As the ancestral homeland of our species, Africa contains elevated levels of genetic diversity and substantial population structure. Importantly, African genomes are heterogeneous: They contain mixtures of multiple ancestries, each of which have experienced different evolutionary histories. In this review, we view population genetics through the lens of admixture, highlighting how multiple demographic events have shaped African genomes. Each of these historical vignettes paints a recurring picture of population divergence followed by secondary contact. First, we give a brief overview of genetic variation in Africa and examine deep population structure within Africa, including the evidence of ancient introgression from archaic “ghost” populations. Second, we describe the genetic legacies of admixture events that have occurred during the past 10,000 years. This includes gene flow between different click-speaking Khoe-San populations, the stepwise spread of pastoralism from eastern to southern Africa, multiple migrations of Bantu speakers across the continent, as well as admixture from the Middle East and Europe into the Sahel region and North Africa. Furthermore, the genomic signatures of more recent admixture can be found in the Cape Peninsula and throughout the African diaspora. Third, we highlight how natural selection has shaped patterns of genetic variation across the continent, noting that gene flow provides a potent source of adaptive variation and that selective pressures vary across Africa. Finally, we explore the biomedical implications of population structure in Africa on health and disease and call for more ethically conducted studies of genetic variation in Africa.

Despite recent progress, African populations are still dramatically underrepresented in genetic studies, and more studies of African genetic variation and population structure are needed. Such studies may not only hold new insights about human origins but are also crucial for equitable biomedical research, with implications that possibly extend beyond Africa. In this review, we provide an overview of our current understanding of how admixture—mostly during the last 10,000 years—has shaped present-day population structure in Africa and how recent genetic studies complement linguistics and archeology in reconstructing the history of African populations.

Linkage disequilibrium (LD)—The nonrandom association of two alleles at different loci.

Effective population size (Ne)—The number of breeding individuals in an idealized randomly mating population. Ne determines the strength of genetic drift acting on a population.

Population structure—Systematic differences in allele frequencies between subpopulations.

Admixture—The interbreeding of individuals from two or more subpopulations that were isolated for a relatively short evolutionary time.

Out-of-Africa (OOA) model —Hypothesis that anatomically modern humans evolved in Africa and subsequently peopled the rest of the world.

Serial founder effect—The successive loss of genetic variation when populations are sequentially founded by a small number of individuals.

Haplotype—A set of linked genetic variants that are coinherited.

Population bottleneck—An event that drastically reduces the effective size of a population, leading to increased genetic drift.

Genetic cline—A gradual change of allele frequencies over a specified geographic area.

Genetic ancestry—The genealogical paths through which an individual inherits DNA from specific ancestors in a reference population. Individuals with shared genetic ancestry tend to be more genetically similar.

Principal components—A set of uncorrelated variables derived from the original data set through linear transformations, which maximize the variance between samples and reduce the dimensionality of the data while preserving the most important information.

Gene flow—The movement of individuals and their genetic material from one population to another population.

Introgression—The interbreeding of individuals from two or more populations that were isolated for a long evolutionary time but are not yet reproductively isolated.

Holocene—The current geological epoch that started after the Last Glacial Maximum ∼12 kya.

Iron Age—The period of time during human prehistory when people began making tools from iron and steel, extending from ∼4 to 1.5 kya in Africa.

Neolithic (New Stone Age)—The period of time when people began using more sophisticated stone tools, leading to the emergence of farming and herding, extending from ∼12 kya to 6.5 kya in Africa.

Paleolithic (Old Stone Age)—The period of time in human evolution when people initially started using stone tools, extending from ∼3.3 million years ago (Mya) to 12 kya.

Uniparental markers—Mitochondrial DNA and Y chromosomes, which are transmitted exclusively maternally or paternally without recombination.

Fine-mapping—The processes of refining the location of trait-associated variants in the genomic region of interest to identify likely causal variants based on association statistics and linkage disequilibrium patterns.

Isolation-by-distance model—A theoretical framework explaining how genetic differentiation between populations increases with geographic distance due to spatially limited gene flow, that is, decreasing migration rate with increasing distance.

FST (Fixation index)—The extent of genetic differentiation of two populations. Higher values of FST are indicative of greater population structure.

Africa exhibits vast cultural and linguistic diversity, including a wide range of subsistence strategies and ∼2,000 spoken languages. In addition, African populations harbor the greatest genetic diversity, exhibit the lowest levels of linkage disequilibrium (LD), have the largest long-term effective population sizes (Ne), and show the deepest split times of all human lineages (Tishkoff et al. 2009; Auton et al. 2015; Mallick et al. 2016; Bergström et al. 2020). For these reasons, Africa is commonly accepted as the cradle of humankind (Henn et al. 2018), and African population history is of exceptional interest to human evolution.

