A genome-wide search for gene-by-obesity interaction loci of dyslipidemia in Koreans shows diverse genetic risk alleles

Abstract

Dyslipidemia is a well-established risk factor for CVD. Studies suggest that similar fat accumulation in a given population might result in different levels of dyslipidemia risk among individuals; for example, despite similar or leaner body composition compared with Caucasians, Asians of Korean descent experience a higher prevalence of dyslipidemia. These variations imply a possible role of gene-obesity interactions on lipid profiles. Genome-wide association studies have identified more than 500 loci regulating plasma lipids, but the interaction structure between genes and obesity traits remains unclear. We hypothesized that some loci modify the effects of obesity on dyslipidemia risk and analyzed extensive gene-environment interactions (G×Es) at genome-wide levels to search for replicated gene-obesity interactive SNPs. In four Korean cohorts (n = 18,025), we identified and replicated 20 gene-obesity interactions, including novel variants (SCN1A and SLC12A8) and known lipid-associated variants (APOA5, BUD13, ZNF259, and HMGCR). When we estimated the additional heritability of dyslipidemia by considering G×Es, the gain was substantial for triglycerides (TGs) but mild for LDL cholesterol (LDL-C) and total cholesterol (Total-C); the interaction explained up to 18.7% of TG, 2.4% of LDL-C, and 1.9% of Total-C heritability associated with waist-hip ratio. Our findings suggest that some individuals are prone to develop abnormal lipid profiles, particularly with regard to TGs, even with slight increases in obesity indices; ethnic diversities in the risk alleles might partly explain the differential dyslipidemia risk between populations. Research about these interacting variables may facilitate knowledge-based approaches to personalize health guidelines according to individual genetic profiles.

Keywords: dyslipidemias; gene-environment interaction; genome-wide interaction scan; high density lipoprotein; lipids; low density lipoprotein; meta-analysis; missing heritability; obesity; triglycerides.

Fig. 1.

Gene-obesity interactive effects on the risk of dyslipidemia. The bar plots on the lower side of each graph describe the OR as the ratio of the probability of dyslipidemia occurring in each exposed group (G ≠ 0 or E ≠ 0) to the probability in a nonexposed group (G = 0 and E = 0). The upper plots, on the other hand, show multiplicative effects of obesity traits for each genetic group. The figure above describes the estimated OR of each lipid abnormality due to the interplay between HMGCR and abdominal obesity based on WHR (A), LOC101928271 and overweight class 1 (B), BUD13 and abdominal obesity class 1 (C), BUD13 and abdominal obesity class 2 (D), LOC101929680/SCN1A and obesity (E), APOA5 and abdominal obesity based on WHR (F), and APOA5 and abdominal obesity based on WHR (G). Further details are provided in supplemental Table S4.

Fig. 2.

Changes in lipid levels due to increments in BMI for each risk group. The participants in each study population were classified into three groups by the number of risk alleles on G×E markers: the low-risk group (individuals with no risk alleles), the high-risk group (individuals with at least one risk allele), and the higher-risk group (the upper 50% of the high-risk group). The figure above describes the trends of lipid levels due to an increment of 1 kg/m² in BMI for each subgroup. A: The differences in the decrement of HDL-C for each genetic subgroup were far clearer in the obese group than in the group with normal BMI. B: The differences in the increment of TG for each risk group were far clearer in the obese group than in the group with normal BMI; further details are presented in supplemental Table S5.

Fig. 3.

Contributions of marginal associations and gene-obesity interactions to the risk of dyslipidemia. The pie plots describe the proportion of phenotypic variation attributable to the overall genetic variation (total heritability), genetic markers assayed by SNP arrays (SNP-based heritability), and the combined set of both GWAS-identified and novel G×E variants. The bar plots, on the other hand, show the proportion of genetic variation explained by marginal and gene-obesity interactive effects. Parts (A) to (C) describe the genetic contributions to abnormal TG due to the interplay between genes and obesity indices classified by BMI (A), WC (B), and WHR (C). D: The genetic contributions to abnormal Remnant-C due to the interplay between genes and obesity indices classified by WHR; further details are presented in Table 3.