Dataset Information

On the cross-population generalizability of gene expression prediction models.

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.

SUBMITTER: Keys KL

PROVIDER: S-EPMC7449671 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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On the cross-population generalizability of gene expression prediction models.

Keys Kevin L KL Mak Angel C Y ACY White Marquitta J MJ Eckalbar Walter L WL Dahl Andrew W AW Mefford Joel J Mikhaylova Anna V AV Contreras María G MG Elhawary Jennifer R JR Eng Celeste C Hu Donglei D Huntsman Scott S Oh Sam S SS Salazar Sandra S Lenoir Michael A MA Ye Jimmie C JC Thornton Timothy A TA Zaitlen Noah N Burchard Esteban G EG Gignoux Christopher R CR

PLoS genetics 20200814 8

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 mode ...[more]

PMID: 32797036

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Dataset Information

On the cross-population generalizability of gene expression prediction models.

Publications

On the cross-population generalizability of gene expression prediction models.

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