Most of our knowledge about African population history is derived from archeological and linguistic studies, as Africa has long been neglected in genetic studies (Popejoy and Fullerton 2016; Martin et al. 2018; Sirugo et al. 2019; Fatumo et al. 2022). However, archeological and linguistic studies are largely unable to disentangle cultural diffusion from demic diffusion, that is, movements of people (Robertson and Bradley 2000; Diamond and Bellwood 2003). By contrast, genetic studies are uniquely equipped to identify large-scale demic movements (e.g., Tishkoff et al. 2009). In the last decade, the importance of studying genetic variation in Africa has become more appreciated, and a heap of genetic studies of contemporary and ancient individuals has revealed complex population structure and history in Africa, complementing archeological and linguistic studies (e.g., Tishkoff et al. 2009; Schlebusch et al. 2012; Choudhury et al. 2020; Lipson et al. 2022).

In this review, we focus on genetic studies that uncovered extensive archaic, prehistoric, and recent gene flow that has occurred in Africa. We start by putting genetic variation in Africa into a global context and giving a brief overview of population structure in Africa inferred from ancient and extant genomes, focusing on hunter–gatherer groups and deep population structure in the continent. We then discuss how this population structure was shaped by archaic and recent admixture, moving from the deeper past to more recent times. Given the scope of this review paper, we cannot comprehensively review the evolutionary history of every population. Instead, we focus on representative examples of major migratory and admixture events. Finally, we briefly review the evidence for local adaption and discuss the biomedical implication of population structure in Africa. In light of this, we call for more (responsibly conducted) studies of genetic variation in Africa and research capacity building on the African continent. Note that we tried to refer to populations according to current naming conventions, and when we refer to admixture between specific populations, this does not necessarily imply the mixing of these exact populations, but rather the mixing of genetically similar populations.

Compared with the rest of the world, each African genome harbors ∼25% more polymorphisms than each non-African genome (Auton et al. 2015; Mallick et al. 2016; Bergström et al. 2020). Furthermore, variants that are rare on a global level ( 10 kya) (Goudie 2003). An additional central Khoe-San–related ancestry component has been identified in more recent studies that leveraged bigger and more diverse data sets (Uren et al. 2016; Montinaro et al. 2017; Vicente, Jakobsson, et al. 2019). Notably, these three ancestry components correlate with geography but not linguistics or present-day subsistence strategy. The Kx`a-speaking Ju|’Hoan and !Xun and the Khoekhoe-speaking Hai||om are representative of the North Khoe-San ancestry component, the Khoekhoe-speaking Nama and Tuu-speaking ‡Khomani and Karretije are representative of the South Khoe-San ancestry component, and all remaining Khoe-San population are representative of the central Khoe-San ancestry component (Montinaro et al. 2017; Vicente, Jakobsson et al. 2019). Interestingly, the pairwise genetic divergences of these three components were found to be similar (i.e., similar fixation index [FST] values), and the divergence time was estimated to be ∼25 kya (95% CI: 18–32 kya) (Montinaro et al. 2017).

Although most of the genetic variation among Khoe-San populations is explained under an isolation-by-distance model (Montinaro et al. 2017; Vicente, Jakobsson, et al. 2019), there is evidence of modest admixture between the three Khoe-San–related ancestry components. In formal admixture tests (f3-analysis), the ‡Khomani (southern component) showed significant evidence of admixture with Taa populations (central), and the Ju|’Hoan (northern) showed significant signs of admixture with the !Xun (northern) and the Naro (central). Additionally, the Naro (central) showed evidence of admixture with the Ju|’Hoan (northern) and another population characterized by the Central Khoe-San component (e.g., Taa or |Gui). However, none of the populations characterized by the central Khoe-San component showed significant evidence of being a mixture between northern and southern Khoe-San groups (Montinaro et al. 2017). Using SpaceMix analyses, Vicente, Jakobsson et al. (2019) found additional evidence for gene flow from the Ju|’Hoan (northern) into the ‡Hoan (central), from the |Gui/Xade San (central) into the Naro (central), and from an undefined Khoe-San population into the Nama (southern). Note that these tests do not definitively establish admixture between specific populations—the actual historical gene flow may have involved other related populations. It has been argued that this gene flow must have occurred within the last 10 ky after the prehistoric lake Makgadikgadi dried up (Barbieri et al. 2014). However, more studies of whole genome sequences are needed for exact dating. For further review of the history of Khoe-San populations, see Pakendorf and Stoneking (2021).

