@article {100, title = {On the cross-population generalizability of gene expression prediction models.}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 08}, pages = {e1008927}, abstract = {

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction.

}, keywords = {African Americans, Gene Expression Profiling, Genome-Wide Association Study, Humans, Models, Genetic, Quantitative Trait Loci, Reference Standards, RNA-Seq, Transcriptome}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008927}, author = {Keys, Kevin L and Mak, Angel C Y and White, Marquitta J and Eckalbar, Walter L and Dahl, Andrew W and Mefford, Joel and Mikhaylova, Anna V and Contreras, Mar{\'\i}a G and Elhawary, Jennifer R and Eng, Celeste and Hu, Donglei and Huntsman, Scott and Oh, Sam S and Salazar, Sandra and LeNoir, Michael A and Ye, Jimmie C and Thornton, Timothy A and Zaitlen, Noah and Burchard, Esteban G and Gignoux, Christopher R} } @article {86, title = {A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits.}, journal = {Am J Hum Genet}, volume = {106}, year = {2020}, month = {2020 01 02}, pages = {71-91}, abstract = {

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.

}, keywords = {Adult, Animals, Computer Simulation, Female, Gene-Environment Interaction, Genetic Markers, Genome-Wide Association Study, Humans, Male, Mental Disorders, Middle Aged, Models, Genetic, Multifactorial Inheritance, Phenomics, Phenotype, Rats}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.11.015}, author = {Dahl, Andy and Nguyen, Khiem and Cai, Na and Gandal, Michael J and Flint, Jonathan and Zaitlen, Noah} } @article {59, title = {Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives.}, journal = {Genetics}, volume = {211}, year = {2019}, month = {2019 04}, pages = {1179-1189}, abstract = {

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.

}, keywords = {Animals, Genome-Wide Association Study, Humans, Models, Genetic, Phenotype, Principal Component Analysis, Quantitative Trait Loci, Reproducibility of Results}, issn = {1943-2631}, doi = {10.1534/genetics.118.301768}, author = {Dahl, Andy and Guillemot, Vincent and Mefford, Joel and Aschard, Hugues and Zaitlen, Noah} } @article {21, title = {Covariate selection for association screening in multiphenotype genetic studies.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 Dec}, pages = {1789-1795}, abstract = {

Testing for associations in big data faces the problem of multiple comparisons, wherein true signals are difficult to detect on the background of all associations queried. This difficulty is particularly salient in human genetic association studies, in which phenotypic variation is often driven by numerous variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. Although successful, this approach does not leverage the environmental and genetic factors shared among the multiple phenotypes collected in contemporary cohorts. Here we developed covariates for multiphenotype studies (CMS), an approach that improves power when correlated phenotypes are measured on the same samples. Our analyses of real and simulated data provide direct evidence that correlated phenotypes can be used to achieve increases in power to levels often surpassing the power gained by a twofold increase in sample size.

}, keywords = {Algorithms, Genetic Association Studies, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Models, Genetic, Multivariate Analysis, Phenotype, Reproducibility of Results, Sample Size}, issn = {1546-1718}, doi = {10.1038/ng.3975}, author = {Aschard, Hugues and Guillemot, Vincent and Vilhjalmsson, Bjarni and Patel, Chirag J and Skurnik, David and Ye, Chun J and Wolpin, Brian and Kraft, Peter and Zaitlen, Noah} }