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Published in final edited form as: Science. 2024 Dec 13;386(6727):eadq3010. doi: 10.1126/science.adq3010

Neandertal ancestry through time: Insights from genomes of ancient and present-day humans

Leonardo N M Iasi 1,†,*, Manjusha Chintalapati 2,†,§, Laurits Skov 2, Alba Bossoms Mesa 1, Mateja Hajdinjak 1,3, Benjamin M Peter 1,4,‡,*, Priya Moorjani 2,5,‡,*
PMCID: PMC12184710  NIHMSID: NIHMS2080160  PMID: 39666853

Abstract

Gene flow from Neandertals has shaped the genetic and phenotypic variation in modern humans. We generate a catalog of Neandertal ancestry segments in more than 300 genomes spanning the last 50,000 years. We examine how Neandertal ancestry is shared among individuals over time. Our analysis reveals that the vast majority of Neandertal gene flow is attributable to a single, shared extended period of gene flow that occurred 50,500–43,500 years ago, as evidenced by ancestry correlation, co-localization of Neandertal segments across individuals, and divergence from the sequenced Neandertals. Most natural selection–positive and negative–on Neandertal variants occurred immediately after the gene flow. Our findings provide new insights into how the contact with Neandertals shaped modern human origins and adaptation.

Main text:

The sequencing of the Neanderthal (1–4) and Denisovan (5, 6) genomes has revealed extensive gene flow between the ancestors of modern humans and archaic hominins. As a result, most non-Africans harbor 1 to 2% of Neanderthal ancestry, with East Asians exhibiting ~20% more Neanderthal ancestry than West Eurasians (6). This gene flow has been inferred to have occurred between 41,000 and 54,000 years ago, but it is still under debate whether there were secondary interactions between Neanderthals and early modern humans (e.g., Oase, Bacho Kiro, and Ust’-Ishim) and what the cause of the population differences between East Asians and West Eurasians is (e.g., secondary pulse in East Asians or dilution in West Eurasian or other demographic events) (7–11). Moreover, previous studies have identified that the distribution of Neanderthal ancestry is not uniform across the genome: Some regions are significantly depleted of Neanderthal ancestry (referred to as “archaic deserts”), whereas other regions contain Neanderthal variants at unusually high frequencies, possibly because they harbor beneficial mutations (“candidates of adaptive introgression”) (12–17). The evolutionary forces, for example, genetic drift or natural selection, that have shaped these patterns are still not fully understood.

Most previous studies have focused on genomes of present-day individuals, for which separating the effects of past demography and selection is challenging (18). In this work, we generated a catalog of Neanderthal ancestry in both ancient and present-day modern humans providing a systematic analysis of Neanderthal ancestry through time and space. We recovered the origin and trajectory of variants inherited from Neanderthals, which allowed us to refine the estimates of when the gene flow occurred (7, 9, 19) and directly observe how selection has shaped the patterns of ancestry across the genome (18, 20). Together, these analyses help characterize the population history and legacy of Neanderthal gene flow in modern humans.

Identifying the location of Neanderthal ancestry in modern humans

We used genomic data from 334 modern humans, including ancient and present-day individuals. Our dataset includes 59 ancient individuals with a sample age of 45,000 to 2200 years before present (B.P.), including 33 individuals that are older than 10,000 years B.P., and the genomes from 275 diverse present-day individuals from worldwide populations that are part of the Simons Genome Diversity Project (SGDP) (21, 22). These data are a combination of whole-genome sequences and variants enriched for target positions using two different single-nucleotide polymorphism (SNP) capture arrays; the “1240k” (containing ~1.2 million sites segregating in modern humans) and the archaic admixture array [containing ~1.7 million Neanderthal and Denisovan ancestry informative sites (8)] [table S1; (22)]. For ancient individuals, we limited our analysis to 59 ancient genomes that were either whole-genome sequenced or captured on the archaic admixture array because we found that the 1240k array has a limited number of archaic informative variants (fig. S2 and table S3). We further clustered individuals into 14 population groups that were stratified by geographic location and time using the data on the 1240k array [Fig. 1A, figs. S14 and S15, and table S9; (22)].

