| Literature DB >> 35120996 |
Eugene P Rhee1, Aditya Surapaneni2, Zihe Zheng3, Linda Zhou2, Diptavo Dutta4, Dan E Arking5, Jingning Zhang4, ThuyVy Duong5, Nilanjan Chatterjee4, Shengyuan Luo6, Pascal Schlosser7, Rupal Mehta8, Sushrut S Waikar9, Santosh L Saraf10, Tanika N Kelly11, Lee L Hamm12, Panduranga S Rao13, Anna V Mathew13, Chi-Yuan Hsu14, Afshin Parsa15, Ramachandran S Vasan16, Paul L Kimmel15, Clary B Clish17, Josef Coresh2, Harold I Feldman18, Morgan E Grams19.
Abstract
Metabolomics genome wide association study (GWAS) help outline the genetic contribution to human metabolism. However, studies to date have focused on relatively healthy, population-based samples of White individuals. Here, we conducted a GWAS of 537 blood metabolites measured in the Chronic Renal Insufficiency Cohort (CRIC) Study, with separate analyses in 822 White and 687 Black study participants. Trans-ethnic meta-analysis was then applied to improve fine-mapping of potential causal variants. Mean estimated glomerular filtration rate was 44.4 and 41.5 mL/min/1.73m2 in the White and Black participants, respectively. There were 45 significant metabolite associations at 19 loci, including novel associations at PYROXD2, PHYHD1, FADS1-3, ACOT2, MYRF, FAAH, and LIPC. The strength of associations was unchanged in models additionally adjusted for estimated glomerular filtration rate and proteinuria, consistent with a direct biochemical effect of gene products on associated metabolites. At several loci, trans-ethnic meta-analysis, which leverages differences in linkage disequilibrium across populations, reduced the number and/or genomic interval spanned by potentially causal single nucleotide polymorphisms compared to fine-mapping in the White participant cohort alone. Across all validated associations, we found strong concordance in effect sizes of the potentially causal single nucleotide polymorphisms between White and Black study participants. Thus, our study identifies novel genetic determinants of blood metabolites in chronic kidney disease, demonstrates the value of diverse cohorts to improve causal inference in metabolomics GWAS, and underscores the shared genetic basis of metabolism across race.Entities:
Keywords: GWAS; metabolomics; trans-ethnic meta-analysis
Mesh:
Year: 2022 PMID: 35120996 PMCID: PMC8940669 DOI: 10.1016/j.kint.2022.01.014
Source DB: PubMed Journal: Kidney Int ISSN: 0085-2538 Impact factor: 10.612