Meta-Analysis and Multivariate GWAS Analyses in 80,950 Individuals of African Ancestry Identify Novel Variants Associated with Blood Pressure Traits

Abstract

High blood pressure (HBP) has been implicated as a major risk factor for cardiovascular diseases in several populations, including individuals of African ancestry. Despite the elevated burden of HBP-induced cardiovascular diseases in Africa and other populations of African descent, limited genetic studies have been carried out to explore the genetic mechanism driving this phenomenon. We performed genome-wide association univariate and multivariate analyses of both systolic (SBP) and diastolic blood pressure (DBP) traits in 80,950 individuals of African ancestry. We used summary statistics data from six independent cohorts, including the African Partnership for Chronic Disease Research (APCDR), the UK Biobank, and the Million Veteran Program (MVP). FUMA was used to annotate, prioritize, visualize, and interpret our findings to gain a better understanding of the molecular mechanism(s) underlying the genetics of BP traits. Finally, we undertook a Bayesian fine-mapping analysis to identify potential causal variants. Our meta-analysis identified 10 independent variants associated with SBP and 9 with DBP traits. Whilst our multivariate GWAS method identified 21 independent signals, 18 of these SNPs have been previously identified. SBP was linked to gene sets involved in biological processes such as synapse assembly and cell-cell adhesion via plasma membrane adhesion. Of the 19 independent SNPs identified in the BP meta-analysis, only 11 variants had posterior probability (PP) of > 50%, including one novel variant: rs562545 (MOBP, PP = 77%). To facilitate further research and fine-mapping of high-risk loci/variants in highly susceptible groups for cardiovascular disease and other related traits, large-scale genomic datasets are needed. Our findings highlight the importance of including ancestrally diverse populations in large GWASs and the need for diversity in genetic research.

Keywords: GWAS; diastolic blood pressure; high blood pressure; multivariate; systolic blood pressure; univariate.

Figure 1

Study design schematic for discovery and validation of loci. APCDR; African Partnership for Control of Disease Research, UKB; United Kingdom Biobank, MVP; Million Veteran Program.

Figure 2

Manhattan plots showing the minimum p-value for the association across (A) DBP and (B) SBP blood pressure traits, computed using inverse-variance fixed-effect meta-analysis from 75,850 individuals. Each point on the Manhattan plots denotes a variant, with the X-axis representing the genomic position and the Y-axis representing the association level −log 10 (p-value). The horizontal red line shows the genome-wide significance threshold p-value = 5 × 10^–8.

Figure 3

MAGMA tissue expression analysis using gene expression per tissue based on GTEx RNAseq data for 53 specific tissue types. Significant tissue is shown in red.

Figure 4

Regional visualization of the GWAS of −log10 of the p-value of genomic location MOBP (rs562545 in purple) (A) and AC074290.1 (rs77534700 in purple) (B), with each dot representing SNP on the corresponding genes at the bottom.

Figure 5

Manhattan plot showing p-values for the association computed from CPASSOC output results. Each point on the Manhattan plot denotes a variant, with the X-axis representing the genomic position and the Y-axis representing the association level −log 10 (p-value). The horizontal red line shows the genome-wide significance threshold p-value = 5 × 10^–8.

Figure 6

Venn diagram comparing the loci found by meta-analysis and multivariate GWAS analysis. Both methods identified known loci; the novel loci are indicated with an asterisk.