%0 Journal Article %J Nat Commun %D 2021 %T Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks. %A Amar, David %A Sinnott-Armstrong, Nasa %A Ashley, Euan A %A Rivas, Manuel A %K Biological Specimen Banks %K Cardiovascular Diseases %K Causality %K Computer Simulation %K Gene Regulatory Networks %K Genetic Pleiotropy %K Genetic Variation %K Genome-Wide Association Study %K Genotype %K Humans %K Mendelian Randomization Analysis %K Models, Theoretical %K Multifactorial Inheritance %K Phenotype %K Risk Factors %X

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.

%B Nat Commun %V 12 %P 350 %8 2021 01 13 %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/33441555?dopt=Abstract %R 10.1038/s41467-020-20516-2 %0 Journal Article %J Nat Commun %D 2018 %T Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study. %A DeBoever, Christopher %A Tanigawa, Yosuke %A Lindholm, Malene E %A McInnes, Greg %A Lavertu, Adam %A Ingelsson, Erik %A Chang, Chris %A Ashley, Euan A %A Bustamante, Carlos D %A Daly, Mark J %A Rivas, Manuel A %K Databases, Nucleic Acid %K Genome-Wide Association Study %K Humans %K Phenotype %K Proteins %K Sequence Deletion %K United Kingdom %X

Protein-truncating variants can have profound effects on gene function and are critical for clinical genome interpretation and generating therapeutic hypotheses, but their relevance to medical phenotypes has not been systematically assessed. Here, we characterize the effect of 18,228 protein-truncating variants across 135 phenotypes from the UK Biobank and find 27 associations between medical phenotypes and protein-truncating variants in genes outside the major histocompatibility complex. We perform phenome-wide analyses and directly measure the effect in homozygous carriers, commonly referred to as "human knockouts," across medical phenotypes for genes implicated as being protective against disease or associated with at least one phenotype in our study. We find several genes with strong pleiotropic or non-additive effects. Our results illustrate the importance of protein-truncating variants in a variety of diseases.

%B Nat Commun %V 9 %P 1612 %8 2018 04 24 %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/29691392?dopt=Abstract %R 10.1038/s41467-018-03910-9