Jean-Tristan Brandenburg ^1,2* Wenlong C. Chen ^1,2,3 Palwende R. BOUA ^1,4 Melanie Ann Govender ^1,5 Godfred Agongo ^6,7 Lisa Micklesfield ⁸ Hermann Sorgho ⁴ Stephen Tollman ⁹ Gershim Asiki ^10,11 Felistas Mashinya ¹² Scott Hazelhurst ^1,13 Andrew P. Morris ¹⁴ June Fabian ¹⁵ Michele Ramsay ^1,5*

• ¹ Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
• ² Strengthening Oncology Services Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
• ³ National Cancer Registry, National Health Laboratory Service, Johannesburg, South Africa
• ⁴ Clinical Research Unit of Nanoro (CRUN), Nanoro, Burkina Faso
• ⁵ Division of Human Genetics, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
• ⁶ Navrongo Health Research Centre, Navrongo, Ghana
• ⁷ Department of Biochemistry and Forensic Sciences, School of Chemical and Biochemical Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, South Africa
• ⁸ SAMRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
• ⁹ Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
• ¹⁰ African Population and Health Research Center (APHRC), Nairobi, Kenya
• ¹¹ Department of Women's and Children's Health, Karolinska Institute (KI), Stockholm, Stockholm, Sweden
• ¹² Department of Pathology and Medical Sciences, School of Health Care Sciences, Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa
• ¹³ School of Electrical and Information Engineering, Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, South Africa
• ¹⁴ Centre for Genetics and Genomics Versus Arthritis, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, England, United Kingdom
• ¹⁵ Wits University Donald Gordon Medical Centre, Johannesburg, South Africa

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<5x10-8) 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.