Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk

全基因组关联研究 代谢组学 转录组 生物 表达数量性状基因座 代谢组 计算生物学 代谢物 小桶 遗传关联 遗传学 疾病 基因 基因表达 生物信息学 单核苷酸多态性 基因型 医学 内科学 生物化学
作者
Xianyong Yin,Debraj Bose,Annie Kwon,Sarah C. Hanks,Anne Jackson,Heather M. Stringham,Ryan Welch,Anniina Oravilahti,Lilian Fernandes Silva,Adam E. Locke,Christian Fuchsberger,Susan Service,Michael R. Erdos,Lori L. Bonnycastle,Johanna Kuusisto,Nathan O. Stitziel,Ira M. Hall,Jean Morrison,Samuli Ripatti,Aarno Palotie,Nelson B. Freimer,Francis S. Collins,Karen L. Mohlke,Laura J. Scott,Eric B. Fauman,Charles F. Burant,Michael Boehnke,Markku Laakso,Xiaoquan Wen
出处
期刊:American Journal of Human Genetics [Elsevier]
卷期号:109 (10): 1727-1741 被引量:9
标识
DOI:10.1016/j.ajhg.2022.08.007
摘要

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.
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