Genome-wide association study identifying novel risk variants associated with glycaemic traits in the continental African AWI-Gen cohort

Vivien J Chebii¹, Alisha N Wade^{2

3

4}, Nigel J Crowther⁵, Engelbert A Nonterah^{6

7

8}, Godfred Agongo⁹, Z Simayi¹⁰, Palwende R Boua^{11

12

13}, Isaac Kisiangani¹⁴, Michèle Ramsay^{11

15}, Ananyo Choudhury¹⁶, Dhriti Sengupta¹⁷; as members of the H3Africa AWI-Gen study

Affiliations

¹ Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. vivien.chebii@wits.ac.za.
² Department of Internal Medicine, School of Clinical Medicine, University of the Witwatersrand, Johannesburg, South Africa.
³ Research in Metabolism and Endocrinology, Division of Endocrinology, Diabetes and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁴ MRC/Wits Rural Public Health and Health Transitions Research Unit, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
⁵ Department of Chemical Pathology, National Health Laboratory Service, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
⁶ Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana.
⁷ Department of Epidemiology, School of Public Health, C.K. Tedam University of Technology and Allied Sciences, Navrongo, Ghana.
⁸ Julius Global Health, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands.
⁹ Department of Biochemistry and Forensic Sciences, School of Chemical and Biochemical Sciences, C.K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana.
¹⁰ Department of Pathology, Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa.
¹¹ Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
¹² Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santè, Nanoro, Burkina Faso.
¹³ MRC Unit The Gambia at London School of Hygiene and Tropical Medicine, Banjul, The Gambia.
¹⁴ African Population and Health Research Center, Nairobi, Kenya.
¹⁵ Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
¹⁶ Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. Ananyo.Choudhury@wits.ac.za.
¹⁷ Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. Dhriti.Sengupta@wits.ac.za.

PMID: 40025146
PMCID: PMC12069158
DOI: 10.1007/s00125-025-06395-6

Genome-wide association study identifying novel risk variants associated with glycaemic traits in the continental African AWI-Gen cohort

Vivien J Chebii et al. Diabetologia. 2025 Jun.

. 2025 Jun;68(6):1184-1196.

doi: 10.1007/s00125-025-06395-6. Epub 2025 Mar 1.

Authors

Affiliations

¹ Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. vivien.chebii@wits.ac.za.
² Department of Internal Medicine, School of Clinical Medicine, University of the Witwatersrand, Johannesburg, South Africa.
³ Research in Metabolism and Endocrinology, Division of Endocrinology, Diabetes and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁴ MRC/Wits Rural Public Health and Health Transitions Research Unit, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
⁵ Department of Chemical Pathology, National Health Laboratory Service, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
⁶ Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana.
⁷ Department of Epidemiology, School of Public Health, C.K. Tedam University of Technology and Allied Sciences, Navrongo, Ghana.
⁸ Julius Global Health, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands.
⁹ Department of Biochemistry and Forensic Sciences, School of Chemical and Biochemical Sciences, C.K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana.
¹⁰ Department of Pathology, Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa.
¹¹ Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
¹² Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santè, Nanoro, Burkina Faso.
¹³ MRC Unit The Gambia at London School of Hygiene and Tropical Medicine, Banjul, The Gambia.
¹⁴ African Population and Health Research Center, Nairobi, Kenya.
¹⁵ Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
¹⁶ Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. Ananyo.Choudhury@wits.ac.za.
¹⁷ Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. Dhriti.Sengupta@wits.ac.za.

PMID: 40025146
PMCID: PMC12069158
DOI: 10.1007/s00125-025-06395-6

Abstract

Aims/hypothesis: Glycaemic traits such as high fasting glucose levels and insulin resistance are positively associated with the risk of type 2 diabetes and other cardiometabolic diseases. Genetic association studies have identified hundreds of associations for each glycaemic trait, yet very few studies have involved continental African populations. We report the results of genome-wide association studies (GWASs) in a pan-African cohort for four glycaemic traits, namely fasting glucose, fasting insulin, insulin resistance (HOMA-IR) and beta cell function (HOMA-B), which are quantitative variables that affect the risk of developing type 2 diabetes.

