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Selection against archaic hominin genetic variation in regulatory regions


Traces of Neandertal and Denisovan DNA persist in the modern human gene pool, but have been systematically purged by natural selection from genes and other functionally important regions. This implies that many archaic alleles harmed the fitness of hybrid individuals, but the nature of this harm is poorly understood. Here, we show that enhancers contain less Neandertal and Denisovan variation than expected given the background selection they experience, suggesting that selection acted to purge these regions of archaic alleles that disrupted their gene regulatory functions. We infer that selection acted mainly on young archaic variation that arose in Neandertals or Denisovans shortly before their contact with humans; enhancers are not depleted of older variants found in both archaic species. Some types of enhancer appear to have tolerated introgression better than others; compared with tissue-specific enhancers, pleiotropic enhancers show stronger depletion of archaic single-nucleotide polymorphisms. To some extent, evolutionary constraint is predictive of introgression depletion, but certain tissues’ enhancers are more depleted of Neandertal and Denisovan alleles than expected given their comparative tolerance to new mutations. Foetal brain and muscle are the tissues whose enhancers show the strongest depletion of archaic alleles, but only brain enhancers show evidence of unusually stringent purifying selection. We conclude that epistatic incompatibilities between human and archaic alleles are needed to explain the degree of archaic variant depletion from foetal muscle enhancers, perhaps due to divergent selection for higher muscle mass in archaic hominins compared with humans.

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Fig. 1: Introgressed variants are depleted from enhancers and exons relative to matched control variants.
Fig. 2: Archaic variant depletion is correlated with the number of cell types in which an enhancer is active.
Fig. 3: Neandertal and Denisovan variant depletion varies between enhancers active in different tissues.
Fig. 4: Different landscapes of young and old introgressed archaic variation.
Fig. 5: Rare variant enrichment reveals that enhancer sequences are weakly selectively constrained.

Data availability

All datasets analysed here are publicly available at the following websites: CRF introgression calls (; SGDP (; RoadMap (; and 1000 Genomes Phase 3 (

Code availability

Summary data files and custom python scripts for reproducing the paper’s main figures are available at


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We are grateful to J. Pritchard, S. Sankararaman, J. Schraiber and members of the Harris Laboratory for helpful discussions. We thank R. Nielsen and B. Vernot for manuscript comments. We acknowledge financial support from the following grants awarded to K.H.: NIH grant 1R35GM133428-01; a Burroughs Wellcome Fund Career Award at the Scientific Interface; a Searle scholarship; a Sloan Research fellowship; and a Pew Biomedical scholarship.

Author information




N.T. and K.H. conceived of and designed the project. N.T., R.A. and K.H. performed the analyses. K.H. wrote the paper.

Corresponding authors

Correspondence to Natalie Telis or Kelley Harris.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Replication of archaic SNP depletion after sampling control SNPs in clusters.

This plot replicates the analysis from Fig. 1b using archaic SNPs and controls sampled to match the clustering induced by LD structure. The depletion of archaic SNPs from exon and enhancers is nearly identical to the depletion measured using controls not sampled to match the spatial clustering of introgressed SNPs.

Extended Data Fig. 2 Human versus archaic reference sequence divergence as a function of enhancer pleiotropy and tissue activity.

a, Within enhancers and exons, we measured divergence of the human reference from the Altai Neandertal, the Altai Denisovan, and the YRI African genomes, then normalized each by the divergence of the same genomes within adjacent control regions. Exon divergence ratios <1 indicate that purifying selection has slowed down their sequence evolution compared to less constrained adjacent regions. In contrast, divergence is accelerated in enhancers relative to control regions. This acceleration is positively correlated with pleiotropy and is stronger for archaic vs. human comparisons than for the African vs. human reference genome comparison. In the absence of selection against archaic enhancer variation, this divergence pattern should cause archaic SNPs to be enriched within high-pleiotropy enhancers, not depleted as we in fact observe. b, We see no correlation across tissue-specific enhancer sets between Neandertal divergence from the human reference and archaic allele depletion from enhancers. This suggests that differences between tissues in the depletion of introgressed archaic variants are not driven by differences in divergence between reference genomes. c, We see no correlation across tissue-specific enhancer sets between Denisovan divergence from the human reference and archaic allele depletion from enhancers. All error bars in panels ac are 95% confidence intervals derived from the binomial approximation to the Bernoulli distribution.

