TY - JOUR T1 - MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations JF - bioRxiv DO - 10.1101/078972 SP - 078972 AU - Gibran Hemani AU - Jie Zheng AU - Kaitlin H Wade AU - Charles Laurin AU - Benjamin Elsworth AU - Stephen Burgess AU - Jack Bowden AU - Ryan Langdon AU - Vanessa Tan AU - James Yarmolinsky AU - Hashem A. Shihab AU - Nicholas Timpson AU - David M Evans AU - Caroline Relton AU - Richard M Martin AU - George Davey Smith AU - Tom R Gaunt AU - Philip C Haycock A2 - Soranzo, Nicole van Heel, David A Okada, Yukinori Tang, Clara S. Garcia-Barcelo, Merce Tam, Paul KH Jacobsen, Kaya Kvarme Jones, Gregory T Bown, Matthew J Albagha, Omar Ralston, Stuart H. Franke, Andre Fischer, Annegret Ellinghaus, David Försti, Asta Thomsen, Hauke Landi, Stefano Cordell, Heather Manichaikul, Ani W Barr, R Graham Lee, Jeffrey E Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/12/16/078972.abstract N2 - Published genetic associations can be used to infer causal relationships between phenotypes, bypassing the need for individual-level genotype or phenotype data. We have curated complete summary data from 1094 genome-wide association studies (GWAS) on diseases and other complex traits into a centralised database, and developed an analytical platform that uses these data to perform Mendelian randomization (MR) tests and sensitivity analyses (MR-Base, http://www.mrbase.org). Combined with curated data of published GWAS hits for phenomic measures, the MR-Base platform enables millions of potential causal relationships to be evaluated. We use the platform to predict the impact of lipid lowering on human health. While our analysis provides evidence that reducing LDL-cholesterol, lipoprotein(a) or triglyceride levels reduce coronary disease risk, it also suggests causal effects on a number of other non-vascular outcomes, indicating potential for adverse-effects or drug repositioning of lipid-lowering therapies. ER -