Methods: GWASs for the four traits were conducted in approximately 10,000 individuals from the Africa Wits-INDEPTH Partnership for Genomics Studies (AWI-Gen) cohort, with participants from Burkina Faso, Ghana, Kenya and South Africa. Association testing was performed using linear mixed models implemented in BOLT-LMM, with age, sex, BMI and principal components as covariates. Replication, fine mapping and functional annotation were performed using standard approaches.

Results: We identified a novel signal (rs574173815) in the intron of the ankyrin repeat domain 33B (ANKRD33B) gene associated with fasting glucose, and a novel signal (rs114029796) in the intronic region of the WD repeat domain 7 (WDR7) gene associated with fasting insulin. SNPs in WDR7 have been shown to be associated with type 2 diabetes. A variant (rs74806991) in the intron of ADAM metallopeptidase with thrombospondin type 1 motif 16 (ADAMTS16) and another variant (rs6506934) in the β-1,4-galactosyltransferase 6 gene (B4GALT6) are associated with HOMA-IR. Both ADAMTS16 and B4GALT6 are implicated in the development of type 2 diabetes. In addition, our study replicated several well-established fasting glucose signals in the GCK-YTK6, SLC2A2 and THORLNC gene regions.

Conclusions/interpretation: Our findings highlight the importance of performing GWASs for glycaemic traits in under-represented populations, especially continental African populations, to discover novel associated variants and broaden our knowledge of the genetic aetiology of glycaemic traits. The limited replication of well-known signals in this study hints at the possibility of a unique genetic architecture of these traits in African populations.

Data availability: The dataset used in this study is available in the European Genome-Phenome Archive (EGA) database ( https://ega-archive.org/ ) under study accession code EGAS00001002482. The phenotype dataset accession code is EGAD00001006425 and the genotype dataset accession code is EGAD00010001996. The availability of these datasets is subject to controlled access by the Data and Biospecimen Access Committee of the H3Africa Consortium. GWAS summary statistics are accessible through the NHGRI-EBI GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ).

Keywords: African ancestry; Fasting glucose; Fasting insulin; GWAS; Type 2 diabetes.

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Conflict of interest statement

Acknowledgements: We thank all AWI-Gen participants for their consent to participate in the study and all the field workers, laboratory scientists, data managers and other support staff who contributed to sample collection and data generation. The complete list of AWI-Gen Consortium contributing members is provided in ESM Table 8. We acknowledge Jean-Tristan Brandenburg for developing and help in troubleshooting the H3A-GWAS pipeline used for quality control analysis and fine mapping, and Scott Hazelhurst for help with the use of the computing infrastructure. This study used the high-performance computing facilities at the University of Witwatersrand. Data on glycaemic traits used for replication have been contributed by MAGIC investigators and have been downloaded from www.magicinvestigators.org.African . We acknowledge other researchers who have publicly provided their data, which is cited accordingly in the manuscript used for replications in this study. Data availability: The dataset used in this study is available in the European Genome–Phenome Archive (EGA) database ( https://ega-archive.org/ ) under study accession code EGAS00001002482. The phenotype dataset accession code is EGAD00001006425 and the genotype dataset accession code is EGAD00010001996. The availability of these datasets is subject to controlled access by the Data and Biospecimen Access Committee of the H3Africa Consortium. GWAS summary statistics are accessible through the NHGRI-EBI GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ; FG-BMIadjusted, GCST90503329; FG-BMIunadjusted, GCST90503330; FI-BMIadjusted, GCST90503331; FI-BMIunadjusted, GCST90503332; HOMA-B-BMIadjusted, GCST90503333; HOMA-B-BMIunadjusted, GCST90503334; HOMA-IR-BMIadjusted, GCST90503335; HOMA-IR-BMIunadjusted, GCST90503336). Other published datasets included in this study are referenced in the Methods section. Code availability: No custom codes were used in this study. We used publicly available software (referenced in the Methods section) in this research. The H3A-Africa GWAS pipeline was used for quality control analysis and fine mapping, and can be accessed at https://github.com/h3abionet/h3agwas . Other scripts used in this study are available at https://github.com/chirrie/GWAS-of-glycaemic-traits . Funding: The AWI-Gen Collaborative Center is funded by the National Human Genome Research Institute (NHGRI; grant number U54HG006938) as part of the Human Heredity and Health in Africa Consortium. Additional funding was granted by the Department of Science and Technology (now the Department of Science and Innovation), South Africa (award number DST/CON 0056/2014). The funders were not involved in the study’s design, in collection, analysis and interpretation of data, or in drafting the report, and did not restrict publication of the research. Authors’ relationships and activities: The authors declare that there are no relationships or activities that might bias, or be perceived to bias, their work. Contribution statement: AC, DS and MR conceived the study. ANW and NJC guided VJC regarding participant inclusion and exclusion criteria. VJC performed all analyses under the guidance of AC, DS and MR. VJC drafted the manuscript with input and supervision from MR, AC and DS. EAN, GA, ZS, PRB and IK directed fieldwork and sample collection. All authors have read, commented on, and approved the submitted version. VJC is responsible for the integrity of the work as whole.

