Zaki Jihan K, Tomasik Jakub, McCune Jade A, Scherman Oren A, Bahn Sabine
Department of Chemistry, University of Cambridge, Lensfield Rd, Cambridge, Cambridgeshire, CB2 1EW, UK.
Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
Sci Rep. 2025 Jul 1;15(1):20375. doi: 10.1038/s41598-025-07518-0.
Genome-wide association studies (GWAS) have substantially enhanced the understanding of genetic influences on phenotypic outcomes; however, realizing their full potential requires an aggregate analysis of numerous studies. Here we represent the first comprehensive meta-analysis of urinary metabolite GWAS studies, aiming to consolidate existing data on metabolite-SNP associations, evaluate consistency across studies, and unravel novel genetic links. Following an extensive literature review and data collection through the EMBL-EBI GWAS Catalog, PubMed, and metabolomix.com, we employed a sample size-based meta-analytic approach to evaluate the significance of previously reported GWAS associations. Our analysis identified 48 independent lead SNPs correlated with the levels of 14 unique urinary metabolites: alanine, 3-aminoisobutyrate, betaine, creatine, creatinine, formate, glycine, glycolate, histidine, 2-hydroxybutyrate, lysine, threonine, trimethylamine, and tyrosine. Notably, the results revealed a novel locus for tyrosine (rs4594899, SLC12A7, P = 6.6 × 10, N = 2623), and three newly associated independent SNPs within known loci: one for glycine (rs1755615, GLDC, P = 2.4 × 10, N = 5319), and two for 3-aminoisobutyrate (rs79053399, RAI14, P = 6.9 × 10, N = 4656; rs36071744, TTC23L, P = 2.97 × 10, N = 4872). These findings underscore the potential of urinary metabolite GWAS meta-analyses in revealing novel genetic factors that may aid in the understanding of disease processes and highlight the necessity for larger and more comprehensive future studies.
全基因组关联研究(GWAS)极大地增进了我们对基因对表型结果影响的理解;然而,要充分发挥其潜力需要对众多研究进行汇总分析。在此,我们展示了首个尿代谢物GWAS研究的全面荟萃分析,旨在整合代谢物与单核苷酸多态性(SNP)关联的现有数据,评估各研究间的一致性,并揭示新的基因联系。通过对EMBL-EBI GWAS目录、PubMed和metabolomix.com进行广泛的文献综述和数据收集后,我们采用基于样本量的荟萃分析方法来评估先前报道的GWAS关联的显著性。我们的分析确定了48个独立的先导SNP,它们与14种独特的尿代谢物水平相关:丙氨酸、3-氨基异丁酸、甜菜碱、肌酸、肌酐、甲酸、甘氨酸、乙醇酸、组氨酸、2-羟基丁酸、赖氨酸、苏氨酸、三甲胺和酪氨酸。值得注意的是,结果揭示了一个新的酪氨酸基因座(rs4594899,SLC12A7,P = 6.6×10,N = 2623),以及已知基因座内三个新关联的独立SNP:一个与甘氨酸相关(rs1755615,GLDC,P = 2.4×10,N = 5319),两个与3-氨基异丁酸相关(rs79053399,RAI14,P = 6.9×10,N = 4656;rs36071744,TTC23L,P = 2.97×10,N = 4872)。这些发现强调了尿代谢物GWAS荟萃分析在揭示可能有助于理解疾病过程的新基因因素方面的潜力,并突出了未来进行更大规模和更全面研究的必要性。