Genetic association and transferability for urinary albumin-creatinine ratio as a marker of kidney disease in four Sub-Saharan African populations and non-continental individuals of African ancestry

Abstract

Background: Genome-wide association studies (GWAS) have predominantly focused on populations of European and Asian ancestry, limiting our understanding of genetic factors influencing kidney disease in Sub-Saharan African (SSA) populations. This study presents the largest GWAS for urinary albumin-to-creatinine ratio (UACR) in SSA individuals, including 8,970 participants living in different African regions and an additional 9,705 non-resident individuals of African ancestry from the UK Biobank and African American cohorts.

Methods: Urine biomarkers and genotype data were obtained from two SSA cohorts (AWI-Gen and ARK), and two non-resident African-ancestry studies (UK Biobank and CKD-Gen Consortium). Association testing and meta-analyses were conducted, with subsequent fine-mapping, conditional analyses, and replication studies. Polygenic scores (PGS) were assessed for transferability across populations.

Results: Two genome-wide significant (P < 5 × 10^-8) UACR-associated loci were identified, one in the BMP6 region on chromosome 6, in the meta-analysis of resident African individuals, and another in the HBB region on chromosome 11 in the meta-analysis of non-resident SSA individuals, as well as the combined meta-analysis of all studies. Replication of previous significant results confirmed associations in known UACR-associated regions, including THB53, GATM, and ARL15. PGS estimated using previous studies from European ancestry, African ancestry, and multi-ancestry cohorts exhibited limited transferability of PGS across populations, with less than 1% of observed variance explained.

Conclusion: This study contributes novel insights into the genetic architecture of kidney disease in SSA populations, emphasizing the need for conducting genetic research in diverse cohorts. The identified loci provide a foundation for future investigations into the genetic susceptibility to chronic kidney disease in underrepresented African populations Additionally, there is a need to develop integrated scores using multi-omics data and risk factors specific to the African context to improve the accuracy of predicting disease outcomes.

Keywords: African diversity; GWAS; Polygenic score; UACR; chronic kidney disease.

FIGURE 1

Study design showing data sources, the analysis strategy and post-GWAS analysis approach.

FIGURE 2

Manhattan plot—GWAS of UACR in the (A) Meta_SSA (B) Meta_NONRES (C) Meta_ALL datasets using the fixed effect model. Lead genome-wide significant SNPs (P < 5 × 10⁻⁰⁸) and gene annotations are highlighted.

FIGURE 3

Regional plot using LocusZoom of genome-wide significant SNPs found in meta-analyses using the fixed effect model, (A) rs9505286 from the result of Meta_SSA, (B) rs9966824 from the result Meta_NONRES (C) rs9966824 from the result Meta_ALL.

FIGURE 4

Percent variance (r2) explained between PGS and residual phenotypes computed using age, sex and 5 PCs. Key- The negative relationship between PGS and the phenotype in the result of the linear model, *p < 0.05, **p < 0.01 and ***p < 0.001. Details in Supplementary Table S6.