Hunter-gatherer genomes reveal diverse demographic trajectories during the rise of farming in Eastern Africa

Berl, Richard E. W.; Myrick, Justin W; Garfield, Zachary H.; Reynolds, Austin W.; Bafens, Barnabas K.; Belbin, Gillian M.; Mastoras, Mira; Williams, Cole; Daya, Michelle; Negash, Akmel N.; Feldman, Marcus W.; Hewlett, Barry S.; Henn, Brenna M.

doi:10.1016/j.cub.2022.02.050

Cited by 23 publications

(48 citation statements)

References 73 publications

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“…We inferred detailed demographic histories using 4x-8x whole-genome sequencing data for four diverse African populations, comprising the Nama (Khoe-San from South Africa, newly presented here), Mende (from Sierra Leone, MSL from the Phase 3 1000 Genomes Project 19 ), Gumuz (recent descendants of a hunter-gatherer group from Ethiopia 20,21 ), and eastern African agriculturalists (Amhara and Oromo from Ethiopia 20 ). The Amhara and Oromo populations, despite speaking distinct Afro-Asiatic languages, are highly genetically similar 22,21 and thus the two groups were combined for a larger sample size (Figure 2). We also included the British (GBR) from the 1000 Genomes Project in our demographic models as a representative source of back-to-Africa gene flow and recent colonial admixture in South Africa.…”

Section: Resultsmentioning

confidence: 99%

A weakly structured stem for human origins in Africa

Ragsdale

Weaver

Atkinson

et al. 2022

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While it is now broadly accepted that Homo sapiens originated within Africa, considerable uncertainty surrounds specific models of divergence and migration across the continent. Progress is hampered by a paucity of fossil and genomic data, as well as variability in prior divergence time estimates. Here we use linkage disequilibrium and diversity-based statistics, optimized for rapid, complex demographic inference to discriminate among such models. We infer detailed demographic models for populations across Africa, including representatives from eastern and western groups, as well as 44 newly whole-genome sequenced individuals from the Nama (Khoe-San). Despite the complexity of African population history, contemporary population structure dates back to Marine Isotope Stage (MIS) 5. The earliest population divergence among contemporary populations occurs 120-135ka, between the Khoe-San and other groups. Prior to the divergence of contemporary African groups, we infer long-lasting structure between two or more weakly differentiated ancestral Homo populations connected by gene flow over hundreds of thousands of years (i.e. a weakly structured stem). We find that weakly structured stem models provide more likely explanations of polymorphism that had previously been attributed to contributions from archaic hominins in Africa. In contrast to models with archaic introgression, we predict that fossil remains from coexisting ancestral populations should be morphologically similar. Despite genetic similarity between these populations, an inferred 1–4% of genetic differentiation among contemporary human populations can be attributed to genetic drift between stem populations. We show that model misspecification explains variation in previous divergence time estimates and argue that studying a suite of models is key to robust inferences about deep history.

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Section: Resultsmentioning

confidence: 99%

A weakly structured stem for human origins in Africa

Ragsdale

Weaver

Atkinson

et al. 2022

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“…Since the results from SPRUCE indicated that fine-scale environments may affect migration and selection, we decided to test this further by performing a genome-wide selection scan on several populations in southwestern Ethiopia who live close within 70km but derive from different ancestries and experience different environments. Four populations are agriculturalists, ranging from small-scale (Majang) to intensive farming (Shekkacho) (Fentaw 2007; Stauder 2007; Gopalan et al 2022). One population, the Chabu, practiced traditional hunting and gathering until recently (Dira and Hewlett 2018; Gopalan et al 2022).…”

Section: Resultsmentioning

confidence: 99%

“…Four populations are agriculturalists, ranging from small-scale (Majang) to intensive farming (Shekkacho) (Fentaw 2007; Stauder 2007; Gopalan et al 2022). One population, the Chabu, practiced traditional hunting and gathering until recently (Dira and Hewlett 2018; Gopalan et al 2022). Some individuals in these populations live at moderately high altitudes; for example, the Chabu live at elevations up to 2500m (Dira and Hewlett 2017).…”

Section: Resultsmentioning

confidence: 99%

“…Since the SPRUCE results indicated that fine-scale differences in environment affect human migration, we ran a genome-wide selection scan on five neighboring populations in southwestern Ethiopia to test the effect of microenvironments on adaptation. Although these populations are close in geographic space, and in some cases closely related (Gopalan et al 2022), they each show a unique pattern of positive selection using the iHS statistic (Fig. S6).…”

Section: Discussionmentioning

confidence: 99%

“…While the SPRUCE dataset described above gave us the broadest possible geographic spread in eastern Africa, some populations had relatively low sample sizes. We therefore considered an alternative high-SNP density, high sample size dataset of five Ethiopian populations previously published in (Gopalan et al 2022) and available in dbGaP at phs001123.v2.p2. We tested for selection in these five populations from southwestern Ethiopia: Bench, Sheko, Majang, Shekkacho, and Chabu.…”

Section: Genomic Datamentioning

confidence: 99%

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Predicting environmental and ecological drivers of human population structure

Pless

Eckburg

Henn

2022

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Landscape, climate, and culture can all structure human populations, but few methods are designed to disentangle the importance of these many variables. We developed a machine learning method for identifying the variables which best explain migration rates, as measured by the coalescent-based program MAPS that uses shared identical by descent tracts to infer and extrapolate spatial migration across a region of interest. We applied our method to 30 human populations in eastern Africa with high density SNP array data. The remarkable diversity of ethnicities, languages, and environments in this region offers a unique opportunity to explore the variables that shape migration and genetic structure in humans. We explored more than twenty spatial variables relating to landscape, climate, and presence of tsetse flies (an important regional disease vector). The full model explained ~40% of variance in migration rate over the past 56 generations. Precipitation, minimum temperature of the coldest month, and altitude were the most important variables. Among the three groups of tsetse flies, the most important was the fusca group which is a vector for livestock trypanosomiasis. We also performed a selection scan on a subgroup of the populations who live in Ethiopia at relatively high altitudes. We did not identify well-known high-altitude genes, but we did find signatures of positive selection related to metabolism and disease (e.g. PNPLA2 and IRF4). We conclude that environment and landscape has notably shaped the migration and adaptation of human populations in eastern Africa; the remaining variance in structure is likely due to cultural factors not captured in our model.

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