行线性回归相当的功效——与使用线性回归分析不相关的英国样本的常见做法相比，有效样本量增加了 93%（英国生物库文档；Bycroft 等人，bioRxiv）。在更广泛的 23 个高度遗传的特征中，检测到的独立 GWAS 位点的总数从 5839 个增加到 10759 个，增加了 84%。我们建议在生物库规模的分析中使用 BOLT-LMM（保留相关个体），并且我们已经公开发布了 23 个分析特征的 BOLT-LMM 汇总关联统计数据，作为所有研究人员的资源。
