%0 Journal Article %J Am J Hum Genet %D 2020 %T A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits. %A Dahl, Andy %A Nguyen, Khiem %A Cai, Na %A Gandal, Michael J %A Flint, Jonathan %A Zaitlen, Noah %K Adult %K Animals %K Computer Simulation %K Female %K Gene-Environment Interaction %K Genetic Markers %K Genome-Wide Association Study %K Humans %K Male %K Mental Disorders %K Middle Aged %K Models, Genetic %K Multifactorial Inheritance %K Phenomics %K Phenotype %K Rats %X

Gene-environment interactions (GxE) can be fundamental in applications ranging from functional genomics to precision medicine and is a conjectured source of substantial heritability. However, unbiased methods to profile GxE genome-wide are nascent and, as we show, cannot accommodate general environment variables, modest sample sizes, heterogeneous noise, and binary traits. To address this gap, we propose a simple, unifying mixed model for gene-environment interaction (GxEMM). In simulations and theory, we show that GxEMM can dramatically improve estimates and eliminate false positives when the assumptions of existing methods fail. We apply GxEMM to a range of human and model organism datasets and find broad evidence of context-specific genetic effects, including GxSex, GxAdversity, and GxDisease interactions across thousands of clinical and molecular phenotypes. Overall, GxEMM is broadly applicable for testing and quantifying polygenic interactions, which can be useful for explaining heritability and invaluable for determining biologically relevant environments.

%B Am J Hum Genet %V 106 %P 71-91 %8 2020 01 02 %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/31901249?dopt=Abstract %R 10.1016/j.ajhg.2019.11.015 %0 Journal Article %J Genetics %D 2019 %T Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives. %A Dahl, Andy %A Guillemot, Vincent %A Mefford, Joel %A Aschard, Hugues %A Zaitlen, Noah %K Animals %K Genome-Wide Association Study %K Humans %K Models, Genetic %K Phenotype %K Principal Component Analysis %K Quantitative Trait Loci %K Reproducibility of Results %X

High-throughput measurements of molecular phenotypes provide an unprecedented opportunity to model cellular processes and their impact on disease. These highly structured datasets are usually strongly confounded, creating false positives and reducing power. This has motivated many approaches based on principal components analysis (PCA) to estimate and correct for confounders, which have become indispensable elements of association tests between molecular phenotypes and both genetic and nongenetic factors. Here, we show that these correction approaches induce a bias, and that it persists for large sample sizes and replicates out-of-sample. We prove this theoretically for PCA by deriving an analytic, deterministic, and intuitive bias approximation. We assess other methods with realistic simulations, which show that perturbing any of several basic parameters can cause false positive rate (FPR) inflation. Our experiments show the bias depends on covariate and confounder sparsity, effect sizes, and their correlation. Surprisingly, when the covariate and confounder have [Formula: see text], standard two-step methods all have [Formula: see text]-fold FPR inflation. Our analysis informs best practices for confounder correction in genomic studies, and suggests many false discoveries have been made and replicated in some differential expression analyses.

%B Genetics %V 211 %P 1179-1189 %8 2019 04 %G eng %N 4 %1 https://www.ncbi.nlm.nih.gov/pubmed/30692194?dopt=Abstract %R 10.1534/genetics.118.301768