Genome-wide association study identifies multiple loci influencing human serum metabolite levels

Johannes Kettunen, Taru Tukiainen, Antti-Pekka Sarin, Alfredo Ortega-Alonso, Emmi Tikkanen, Leo-Pekka Lyytikäinen, Antti J Kangas, Pasi Soininen, Peter Würtz, Kaisa Silander, Danielle M Dick, Richard J Rose, Markku J Savolainen, Jorma Viikari, Mika Kähönen, Terho Lehtimäki, Kirsi H Pietiläinen, Michael Inouye, Mark I McCarthy, Antti Jula, Johan Eriksson, Olli T Raitakari, Veikko Salomaa, Jaakko Kaprio, Marjo-Riitta Järvelin, Leena Peltonen, Markus Perola, Nelson B Freimer, Mika Ala-Korpela, Aarno Palotie, Samuli Ripatti, Johannes Kettunen, Taru Tukiainen, Antti-Pekka Sarin, Alfredo Ortega-Alonso, Emmi Tikkanen, Leo-Pekka Lyytikäinen, Antti J Kangas, Pasi Soininen, Peter Würtz, Kaisa Silander, Danielle M Dick, Richard J Rose, Markku J Savolainen, Jorma Viikari, Mika Kähönen, Terho Lehtimäki, Kirsi H Pietiläinen, Michael Inouye, Mark I McCarthy, Antti Jula, Johan Eriksson, Olli T Raitakari, Veikko Salomaa, Jaakko Kaprio, Marjo-Riitta Järvelin, Leena Peltonen, Markus Perola, Nelson B Freimer, Mika Ala-Korpela, Aarno Palotie, Samuli Ripatti

Abstract

Nuclear magnetic resonance assays allow for measurement of a wide range of metabolic phenotypes. We report here the results of a GWAS on 8,330 Finnish individuals genotyped and imputed at 7.7 million SNPs for a range of 216 serum metabolic phenotypes assessed by NMR of serum samples. We identified significant associations (P < 2.31 × 10(-10)) at 31 loci, including 11 for which there have not been previous reports of associations to a metabolic trait or disorder. Analyses of Finnish twin pairs suggested that the metabolic measures reported here show higher heritability than comparable conventional metabolic phenotypes. In accordance with our expectations, SNPs at the 31 loci associated with individual metabolites account for a greater proportion of the genetic component of trait variance (up to 40%) than is typically observed for conventional serum metabolic phenotypes. The identification of such associations may provide substantial insight into cardiometabolic disorders.

Conflict of interest statement

COMPETING FINANCIAL INTERESTS

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1
The heritability estimates and proportion of variance explained for all traits. Heritability estimates are presented for lipids, small molecules, ratios used in this study and lipoproteins. Labels in the lipoprotein subclasses describe the properties measured in each subfraction (P, concentration of particles; L, total lipids; PL, phospholipids; C, total cholesterol; CE, cholesterol esters; FC, free cholesterol; TG, triglycerides). Abbreviations are explained in detail in Supplementary Table 1.
Figure 2
Figure 2
Overall summary of basic metabolism, key constituents of the NMR-measurable serum metabolome and associated genetic loci. The green shapes represent various dietary ingredients and systemic metabolic measures reflecting human body functions. A key selection of metabolites quantified in each metabolic category is given. Red rectangles represent the newly identified genetic loci found in this work, and blue rectangles indicate loci that were previously identified. The genes are categorized by the lead trait associations given in Tables 2 and 3. In particular, ALB and FCGR2B were associated with serum cholesterol, PPP1R11 with very high VLDL measures, CPT1A with the LA/PUFA ratio, SLC25A1 with citrate, F12 with phenylalanine, TAT with tyrosine, DHDPSL with glutamine, SLC1A4 with valine and PPM1K and SLC2A4 with Fischer’s ratio.

Source: PubMed

Подписаться