Abstract
Association testing of multiple correlated phenotypes offers better power than univariate analysis of single traits. We analyzed 6,600 individuals from two population-based cohorts with both genome-wide SNP data and serum metabolomic profiles. From the observed correlation structure of 130 metabolites measured by nuclear magnetic resonance, we identified 11 metabolic networks and performed a multivariate genome-wide association analysis. We identified 34 genomic loci at genome-wide significance, of which 7 are novel. In comparison to univariate tests, multivariate association analysis identified nearly twice as many significant associations in total. Multi-tissue gene expression studies identified variants in our top loci, SERPINA1 and AQP9, as eQTLs and showed that SERPINA1 and AQP9 expression in human blood was associated with metabolites from their corresponding metabolic networks. Finally, liver expression of AQP9 was associated with atherosclerotic lesion area in mice, and in human arterial tissue both SERPINA1 and AQP9 were shown to be upregulated (6.3-fold and 4.6-fold, respectively) in atherosclerotic plaques. Our study illustrates the power of multi-phenotype GWAS and highlights candidate genes for atherosclerosis.
Highlights
Five years of genome-wide association studies (GWAS) have successfully identified common variants at .1,000 genomic loci robustly associated with a wide range of human conditions and quantitative traits [1]
We aim to identify novel genetic variants for metabolism, characterize their effects on nearby genes, and show that the nearby genes are associated with metabolism and atherosclerosis
To discover new genetic variants, we use an alternative approach to traditional genome-wide association studies: we leverage the information in phenotype covariance to increase our statistical power
Summary
Five years of genome-wide association studies (GWAS) have successfully identified common variants at .1,000 genomic loci robustly associated with a wide range of human conditions and quantitative traits [1]. Using high-throughput nuclear magnetic resonance assays, quantitative profiles of 130 metabolites in two population-based cohorts from Finland, the Cardiovascular Risk in Young Finns Study (YFS) [10] and the Northern Finland Birth Cohort 1966 (NFBC66) [11] have been determined. These metabolites included lipoprotein subclasses of VLDL, LDL, IDL and HDL as well as lipids, amino acids and other small molecules (Materials S1)
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