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Rare and common genetic determinants of metabolic individuality and their effects on human health.

ABSTRACT: Garrod's concept of 'chemical individuality' has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P < 1.25 × 10^-11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships.

SUBMITTER: Surendran P

PROVIDER: S-EPMC9671801 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Rare and common genetic determinants of metabolic individuality and their effects on human health.

Surendran Praveen P Stewart Isobel D ID Au Yeung Victoria P W VPW Pietzner Maik M Raffler Johannes J Wörheide Maria A MA Li Chen C Smith Rebecca F RF Wittemans Laura B L LBL Bomba Lorenzo L Menni Cristina C Zierer Jonas J Rossi Niccolò N Sheridan Patricia A PA Watkins Nicholas A NA Mangino Massimo M Hysi Pirro G PG Di Angelantonio Emanuele E Falchi Mario M Spector Tim D TD Soranzo Nicole N Michelotti Gregory A GA Arlt Wiebke W Lotta Luca A LA Denaxas Spiros S Hemingway Harry H Gamazon Eric R ER Howson Joanna M M JMM Wood Angela M AM Danesh John J Wareham Nicholas J NJ Kastenmüller Gabi G Fauman Eric B EB Suhre Karsten K Butterworth Adam S AS Langenberg Claudia C

Nature medicine 20221110 11

Garrod's concept of 'chemical individuality' has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P < 1.25 × 10<sup>-11</sup>) within 330 genomic regions, with rare variants (minor allele freq ...[more]

PMID: 36357675

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Project description:BackgroundCommon metabolic diseases, including type 2 diabetes, coronary artery disease, and hypertension, arise from disruptions of the body's metabolic homeostasis, with relatively strong contributions from genetic risk factors and substantial comorbidity with obesity. Although genome-wide association studies have revealed many genomic loci robustly associated with these diseases, biological interpretation of such association is challenging because of the difficulty in mapping single-nucleotide polymorphisms (SNPs) onto the underlying causal genes and pathways. Furthermore, common diseases are typically highly polygenic, and conventional single variant-based association testing does not adequately capture potentially important large-scale interaction effects between multiple genetic factors.MethodsWe analyzed moderately sized case-control data sets for type 2 diabetes, coronary artery disease, and hypertension to characterize the genetic risk factors arising from non-additive, collective interaction effects, using a recently developed algorithm (discrete discriminant analysis). We tested associations of genes and pathways with the disease status while including the cumulative sum of interaction effects between all variants contained in each group.ResultsIn contrast to non-interacting SNP mapping, which produced few genome-wide significant loci, our analysis revealed extensive arrays of pathways, many of which are involved in the pathogenesis of these metabolic diseases but have not been directly identified in genetic association studies. They comprised cell stress and apoptotic pathways for insulin-producing β-cells in type 2 diabetes, processes covering different atherosclerotic stages in coronary artery disease, and elements of both type 2 diabetes and coronary artery disease risk factors (cell cycle, apoptosis, and hemostasis) associated with hypertension.ConclusionsOur results support the view that non-additive interaction effects significantly enhance the level of common metabolic disease associations and modify their genetic architectures and that many of the expected genetic factors behind metabolic disease risks reside in smaller genotyping samples in the form of interacting groups of SNPs.

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Rare and common genetic determinants of metabolic individuality and their effects on human health.

Publications

Rare and common genetic determinants of metabolic individuality and their effects on human health.

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