Fig. 1.

Fig. 1.

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Calling of Neanderthal ancestry in ancient and present-day individuals worldwide. (A) Sample overview of the population clusters through time. Bars represent the span from oldest to youngest individual; n is the number of individuals. ancientEAS, Ancient East Asians; ANE, ancient North Eurasians; ANS, ancient North Siberians; EarlyOoA, early out of Africa; EAS, East Asians; postLGM-WEurHG, post-LGM West Eurasian hunter-gatherers; preLGM-WEurHG, pre-LGM West Eurasian hunter-gatherers; SA, South Asians; SWA, Southwest Asians; WEur, West Eurasians. (B) Sample location and age of individuals.

Triangles represent present-day individuals, and dots represent ancient individuals. kya, thousand years ago. (C) Workflow overview for admixfrog. High-quality genomes were used as representatives for the unobserved ancestries (blue, red, and green) of a given target genome. The genotypes of all SNPs in a small bin are latent states that were coestimated from aligned sequencing reads. (D) Inferred ancestry segments across the autosomes in three ancient individuals. Red segments represent Neanderthal ancestry. (E) Ancestry covariance for the same individuals calculated on the genotype likelihoods from admixfrog. The dashed line indicates the inferred decay of coancestry.

To infer Neanderthal ancestry segments in a single target modern human genome, we used admixfrog, a hidden Markov model–based approach (23). For each diploid individual of interest in each genomic window [0.005 centimorgan (cM)], admixfrog estimates a combination of two ancestries from three possibilities: (i) Neanderthal [using the three high-coverage Neanderthal genomes as references (2–4)], (ii) Denisovan [using the high-coverage Altai Denisovan genome (6)], or (iii) modern human [using a panel of individuals of sub-Saharan African–related ancestry who have minimal Neanderthal ancestry (24)]. admixfrog coestimates the genotype likelihoods and contamination and is thus well suited for the analysis of ancient DNA [Fig. 1C; (22)].

We performed extensive simulations to test the performance of admixfrog. We found that for samples with coverage of at least 0.2x, the method works reliably for shotgun genomes and the archaic admixture array but it had lower power for the 1240k capture array, which has few archaic informative markers [figs. S2 to S9 and table S3; (22)]. Because we had access to only one Denisovan genome that is distantly related to the introgressing Denisovan related to many human populations (25), our ability to reliably detect Denisovan ancestry was limited (figs. S12 and S13 and tables S6 and S7). Thus, we focused on Neanderthal ancestry only. Using admixfrog, we obtained reliable evidence of Neanderthal ancestry in all individuals of nonAfrican ancestry (58 ancient and 231 present-day individuals) (Fig. 1B). We focused on this data to infer the source, timing, and function of Neanderthal ancestry in modern humans (Fig. 1, D and E).

Spatiotemporal patterns of Neanderthal ancestry

The distribution of Neanderthal ancestry across populations offers insights into the historical interactions between modern humans and Neanderthals. After the initial gene flow, the patterns of variation of Neanderthal alleles would be shaped by the demographic history of the modern populations, including genetic drift, bottlenecks, and secondary gene flow events. Neanderthal segments that originated from the same introgression event would be shared by descendant populations, and the sharing patterns would thus reflect the relatedness observed at random neutral sites in the genome. The amount of unique Neanderthal ancestry in any individual would in turn be small. By contrast, secondary Neanderthal gene flow events (private to some populations) would introduce ancestry at new genomic locations and would thus lead to populations with largely uncorrelated ancestry patterns and increased levels of unique ancestry (26). Furthermore, gene flow events from genetically differentiated Neanderthal populations (27) would result in differences in divergence estimates between the introgressing segments and the reference Neanderthal genomes (25).

