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Harms, A. C.

Publications and source records attributed to Harms, A. C..

2 recordsLinked to original sources

The genomic architecture of blood metabolites based on a decade of genome-wide analyses

Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes and lipid species. We performed a review of all genetic association studies, and identified > 800 class-specific metabolite loci that influence metabolite levels. In a twin-family cohort (N = 5,117), these metabolite loci were leveraged to simultaneously estimate total heritability (h2total), and the proportion of heritability captured by known metabolite loci (h2Metabolite-hits) for 309 lipids and 52 organic acids. Our study revealed significant differences in h2Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation had higher h2Metabolite-hits estimates than phosphatidylcholines with a low degree of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes and lipid species.

genetics

Genome-wide association study of plasma lipids

There is continuous interest in the genetic determinants of plasma triglycerides (TGs) and phospholipids and their role in the etiology of cardiovascular disease (CVD). Here, we report the results of a Dutch genome wide association study (GWAS) of an in-house developed lipidomics platform, focusing on 90 plasma lipids. Lipids were assessed by liquid chromatography mass spectrometry in participants from the Leiden Longevity Study, the Netherlands Twin Register and the Erasmus Rucphen Family (ERF) study and meta-analysed, resulting in a sample size of 5537 participants. In addition, we performed genetic correlation analyses between the 90 plasma lipids and markers of metabolic health, as well as vascular pathology and CVD combining our GWAS results with publicly available GWAS outputs. We replicated previously known associations between 34 lipids and 10 lipid quantitative trait loci (lipQTL) (GCKR, APOA1, FADS1, SGPP1,TMEM229B, LIPC, PDXDC1, CETP, CERS4 and SPTLC3) with metabolome-wide (P < 1.61 x 10-9) significance. Moreover, we report 6 novel phospholipid-related and 5 triglyceride (TG)-related loci: SGGP1 (SM21:0), SPTLC3 (SM21:0 and SM25:1), FADS1 (LPCO16:1, PC38:2, PEO36:5, PEO38:5, TG56:5, TG56:6, and TG56:7), TMEM229 (LPCO16:1), GCKR (TG50:2), and APOA1 (TG54:4). In addition, we report suggestively significant (P < 5 x 10-8) associations mapping to eleven novel lipid quantitative trait loci (lipQTLs), three of which are supported by mining previous GWAS data: MAU (PC34:4), LDLR (SM16:0), and MLXIPL (TG48:1 and TG50:1)). Genetic correlation analysis indicates that one specific specific sphingomyelin, SM22:0, shares common genetic background with CVD. Levels of SM22:0 also positively associate with carotid artery intima-media thickness in the ERF study, and this observation is independent of LDL-C level. Our findings yield higher resolution of plasma lipid species and new insights in the biology of circulating phosholipids and their relation to CVD risk.

epidemiology