Open AccessMendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics
Author(s) -
Jean Morrison,
Nicholas W. Knoblauch,
Joseph Marcus,
Matthew Stephens,
Xin He
Publication year - 2020
Publication title -
nature genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 18.861
H-Index - 573
eISSN - 1546-1718
pISSN - 1061-4036
DOI - 10.1038/s41588-020-0631-4
Subject(s) - mendelian randomization , pleiotropy , biology , false positive paradox , genome wide association study , uncorrelated , genetics , mendelian inheritance , genetic association , computational biology , evolutionary biology , statistics , genetic variants , single nucleotide polymorphism , phenotype , gene , mathematics , genotype
Mendelian randomization (MR) is a valuable tool for detecting causal effects by using genetic variant associations. Opportunities to apply MR are growing rapidly with the increasing number of genome-wide association studies (GWAS). However, existing MR methods rely on strong assumptions that are often violated, leading to false positives. Correlated horizontal pleiotropy, which arises when variants affect both traits through a heritable shared factor, remains a particularly challenging problem. We propose a new MR method, Causal Analysis Using Summary Effect estimates (CAUSE), that accounts for correlated and uncorrelated horizontal pleiotropic effects. We demonstrate, in simulations, that CAUSE avoids more false positives induced by correlated horizontal pleiotropy than other methods. Applied to traits studied in recent GWAS studies, we find that CAUSE detects causal relationships that have strong literature support and avoids identifying most unlikely relationships. Our results suggest that shared heritable factors are common and may lead to many false positives using alternative methods.