Abstract
Genetic studies of metabolites have identified thousands of variants, many of which are associated with downstream metabolic and obesogenic disorders. However, these studies have relied on univariate analyses, reducing power and limiting context-specific understanding. Here we aim to provide an integrated perspective of the genetic basis of metabolites by leveraging the Finnish Metabolic Syndrome In Men (METSIM) cohort, a unique genetic resource which contains metabolic measurements, mostly lipids, across distinct time points as well as information on statin usage. We increase effective sample size by an average of two-fold by applying the Covariates for Multi-phenotype Studies (CMS) approach, identifying 588 significant SNP-metabolite associations, including 228 new associations. Our analysis pinpoints a small number of master metabolic regulator genes, balancing the relative proportion of dozens of metabolite levels. We further identify associations to changes in metabolic levels across time as well as genetic interactions with statin at both the master metabolic regulator and genome-wide level.
Highlights
Genetic studies of metabolites have identified thousands of variants, many of which are associated with downstream metabolic and obesogenic disorders
genome-wide association studies (GWAS) was performed using standard linear regression (STD), and using the Covariates for Multi-phenotype Studies (CMS) approach[21], a powerful method we recently developed for the analysis of multivariate data sets (Online Methods)
We found that 13 master metabolic regulator genes (LIPC, APOA5, CETP, PCSK9, LDLR, GCKR, APOC1, LPL, GALNT2, CELSR2, TRIB1, DOCK7, and FADS2) capture over 75% (N = 457) of all associations (Supplementary Fig. 5)
Summary
Genetic studies of metabolites have identified thousands of variants, many of which are associated with downstream metabolic and obesogenic disorders These studies have relied on univariate analyses, reducing power and limiting context-specific understanding. A number of genome-wide association studies (GWAS) of metabolites have been performed These studies identified hundreds of genetic variant–metabolite associations[11], provided estimation of the heritability of multiple metabolites[12], and highlighted the biological and clinical relevance of some of these findings[13]. All of these studies relied on standard univariate analyses and assessed marginal additive effects of genetic variants only. Our analysis provides a step towards richer understanding of genetic regulation of metabolites as a function of environmental factors
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