Figures

**Fig. 1**
(a) Manhattan plot for the FG GWAS. The red horizontal line indicates the genome-wide significance threshold (p=5 × 10⁻⁸); the blue horizontal line shows the suggestive significance threshold of p=1 × 10⁻⁶. (b) Regional plot of chromosome 5 (including the *ANKRD33B* gene region), showing association with FG in the African AWI-Gen cohort: the lead SNP (rs574173815) is shown as a black and purple diamond, and SNPs in LD with the lead SNP are shown as coloured triangles. The x-axis shows the genomic positions based on the Human Build 37 reference genome (GRCh37). The blue peaks indicate the recombination rates at each position. Genes overlapping with or near the locus are shown below the x-axis. LD was based on the African 1000 Genomes Project reference panel

**Fig. 2**
(a) Manhattan plot for the FI GWAS. The red horizontal line indicates the genome-wide significance threshold (p=5 × 10⁻⁸); the blue horizontal line shows the suggestive significance threshold of p=1 × 10⁻⁶. (b) Regional plot of chromosome 18 (*WDR7* and *BODIL2* gene region) showing associations with FI in the African AWI-Gen cohort: the lead SNP (rs114029796) is shown as a black and purple diamond, and SNPs in LD with the lead SNP are shown as coloured triangles. The x-axis shows the genomic positions based on the Human Build 37 reference genome (GRCh37). The blue peaks indicate the recombination rates at each position. Genes overlapping with or near the locus are shown below the x-axis. LD was based on the African 1000 Genomes Project LD panel

**Fig. 3**
(a) Manhattan plot for the HOMA-IR GWAS. The red horizontal line indicates the genome-wide significance threshold (p=5 × 10⁻⁸); the blue horizontal line shows the suggestive significance threshold of p=1 × 10⁻⁶. (b) Regional plot of chromosome 5 (including the *ADAMTS16* gene region) showing association with HOMA-IR in the African AWI-Gen cohort: the lead SNP (rs74806991) is shown as a black and purple diamond, and SNPs in LD with the lead SNP are shown as coloured triangles. (c) Regional plot of chromosome 18 (including the *B4GALT6* gene region) showing an association with HOMA-IR in the African AWI-Gen cohort: the lead SNP (rs6506934) is shown as a black and purple diamond, and SNPs in LD with the lead SNP are shown as coloured triangles. The x-axis shows the genomic positions based on the Human Build 37 reference genome (GRCh37). The blue peaks indicate the recombination rates at each position. Genes overlapping with or near the locus are shown below the x-axis. LD was based on the African 1000 Genomes Project LD panel

See this image and copyright information in PMC

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Genome-wide association study identifying novel risk variants associated with glycaemic traits in the continental African AWI-Gen cohort

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Genome-wide association study identifying novel risk variants associated with glycaemic traits in the continental African AWI-Gen cohort

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Abstract

Conflict of interest statement

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