Gene-Environment Interactions Related to Hyperlipidemia Among African-Americans

Purpose: African Americans (AA) in the United States face significant health disparities in chronic health conditions. When compared to Caucasians, AAs fare worse with the following indices: 1) highest incidence and prevalence of hypertension, obesity, diabetes, low birth weight, and infant mortality; 2) highest death rates from heart disease, stroke, and colorectal cancer; and 3) shorter life expectancy ¹. Health outcomes and life expectancy are strongly influenced by the characteristics of one’s environment ² and health disparities among AAs have been associated with genomic underpinnings, social inequalities, disproportionate burdens of pollution, and unequal access to quality health care ³. Despite some of the genome wide association studies that have identified the independent effects of risk alleles for hypertension and other chronic disease among AAs, very few examine multiple omic methods together, such as single nucleotide polymorphisms (SNP) and DNA methylation (DNAm), to illuminate both the contribution of genetic and environmentally mediated (DNAm) influences on phenotypic expression of disease. In this study, we integrate analysis of SNP and DNAm data to investigate the interaction between genetic and epigenetic factors that may contribute to chronic disease risk in AA for hyperlipidemia.

Methods: Data to be analyzed is from community-based prospective study that recruited individuals with 2 or more siblings that were diagnosed with primary hypertension prior to age 60 (N=1,854 AAs). Peripheral blood samples were collected to measure plasma concentrations of lipid traits [i.e. total cholesterol (cholesterol), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL) and triglycerides] were evaluated for all participants who fasted >10 hours prior to serum collection. Genotyping was completed using Affymetix Array 6.0 and DNAm measured using Illumnia 27K array. Linear mixed model analyses were conducted separately within each lipid trait. Age, sex, lipid medications, and ancestry informative principal components were used as covariates in all analyses. To control for population stratification, the top 4 principal components (PC) extracted for AAs from genome-wide SNP data were included in all evaluation models. 

Results: Mean age of participants is 63 years (26-94), and 71% of the sample is female (N=1,050). Sixteen percent (N=242 > 240 mg/dL) meet the clinical diagnostic criteria for elevated cholesterol, 84% for decreased HDL (N=1250, <40 mg/dL), 7% for increased LDL (N=108 >160 mg/dL), and 21% elevated triglycerides (N=316 ≥ 150mg/dL). Fewer SNPs were significantly associated with variation in lipid traits (cholesterol, HDL, LDL, triglycerides) in AAs than in the European Ancestry cohort ^4,5 due to differences in allele frequencies and interaction effects. DNAm near SNPs associated with lipid trait variation explained additional contribution to variance, over and above that explained by SNPs alone, seen in lipid trait levels in AA.

Conclusion: SNPs associated with variation in lipid traits in AAs and SNPs that are somewhat inconsistent with previous GWAS studies in cohorts of European ancestry. The findings could identify why certain therapies are less effective in AA populations. In future studies we aim to identify optimal therapeutic targets for future interventional and translational studies for clinical prevention and treatment of lipid trait variation in AA. Reductions in morbidity and mortality due to variation in lipid traits in AAs may be achieved by a better understanding of the genetic and epigenetic factors associated lipid traits for early and appropriate screening and treatment.