Recent genetic studies paint a complex picture of population continuity and admixture in eastern Africa since the introduction of pastoralism in northeastern Africa some 8 kya (e.g., Haber et al. 2016; Skoglund et al. 2017; Prendergast et al. 2019; Naidoo et al. 2020; Wang et al. 2020a). Using DNA from ancient individuals from Kenya and Tanzania, it has been proposed that herding and farming spread in multiple steps into eastern Africa (Prendergast et al. 2019). First, in northeastern Africa, admixture between a population related to contemporary Nilo-Saharan speakers (e.g., the Dinka or Nuer) and a population related to modern groups from northern Africa or the Levant created a group of “early northeastern pastoralists.” This group then migrated to eastern Africa and admixed with local foragers ∼4 kya, receiving ∼20% ancestry from a group related to a 4,500-year-old ancient individual from the Mota cave in Ethiopia that is genetically similar to the isolated, Afro-Asiatic–speaking Aari (Gallego Llorente et al. 2015) and present-day Afro-Asiatic speakers (fig. 4A). Given the high genetic affinity of a pastoralist individual who lived 4000 years ago in northern Sudan with ancient individuals from Kenya and Tanzania, it has been argued that this initial dispersal of northeastern pastoralists into East Africa occurred rapidly (Wang et al. 2022). Lastly, this group received another pulse of gene flow from a population related to Nilo-Saharan–speaking Dinka in Sudan ∼2.2 kya, that is, during the Iron Age (fig. 4A; Prendergast et al. 2019; Fan et al. 2019). Based on varying amounts of Mota-related and Dinka-related ancestry in ancient individuals from the Democratic Republic of Congo, Uganda, and Botswana, it has been argued that a model with repeated, unidirectional gene flow from east African forager groups and Nilo-Saharan–speaking groups into the “early northeastern pastoralist” group provides a better fit (Wang et al. 2020a). However, with the currently available data, it is impossible to distinguish between multiple waves of migration and complex population structure.

Visual summary of key admixture events in Africa. (A) The stepwise spread of lactose persistence from northeastern Africa into eastern Africa and subsequently into southern Africa. (B) Southward migration of Bantu-speaking people through the rainforest to modern-day Angola (ANG) and Zambia (ZMB) before splitting into eBSPs and seBSPs, in concordance with the late-split hypothesis. (C) Extensive admixture between Sahelian populations with European groups in the West and Middle Eastern groups in the East, but only limited gene flow among Sahelian populations. (D) Repetitive gene flow from the Middle East/Europe and sub-Saharan Africa into Northern African populations.

In line with archeological studies, genetic studies of Khoe-San confirmed that pastoralism spread from East Africa to southern Africa by demic diffusion (Breton et al. 2014; Macholdt et al. 2014; Ranciaro et al. 2014; Schlebusch et al. 2017; Skoglund et al. 2017). Khoekhoe-speaking populations (e.g., the Nama), who currently practice a pastoralist lifestyle, have a high-frequency lactase persistence (LP) allele that is also found in East African populations (Schlebusch et al. 2012; Breton et al. 2014; Macholdt et al. 2014). This “East African” LP single nucleotide polymorphism (SNP) (14010G > C) is distinct from the “European” LP SNP (1391 °C > T) and is rare in southern African Bantu-speaking groups (Breton et al. 2014; Macholdt et al. 2014). Among Khoe-San groups, this “East African” LP SNP is found at the highest frequency in the Nama with a frequency of ∼35%, which is much higher than expected given the ∼13% East African admixture fraction in the Nama, suggesting positive selection (Breton et al. 2014; Macholdt et al. 2014). Interestingly, Prendergast et al. found that the “East African” LP allele is largely absent from ancient pastoralist individuals from Kenya and Tanzania, indicating that east African pastoralists were lactose intolerant as recently as 3–1 kya (Prendergast et al. 2019). However, it is also possible that this allele has not been detected in ancient samples due to a limited number of surveyed individuals.

