Genetics Meets Metabolomics: A Genome-Wide Association Study of Metabolite Profiles in Human Serum

The rapidly evolving field of metabolomics aims at a comprehensive measurement of ideally all endogenous metabolites in a cell or body fluid. It thereby provides a functional readout of the physiological state of the human body. Genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, or amino acids are not only expected to display much larger effect sizes due to their direct involvement in metabolite conversion modification, but should also provide access to the biochemical context of such variations, in particular when enzyme coding genes are concerned. To test this hypothesis, we conducted what is, to the best of our knowledge, the first GWA study with metabolomics based on the quantitative measurement of 363 metabolites in serum of 284 male participants of the KORA study. We found associations of frequent single nucleotide polymorphisms (SNPs) with considerable differences in the metabolic homeostasis of the human body, explaining up to 12% of the observed variance. Using ratios of certain metabolite concentrations as a proxy for enzymatic activity, up to 28% of the variance can be explained (p-values 10?16 to 10?21). We identified four genetic variants in genes coding for enzymes (FADS1, LIPC, SCAD, MCAD) where the corresponding metabolic phenotype (metabotype) clearly matches the biochemical pathways in which these enzymes are active. Our results suggest that common genetic polymorphisms induce major differentiations in the metabolic make-up of the human population. This may lead to a novel approach to personalized health care based on a combination of genotyping and metabolic characterization. These genetically determined metabotypes may subscribe the risk for a certain medical phenotype, the response to a given drug treatment, or the reaction to a nutritional intervention or environmental challenge.

蓬勃发展的代谢组学（metabolomics）领域，旨在全面精准地测定细胞或体液内几乎所有内源性代谢物，以此获取人体生理状态的功能性读值。与关键脂质、碳水化合物或氨基酸稳态失衡相关的遗传变异，不仅因直接参与代谢物的转化修饰，有望展现出更为显著的效应量，还可为解析这类变异的生化背景提供途径，尤其在涉及酶编码基因时。为验证这一假说，我们依托KORA研究中284名男性受试者的血清样本，基于363种代谢物的定量检测结果，开展了据我们所知首例结合代谢组学的全基因组关联研究（Genome-Wide Association Study, GWA）。我们发现，常见单核苷酸多态性（Single Nucleotide Polymorphism, SNPs）与人体代谢稳态的显著差异存在关联，该关联可解释高达12%的观测变异。以特定代谢物浓度的比值作为酶活性的替代指标，可解释高达28%的变异（P值范围为10⁻¹⁶至10⁻²¹）。我们在4个酶编码基因（FADS1、LIPC、SCAD、MCAD）中鉴定出4处遗传变异，其对应的代谢表型（metabotype）与这些酶所参与的生化通路高度契合。我们的研究结果表明，常见遗传多态性会引发人群代谢特征的显著分化。这或将推动一种基于基因分型与代谢特征表征相结合的新型个性化医疗方案的诞生。这些由遗传决定的代谢表型，或可用于提示特定医学表型的患病风险、对特定药物治疗的应答效果，或是对营养干预或环境挑战的反应情况。