Faculty, Staff and Student Publications
Publication Date
4-7-2022
Journal
American Journal of Human Genetics
Abstract
One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB50k) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB50k, demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB50k analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.
Keywords
Cardiovascular Diseases, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Life Style, Polymorphism, Single Nucleotide, Transcriptome, PrediXcan, TWAS, ancestrally diverse, gene expression, cardiometabolic traits, PAGE
DOI
10.1016/j.ajhg.2022.02.013
PMID
35263625
PMCID
PMC9069067
PubMedCentral® Posted Date
3-8-2022
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Graphical Abstract