We examined the population structure at Neanderthal ancestry segments using principal components analysis (PCA). We found that PC1 differentiates individuals from East Asia and Europe and PC2 separates individuals from Oceania and other worldwide groups (Fig. 2A). The differentiation observed in the PCA is driven by differences in frequency rather than the presence or absence of Neanderthal segments among populations (fig. S16). We showed the robustness of this pattern across datasets by repeating this analysis using individuals from the 1000 Genomes Project (fig. S17). To determine whether this pattern can be explained by the demographic history of modern populations, we compared genomic sharing at Neanderthal ancestry segments with the patterns at sites segregating within modern human populations. We found that the correlation matrix of shared Neanderthal ancestry segments is highly concordant with the f3-drift-matrix (inferred using 1240k sites) that measures genome-wide allele sharing across individuals [Pearson correlation = 0.78, p < 2.2 × 10−16; Fig. 2C, fig. S18, and tables S8, S10, and S11; (22)]. The notable exception is the early out of Africa (EarlyOoA) cluster (which includes individuals with sampling age older than 40,000 years B.P., including Ust’-Ishim, Oase, Bacho Kiro, and Zlatý kůň), which has a significantly weaker correlation (mean Pearson’s correlation = 0.05, maximum p < 0.0012 by Holm-Bonferroni adjusted t test; table S11).

Fig. 2.

Fig. 2.

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Analysis of the spatiotemporal pattern on inferred Neanderthal segments on the autosomes. (A) PCA of the sharing of Neanderthal segments. (B) Amount of unique Neanderthal ancestry per population cluster (with n giving the number of individuals per cluster) for a randomly sampled individual. Error bars were calculated by resampling the individuals and represent one standard deviation. (C) Comparison of the differences in pairwise f3 values (upper part of the matrix) and the pairwise correlation of Neanderthal segments (lower part of the matrix). One individual was analyzed per site. Two random present-day SGDP individuals are included for clusters that contain present-day samples. (D) Matching rate of Neanderthal segments with at least 15 informative SNPs to the Chagyrskaya Neanderthal, stratified by data type (shotgun sequenced in blue, captured in red). Individuals are grouped in population clusters ordered from oldest to youngest. Population clusters have the same labels as in Fig. 1.

After comparing the sharing of Neanderthal ancestry segments across individuals, we found that individuals that postdate the Last Glacial Maximum (LGM), including present-day individuals, have around 6% unique Neanderthal ancestry, with few differences between population clusters [fig. S19 and table S12; (22)]. Individuals that predate the LGM have a higher amount of unique Neanderthal ancestry, with the highest proportion in EarlyOoA individuals (296 Mb or 34%). The results remain significant after controlling for sample size (p < 0.0002 for pairwise t test with Holm-Bonferroni multiple testing correction) (Fig. 2B and tables S13 and S14). Because some of the unique Neanderthal ancestry might be at low frequency in a population, we repeated the analysis using the 1000 Genomes Project dataset (28), which has many more individuals per population and obtained qualitatively similar results (figs. S20 and S21 and table S13).

Among post-LGM individuals, we found that the amount of unique Neanderthal ancestry is not significantly different between West Eurasians and East Asians, despite East Asians harboring ~20% more Neanderthal ancestry (22 Mb unique in East Asians versus 19 Mb unique in West Eurasians, p = 1) (table S14). There is no significant difference in the overall Neanderthal ancestry between ancient East Asians (n = 2) and preand post-LGM West Eurasian hunter-gatherers (n = 11), albeit the sample size is very small [figs. S22 and S23 and table S5; (22)]. Among present-day individuals, we found the largest amount of unique Neanderthal ancestry in the Oceanian cluster, possibly owing to a contribution of some misclassified Denisovan ancestry segments (6) (table S5). The lowest amount of unique Neanderthal ancestry per individual is seen in the Satsurblia and Yamnaya clusters. The Caucasus huntergatherer ancestry in these population clusters is widespread in present-day individuals, with substantial contributions to the West Eurasians and South Asians in our data (29, 30).

