Detailed metabolic and genetic characterization reveals new associations for 30 known lipid loci

Taru Tukiainen, Johannes Kettunen, Antti J. Kangas, Leo-Pekka Lyytikäinen, Pasi Soininen, Antti-Pekka Sarin, Emmi Tikkanen, Paul F. O'reilly, Markku J. Savolainen, Kimmo Kaski, Anneli Pouta, Antti Jula, Terho Lehtimäki, Mika Kähönen, Jorma Viikari, Marja-Riitta Taskinen, Matti Jauhiainen, Johan G. Eriksson, Olli Raitakari, Veikko SalomaaMarjo-Riitta Järvelin, Markus Perola, Aarno Palotie, Mika Ala-Korpela, Samuli Ripatti*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

69 Citations (Scopus)

Abstract

Almost 100 genetic loci are known to affect serum cholesterol and triglyceride levels. For many of these loci, the biological function and causal variants remain unknown. We performed an association analysis of the reported 95 lipid loci against 216 metabolite measures, including 95 measurements on lipids and lipoprotein subclasses, obtained via serum nuclear magnetic resonance metabolomics and four enzymatic lipid traits in 8330 individuals from Finland. The genetic variation in the loci was investigated using a dense set of 440 807 directly genotyped and imputed variants around the previously identified lead single nucleotide polymorphisms (SNPs). For 30 of the 95 loci, we identified new metabolic or genetic associations (P < 5 × 10 -8). In the majority of the loci, the strongest association was to a more specific metabolite measure than the enzymatic lipids. In four loci, the smallest high-density lipoprotein measures showed effects opposite to the larger ones, and 14 loci had associations beyond the individual lipoprotein measures. In 27 loci, we identified SNPs with a stronger association than the previously reported markers and 12 loci harboured multiple, statistically independent variants. Our data show considerable diversity in association patterns between the loci originally identified through associations with enzymatic lipid measures and reveal association profiles of far greater detail than from routine clinical lipid measures. Additionally, a dense marker set and a homogeneous population allow for detailed characterization of the genetic association signals to a resolution exceeding that achieved so far. Further understanding of the rich variability in genetic effects on metabolites provides insights into the biological processes modifying lipid levels.

Original languageEnglish
Article numberddr581
Pages (from-to)1444-1455
Number of pages12
JournalHUMAN MOLECULAR GENETICS
Volume21
Issue number6
DOIs
Publication statusPublished - Mar 2012
MoE publication typeA1 Journal article-refereed

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