A direct link between Afro-Asiatic–speaking eastern African (i.e., Amhara- or Oromo-related ancestry) and southern African pastoralists has been established by showing that a 1,200-year-old individual from southern Africa, who has genetic similarities with modern Khoekhoe-speaking pastoralist groups (e.g., the Nama), traces ∼40% of their ancestry to a Eurasian admixed group related to a 3,100-year-old pastoralist individual from Luxmanda, Tanzania (Skoglund et al. 2017). Thus, this study indicates that admixture of Khoe-San groups with eastern African pastoralists occurred at least ∼1.2 kya (fig. 4A). Concordantly, another study estimated that all modern Khoe-San populations received 9–30% gene flow from an admixed East African/Eurasian pastoralist group 1.5–1.3 kya (Schlebusch et al. 2017). Furthermore, east African pastoralist contributions to Khoe-San groups are lower on X chromosomes than autosomes (Vicente et al. 2021), indicating that male-biased admixture occurred. Overall, these results suggest that eastern African pastoralists reached southern Africa prior to and independently of Bantu-speaking groups. For a detailed review of the spread of lactase persistence in Africa, see Campbell and Ranciaro (2021).

Genetic studies showed that the spread of Bantu languages, agricultural practices, and iron use 5–3 kya was accompanied by multiple migration waves of Bantu speakers from western Africa (i.e., current eastern Nigeria and western Cameroon) to other regions in sub-Saharan Africa (Tishkoff et al. 2009; Schlebusch et al. 2012; Li et al. 2014). Consequently, the Bantu expansion extensively contributed to population structure due to differential levels of admixture with and replacement of local hunter–gatherer groups over the past 3,500 years (Skoglund et al. 2017; Sengupta et al. 2021; González-Santos et al. 2022).

Two major migratory routes of Bantu-speaking populations (BSPs) have been hypothesized. The early-split hypothesis suggests that BSPs split at an early stage north of the rainforest, with one group then moving directly South through the rainforest, whereas the other migrated East, north of the rainforest, toward the Great African Lakes. In contrast, the late-split hypothesis states that BSPs first migrated South through the rainforest before splitting into two groups, with one moving further South and the other one migrating East toward the Great African Lakes. Similarly to phylolinguistics (e.g., Rexová et al. 2006), genetics are in favor of the late-split hypothesis, as eastern BSPs (eBSPs) and south-eastern BSPs (seBSPs) are genetically closer to western BSPs (wBSPs) south of the rainforest (i.e., Angola) than to wBSPs north of the rainforest (Patin et al. 2017). A subsequent study using samples from wider geographic and ethnolinguistic groups showed that eBSPs, seBSPs, and southwestern BSPs (swBSPs) are genetically closest to Bantu speakers from Zambia (Choudhury et al. 2020). Together, these findings suggest that Bantu speakers first migrated South through the rainforest to Angola and subsequently to Zambia before splitting into two groups (fig. 4B).

In western Africa, wBSPs asymmetrically mixed with resident RHG groups, with RHG groups receiving higher amounts of gene flow from wBSPs (Jarvis et al. 2012; Hsieh, Veeramah, et al. 2016; Patin et al. 2017; Lopez et al. 2018). wBSPs in Angola have small amounts of RHG-related ancestry from an admixture event that occurred after the split of BSPs ∼800 ya (Patin et al. 2017), although a recent study inferred a more ancient admixture date of ∼1.9 kya for Bantu speakers in Cabinda, Angola (Tallman et al. 2022). The amount of gene flow from wBSPs into individual RHG groups varied. Whereas the Mbuti and the Biaka have 350 ya (de Wit et al. 2010; Petersen et al. 2013; Choudhury et al. 2017). The SAC population represents >49% of the estimated 7 million inhabitants in this province, with the vast majority being historically Afrikaans speakers (a unique South African language ancestrally linked to Dutch), although this is more recently changing (Patterson et al. 2010; Republic of South Africa 2021). Their complex origin of admixture is attributed to significant historical events that occurred within the last few millennia, starting ∼1.7 kya with the arrival of Bantu-speaking agro-pastoralists in South Africa (Sengupta et al. 2021). During the last few centuries, European colonization of the Cape by the Dutch, Germans, and French, later followed by British seizure and rule, contributed to the complex admixture patterns at the Western Cape. During this time, slaves were trade by the Dutch East India Company from East Africa, Madagascar and surrounding islands, India, and Indonesia, leading to settler–slave admixture, including indigenous Khoe-San people (de Wit et al. 2010; Patterson et al. 2010; Montinaro and Capelli 2018). Genomic studies of the SAC population revealed that these historic events correlate with the complex five-way admixture observed in this population, with ancestral contributions occurring predominantly from the indigenous Khoe-San, the Bantu-speaking Africans, European-descent groups, and Southeast Asian and South Asian populations (de Wit et al. 2010; Daya et al. 2013; Petersen et al. 2013; Chimusa et al. 2014; Choudhury et al. 2017; Swart et al. 2020). In addition, cultural and religious practices contributed to the high degree of heterogeneity in ancestral contributions among SAC individuals sampled from different regions of South Africa (de Wit et al. 2010; Daya et al. 2014; Choudhury et al. 2017).