Next, we investigated how many genetically distinct Neanderthal populations may have contributed to the introgression by calculating the number of differences between the inferred Neanderthal segments to the sequenced Chagyrskaya Neanderthal genome (which was not used in the design of the archaic admixture array) and found a unimodal distribution in all clusters [Fig. 2D; (22)]. This suggests that a single Neanderthal group or multiple closely related populations contributed the bulk of inferred ancestry in modern humans. One exception is the EarlyOoA cluster, which has a bimodal distribution, with ~6% of ancestry segments that are significantly more diverged from the Chagyrskaya Neanderthal [table S15; (22)]. We found consistent results when comparing the proportion of introgressed segments coming from any of the four reference archaics estimated using a generalized linear mixed model [figs. S26 to S28 and tables S16 and S17; (22)].

Timing of Neanderthal gene flow

The lengths of Neanderthal ancestry segments in modern humans provide insights about the timing and duration of gene flow, as these segments become progressively shorter with each generation due to recombination (19, 31, 32). Instead of using the decay in Neanderthal segments, which can preferentially miss shorter segments, we measured the ancestry covariance across the genome between pairs of alleles of putative Neanderthal ancestry (19, 22). In a previous study (19), it was shown using simulations that this approach works reliably for single ancient genomes that are older than 10,000 years, even with low coverage (~1x). Because some EarlyOoA individuals have very recent Neanderthal ancestry and possibly evidence for multiple pulses of gene flow (7–9, 19), we analyzed these individuals separately.

If we assume that the gene flow occurred instantaneously within a single or few generations [instantaneous gene flow (IG) model], we expect the decay of ancestry covariance in each individual to follow an exponential distribution. We measured the ancestry covariance for each of the 16 ancient individuals that lived between 40,000 and 20,000 years B.P. and inferred that the Neanderthal gene flow occurred between 321 and 950 generations before these individuals lived [fig. S31 and table S18; (22)]. By leveraging the linear relationship between the dates of Neanderthal gene flow (in generations) and the individuals’ sampling age (in years), we jointly inferred the average generation interval as 28.4 years [95.5% confidence interval (CI): 27 to 30 years] and the time of the shared pulse of Neanderthal gene flow as 46,364 years B.P. (95.5% CI: 45,682 to 47,045 years B.P.). Our estimates are consistent with previous studies (19, 33), though they are more precise because of our larger sample size (Fig. 3A). We found that our results are robust to the inclusion of individuals in the EarlyOoA cluster (table S20).

Fig. 3.

Fig. 3.

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Dating of the Neanderthal admixture event in 22 individuals older than 20,000 years B.P. (A) Time since the Neanderthal admixture using ancestry covariance curves with 95.5% CIs (y axis) versus the age of the individual and the 95.5% CIs (x axis). The date for Zlatý kůň is a genetic date. The black line indicates the fit of a linear model, with the uncertainty in gray shades. (B) Log-likelihood surfaces of the joint estimate of gene flow duration under the extended pulse model with the EarlyOoA individuals excluded (n = 16). Shades of red indicate the difference between any log-likelihood and the highest log-likelihood [maximum log-likelihood (MLL)].

Previous studies have suggested that there may have been two distinct pulses of Neanderthal gene flow in East Asians (10, 14, 15, 34). We tested this hypothesis for two ancient East Asian genomes, ~40,000-year-old Tianyuan and ~35,000-year-old Salkhit, by examining whether a model of a single pulse or two pulses of gene flow fits the data better. By applying a likelihood ratio test, we found that we were unable to reject the model with a single pulse for either of the ancient East Asian genomes (table S19), despite adequate power to detect a secondary pulse that contributed at least 10% of the ancestry compared with the first pulse (fig. S32).

To determine whether the estimate of the time of the shared pulse of Neanderthal gene flow would differ if admixture took place over an extended duration, we next compared the IG model to the extended pulse (EP) model, which assumes that gene flow occurred over multiple generations (32). We focused on individuals younger than 40,000 years B.P. because the joint modeling of the IG and EP models assumes that no major gene flow occurs after the sampling age of the oldest individual (because there is large uncertainty in the sampling age of EarlyOoA individuals, we excluded them). We obtained a significantly better fit for the EP model than the IG model (likelihood ratio test, p < 2.2 × 10−16), with a mean time of gene flow of around 47,124 years B.P. (46,872 to 47,404 years B.P.) and a duration of around 6832 years (2044 to 9968 years). However, uncertainties in local recombination rates over time and the sampling ages of ancient individuals affect the precision of the duration estimates (Fig. 3B, figs. S35 to S37, and tables S21 to S24) (35, 36).

