RT Journal Article
SR Electronic
T1 MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 078972
DO 10.1101/078972
A1 Gibran Hemani
A1 Jie Zheng
A1 Kaitlin H Wade
A1 Charles Laurin
A1 Benjamin Elsworth
A1 Stephen Burgess
A1 Jack Bowden
A1 Ryan Langdon
A1 Vanessa Tan
A1 James Yarmolinsky
A1 Hashem A. Shihab
A1 Nicholas Timpson
A1 David M Evans
A1 Caroline Relton
A1 Richard M Martin
A1 George Davey Smith
A1 Tom R Gaunt
A1 Philip C Haycock
A1 Nicole Soranzo
A1 David A van Heel
A1 Yukinori Okada
A1 Clara S. Tang
A1 Merce Garcia-Barcelo
A1 Paul KH Tam
A1 Kaya Kvarme Jacobsen
A1 Gregory T Jones
A1 Matthew J Bown
A1 Omar Albagha
A1 Stuart H. Ralston
A1 Andre Franke
A1 Annegret Fischer
A1 David Ellinghaus
A1 Asta Försti
A1 Hauke Thomsen
A1 Stefano Landi
A1 Heather Cordell
A1 Ani W Manichaikul
A1 R Graham Barr
A1 Jeffrey E Lee
YR 2016
UL http://biorxiv.org/content/early/2016/12/16/078972.abstract
AB 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.