Several studies have revealed a sex-biased gene flow in SAC that supports the historical records indicating that almost all mixed marriages were between a male settler and either a free Black female (where the man bought the slave their freedom) or an indigenous Khoe-San female (Patterson et al. 2010; Petersen et al. 2013; Choudhury et al. 2017). Petersen et al. (2013) used uniparental markers to ascertain likely ancestral contributions using unique population-specific mtDNA and Y chromosomal haplogroup identifiers. Khoe-San derived maternal lineage L0d had a 68% representation in the SAC group studied, while the M/N Eurasian mtDNA lineages were only represented at low frequencies. In contrast, there was a significant Eurasian paternal contribution (71.4%) defined by haplogroups R/I/G/N/O/J in the same group, and the Western European R1b haplogroup was prevalent at 44.4%. Similar distributions of mtDNA and Y haplogroups were observed from whole genome sequencing data of a small group of SAC males from the Western Cape region (Choudhury et al. 2017). Overall, these findings demonstrate that recent admixture involved sex-biased gene flow.

As a consequence of the transatlantic slave trade, >12.5 million people were forcefully displaced from Africa to the Americas between the sixteenth and nineteenth centuries, creating the largest present-day African diaspora (Eltis 2007). Subsequent admixture with European-like ancestry and Native American-like ancestry populations was spatially and temporally complex, leading to varying amounts of recent African-like ancestry in admixed populations in the Americas (Bryc et al. 2010; Ongaro et al. 2019; Gouveia et al. 2020; Micheletti et al. 2020). African-related ancestry is the highest in the British Caribbean (∼75%) and the United States (∼71%) and the lowest in South America (∼11–12%) and Central America (∼8%, including Mexico) (Micheletti et al. 2020). The remaining ancestry can be predominantly assigned as European-like, with minor contributions from Native American groups in some populations (Micheletti et al. 2020). Additionally, despite more males being deported to the Americas, it has been shown that African contributions to gene pools in the Americas were likely female-biased, whereas European contributions were likely male-biased (Mathias et al. 2016; Ongaro et al. 2019; Micheletti et al. 2020). However, the magnitude of the sex bias is difficult to pinpoint from X chromosomal and autosomal ancestry proportions due to potential confounding from complex demographic histories, among others (Pfennig and Lachance 2023).

Broadly in agreement with historical records of the transatlantic slave trade, genetic studies of admixed populations from the Americas showed that most of the African ancestry can be traced to West–Central Africa, for example, similar to the Yoruba or Esan from Nigeria, with a smaller fraction being similar to south-eastern African ancestry, for example, Mbukushu-like from Botswana and/or Luhya-like from Kenya (Patin et al. 2014; Gouveia et al. 2020; Micheletti et al. 2020). However, the distribution of these African ancestries varies between different populations in the Americas, with western/central African-related ancestry being more common in the northern parts, for example, the United States, and south-eastern African-related ancestry being more common in the southern parts, for example, Brazil (Gouveia et al. 2020). The different sources of African-like ancestry and the different timing of admixture for different African source populations in the Americas may be attributed to geography and changing geopolitics at the time, influencing the voyage routes (Ongaro et al. 2019; Gouveia et al. 2020). Interestingly, there is less differentiation between the African ancestries found in admixed genomes in the Americas (as quantified by FST statistics) compared with what is seen between each of the contributing ancestries in Africa (Gouveia et al. 2020). For a more granular review of the demographic histories in light of the transatlantic slave trade of admixed population in the Americas, see Fortes-Lima and Verdu (2021).

Environmental conditions vary over time and space. Because of this, African populations have experienced a heterogeneous mix of selection pressures. Nonetheless, African populations are connected via gene flow, which can serve as a potent source of adaptive variation. Although the specific genes implicated in African scans of selection vary by the method used and population studied, some common themes arise. Regulatory DNA appears to be a frequent target of adaptation in African genomes (Quiver and Lachance 2022). Furthermore, many noteworthy instances of selection in Africa are associated with physiology, diet, or pathogen pressure.