Neanderthal ancestry across the genome

Neanderthal ancestry has played a major role in human adaptation and disease (37, 38), but few studies have tracked how the frequency of Neanderthal variants has changed through time (39–41). Using Neanderthal segments in ancient and present-day individuals, we recovered Neanderthal ancestry in 61.7% (1551 Mb) of the autosomal callable genome (fig. S38). On the X chromosome, we found Neanderthal ancestry only in 20.2% (29 of 144.86 Mb) of the genome. Neanderthal ancestry segments on the X chromosome are nonuniform and nonrandomly distributed, with large genomic regions devoid of any Neanderthal segments (Fig. 4A). Indeed, when we measured the entropy on the X chromosome, we found that the distribution on the X chromosome is significantly more ordered than on the autosomes [Shannon’s entropy H = 0.03 (X chromosomes) versus 0.11 (autosomes), Wilcoxon rank sum test p < 2.2 × 10−16].

Fig. 4.

Fig. 4.

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Regions of high Neanderthal ancestry through time. (A) Mean posterior probability of Neanderthal ancestry on the X chromosome for ancient (teal) and present-day (orange) individuals. The dotted lines give the average posterior probability for Neanderthal ancestry throughout the genome. (B) Mean posterior probability of Neanderthal ancestry on chromosome 9. The colored rectangles indicate the position of the high-frequency regions and correspond to the analyses in (C) to (E). (C) Regions of high frequency in both present-day and ancient genomes. This genomic interval (shown on top) overlaps the gene BNC2. (D and E) Regions at high frequency in present-day, but not ancient, individuals (D) and regions at high frequency in ancient, but not present-day, individuals (E). The number of dots corresponds to the number of individuals at the sampling time depth shown on the x axis. Genomic coordinates are shown in human genome reference build hg19. Frequencies in (C) and (D) are estimated, including 50 kb flanking up and downstream of the region.

Previous studies have shown that the landscape of Neanderthal ancestry in present-day individuals is correlated with recombination rate (42) and B-statistics, a measure of background selection (12, 43, 44). We grouped our individuals into four different time intervals: non-African present-day individuals (n = 231), ancient individuals younger than 10,000 years B.P. (n = 25), ancient individuals between 10,000 and 30,000 years B.P. (n = 13), and ancient individuals older than 30,000 years B.P. (n = 20). We also tested individuals from the EarlyOoA cluster alone. We found that the local recombination rate is positively correlated with Neanderthal ancestry in ancient and present-day individuals, except in EarlyOoA, where we lacked power [table S25; (22)]. Across time intervals, we also found that evolutionarily constrained regions (low B-scores) consistently harbor less Neanderthal ancestry than the rest of the genome (fig. S40). For instance, in individuals older than 30,000 years B.P., mean Neanderthal ancestry in the lowest B-score bin is 3.2% and in the highest B-score bin is 6.2%, suggesting that initial gene flow may have been >5% in modern humans. For the X chromosome, we found a weak correlation with recombination rate at fine scales (Spearman correlation at 20 kb = 0.05, p = 0.04), but the correlation is not significant at larger distances owing to limited power (table S31).

To identify candidate regions of natural selection, we examined how the frequency of Neanderthal segments has changed with time. Segments that harbor beneficial alleles may increase in frequency as a result of positive selection or adaptive introgression, whereas segments carrying deleterious alleles are pre-dicted to be purged quickly, leading to Neanderthal deserts (12). We thus scanned the genome for regions where the frequency of Neanderthal ancestry is unexpectedly high (or low) compared with the genome-wide average estimates and also examined how the frequency changed over time (Fig. 4B and table S26). Regions of elevated archaic ancestry are likely candidates of natural selection, though certain demographic events—population structure or strong founder events—can potentially also lead to local increases in frequency owing to genetic drift alone (41, 45). To minimize the impact of population stratification, we focused on present-day and ancient individuals of West Eurasian–related ancestry, which included 45 ancient and 101 present-day individuals.

