American journal of human genetics · 2022
Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk.
СтатьяПоддержка исследования: вне правительства СШАПоддержка NIH: внешняя программаResearch Support, N.I.H., IntramuralНазвание и аннотация: Перевод пока не подготовлен
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Yin Xianyong, Bose Debraj, Kwon Annie, Hanks Sarah C, Jackson Anne U, Stringham Heather M, Welch Ryan, Oravilahti Anniina, Fernandes Silva Lilian, FinnGen, Locke Adam E, Fuchsberger Christian, Service Susan K, Erdos Michael R, Bonnycastle Lori L, Kuusisto Johanna, Stitziel Nathan O, Hall Ira M, Morrison Jean, Ripatti Samuli, Palotie Aarno, Freimer Nelson B, Collins Francis S, Mohlke Karen L, Scott Laura J, Fauman Eric B, Burant Charles, Boehnke Michael, Laakso Markku, Wen Xiaoquan
Аннотация
Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.