One key evolutionary challenge involves physiological responses to extreme conditions, including high-altitude desert environments. In Africa, the Ethiopian Highlands are 1,500 meters above sea level, with summits as high as 4,550 meters above sea level. For example, the Amahara people have adapted to low barometric pressure and hypoxia in the Ethiopian Highlands over the past 5,000 years. Interestingly, the specific adaptive mutations seen in the Ethiopian Highlands differ from what has been observed in the Tibetan Plateau and the Andean Altiplano. Selection scans comparing Amhara individuals living at high altitude to individuals living in lowland areas have implicated a number of adaptive loci, including rs10803083, a SNP that is associated with hemoglobin levels (Alkorta-Aranburu et al. 2012); BHLHE41, a gene that is involved in hypoxia response and circadian rhythm (Huerta-Sánchez et al. 2013); and EGNL1, a gene that plays a central role in mammalian oxygen homeostasis (Scheinfeldt et al. 2012). Intriguingly, EGLN1 has also been implicated in selection scans of the click-speaking Sandawe people, who are traditional foragers from Tanzania (Lachance et al. 2012). This suggests that the benefits of adaptive EGLN1 haplotypes may extend beyond high-altitude conditions.

Arid desert environments also present an evolutionary challenge in Africa. For instance, despite frequent droughts, the ‡Khomani San have lived in the Kalahari Desert for thousands of years. Using SWIF(r), an approach that combines multiple statistics to generate posterior probabilities of sweeps, researchers have identified multiple genes associated with adiponectin, body mass index (BMI), and metabolism as potential targets of selection in the ‡Khomani San (Sugden et al. 2018).

Another example of adaptation to extreme conditions are RHG groups, who evolved a short stature (mean adult height 0.05) but rare outside of Africa (i.e., frequency

Google Scholar

WorldCat

Ansari-Pour N, Plaster CA, Bradman N.

Google Scholar

WorldCat

Arauna LR, et al. 

Google Scholar

WorldCat

Auton A, et al. 

Google Scholar

WorldCat

Bajić V, et al. 

Google Scholar

WorldCat

Barbieri C, et al. 

Google Scholar

WorldCat

Batai K, Hooker S, Kittles RA.

Google Scholar

WorldCat

Bergström A, et al. 

Google Scholar

WorldCat

Bower J.

Google Scholar

WorldCat

Bress A, et al. 

Google Scholar

WorldCat

Breton G, et al. 

Google Scholar

WorldCat

Bryc K, et al. 

Google Scholar

WorldCat

Busby GB, et al. 

Google Scholar

WorldCat

Campbell MC, Ranciaro A.

Google Scholar

WorldCat

Cavalli-Sforza LL, Piazza A.

Google Scholar

WorldCat

Černý V, Fortes-Lima C, Tříska P.

Google Scholar

WorldCat

Chaichoompu K, et al. 

Google Scholar

WorldCat

Chen L, Wolf AB, Fu W, Li L, Akey JM.

Google Scholar

WorldCat

Chimusa ER, et al. 

Google Scholar

WorldCat

Chimusa ER, et al. 

Google Scholar

WorldCat

Choudhury A, et al. 

Google Scholar

WorldCat

Choudhury A, et al. 

Google Scholar

WorldCat

Choudhury A, Sengupta D, Ramsay M, Schlebusch C.

Google Scholar

WorldCat

Čížková M, et al. 

Google Scholar

WorldCat

Coelho M, Sequeira F, Luiselli D, Beleza S, Rocha J.

Google Scholar

WorldCat

Crawford NG, et al. 

Google Scholar

WorldCat

Dandara C, et al. 

Google Scholar

WorldCat

D’Atanasio E, et al. 

Google Scholar

WorldCat

Daya M, et al. 

Google Scholar

WorldCat

Daya M, et al. 

Google Scholar

WorldCat

de Wit E, et al. 

Google Scholar

WorldCat

Diallo MY, et al. 

Google Scholar

WorldCat

Diamond J, Bellwood P.

Google Scholar

WorldCat

Durvasula A, Sankararaman S.

Google Scholar

WorldCat

Eltis D.

Fadhlaoui-Zid K, et al. 

Google Scholar

WorldCat

Fadhlaoui-Zid K, et al. 

Google Scholar

WorldCat

Fan S, et al. 

Google Scholar

WorldCat

Fan S, et al. 

Google Scholar

WorldCat

Fatumo S, et al. 

Google Scholar

WorldCat

Fortes-Lima C, et al. 

Google Scholar

WorldCat

Fortes-Lima C, Verdu P.

Google Scholar

WorldCat

Fregel R, et al. 

Google Scholar

WorldCat

Gallego Llorente M, et al. 

Google Scholar

WorldCat

Gibbon VE.

Google Scholar

WorldCat

González-Santos M, et al. 

Google Scholar

WorldCat

Gopalan S, et al. 

