Genetic signatures of gene flow and malaria-driven natural selection in sub-Saharan populations of the "endemic Burkitt Lymphoma belt"

Abstract

Populations in sub-Saharan Africa have historically been exposed to intense selection from chronic infection with falciparum malaria. Interestingly, populations with the highest malaria intensity can be identified by the increased occurrence of endemic Burkitt Lymphoma (eBL), a pediatric cancer that affects populations with intense malaria exposure, in the so called "eBL belt" in sub-Saharan Africa. However, the effects of intense malaria exposure and sub-Saharan populations' genetic histories remain poorly explored. To determine if historical migrations and intense malaria exposure have shaped the genetic composition of the eBL belt populations, we genotyped ~4.3 million SNPs in 1,708 individuals from Ghana and Northern Uganda, located on opposite sides of eBL belt and with ≥ 7 months/year of intense malaria exposure and published evidence of high incidence of BL. Among 35 Ghanaian tribes, we showed a predominantly West-Central African ancestry and genomic footprints of gene flow from Gambian and East African populations. In Uganda, the North West population showed a predominantly Nilotic ancestry, and the North Central population was a mixture of Nilotic and Southern Bantu ancestry, while the Southwest Ugandan population showed a predominant Southern Bantu ancestry. Our results support the hypothesis of diverse ancestral origins of the Ugandan, Kenyan and Tanzanian Great Lakes African populations, reflecting a confluence of Nilotic, Cushitic and Bantu migrations in the last 3000 years. Natural selection analyses suggest, for the first time, a strong positive selection signal in the ATP2B4 gene (rs10900588) in Northern Ugandan populations. These findings provide important baseline genomic data to facilitate disease association studies, including of eBL, in eBL belt populations.

Fig 1. Populations studied in relation to major geographical barriers and analyses of population structure based on genotype data.

(A) Map of Africa showing the geographical origin of the Pan-African populations used in the study comprising 22 populations from previous studies and three new populations in Ghana and the Uganda EMBLEM study (S1 Table). Horizontal dotted lines are the geographical extent of the endemic Burkitt lymphoma (eBL) belt (see S1 Fig and S1 Table for detailed information on Pan-African populations in the eBL belt). The map highlights major geographical features, such as the Congo rainforest (light green), major rivers and lakes, and the East and West African Rift valley systems (pink) that may have shaped migratory routes or constituted barriers to gene flow. (B) Principal Component Analysis (PCA) based on analysis of the genome-wide dataset of Uganda and Ghana integrated with 22 other pan-African populations. For better visualization, open symbols are used for the PCA plot and solid symbols are used in the map. The axes of the plot show the first and third principal components (See S10 Fig for other principal components). The PCA was repeated using similar number of individuals for each studied population (S9 Fig). (C) ADMIXTURE plot showing ancestry clusters in 28 populations from Africa (details in S1, S1A and S1B Table). The populations are listed left to right based on their geographical location in Africa from West to East and North to South. The colors represent different ancestral clusters, with K = 6 being the most likely number of clusters on admixture analysis (See S4 Fig). This ADMIXTURE analysis was repeated using similar number of individuals for each studied population (S6 Fig).

Fig 2. Populations movement routes in relation to major geographical barriers based on genetic inferences.

(A) Map of Africa showing the geographical origin of the 22 previously reported Pan-African populations used herein, plus the three new populations from Ghana and the Uganda EMBLEM study (S1 Table). The arrows in the map indicate population gene flow based on significant f3 statistic tests (S2 Table). Colors of the arrows and the pie charts represent the ancestries inferred by ADMIXTURE (Fig 1C). Rather than shortest geographical distance, shapes of the arrows consider the major geographical barriers such as the Congo rainforest (light green), the East and West African Rift valley systems (pink), and the corresponding Rift Valley lakes and rivers (light blue). (B) The zoomed-out map shows the postulated dispersal routes in greater detail, as well as locations of the populations in the East African Rift Valley plateau sampled in previous studies (2 = Barundi, 3 = Banyarwanda, 4 = Baganda in Uganda; 7 = Luhya, 8 = Kalenjin, and 9 = Kikuyu in Kenya; and 10 = Sandawe in Tanzania), and the Uganda EMBLEM study populations [5 = Uganda North West (UNW), 6 = Uganda North Central (UNC)].

Fig 3. Malaria-driven natural selection analysis of rs10900588-G derived allele of gene ATP2B4. (A) Genome-wide population branch statistic (PBS).

The mean PBS values (20 SNP windows) are represented by a Manhattan plot for Uganda North West (UNW). SNP rs10900588 on chromosome 1 showed the strongest signal of selection (See S15 Fig for LD graphs). (B) World-wide frequencies (red intensity) of the rs10900588-G derived allele, with map of the Pan-African plus UNW and UNC populations. (C) Haplotype bifurcation diagrams for the core haplotype at ATP2B4 gene in Uganda North West (UNW), Uganda North Central (UNC), and Shoto/Zulu. ASW—Americans of African Ancestry in SW USA; MXL—Mexican Ancestry from Los Angeles USA; PUR—Puerto Ricans from Puerto Rico; ACB—African Caribbeans in Barbados; CLM—Colombians from Medellin, Colombia; PEL—Peruvians from Lima, Peru; KHV—Kinh in Ho Chi Minh City, Vietnam; JPT—Japanese in Tokyo, Japan; CDX—Chinese Dai in Xishuangbanna, China; CHB—Han Chinese in Beijing, China; CHS—Southern Han Chinese; GIH—Gujarati Indian from Houston, Texas.