Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populations

Abstract

Hypertension is a leading cause of global disease, mortality, and disability. While individuals of African descent suffer a disproportionate burden of hypertension and its complications, they have been underrepresented in genetic studies. To identify novel susceptibility loci for blood pressure and hypertension in people of African ancestry, we performed both single and multiple-trait genome-wide association analyses. We analyzed 21 genome-wide association studies comprised of 31,968 individuals of African ancestry, and validated our results with additional 54,395 individuals from multi-ethnic studies. These analyses identified nine loci with eleven independent variants which reached genome-wide significance (P < 1.25×10−8) for either systolic and diastolic blood pressure, hypertension, or for combined traits. Single-trait analyses identified two loci (TARID/TCF21 and LLPH/TMBIM4) and multiple-trait analyses identified one novel locus (FRMD3) for blood pressure. At these three loci, as well as at GRP20/CDH17, associated variants had alleles common only in African-ancestry populations. Functional annotation showed enrichment for genes expressed in immune and kidney cells, as well as in heart and vascular cells/tissues. Experiments driven by these findings and using angiotensin-II induced hypertension in mice showed altered kidney mRNA expression of six genes, suggesting their potential role in hypertension. Our study provides new evidence for genes related to hypertension susceptibility, and the need to study African-ancestry populations in order to identify biologic factors contributing to hypertension.

Genome-Wide Association Studies of Hypertension and Several Other Cardiovascular Diseases

“…There are fewer loci reaching GWAS significance in African populations than in Europeans or Asians. The largest GWAS performed in an African-origin population analyzed 21 GWAS comprising 31,968 individuals of African ancestry and validated their results with an additional 54,395 individuals from multiethnic studies [15]. The authors found 9 loci with 11 independent variants for either SBP, DBP, hypertension, or combined traits.…”

Advancing stroke genomic research in the age of Trans-Omics big data science: Emerging priorities and opportunities

“…This research identified three novel genomic regions associated with blood pressure and utilized transomic techniques to unravel the associated pathways, which have not been previously reported in studies of other race/ethnicity. 62 Likewise, stroke research utilizing Africans and African ancestry populations can produce significant dividends because of the association between high blood pressure and stroke in African ancestry populations. 62 …”

“…62 Likewise, stroke research utilizing Africans and African ancestry populations can produce significant dividends because of the association between high blood pressure and stroke in African ancestry populations. 62 …”

Adjustment for covariates using summary statistics of genome‐wide association studies

“…Together, these efforts have already yielded the successful discovery of over thousands of disease associated loci (Ehret et al, 2016; Ehret et al, 2011; Evangelou, 2018; Hoffmann et al, 2017; Lane et al, 2016; Liang et al, 2017; Liu et al, 2016; Marouli et al, 2017; Turcot et al, 2018). The commonly-used approach for analyzing a continuous trait still relies on a simple regression model.…”

“…Genome-wide association studies (GWAS), whole-exome sequencing studies and whole-genome sequencing studies have produced many large data sets of genetic variation in hundreds of thousands to millions of human subjects (Boyle, Li, & Pritchard, 2017), which has motivated the development of new statistical methods to analyze these data sets for addressing important biological questions. Together, these efforts have already yielded the successful discovery of over thousands of disease-associated loci (Ehret et al, 2011;Ehret et al, 2016;Evangelou, Warren & Mosen-Ansorena, 2017;Hoffmann et al, 2017;Lane et al, 2016;Liang et al, 2017;Liu et al, 2016;Marouli et al, 2017;Turcot et al, 2018). The commonly used approach for analyzing a continuous trait still relies on a simple regression model.…”