biostudies-literatureHu XResearch Grants Council, University Grants CommitteeChinese University of Hong KongInnovation and Technology Commission - Hong KongHong Kong University of Science and Technologye2106858119https://www.ebi.ac.uk/biostudies/studies/S-EPMC9282238biostudies-literatureUnknown119(28)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 instrumeProceedings of the National Academy of Sciences of the United States of AmericaMendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics.PMC928223816301419405342316307818493018116308120PRP/029/19FX2430141912303618R9405405336014301120Peng HHu XLin ZWan XWang YZhao JYang CZhao HfalseMendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics.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 instrume2022-01-01T00:00:00Z2022 Jul2025-04-04T10:59:10.314Z2025-04-04T10:59:10.314ZS-EPMC92822383578705010.1073/pnas.2106858119