A genome-wide association study for rheumatoid arthritis replicates previous HLA and non-HLA associations in a cohort from South Africa

Abstract

The complex pathogenesis of rheumatoid arthritis (RA) is not fully understood, with few studies exploring the genomic contribution to RA in patients from Africa. We report a genome-wide association study (GWAS) of South-Eastern Bantu-Speaking South Africans (SEBSSAs) with seropositive RA (n = 531) and population controls (n = 2653). Association testing was performed using PLINK (logistic regression assuming an additive model) with sex, age, smoking and the first three principal components as covariates. The strong association with the Human Leukocyte Antigen (HLA) region, indexed by rs602457 (near HLA-DRB1), was replicated. An additional independent signal in the HLA region represented by the lead SNP rs2523593 (near the HLA-B gene; Conditional P-value = 6.4 × 10-10) was detected. Although none of the non-HLA signals reached genome-wide significance (P < 5 × 10-8), 17 genomic regions showed suggestive association (P < 5 × 10-6). The GWAS replicated two known non-HLA associations with MMEL1 (rs2843401) and ANKRD55 (rs7731626) at a threshold of P < 5 × 10-3 providing, for the first time, evidence for replication of non-HLA signals for RA in sub-Saharan African populations. Meta-analysis with summary statistics from an African-American cohort (CLEAR study) replicated three additional non-HLA signals (rs11571302, rs2558210 and rs2422345 around KRT18P39-NPM1P33, CTLA4-ICOS and AL645568.1, respectively). Analysis based on genomic regions (200 kb windows) further replicated previously reported non-HLA signals around PADI4, CD28 and LIMK1. Although allele frequencies were overall strongly correlated between the SEBSSA and the CLEAR cohort, we observed some differences in effect size estimates for associated loci. The study highlights the need for conducting larger association studies across diverse African populations to inform precision medicine-based approaches for RA in Africa.

Figure 1

Summary of associations detected in the GWAS and the meta-analysis. (A) Miami plot showing association signals from the SEBSSA GWAS (upward facing) and SEBSSA-CLEAR meta-analysis (downward facing). Signals with P-value<10⁻¹⁰ have been shown as P-value = 10⁻¹⁰ to enhance the visibility of suggestive signals. Only SNPs that were common to the two studies are shown. The red horizontal line corresponds to the genome-wide significance threshold (P-value = 5 × 10⁻⁸) and the orange dots represent SNPs below the suggestive threshold (P-value = 5 × 10⁻⁶). (B) Regional association plot for HLA in the SEBSSA study. Three independent signals identified using conditional analysis are shown in blue. Locuszoom plots showing LD and association statistics in the +/−200 kb region around the lead SNPs corresponding to the three independent signals (C) rs2523593 (D) rs29001652 and (E) rs602457. The LD estimates were based on the SEBSSA dataset.

Figure 2

Comparison of effect sizes and EAFs in SEBSSA and CLEAR cohorts. Known RA-associated SNPs (from GWAS Catalog) that show a P-value<0.05 for non-HLA and P-value<0.005 for HLA in the SEBSSA summary statistics are included. (A) Effect size comparisons of HLA signals. (B) Effect size comparisons of non-HLA signals. (C) EAF comparisons of HLA signals (D). EAF comparisons of non-HLA signals. A more comprehensive set of SNPs are shown in Supplementary Material, Figure S9.