Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks.

TitleGraphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks.
Publication TypeJournal Article
Year of Publication2021
AuthorsAmar, D, Sinnott-Armstrong, N, Ashley, EA, Rivas, MA
JournalNat Commun
Volume12
Issue1
Pagination350
Date Published2021 01 13
ISSN2041-1723
KeywordsBiological Specimen Banks, Cardiovascular Diseases, Causality, Computer Simulation, Gene Regulatory Networks, Genetic Pleiotropy, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Mendelian Randomization Analysis, Models, Theoretical, Multifactorial Inheritance, Phenotype, Risk Factors
Abstract

Causal inference via Mendelian randomization requires making strong assumptions about horizontal pleiotropy, where genetic instruments are connected to the outcome not only through the exposure. Here, we present causal Graphical Analysis Using Genetics (cGAUGE), a pipeline that overcomes these limitations using instrument filters with provable properties. This is achievable by identifying conditional independencies while examining multiple traits. cGAUGE also uses ExSep (Exposure-based Separation), a novel test for the existence of causal pathways that does not require selecting instruments. In simulated data we illustrate how cGAUGE can reduce the empirical false discovery rate by up to 30%, while retaining the majority of true discoveries. On 96 complex traits from 337,198 subjects from the UK Biobank, our results cover expected causal links and many new ones that were previously suggested by correlation-based observational studies. Notably, we identify multiple risk factors for cardiovascular disease, including red blood cell distribution width.

DOI10.1038/s41467-020-20516-2
Alternate JournalNat Commun
PubMed ID33441555
PubMed Central IDPMC7806647
Grant ListMC_PC_17228 / MR / Medical Research Council / United Kingdom
MC_QA137853 / MR / Medical Research Council / United Kingdom
R01 HG010140 / HG / NHGRI NIH HHS / United States
U01 HG009080 / HG / NHGRI NIH HHS / United States