Extended Data Fig. 3 Neandertal and Denisovan variant depletion as a function of enhancer tissue activity.

These plots show the data from Fig. 3a with Neandertal and Denisovan odds ratios on separate plots for clarity.

Extended Data Fig. 4 Joint distributions of Neandertal and Denisovan SNP depletion within each SGDP population.

Although there are differences between populations, particularly since Denisovan introgression is sparse and noisy, all show that brain and fetal muscle enhancers are the most depleted of introgression. In most populations the ‘Blood & T-cell’ tissue is least depleted of introgression.

Extended Data Fig. 5 Counts of young and old archaic alleles present in modern populations and shared by archaic reference genomes.

a, Recall that ‘young’ introgression calls are SNPs that appear in Call Set 2 generated by Sankararaman, et al. while ‘old’ calls appear in Call Set 1 for at least one archaic species but not in either set of young calls. In every modern human population, we find that 20-30% of old introgressed SNPs are shared with both the Altai Neandertal and Altai Denisovan, suggesting they likely predate the divergence of Neandertals and Denisovans or are at least old enough to have passed between the two species by gene flow. b, In contrast, only 10-20% of young introgressed SNPs are present in both archaic reference genomes. Over 45% of young Neandertal alleles are shared with the Altai Neandertal but not the Altai Denisovan; conversely, over 45% of young Denisovan alleles are shared with only the Denisovan reference. Compared to the sets of young Neandertal and Denisovan alleles, old Neandertal and Denisovan alleles look more similar to each other in their archaic reference sharing profiles: each contains 10-25% Neandertal-specific alleles and 2-10% Denisovan-specific alleles. These patterns support our hypothesis that the old calls are indeed older than the young calls. c, This panel shows the numbers of introgressed SNPs classified as young versus old within each population. Each SNP set is further subdivided into SNPs that appear in the Neandertal call set only, the Denisovan call set only, or the intersection of both call sets.

Extended Data Fig. 6 Site frequency spectra of introgressed SNPs classified as young versus old in each SGDP population.

For each call set, the corresponding vertical line demarcates the mean allele frequency of that category. In each population, the old ‘1 minus 2’ call set has the highest mean allele frequency, adding support to our hypothesis that these variants are older and/or less deleterious than either population-specific Call Set 2.

Extended Data Fig. 7 GC content cannot explain differences in singleton enrichment between tissues.

a, We partitioned the site frequency spectra of enhancers into SNPs that have GC ancestral alleles and SNPs that have AT ancestral alleles. Using each of these disjoint variants sets, we then computed singleton enrichment in enhancers versus adjacent control regions. GC-biased gene conversion is expected to have opposite effects on the two frequency spectra, increasing the proportion of GC-ancestral singletons and decreasing the proportion of AT-ancestral singletons. Despite this confounder, the finding that brain enhancers are enriched for singletons holds up when we restrict to either GC-ancestral SNPs or ATancestral SNPs. b, Across tissues, enhancers are enriched for GC base pairs compared to adjacent genomic regions. However, there is no correlation between GC content enrichment and the singleton enrichment that we attribute to purifying selection.

Extended Data Fig. 8 Joint distribution across tissues of enhancer singleton enrichment and introgression depletion.

Although singleton enrichment is correlated with depletion of young Neandertal alleles and young Denisovan alleles, the significance of this correlation disappears when all brain related tissues are excluded from the regression.

Extended Data Fig. 9 Mean enhancer phastCons scores partitioned by tissue activity.

Across all tissues, enhancers have a mean phastCons score that is slightly elevated above the genomic mean, indicating that these regions are slightly conserved over phylogenetic timescales. Fetal brain and neurosphere enhancers have a higher mean phastCons score than enhancers active in any other tissues. This result mirrors our findings on the landscape of recent purifying selection as measured by site frequency skew: fetal brain enhancers are more conserved than other regulatory elements, but fetal muscle enhancers are not.

Extended Data Fig. 10 Random sampling of control SNPs to match introgressed SNPs that have been pooled across populations.

This figure illustrates how we construct the set of SNPs that are eligible to be chosen as control SNPs that match introgressed SNPs for allele frequency and B statistic after these introgressed SNPs have been pooled across all SGDP populations.

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Telis, N., Aguilar, R. & Harris, K. Selection against archaic hominin genetic variation in regulatory regions. Nat Ecol Evol 4, 1558–1566 (2020).

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