We identified 86 regions (347 genes) that are at high frequency (Z score > 4.5, corresponding to approximately the 99.9th percentile, assuming a normal distribution) in both present-day and ancient individuals and may be candidates of immediate adaptation [Fig. 4, B and C; (22)]. Using a permutation Gene Ontology (GO) analysis, we found that these candidate regions are enriched for pathways related to skin pigmentation, metabolism, and immunity (table S28), compared with a set of genes with similar genomic features. These pathways have also been identified in surveys of presentday individuals (12, 13), suggesting that many of these genes may have been immediately beneficial to modern humans as they encountered new environmental pressures outside Africa.

We found 91 candidate regions (169 genes) at high frequency in present-day individuals but not in ancient individuals, indicating that these regions may contain variants that became adaptive later on (selection on standing introgressed variation) (Fig. 4, B and D). We also found 32 candidate regions (102 genes) that were at high frequency in ancient individuals but not in present-day individuals (Fig. 4, B and E). Many of these regions (~44%) are located within 1 Mb of candidate adaptive regions, suggesting that these haplotypes hitchhiked with beneficial mutations and decreased in frequency as recombination occurred.

Examining the trajectory of 11 previously published candidate regions of adaptive introgression found in present-day European populations [inferred in multiple surveys (12, 13, 15, 41)], we replicated 72% of the signals, including seven regions that may have been immediately adaptive and one candidate of selection on standing variation. Among these regions, a notable example is the 2-Mb region on chromosome 2, where the highest Neanderthal ancestry in ancient individuals is 64% and in present-day individuals is 67%. This region contains 12 genes, including TANC1 and BAZ2B, which play a role in neuronal signaling and the nervous system (46, 47). Another example is BNC2, a gene that plays a role in skin pigmentation (48), which is at ~22% frequency in individuals older than 30,000 years B.P. and at ~65% frequency in present-day individuals (Fig. 4C). This indicates that variants at this locus may have been immediately beneficial and increased over time in modern humans, unlike previous reports that rejected immediate selection at this locus, possibly owing to limited samples at earlier timescales (41) (fig. S41 and table S27).

To understand the genesis of Neanderthal deserts, we examined Neanderthal ancestry over time in five regions of limited (or no) Neanderthal ancestry that were identified in previous studies (13, 15). We found that the deserts are in the 0.1th percentile of the empirical distribution of archaic ancestry genomewide across time bins, including in individuals in the earliest time intervals (greater than 30,000 years B.P.) (table S29). Notably, we found almost no introgressed Neanderthal segments within the boundaries of four out of five deserts in ancient or present-day individuals (fig. S43) [similar to a recent study in South Asians (45), we did not replicate the desert on chromosome 1]. This indicates that the deserts formed rapidly after the initial gene flow, consistent with theoretical expectations (18, 49).

We found that Neanderthal ancestry on the X chromosome is substantially depleted compared with that on the autosomes, including individuals older than 30,000 years B.P., with the X-to-autosome ratio remaining stable over time (0.216 to 0.409 for females and 0.239 to 0.288 for males) (fig. S45). In accordance, we found large regions that are depleted of Neanderthal ancestry in our earliest time intervals and persist over time (figs. S47 and S48). Among the two previously identified deserts on the X chromosome (13), only one of them is completely devoid of Neanderthal ancestry (chrX:62,000, 000–78,000,000), whereas we found substantial Neanderthal ancestry in the other (chrX: 109,000,000–143,000,000) (fig. S49). Interestingly, most deserts overlap with previously reported putative selective sweeps (or extended common haplotypes) in non-Africans (50), and all but one of these 19 haplotypes are devoid of Neanderthal ancestry in our data. That the depletion is present even in our earliest samples suggests thatthe selection on these haplotypes may have occurred rapidly during and immediately after Neanderthal gene flow.

