Michigan State University, Department of Epidemiology & Biostatistics, East Lansing, MI, USA.
Michigan State University, Department of Statistics & Probability, East Lansing, MI, USA.
Eur J Hum Genet. 2023 Mar;31(3):313-320. doi: 10.1038/s41431-022-01135-5. Epub 2022 Jul 19.
Modern GWAS studies use an enormous sample size and ultra-high density SNP genotypes. These conditions reduce the mapping resolution of marginal association tests-the method most often used in GWAS. Multi-locus Bayesian Variable Selection (BVS) offers a one-stop solution for powerful and precise mapping of risk variants and polygenic risk score (PRS) prediction. We show (with an extensive simulation) that multi-locus BVS methods can achieve high power with a low false discovery rate and a much better mapping resolution than marginal association tests. We demonstrate the performance of BVS for mapping and PRS prediction using data from blood biomarkers from the UK-Biobank (~300,000 samples and ~5.5 million SNPs). The article is accompanied by open-source R-software that implement the methods used in the study and scales to biobank-sized data.
现代全基因组关联研究使用了巨大的样本量和超高密度 SNP 基因型。这些条件降低了边缘关联测试的映射分辨率-这是 GWAS 中最常用的方法。多基因座贝叶斯变量选择 (BVS) 为风险变异和多基因风险评分 (PRS) 预测的强大而精确的映射提供了一站式解决方案。我们通过广泛的模拟表明，多基因座 BVS 方法可以以低错误发现率和比边缘关联测试更好的映射分辨率实现高功效。我们使用来自英国生物银行的血液生物标志物数据（约 30 万样本和 550 万 SNPs）展示了 BVS 在映射和 PRS 预测方面的性能。本文还提供了开源 R 软件，其中实现了研究中使用的方法，并可以扩展到生物库规模的数据。