Google Scholar

WorldCat

Goudie AS.

Google Scholar

Google Preview

WorldCat

Gouveia MH, et al. 

Google Scholar

WorldCat

Green RE, et al. 

Google Scholar

WorldCat

Gurdasani D, et al. 

Google Scholar

WorldCat

Haak W, et al. 

Google Scholar

WorldCat

Haber M, et al. 

Google Scholar

WorldCat

Hamid I, Korunes KL, Beleza S, Goldberg A.

Google Scholar

WorldCat

Hammer MF, Woerner AE, Mendez FL, Watkins JC, Wall JD.

Google Scholar

WorldCat

Harvati K, et al. 

Google Scholar

WorldCat

Henn BM, et al. 

Google Scholar

WorldCat

Henn BM, et al. 

Google Scholar

WorldCat

Henn BM, Steele TE, Weaver TD.

Google Scholar

WorldCat

Hervella M, et al. 

Google Scholar

WorldCat

Hey J, et al. 

Google Scholar

WorldCat

Hindorff LA, et al. 

Google Scholar

WorldCat

Hollfelder N, et al. 

Google Scholar

WorldCat

Hollfelder N, Breton G, Sjödin P, Jakobsson M.

Google Scholar

WorldCat

Hsieh P, Veeramah KR, et al. 

Google Scholar

WorldCat

Hsieh P, Woerner AE, et al. 

Google Scholar

WorldCat

Huerta-Sánchez E, et al. 

Google Scholar

WorldCat

Jallow M, et al. 

Google Scholar

WorldCat

Jarvis JP, et al. 

Google Scholar

WorldCat

Johnson J, et al. 

Google Scholar

WorldCat

Kariuki SN, Williams TN.

Google Scholar

WorldCat

Karlsson EK, Kwiatkowski DP, Sabeti PC.

Google Scholar

WorldCat

Kim HL, et al. 

Google Scholar

WorldCat

Lachance J, et al. 

Google Scholar

WorldCat

Lazaridis I, et al. 

Google Scholar

WorldCat

Lemke AA, et al. 

Google Scholar

WorldCat

Li S, Schlebusch C, Jakobsson M.

Google Scholar

WorldCat

Lipson M, et al. 

Google Scholar

WorldCat

Lipson M, et al. 

Google Scholar

WorldCat

Lokki AI, et al. 

Google Scholar

WorldCat

Lopez M, et al. 

Google Scholar

WorldCat

Lorente-Galdos B, et al. 

Google Scholar

WorldCat

Lucas-Sánchez M, Font-Porterias N, Calafell F, Fadhlaoui-Zid K, Comas D.

Google Scholar

WorldCat

Lucas-Sánchez M, Serradell JM, Comas D.

Google Scholar

WorldCat

Macholdt E, et al. 

Google Scholar

WorldCat

Majara L, et al. 

Google Scholar

WorldCat

Mallick S, et al. 

Google Scholar

WorldCat

Manning K, Timpson A.

Google Scholar

WorldCat

Marcus J, Ha W, Barber RF, Novembre J.

Google Scholar

WorldCat

Marshall F, Hildebrand E.

Google Scholar

WorldCat

Martin AR, Teferra S, Möller M, Hoal EG, Daly MJ.

Google Scholar

WorldCat

Mathias RA, et al. 

Google Scholar

WorldCat

Matjuda EN, Engwa GA, Anye SNC, Nkeh-Chungag BN, Goswami N.

Google Scholar

WorldCat

Meyer M, et al. 

Google Scholar

WorldCat

Micheletti SJ, et al. 

Google Scholar

WorldCat

Mölder F, et al. 

Google Scholar

WorldCat

Montinaro F, et al. 

Google Scholar

WorldCat

Montinaro F, Capelli C.

Google Scholar

WorldCat

Naidoo T, et al. 

Google Scholar

WorldCat

Ndadza A, et al. 

Google Scholar

WorldCat

Newman J.

Google Scholar

Google Preview

WorldCat

Norris ET, et al. 

Google Scholar

WorldCat

Nováčková J, et al. 

Google Scholar

WorldCat

Ongaro L, et al. 

Google Scholar

WorldCat

Pagani L, et al. 

Google Scholar

WorldCat

Pagani L, et al. 

Google Scholar

WorldCat

Pakendorf B, Stoneking M.

Google Scholar

WorldCat

Patin E, et al. 

Google Scholar

WorldCat

Patin E, et al. 

Google Scholar

WorldCat

Patin E, Quintana-Murci L.

Google Scholar

WorldCat

Patterson N, et al. 

