[Show abstract][Hide abstract] ABSTRACT: The study of the genetic regulation of metabolism in human serum samples can contribute to a better understanding of the intermediate biological steps that lead from polymorphism to disease. Here, we conducted a genome-wide association study to discover metabolic quantitative trait loci (mQTLs) utilizing samples from a study of prostate-cancer in Swedish men, consisting of 402 individuals (214 cases and 188 controls) in a discovery set and 489 case-only samples in a replication set. A global non-targeted metabolite profiling approach was utilized resulting in the detection of 6,138 molecular features followed by targeted identification of associated metabolites. Seven replicating loci were identified (PYROXD2, FADS1, PON1, CYP4F2, UGT1A8, ACADL, and LIPC) with associated sequence variants contributing significantly to trait variance for one or more metabolites (P = 10(-13) to 10(-91) ). Regional mQTL enrichment analyses implicated two loci that included FADS1 and a novel locus near PDGFC. Biological pathway analysis implicated ACADM, ACADS, ACAD8, ACAD10, ACAD11, and ACOXL, reflecting significant enrichment of genes with acyl-CoA dehydrogenase activity. mQTL SNPs and mQTL-harboring genes were over-represented across genome-wide association studies conducted to date, suggesting that these data may have utility in tracing the molecular basis of some complex disease associations.
[Show abstract][Hide abstract] ABSTRACT: Genome-wide association studies (GWAS) have identified ∼30 single-nucleotide polymorphisms (SNPs) consistently associated with prostate cancer (PCa) risk. To test the hypothesis that other sequence variants in the genome may interact with those 32 known PCa risk-associated SNPs identified from GWAS to affect PCa risk, we performed a systematic evaluation among three existing PCa GWAS populations: CAncer of the Prostate in Sweden population, a Johns Hopkins Hospital population, and the Cancer Genetic Markers of Susceptibility population, with a total sample size of 4723 PCa cases and 4792 control subjects. Meta-analysis of the interaction term between each of those 32 SNPs and SNPs in the genome was performed in three PCa GWAS populations. The most significant interaction detected was between rs12418451 in MYEOV and rs784411 in CEP152, with a P(interaction) of 1.15 × 10(-7) in the meta-analysis. In addition, we emphasized two pairs of interactions with potential biological implication, including an interaction between rs7127900 near insulin-like growth factor-2 (IGF2)/IGF2AS and rs12628051 in TNRC6B, with a P(interaction) of 3.39 × 10(-6) and an interaction between rs7679763 near TET2 and rs290258 in SYK, with a P(interaction) of 1.49 × 10(-6). Those results show statistical evidence for novel loci interacting with known risk-associated SNPs to modify PCa risk. The interacting loci identified provide hints on the underlying molecular mechanism of the associations with PCa risk for the known risk-associated SNPs. Additional studies are warranted to further confirm the interaction effects detected in this study.