基于两样本汇总数据的孟德尔随机化中选择偏倚的影响。
Effect of selection bias on two sample summary data based Mendelian randomization.
机构信息
Department of Biostatistics, The University of Iowa, Iowa City, 52242, USA.
Lieber Institute for Brain Development, Johns Hopkins School of Medicine, Baltimore, 21205, USA.
出版信息
Sci Rep. 2021 Apr 7;11(1):7585. doi: 10.1038/s41598-021-87219-6.
Abstract
Mendelian randomization (MR) is becoming more and more popular for inferring causal relationship between an exposure and a trait. Typically, instrument SNPs are selected from an exposure GWAS based on their summary statistics and the same summary statistics on the selected SNPs are used for subsequent analyses. However, this practice suffers from selection bias and can invalidate MR methods, as showcased via two popular methods: the summary data-based MR (SMR) method and the two-sample MR Steiger method. The SMR method is conservative while the MR Steiger method can be either conservative or liberal. A simple and yet more powerful alternative to SMR is proposed.
摘要
孟德尔随机化（MR）越来越受欢迎，可用于推断暴露因素与特征之间的因果关系。通常，工具 SNPs 是根据暴露因素 GWAS 的汇总统计数据从其中选择的，并且所选 SNPs 的相同汇总统计数据用于后续分析。然而，这种做法存在选择偏差，并可能使 MR 方法无效，如两种流行的方法：基于汇总数据的 MR（SMR）方法和两样本 MR Steiger 方法所展示的那样。SMR 方法保守，而 MR Steiger 方法可以保守或宽松。因此，提出了一种简单但更强大的 SMR 替代方法。
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