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

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.

SUBMITTER: Amar D

PROVIDER: S-EPMC7806647 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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

Amar David D Sinnott-Armstrong Nasa N Ashley Euan A EA Rivas Manuel A MA

Nature communications 20210113 1

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 Separatio ...[more]

PMID: 33441555

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Project description:BackgroundIndividuals with osteoarthritis present with comorbidities, and the potential causal associations remain incompletely elucidated. The present study undertook a large-scale investigation about the causality between osteoarthritis and variable traits, using the summary-level data of genome-wide association studies (GWAS).MethodsThe present study included the summary-level GWS data of knee osteoarthritis, hip osteoarthritis, hip or knee osteoarthritis, hand osteoarthritis, and other 1355 traits. Genetic correlation analysis was conducted between osteoarthritis and other traits through cross-trait bivariate linkage disequilibrium score regression. Subsequently, latent causal variable analysis was performed to explore the causal association when there was a significant genetic correlation. Genetic correlation and latent causal variable analysis were conducted on the Complex Traits Genomics Virtual Lab platform ( https://vl.genoma.io/ ).ResultsWe found 133 unique phenotypes showing causal relationships with osteoarthritis. Our results confirmed several well-established risk factors of osteoarthritis, such as obesity, weight, BMI, and meniscus derangement. Additionally, our findings suggested putative causal links between osteoarthritis and multiple factors. Socioeconomic determinants such as occupational exposure to dust and diesel exhaust, extended work hours exceeding 40 per week, and unemployment status were implicated. Furthermore, our analysis revealed causal associations with cardiovascular and metabolic disorders, including heart failure, deep venous thrombosis, type 2 diabetes mellitus, and elevated cholesterol levels. Soft tissue and musculoskeletal disorders, such as hallux valgus, internal derangement of the knee, and spondylitis, were also identified to be causally related to osteoarthritis. The study also identified the putative causal associations of osteoarthritis with digestive and respiratory diseases, such as Barrett's esophagus, esophagitis, and asthma, as well as psychiatric conditions including panic attacks and manic or hyperactive episodes. Additionally, we observed osteoarthritis causally related to pharmacological treatments, such as the use of antihypertensive medications, anti-asthmatic drugs, and antidepressants.ConclusionOur study uncovered a wide range of traits causally associated with osteoarthritis. Further studies are needed to validate and illustrate the detailed mechanism of those causal associations.

Dataset Information

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

Publications

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

Similar Datasets

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