Performance and Accuracy Evaluation of Reference Panels for Genotype Imputation in Sub-Saharan African Populations

Overview

Journal Cell Genom

Date 2023 Jun 30

PMID 37388906

Authors

Dhriti Sengupta

Gerrit Botha

Ayton Meintjes

Mamana Mbiyavanga

Scott Hazelhurst

Nicola Mulder

Michele Ramsay

Ananyo Choudhury

Affiliations

Soon will be listed here.

Abstract

Based on evaluations of imputation performed on a genotype dataset consisting of about 11,000 sub-Saharan African (SSA) participants, we show Trans-Omics for Precision Medicine (TOPMed) and the African Genome Resource (AGR) to be currently the best panels for imputing SSA datasets. We report notable differences in the number of single-nucleotide polymorphisms (SNPs) that are imputed by different panels in datasets from East, West, and South Africa. Comparisons with a subset of 95 SSA high-coverage whole-genome sequences (WGSs) show that despite being about 20-fold smaller, the AGR imputed dataset has higher concordance with the WGSs. Moreover, the level of concordance between imputed and WGS datasets was strongly influenced by the extent of Khoe-San ancestry in a genome, highlighting the need for integration of not only geographically but also ancestrally diverse WGS data in reference panels for further improvement in imputation of SSA datasets. Approaches that integrate imputed data from different panels could also lead to better imputation.

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References

Li H, Durbin R . Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009; 25(14):1754-60. PMC: 2705234. DOI: 10.1093/bioinformatics/btp324. View

Ramsay M, Crowther N, Tambo E, Agongo G, Baloyi V, Dikotope S . H3Africa AWI-Gen Collaborative Centre: a resource to study the interplay between genomic and environmental risk factors for cardiometabolic diseases in four sub-Saharan African countries. Glob Health Epidemiol Genom. 2017; 1:e20. PMC: 5732578. DOI: 10.1017/gheg.2016.17. View

Das S, Abecasis G, Browning B . Genotype Imputation from Large Reference Panels. Annu Rev Genomics Hum Genet. 2018; 19:73-96. DOI: 10.1146/annurev-genom-083117-021602. View

. The GenomeAsia 100K Project enables genetic discoveries across Asia. Nature. 2019; 576(7785):106-111. PMC: 7054211. DOI: 10.1038/s41586-019-1793-z. View

Chang C, Chow C, Tellier L, Vattikuti S, Purcell S, Lee J . Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015; 4:7. PMC: 4342193. DOI: 10.1186/s13742-015-0047-8. View

Durbin R . Efficient haplotype matching and storage using the positional Burrows-Wheeler transform (PBWT). Bioinformatics. 2014; 30(9):1266-72. PMC: 3998136. DOI: 10.1093/bioinformatics/btu014. View

Browning B, Browning S . Genotype Imputation with Millions of Reference Samples. Am J Hum Genet. 2016; 98(1):116-26. PMC: 4716681. DOI: 10.1016/j.ajhg.2015.11.020. View

Lex A, Gehlenborg N, Strobelt H, Vuillemot R, Pfister H . UpSet: Visualization of Intersecting Sets. IEEE Trans Vis Comput Graph. 2015; 20(12):1983-92. PMC: 4720993. DOI: 10.1109/TVCG.2014.2346248. View

Danecek P, Auton A, Abecasis G, Albers C, Banks E, DePristo M . The variant call format and VCFtools. Bioinformatics. 2011; 27(15):2156-8. PMC: 3137218. DOI: 10.1093/bioinformatics/btr330. View

10.

Sun Q, Liu W, Rosen J, Huang L, Pace R, Dang H . Leveraging TOPMed imputation server and constructing a cohort-specific imputation reference panel to enhance genotype imputation among cystic fibrosis patients. HGG Adv. 2022; 3(2):100090. PMC: 8804187. DOI: 10.1016/j.xhgg.2022.100090. View

11.

Choudhury A, Aron S, Botigue L, Sengupta D, Botha G, Bensellak T . High-depth African genomes inform human migration and health. Nature. 2020; 586(7831):741-748. PMC: 7759466. DOI: 10.1038/s41586-020-2859-7. View

12.

Fatumo S, Chikowore T, Choudhury A, Ayub M, Martin A, Kuchenbaecker K . A roadmap to increase diversity in genomic studies. Nat Med. 2022; 28(2):243-250. PMC: 7614889. DOI: 10.1038/s41591-021-01672-4. View

13.

Taliun D, Harris D, Kessler M, Carlson J, Szpiech Z, Torres R . Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature. 2021; 590(7845):290-299. PMC: 7875770. DOI: 10.1038/s41586-021-03205-y. View

14.

McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood A, Teumer A . A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016; 48(10):1279-83. PMC: 5388176. DOI: 10.1038/ng.3643. View

15.

Ali S, Soo C, Agongo G, Alberts M, Amenga-Etego L, Boua R . Genomic and environmental risk factors for cardiometabolic diseases in Africa: methods used for Phase 1 of the AWI-Gen population cross-sectional study. Glob Health Action. 2018; 11(sup2):1507133. PMC: 6161608. DOI: 10.1080/16549716.2018.1507133. View

16.

Bentley A, Callier S, Rotimi C . Evaluating the promise of inclusion of African ancestry populations in genomics. NPJ Genom Med. 2020; 5:5. PMC: 7042246. DOI: 10.1038/s41525-019-0111-x. View

17.

Gurdasani D, Carstensen T, Tekola-Ayele F, Pagani L, Tachmazidou I, Hatzikotoulas K . The African Genome Variation Project shapes medical genetics in Africa. Nature. 2014; 517(7534):327-32. PMC: 4297536. DOI: 10.1038/nature13997. View

18.

Patin E, Lopez M, Grollemund R, Verdu P, Harmant C, Quach H . Dispersals and genetic adaptation of Bantu-speaking populations in Africa and North America. Science. 2017; 356(6337):543-546. DOI: 10.1126/science.aal1988. View

19.

Schlebusch C, Sjodin P, Breton G, Gunther T, Naidoo T, Hollfelder N . Khoe-San Genomes Reveal Unique Variation and Confirm the Deepest Population Divergence in Homo sapiens. Mol Biol Evol. 2020; 37(10):2944-2954. PMC: 7530619. DOI: 10.1093/molbev/msaa140. View

20.

Schlebusch C, Skoglund P, Sjodin P, Gattepaille L, Hernandez D, Jay F . Genomic variation in seven Khoe-San groups reveals adaptation and complex African history. Science. 2012; 338(6105):374-9. PMC: 8978294. DOI: 10.1126/science.1227721. View