Credible set is sensitive to imputation quality and missing variants

Yanyu Liang, 23andMe Research Team, Adam Auton, Xin Wang
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Abstract

Bayesian fine-mapping to obtain credible sets has been widely applied post GWAS to pinpoint causal variants. The calculation of credible sets generally assumes that all variants have been equally well genotyped, which is often not the case when a GWAS has been run on imputed data. In this work, we investigate the behavior of credible sets in imputed datasets utilizing 'held out' genotyped variants to measure accuracy. We show, via simulation, that: i) the coverage of credible sets decreases when using imputed variants in GWAS; ii) rare causal variants often fail to be tagged in credible sets when they are not present in the GWAS variant set. We develop a reweighting approach to take imputation quality into account during fine-mapping that only requires summary statistics, and demonstrate the approach with real data.
可信集合对估算质量和缺失变异很敏感
贝叶斯精细映射法获得可信集的方法已被广泛应用于 GWAS 后,以确定因果变异。可信集的计算通常假定所有变异的基因分型都一样好,而当 GWAS 在估算数据上运行时,情况往往并非如此。在这项工作中,我们利用 "保留 "的基因分型变异来衡量准确性,从而研究了可信集在估算数据集中的行为。通过模拟,我们发现:i)当在 GWAS 中使用归类变异时,可信集的覆盖率会降低;ii)当稀有因果变异不存在于 GWAS 变异集时,可信集往往无法标记这些变异。我们开发了一种重新加权方法,在精细绘图过程中考虑估算质量,这种方法只需要汇总统计数据,并用真实数据演示了这种方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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