PDF(Pubmed)
Abstract：
Mendelian randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods often rely on strong assumptions, resulting in many false-positive findings. To relax MR assumptions, ongoing research has been primarily focused on accounting for confounding due to pleiotropy. Here, we show that sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap. We propose a unified MR approach, MR-APSS, which 1) accounts for pleiotropy and sample structure simultaneously by leveraging genome-wide information; and 2) allows the inclusion of more genetic variants with moderate effects as instrument variables (IVs) to improve statistical power without inflating type I errors. We first evaluated MR-APSS using comprehensive simulations and negative controls and then applied MR-APSS to study the causal relationships among a collection of diverse complex traits. The results suggest that MR-APSS can better identify plausible causal relationships with high reliability. In particular, MR-APSS can perform well for highly polygenic traits, where the IV strengths tend to be relatively weak and existing summary-level MR methods for causal inference are vulnerable to confounding effects.
摘要：
孟德尔随机化（MR）是使用全基因组关联研究（GWAS）的汇总统计数据推断各种性状之间因果关系的有价值的工具。现有的汇总级MR方法通常依赖于强假设，导致许多假阳性结果。为了放松MR的假设，正在进行的研究主要集中在考虑由于多效性造成的混杂因素。这里,我们发现样本结构是另一个主要的混杂因素，包括人口分层，神秘的亲密关系，和样本重叠。我们提出了一种统一的MR方法，MR-APSS,其中1）通过利用全基因组信息同时解释多效性和样本结构；2）允许包含更多具有中等影响的遗传变异作为仪器变量（IV），以提高统计能力而不增加I型错误。我们首先使用综合模拟和阴性对照评估MR-APSS，然后应用MR-APSS研究各种复杂性状之间的因果关系。结果表明，MR-APSS可以更好地识别合理的因果关系，具有很高的可靠性。特别是,MR-APSS对高度多基因性状表现良好，其中IV强度往往相对较弱，并且用于因果推断的现有摘要级MR方法容易受到混杂效应的影响。
