Genomics and Biobank Research for Health Equity

National Institute On Minority Health And Health Disparities

Investigators

Linked publications, trials & patents

Abstract

Today we are starting to fulfill the promise of genomics for personalized medicine and healthcare. Interestingly, and for a variety of reasons, publicly funded databases continue to miss a vast portion of the world's genetic variation. As of January 2018, the GWAS catalog has registered 78% of individuals from European ancestry while underrepresented populations make up less than 4% including African (2.4%), Hispanic or Latin American (1.3%) and Native American (0.03%). The sampling bias is referred to as the genomics research gap and has the potential to exacerbate existing health disparities among underrepresented and underserved populations. Populations underrepresented in biomedical research bear a disproportionate burden for many diseases, including diabetes type 2. This is particularly true for Native American communities in the US, who have among the lowest levels of participation in genomic studies seen for any ethnic group. Genomic studies must be more representative of all populations so that all people can benefit from the upcoming genomic revolution in healthcare. We are interested in studying how combinations of genetic ancestry in admixed Latin American populations may impact genomic determinants of health and disease. We have found ancestry-enriched SNPs in Latin American populations having a substantial effect on health- and disease-related phenotypes. We just published a study aimed to understand the combined effects of socioeconomic deprivation (SED) and genetic ancestry (GA) on type 2 diabetes (T2D) disparities in the UK. The study included 474,184 adults between 40 and 70 years of age enrolled in the UK Biobank between 2006 and 2010, of which 5.9% had T2D. We used the Townsend deprivation index to measure SED, which incorporates unemployment, non-car ownership, non-home ownership, and household crowding in a given area. Through clustering genetic principal components, researchers were able to create GA groupingsAfrican, European, and South Asianto capture the genetic diversity among the study cohort. Together, the data was analyzed to determine whether terciles of SED (low, medium, or high) and/or GA are associated with T2D prevalence within Asian, Black, and White ethnic groups in the UK. The study investigators identified that the prevalence of T2D was highest among Asians (17.9%) in the UK, followed by Blacks (11.7%) and Whites (5.5%). Using White Europeans with low SED as a baseline, medium and higher SED levels were associated with increased risk of T2D across all White, African, and South Asian ancestries. However, SED and GA interact in non-additive way, yielding group-specific effects of SED on T2D. The effect of SED on T2D risk was greatest for individuals with South Asian and African ancestry, even those with low SED, when compared with low SED Whites. In particular, South Asians with high SED had the greatest overall risk with a reported odds ratio of 7.95 (95% CI, 7.32-8.63). The study results may be attributed to lifestyle and environmental exposures that often occur with higher SED. For example, SED-related experiences of structural oppression may differ among different ethnic and ancestral groups and increase risk of developing T2D. The findings from this study support the need for community- and population-specific interventions that recognize the importance of SED for T2D risk across different ethnic groups in the UK. Policies that support these interventions may help decrease T2D health disparities. Our group is also developing algorithms and software for local and global ancestry estimation that scale well with biobank data and are actively maintaining a repository on GitHub: https://github.com/healthdisparities.