Abstract
Low HDL-C is a major risk factor for CVD. To elucidate potential novel pathways behind low HDL-C, we have employed 3 different omics: genomics, transcriptomics, and lipidomics. We performed GWAS on 450 Finns with low or high HDL-C (90th percentiles). Out of 54 individuals, we obtained subcutaneous (SC) fat biopsies for transcriptome analysis, and isolated plasma HDL particles for lipidomics analysis with MS technology. We first conducted gene network analysis for loci associated with low HDL-C observing that SNPs within 4 inflammatory pathways (e.g. antigen-presentation) were enriched among the low HDL-C associated genes (p=10^-5). Also, these inflammatory pathways were over-expressed in SC fat of low-HDL-subjects (p=10^-10). We then calculated genetic risk scores based on low HDL-C associating SNPs from these pathways, observing that high risk score resulted in both decreased HDL-C-levels and increased expression of these inflammatory pathways. Moreover, individual genes of these pathways (e.g. HLA-DRB1 [p=10^-7, TAP2 p=10^-6) exhibited cis-eQTLs, their expression inversely correlating with HDL-C. Consistent with this, the inflammatory nature of the HDL particle of low HDL-C subjects was further highlighted by the elevation of proinflammatory ceramides and reduction of anti-oxidative plasmalogens in the lipidomic analysis, which was also genetic risk score dependent. In a replication analysis of 5 Finnish population cohorts (n=11,211) the low HDL-C associated SNPs were not associated with HDL-C as quantitative trait, but only after using 75, 90, and 95 percentiles as low/high HDL-C criteria. Interestingly, the effect sizes of individual SNPs became stronger when more extreme phenotype criteria for low HDL-C were implemented (e.g. HLA-DRB1; OR for low HDL-C 75 % = 1.11, OR for 90 % = 1.38, OR for 95 % = 1.77). Our findings imply that genetic variation influences inflammatory processes associated with low HDL-cholesterol on both expression and metabolite levels, suggesting more inflammatory and less vasoprotective role for HDL particles in low-HDL-subjects. Using the extreme phenotype approach, we also demonstrate the presence of ‘low HDL genes’ in addition to the population GWAS identified ‘HDL genes.’
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