Whole-genome sequencing for an enhanced understanding of genetic variation among South Africans

Ananyo Choudhury, Michèle Ramsay (), Scott Hazelhurst, Shaun Aron, Soraya Bardien, Gerrit Botha, Emile R. Chimusa, Alan Christoffels, Junaid Gamieldien, Mahjoubeh J. Sefid-Dashti, Fourie Joubert, Ayton Meintjes, Nicola Mulder, Raj Ramesar, Jasper Rees, Kathrine Scholtz, Dhriti Sengupta, Himla Soodyall, Philip Venter, Louise Warnich and Michael S. Pepper ()
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Ananyo Choudhury: University of the Witwatersrand
Michèle Ramsay: University of the Witwatersrand
Scott Hazelhurst: University of the Witwatersrand
Shaun Aron: University of the Witwatersrand
Soraya Bardien: Stellenbosch University
Gerrit Botha: University of Cape Town
Emile R. Chimusa: University of Cape Town
Alan Christoffels: University of the Western Cape
Junaid Gamieldien: University of the Western Cape
Mahjoubeh J. Sefid-Dashti: University of the Western Cape
Fourie Joubert: University of Pretoria
Ayton Meintjes: University of Cape Town
Nicola Mulder: University of Cape Town
Raj Ramesar: University of Cape Town
Jasper Rees: Agricultural Research Council
Kathrine Scholtz: University of Limpopo
Dhriti Sengupta: University of the Witwatersrand
Himla Soodyall: University of the Witwatersrand
Philip Venter: University of Limpopo
Louise Warnich: Stellenbosch University
Michael S. Pepper: University of Pretoria

Nature Communications, 2017, vol. 8, issue 1, 1-12

Abstract: Abstract The Southern African Human Genome Programme is a national initiative that aspires to unlock the unique genetic character of southern African populations for a better understanding of human genetic diversity. In this pilot study the Southern African Human Genome Programme characterizes the genomes of 24 individuals (8 Coloured and 16 black southeastern Bantu-speakers) using deep whole-genome sequencing. A total of ~16 million unique variants are identified. Despite the shallow time depth since divergence between the two main southeastern Bantu-speaking groups (Nguni and Sotho-Tswana), principal component analysis and structure analysis reveal significant (p

Date: 2017
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DOI: 10.1038/s41467-017-00663-9

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