On the cross-population generalizability of gene expression prediction models

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...

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Bibliographic Details
Published in:PLoS genetics Vol. 16; no. 8; p. e1008927
Main Authors: Keys, Kevin L., Mak, Angel C. Y., White, Marquitta J., Eckalbar, Walter L., Dahl, Andrew W., Mefford, Joel, Mikhaylova, Anna V., Contreras, María G., Elhawary, Jennifer R., Eng, Celeste, Hu, Donglei, Huntsman, Scott, Oh, Sam S., Salazar, Sandra, Lenoir, Michael A., Ye, Jimmie C., Thornton, Timothy A., Zaitlen, Noah, Burchard, Esteban G., Gignoux, Christopher R.
Format: Journal Article
Language:English
Published: United States Public Library of Science 14.08.2020
Public Library of Science (PLoS)
Subjects:
Asthma
Bioengineering
Biology and Life Sciences
Black or African American - genetics
Datasets
Gene expression
Gene Expression Profiling - methods
Gene Expression Profiling - standards
Genetic control
Genome-Wide Association Study - methods
Genome-Wide Association Study - standards
Genomes
Genotype & phenotype
Genotypes
Humans
Methods
Models, Genetic
People and Places
Physical Sciences
Population
Population genetics
Precision medicine
Prediction models
Quantitative Trait Loci
Reference Standards
Research and Analysis Methods
Ribonucleic acid
RNA
RNA sequencing
RNA-Seq - methods
RNA-Seq - standards
Software
Supervision
Transcriptome
United States
San Francisco California
California
United States > US
Colorado
ISSN:1553-7404, 1553-7390, 1553-7404
Online Access:Get full text
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Summary: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.
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We have read the journal's policy and one author of this manuscript (Chris Gignoux) has the following competing interests: ownership of stock in 23andMe. The remaining authors have declared that no competing interests exist.
These authors are joint senior authors on this work.
ISSN:1553-7404
1553-7390
1553-7404
DOI:10.1371/journal.pgen.1008927