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Non-additive effects of genes in human metabolomics.
Genetics 200, 707-718 (2015)
Genome-wide association studies (GWAS) are widely applied to analyze the genetic effects on phenotypes. With the availability of high-throughput technologies for metabolite measurements, GWAS successfully identified loci that affect metabolite concentrations and underlying pathways. In most GWAS the effect of each SNP on the phenotype is assumed to be additive. Other genetic models such as recessive, dominant or over-dominant were considered only by very few studies. In contrast to that, there are theories that emphasize the relevance of non-additive effects as a consequence of physiological mechanisms. This might be especially important for metabolites as these intermediate phenotypes are closer to the underlying pathways than other traits or diseases. In this study we analyzed systematically non-additive effects on a large panel of serum metabolites and all possible ratios (22,801 in total) in a population based study (KORA F4, N=1,785). We applied four different 1 df tests corresponding to an additive, dominant, recessive and over-dominant trait model and additionally a genotypic model with 2 df that allows a more general consideration of genetic effects. Twenty three loci were found to be genome-wide significantly associated (Bonferroni corrected p-value ≤2.19x10(-12)) with at least one metabolite or ratio. For five of them we show the evidence of non-additive effects. We replicated seventeen loci including three loci with non-additive effects in an independent study (TwinsUK, N=846). In conclusion, we found that most genetic effects on metabolite concentrations and ratios were indeed additive, which verifies the practice of using the additive model for analyzing SNP effects on metabolites.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Kora ; Genome-wide Association Studies ; Genotypic Model ; Metabolomics ; Non-additive Models; Genome-wide Association; Fishers Theory; Dominance; Phenotypes; Mutations; Origin; Model