Discussion

We present a comprehensive study of Neanderthal ancestry variation in modern humans over the past 50,000 years, integrating data from both ancient and present-day individuals. Using recently developed methods, we generated a catalog of Neanderthal introgressed segments covering nearly 1.6 Gb of the human genome (1551 Mb on the autosomes and 29 Mb on the X chromosome). We found that the vast majority of Neanderthal ancestry in modern humans is attributable to a single, shared extended period of gene flow into the common ancestors of non-Africans, although we cannot rule out the possibility that some populations may have received minor contributions of additional Neanderthal ancestry.

The earliest individuals—Oase, Ust’-Ishim, Zlaty’kun, and Bacho Kiro—possess substantial unique Neanderthal ancestry, distinct matching profiles to the sequenced Neanderthals, and the weakest correlation of introgressed segment locations with other ancient or presentday individuals. This suggests that some Neanderthal ancestry in these early individuals is not shared with modern humans after 40,000 years. Consistent with previous studies (6), we found that present-day East Asians harbor ~20% more Neanderthal ancestry than West Eurasians. However, this difference was not observed when comparing ancient East Asians (Tianyuan and Salkhit) with preLGM West Eurasians. Additionally, we found that a model of a single Neanderthal gene flow provides the best fit to the ancestry covariance curves in ancient East Asians, compared with models that include secondary gene flow events. These results all hint at the possibility that some differences among Eurasians may have arisen more recently, though our findings remain tentative owing to the limited availability of ancient East Asian genomes.

We infer that the major Neanderthal gene flow in modern humans occurred 50,500 to 43,500 years ago, which is consistent with archaeological evidence for the overlap of modern humans and Neanderthals in Europe (51, 52). These dates have several implications for the spread of humans after the out-ofAfrica event. Because 1 to 2% of the ancestry of most non-Africans today is derived from Neanderthals, the timing of this gene flow into the common ancestors of non-Africans provides a lower bound on the timing of the out-of-Africa migration and settlement of regions outside Africa. For instance, this suggests that the major out-of-Africa migration occurred no later than 43,500 years ago. Moreover, the population receiving Neanderthal ancestry might have been highly structured during the gene flow event. The diversification of people outside Africa may have started during or soon after the Neanderthal gene flow, which could partially explain different levels of Neanderthal ancestry among nonAfrican populations and also reconcile our dates with archaeological evidence for the presence of modern humans in Southeast Asia and Oceania by ~47,000 years (17, 53). Furthermore, our results underscore the need for caution in comparing genetic and archaeological dates because processes like gene flow can be highly complex; thus, relying on point estimates can provide a limited and incomplete understanding. Further analysis, including studies of ancient genomes from Eurasia and Oceania, will be critical for inferring the timing of human dispersal across Eurasia and the Pacific region.

Finally, we demonstrate that the landscape of Neanderthal ancestry across the genome was formed rapidly after the gene flow. Most natural selection—positive and negative—on the Neanderthal ancestry variants occurred within ~100 generations after the gene flow and was also evident in the EarlyOoA individuals. Notably, we found that the depletion of Neanderthal ancestry and the strong sweeps on the X chromosomes in non-Africans occurred rapidly.

Together, our catalog of Neanderthal introgressed segments in ancient and present-day genomes allows us to elucidate the evolutionary history of Neanderthal gene flow in modern humans and provides new insights about human origins and adaptation.

Materials and methods summary

We assembled a dataset of 59 ancient genomes (7–9, 11, 26, 29, 39, 54–74) and 275 high-coverage present-day genomes from the SGDP (21), which are either shotgun-sequenced or captured using the archaic admixture array (8). We clustered each individual into a superpopulation and population clusters based on outgroup-f3 statistics (62, 75). We applied a hidden Markov model– based ancestry inference method admixfrog (23) to identify Neanderthal introgressed segments using three high-coverage Neanderthals (2–4), one Denisovan (6), and a set of presentday individuals with sub-Saharan African–related ancestry (28) as reference populations focusing on sites in the archaic admixture array.

