Joint Influence of SNPs and DNA Methylation on Lipids in African Americans From Hypertensive Sibships

Mapping Epigenetic Gene Variant Dynamics: Comparative Analysis of Frequency, Functional Impact and Trait Associations in African and European Populations

Epigenetic modifications influence gene expression levels, impact organismal traits, and play a role in the development of diseases. Therefore, variants in genes involved in epigenetic processes are likely to be important in disease susceptibility, and the frequency of variants may vary between populations with African and European ancestries. Here, we analyse an integrated dataset to define the frequencies, associated traits, and functional impact of epigenetic gene variants among individuals of African and European ancestry represented in the UK Biobank. We find that the frequencies of 88.4% of epigenetic gene variants significantly differ between these groups. Furthermore, we find that the variants are associated with many traits and diseases, and some of these associations may be population-specific owing to allele frequency differences. Additionally, we observe that variants associated with traits are significantly enriched for quantitative trait loci that affect DNA methylation, chromatin accessibility, and gene expression. We find that methylation quantitative trait loci account for 71.2% of the variants influencing gene expression. Moreover, variants linked to biomarker traits exhibit high correlation. We therefore conclude that epigenetic gene variants associated with traits tend to differ in their allele frequencies among African and European populations and are enriched for QTLs.

Lipid Phenotypes and DNA Methylation: a Review of the Literature

PURPOSE OF REVIEW

Epigenetic modifications via DNA methylation have previously been linked to blood lipid levels, dyslipidemias, and atherosclerosis. The purpose of this review is to discuss current literature on the role of DNA methylation on lipid traits and their associated pathologies.

RECENT FINDINGS

Candidate gene and epigenome-wide approaches have identified differential methylation of genes associated with lipid traits (particularly CPT1A, ABCG1, SREBF1), and novel approaches are being implemented to further characterize these relationships. Moreover, studies on environmental factors have shown that methylation variations at lipid-related genes are associated with diet and pollution exposure. Further investigation is needed to elucidate the directionality of the associations between the environment, lipid traits, and epigenome. Future studies should also seek to increase the diversity of cohorts, as European and Asian ancestry populations are the predominant study populations in the current literature.

African American mothers' attitudes towards genetic testing in the InterGEN study

The paucity of representation of people of color, particularly those of African ancestry, is a major issue in contemporary omics research. Metadata summarizing genome-wide association studies from 2005 to 2015 suggest that nearly 80% of participants are of European ancestry and only 2.4% are of African ancestry. Negative attitude towards genetic testing is a commonly cited belief as to why there is low representation of Americans of African ancestry participating in genetic studies. Using the attitudes towards genetic testing survey, administered as part of our parent (epi)genome-wide association study, we characterized the perceptions of genetic research among our cohort of African ancestry women (n = 168). Our data show generally favorable perceptions of genetic testing among our cohort. Further, we demonstrate that more favorable attitudes towards genetic testing correlated with higher levels of income, even when accounting for commonly cited negative predictors such as maternal age, education, country of origin, and religion. Overall, our data characterize generally positive perceptions of genetic testing among women of African ancestry.

Using Genetic Burden Scores for Gene-by-Methylation Interaction Analysis on Metabolic Syndrome in African Americans

With the rapid advancement of omics-based research, particularly big data such as genome- and epigenome-wide association studies that include extensive environmental and clinical variables, data analytics have become increasingly complex. Researchers face significant challenges regarding how to analyze multifactorial data and make use of the findings for clinical translation. The purpose of this article is to provide a scientific exemplar for use of genetic burden scores as a data analysis method for studies with both genotype and DNA methylation data in which the goal is to evaluate associations with chronic conditions such as metabolic syndrome (MetS). This study included 739 African American men and women from the Genetic Epidemiology Network of Arteriopathy Study who met diagnostic criteria for MetS and had available genetic and epigenetic data. Genetic burden scores for evaluated genes were not significant after multiple testing corrections, but DNA methylation at 2 CpG sites (dihydroorotate dehydrogenase cg22381196 pFDR = .014; CTNNA3 cg00132141 pFDR = .043) was significantly associated with MetS after controlling for multiple comparisons. Interactions between the marginally significant CpG sites and burden scores, however, were not significant. More work is required in this area to identify intermediate biological pathways influenced by environmental, genetic, and epigenetic variation that may explain the high prevalence of MetS among African Americans. This study does serve, however, as an example of the use of the genetic burden score as an alternative data analysis approach for complex studies involving the analysis of genetic and epigenetic data simultaneously.

NuRsing Research in the 21st Century: R You Ready?

Nurse scientists are adept at translating findings from basic science into useful clinical- and community-based interventions to improve health. Over time, the focus of some nursing research has grown to include the assessment and evaluation of genomic and other output from high-throughput, or "omic," technologies as indicators related to health and disease. To date, the growth in the application of omics technologies in nursing research has included calls to increase attention to omics in nursing school curricula and educational training opportunities, such as the Summer Genetics Institute offered by the National Institute of Nursing Research. However, there has been scant attention paid in the nursing literature to the complexity of data analysis or issues of reproducibility related to omics studies. The goals of this article are to (1) familiarize nurse scientists with tools that encourage reproducibility in omics studies, with a focus on the free and open-source data processing and analysis pipeline, and (2) provide a baseline understanding of how these tools can be used to improve collaboration and cohesion among interdisciplinary research team members. Knowledge of these tools and skill in applying them will be important for communication across disciplines and imperative for the advancement of omics research in nursing.

From lipid locus to drug target through human genomics

In the last decade, over 175 genetic loci have robustly been associated to levels of major circulating blood lipids. Most loci are specific to one or two lipids, whereas some (SUGP1, ZPR1, TRIB1, HERPUD1, and FADS1) are associated to all. While exposing the polygenic architecture of circulating lipids and the underpinnings of dyslipidaemia, these genome-wide association studies (GWAS) have provided further evidence of the critical role that lipids play in coronary heart disease (CHD) risk, as indicated by the 2.7-fold enrichment for macrophage gene expression in atherosclerotic plaques and the association of 25 loci (such as PCSK9, APOB, ABCG5-G8, KCNK5, LPL, HMGCR, NPC1L1, CETP, TRIB1, ABO, PMAIP1-MC4R, and LDLR) with CHD. These GWAS also confirmed known and commonly used therapeutic targets, including HMGCR (statins), PCSK9 (antibodies), and NPC1L1 (ezetimibe). As we head into the post-GWAS era, we offer suggestions for how to move forward beyond genetic risk loci, towards refining the biology behind the associations and identifying causal genes and therapeutic targets. Deep phenotyping through lipidomics and metabolomics will refine and increase the resolution to find causal and druggable targets, and studies aimed at demonstrating gene transcriptional and regulatory effects of lipid associated loci will further aid in identifying these targets. Thus, we argue the need for deeply phenotyped, large genetic association studies to reduce costs and failures and increase the efficiency of the drug discovery pipeline. We conjecture that in the next decade a paradigm shift will tip the balance towards a data-driven approach to therapeutic target development and the application of precision medicine where human genomics takes centre stage.