Google Scholar

WorldCat

Pennarun E, et al. 

Google Scholar

WorldCat

Pereira L, et al. 

Google Scholar

WorldCat

Pereira L, Mutesa L, Tindana P, Ramsay M.

Google Scholar

WorldCat

Perera MA, et al. 

Google Scholar

WorldCat

Perry GH, et al. 

Google Scholar

WorldCat

Petersen DC, et al. 

Google Scholar

WorldCat

Pfennig A, Lachance J.

Google Scholar

WorldCat

Pickrell JK, et al. 

Google Scholar

WorldCat

Plagnol V, Wall JD.

Google Scholar

WorldCat

Popejoy AB, Fullerton SM.

Google Scholar

WorldCat

Prendergast ME, et al. 

Google Scholar

WorldCat

Prendergast ME.

Google Scholar

WorldCat

Prendergast ME, Sawchuk EA, Sirak KA.

Google Scholar

WorldCat

Priehodová E, et al. 

Google Scholar

WorldCat

Priehodová E, et al. 

Google Scholar

WorldCat

Privé F, et al. 

Google Scholar

WorldCat

Quiver MH, Lachance J.

Google Scholar

WorldCat

Ragsdale AP et al. 

Google Scholar

WorldCat

Ranciaro A, et al. 

Google Scholar

WorldCat

Rasmussen M, et al. 

Google Scholar

WorldCat

Rees JS, Castellano S, Andrés AM.

Google Scholar

WorldCat

Republic of South Africa SD.

Rexová K, Bastin Y, Frynta D.

Google Scholar

WorldCat

Robertson JH, Bradley R.

Google Scholar

WorldCat

Rotimi CN, Jorde LB.

Google Scholar

WorldCat

Schaefer NK, Shapiro B, Green RE.

Google Scholar

WorldCat

Scheinfeldt LB, et al. 

Google Scholar

WorldCat

Scheinfeldt LB, et al. 

Google Scholar

WorldCat

Schlebusch CM, et al. 

Google Scholar

WorldCat

Schlebusch CM, et al. 

Google Scholar

WorldCat

Schlebusch CM, et al. 

Google Scholar

WorldCat

Schlebusch CM, Jakobsson M.

Google Scholar

WorldCat

Schlebusch CM, Sjödin P, Skoglund P, Jakobsson M.

Google Scholar

WorldCat

Segurel L, Bon C.

Google Scholar

WorldCat

Semo A, et al. 

Google Scholar

WorldCat

Sengupta D, et al. 

Google Scholar

WorldCat

Serra-Vidal G, et al. 

Google Scholar

WorldCat

Shriner D, Rotimi CN.

Google Scholar

WorldCat

Shriner D, Rotimi CN.

Google Scholar

WorldCat

Sirugo G, Williams SM, Tishkoff SA.

Google Scholar

WorldCat

Sjöstrand AE, et al. 

Google Scholar

WorldCat

Skoglund P, et al. 

Google Scholar

WorldCat

Solé-Morata N, et al. 

Google Scholar

WorldCat

Sugden LA, et al. 

Google Scholar

WorldCat

Swart Y, Uren C, van Helden PD, Hoal EG, Möller M.

Google Scholar

WorldCat

Swart Y, van Eeden G, Sparks A, Uren C, Möller M.

Google Scholar

WorldCat

Tallman S, Sungo M das D, Saranga S, Beleza S.

Google Scholar

WorldCat

Tishkoff SA, et al. 

Google Scholar

WorldCat

Triska P, et al. 

Google Scholar

WorldCat

Uren C, et al. 

Google Scholar

WorldCat

Vai S, et al. 

Google Scholar

WorldCat

Van De Loosdrecht M, et al. 

Google Scholar

WorldCat

Verdu P, et al. 

Google Scholar

WorldCat

Vicente M, et al. 

Google Scholar

WorldCat

Vicente M, Jakobsson M, Ebbesen P, Schlebusch CM.

Google Scholar

WorldCat

Vicente M, Priehodová E, et al. 

Google Scholar

WorldCat

Wall JD, et al. 

Google Scholar

WorldCat

Wang K, et al. 

Google Scholar

WorldCat

Wang K, et al. 

Google Scholar

WorldCat

Wang K, Mathieson I, O’Connell J, Schiffels S.

Google Scholar

WorldCat

Wohlers I, et al. 

Google Scholar

WorldCat

World Health Organization.

Xu D, et al. 

Google Scholar

WorldCat

Zhou Q, Zhao L, Guan Y.

Google Scholar

WorldCat