To characterize the spatiotemporal patterns of Neanderthal ancestry sharing among modern humans, we performed PCA on regions of introgressed Neanderthal ancestry and compared the observed patterns to overall ancestry sharing at 1.2 million sites segregating in modern humans (1240k ascertained sites). We identified the amount of unique Neanderthal ancestry across individuals, among ancient individuals, and across ancient and present-day individuals. To identify whether there were contributions from genetically distinct Neanderthal groups, we calculated the number of differences between the inferred Neanderthal segments to the sequenced Chagyrskaya Neanderthal genome (which was not used in the design of the archaic admixture array and hence provides unbiased estimates to the divergence to introgressing group).

We inferred the timing and duration of Neanderthal ancestry by measuring the extent of ancestry covariance across the genome between pairs of alleles of putative Neanderthal ancestry (ACov ascertainment). We jointly modeled the shared Neanderthal admixture time and generation interval by studying the relationship between dates of admixture (in generations) and radiocarbon dates (in years) using a Bayesian linear model (19). We estimated the duration of Neanderthal admixture using an EP model and compared this model with the IG model using a likelihood ratio test.

By assembling nonoverlapping Neanderthal ancestry segments extracted from all individuals, we reconstructed a linear genome of the introgressing Neanderthal population (for both autosomes and the X chromosome). We examined the frequency of Neanderthal segments at a given genomic position and correlated that with the local recombination rate and a measure of background constraint (B-score) inferred using the B-statistic (43). We surveyed genomic regions harboring significantly high frequency of Neanderthal ancestry [that are 4.5 standard deviations away from the genomewide mean, which corresponds to the 99.9th percentile, assuming a normal distribution (76)]. After merging neighboring genomic windows, we performed GO enrichment analysis using a permutation of candidate genes with a set of control genes that are matched for genomic features such as gene length, average B-score, number of SNPs or ascertained markers, and average recombination rate. We investigated the genesis of Neanderthal deserts and published candidates of adaptive introgression by examining the trajectory of archaic ancestry through time, considering four discrete time intervals.

Finally, we examined Neanderthal ancestry on the X chromosome by applying admixfrog to 289 shotgun-sequenced genomes. We investigated the distribution of Neanderthal ancestry compared with the autosomes, the amount of Neanderthal ancestry on the X chromosome through time, the correlation in the frequency of Neanderthal ancestry with the local recombination rate and B-scores, and the timing of the formations of regions devoid of archaic ancestry that were identified in previous studies.

Supplementary Material

Supplementary Tables
Supplementary Material

ACKNOWLEDGMENTS

We thank J. Kelso, M. Schumer, N. Patterson, S. Peyrégne, M. Slatkin, E. Zavala, S. Johnson, E. Kerdoncuff, S. Schiffels, I. Lazaridis, J. Lachance, and E. Vacca for helpful comments on the manuscript. Funding: P.M. was supported by the Burroughs Wellcome Fund (Career Award at the Scientific Interface). P.M., M.C., and L.S. were supported by National Institutes of Health (NIH) grant no. R35GM142978. L.N.M.I. and B.M.P. were funded by the European Union (ERC, NEADMIX, 101042421). M.H. was supported by Marie Skłodowska Curie Actions (grant no. 844014).

Footnotes

Competing interests: The authors declare no competing interests.

Data and materials availability:

No new data were generated for this study. The catalog of Neanderthal segments is available through Dryad (77). All scripts are available through GitHub (https://github.com/LeonardoIasi/Neandertal-ancestry-through-time) and Zenodo (78).

REFERENCES AND NOTES

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Tables
NIHMS2080160-supplement-Supplementary_Tables.xlsx (2.6MB, xlsx)
Supplementary Material
NIHMS2080160-supplement-Supplementary_Material.docx (22.1MB, docx)

Data Availability Statement

No new data were generated for this study. The catalog of Neanderthal segments is available through Dryad (77). All scripts are available through GitHub (https://github.com/LeonardoIasi/Neandertal-ancestry-through-time) and Zenodo (78).

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