On the cross-population generalizability of gene expression prediction models

Autoři: Kevin L. Keys aff001;  Angel C. Y. Mak aff001;  Marquitta J. White aff001;  Walter L. Eckalbar aff001;  Andrew W. Dahl aff001;  Joel Mefford aff001;  Anna V. Mikhaylova aff003;  María G. Contreras aff001;  Jennifer R. Elhawary aff001;  Celeste Eng aff001;  Donglei Hu aff001;  Scott Huntsman aff001;  Sam S. Oh aff001;  Sandra Salazar aff001;  Michael A. Lenoir aff005;  Jimmie C. Ye aff006;  Timothy A. Thornton aff003;  Noah Zaitlen aff008;  Esteban G. Burchard aff001;  Christopher R. Gignoux aff009
Působiště autorů: Department of Medicine, University of California, San Francisco, California, United States of America aff001;  Berkeley Institute for Data Science, University of California, Berkeley, California, United States of America aff002;  Department of Biostatistics, University of Washington, Seattle, Washington, United States of America aff003;  San Francisco State University, San Francisco, California, United States of America aff004;  Bay Area Pediatrics, Oakland, California, United States of America aff005;  Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America aff006;  Department of Bioengineering and Therapeutic Biosciences, University of California, San Francisco, California, United States of America aff007;  Department of Neurology, University of California, Los Angeles, California, United States of America aff008;  Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America aff009;  Department of Biostatistics and Informatics, School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America aff010
Vyšlo v časopise: On the cross-population generalizability of gene expression prediction models. PLoS Genet 16(8): e32767. doi:10.1371/journal.pgen.1008927
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008927


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.

Klíčová slova:

African American people – Europe – Forecasting – Gene expression – Gene prediction – Phenotypes – Population genetics – Serial analysis of gene expression


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