Recovering signals of ghost archaic introgression in African populations

Abstract

While introgression from Neanderthals and Denisovans has been documented in modern humans outside Africa, the contribution of archaic hominins to the genetic variation of present-day Africans remains poorly understood. We provide complementary lines of evidence for archaic introgression into four West African populations. Our analyses of site frequency spectra indicate that these populations derive 2 to 19% of their genetic ancestry from an archaic population that diverged before the split of Neanderthals and modern humans. Using a method that can identify segments of archaic ancestry without the need for reference archaic genomes, we built genome-wide maps of archaic ancestry in the Yoruba and the Mende populations. Analyses of these maps reveal segments of archaic ancestry at high frequency in these populations that represent potential targets of adaptive introgression. Our results reveal the substantial contribution of archaic ancestry in shaping the gene pool of present-day West African populations.

Fig. 1. Demography relating known and proposed archaic lineages to modern human populations.

(A) Basic demographic model with CSFS fit. W Afr, West Africans; Eur, European; N, Neanderthal; D, Denisovan; UA, unknown archaic [see (18)]. Below, we show the CSFS in the West African YRI when restricting to SNPs where a randomly sampled allele from the high-coverage Vindija Neanderthal was observed to be derived [Neanderthal (data)], as well as where a randomly sampled allele from the high-coverage Denisovan genome was observed to be derived [Denisovan (data)]. We also show the CSFS under the proposed model [Neanderthal (model) and Denisova (model)]. Migration between Europe and West Africa introduces an excess of low-frequency variants but does not capture the decrease in intermediate frequency variants and increase in high-frequency variants. (B) Newly proposed model involving introgression into the modern human ancestor from an unknown hominin that separated from the human ancestor before the split of modern humans and the ancestors of Neanderthals and Denisovans. Below, we show the CSFS fit from the proposed model, which captures the U-shape observed in the data.

Fig. 2. ABC estimates of the demographic parameters of the archaic ghost population across four West African populations (YRI, ESN, GWD, and MSL).

Posterior means are denoted by diamonds, and 95% credible intervals are denoted by lines. (A) The admixture time t_a, (B) the admixture fraction α, (C) the split time of the introgressing population t_s, and (D) the effective population size of the introgressing population N_e are shown. The parameter estimates are largely consistent across the African populations: We estimate split times of 360 ka to 1.02 Ma B.P., admixture times of 0 to 124 ka B.P., admixture fractions that range from 0.02 to 0.19, and effective population sizes that range from 22,000 to 28,000.

Fig. 3. Analysis of segments of archaic ghost ancestry found in the Yoruba and Mende populations.

(A) Inference of segments of archaic ancestry was performed with ArchIE. ArchIE proceeds by simulating data under a model of archaic introgression, calculating population genetic summary statistics, and training a model to predict the probability that a 50-kb window in an individual comes from an archaic population. We apply the resulting predictor to genome sequences from the Yoruba and Mende populations. (B) Comparison of TMRCA between inferred archaic and nonarchaic segments to the TMRCA of a pair of nonarchaic segments in the Yoruba. On average, archaic segments are 1.69× older than nonarchaic segments. (C) Estimates of the divergence times of archaic segments inferred in Yoruba from KhoeSan, Jul‘hoan, two modern human pygmy genomes (Mbuti and Biaka), and Neanderthal and Denisovan genomes compared to divergence times of nonarchaic segments. P values are computed via block jackknife. Archaic segments are more diverged from all six genomes than nonarchaic segments.