Functionally oriented analysis of cardiometabolic traits in a trans-ethnic sample.

TitleFunctionally oriented analysis of cardiometabolic traits in a trans-ethnic sample.
Publication TypeJournal Article
Year of Publication2019
AuthorsPetty, LE, Highland, HM, Gamazon, ER, Hu, H, Karhade, M, Chen, H-H, de Vries, PS, Grove, ML, Aguilar, D, Bell, GI, Huff, CD, Hanis, CL, Doddapaneni, H, Munzy, DM, Gibbs, RA, Ma, J, Parra, EJ, Cruz, M, Valladares-Salgado, A, Arking, DE, Barbeira, A, Im, HKyung, Morrison, AC, Boerwinkle, E, Below, JE
JournalHum Mol Genet
Volume28
Issue7
Pagination1212-1224
Date Published2019 Apr 01
ISSN1460-2083
Abstract

Interpretation of genetic association results is difficult because signals often lack biological context. To generate hypotheses of the functional genetic etiology of complex cardiometabolic traits, we estimated the genetically determined component of gene expression from common variants using PrediXcan (1) and determined genes with differential predicted expression by trait. PrediXcan imputes tissue-specific expression levels from genetic variation using variant-level effect on gene expression in transcriptome data. To explore the value of imputed genetically regulated gene expression (GReX) models across different ancestral populations, we evaluated imputed expression levels for predictive accuracy genome-wide in RNA sequence data in samples drawn from European-ancestry and African-ancestry populations and identified substantial predictive power using European-derived models in a non-European target population. We then tested the association of GReX on 15 cardiometabolic traits including blood lipid levels, body mass index, height, blood pressure, fasting glucose and insulin, RR interval, fibrinogen level, factor VII level and white blood cell and platelet counts in 15 755 individuals across three ancestry groups, resulting in 20 novel gene-phenotype associations reaching experiment-wide significance across ancestries. In addition, we identified 18 significant novel gene-phenotype associations in our ancestry-specific analyses. Top associations were assessed for additional support via query of S-PrediXcan (2) results derived from publicly available genome-wide association studies summary data. Collectively, these findings illustrate the utility of transcriptome-based imputation models for discovery of cardiometabolic effect genes in a diverse dataset.

DOI10.1093/hmg/ddy435
Alternate JournalHum. Mol. Genet.
PubMed ID30624610
PubMed Central IDPMC6423424
Grant ListR01 HL142302 / HL / NHLBI NIH HHS / United States