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Resurrecting the alternative splicing landscape of archaic hominins using machine learning

Nature Ecology & Evolution volume 7, pages 939–953 (2023)Cite this article

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Abstract

Alternative splicing contributes to adaptation and divergence in many species. However, it has not been possible to directly compare splicing between modern and archaic hominins. Here, we unmask the recent evolution of this previously unobservable regulatory mechanism by applying SpliceAI, a machine-learning algorithm that identifies splice-altering variants (SAVs), to high-coverage genomes from three Neanderthals and a Denisovan. We discover 5,950 putative archaic SAVs, of which 2,186 are archaic-specific and 3,607 also occur in modern humans via introgression (244) or shared ancestry (3,520). Archaic-specific SAVs are enriched in genes that contribute to traits potentially relevant to hominin phenotypic divergence, such as the epidermis, respiration and spinal rigidity. Compared to shared SAVs, archaic-specific SAVs occur in sites under weaker selection and are more common in genes with tissue-specific expression. Further underscoring the importance of negative selection on SAVs, Neanderthal lineages with low effective population sizes are enriched for SAVs compared to Denisovan and shared SAVs. Finally, we find that nearly all introgressed SAVs in humans were shared across the three Neanderthals, suggesting that older SAVs were more tolerated in human genomes. Our results reveal the splicing landscape of archaic hominins and identify potential contributions of splicing to phenotypic differences among hominins.

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Fig. 1: The identification, distribution and origin of archaic SAVs.
Fig. 2: Genes with archaic-specific SAVs are enriched for roles in many phenotypes.
Fig. 3: Most SAVs result in isoforms that trigger NMD or yield altered transcripts and proteins.
Fig. 4: Lineage-specific archaic variants are enriched for SAVs compared to shared archaic variants.
Fig. 5: Introgressed SAVs present in modern humans were shared across archaic individuals and are associated with increased tissue specificity.
Fig. 6: Example archaic SAVs leading to NMD in loci with evidence of recent adaptive evolution.

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Data availability

The SpliceAI annotated archaic variant dataset is available on Dryad94. Source data are provided with this paper.

Code availability

The archived version of the code used to conduct analyses and generate figures has been deposited in Zenodo95. A non-archived version is available on GitHub (https://github.com/brandcm/Archaic_Splicing).

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Acknowledgements

We thank M. L. Benton for kindly sharing data on tissue specificity and Z. Gao for helpful discussion on recurrent mutations. E. McArthur and D. Rinker provided comments that improved this manuscript. We also thank members of the Capra Lab for feedback on figures. This research greatly benefited from access to the Wynton high-performance compute cluster at the University of California, San Francisco. L.L.C. was funded by National Institutes of Health grant no. T32HG009495 to the University of Pennsylvania. J.A.C. and C.M.B. were funded by National Institutes of Health grant no. R35GM127087.

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Authors and Affiliations

  1. Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA

    Colin M. Brand & John A. Capra

  2. Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA

    Colin M. Brand & John A. Capra

  3. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Laura L. Colbran

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The work was conceived by C.M.B., L.L.C. and J.A.C. Formal analysis was undertaken by C.M.B. and L.L.C. The manuscript was drafted, reviewed and edited by all authors.

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Correspondence to John A. Capra.

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Extended data

Extended Data Fig. 1 Shared phenotype enrichment.

(A) Phenotype associations enriched among genes with archaic-specific shared SAVs based on annotations from the 2019 GWAS Catalog. Phenotypes are ordered by increasing enrichment within manually curated systems. Circle size indicates enrichment magnitude. Enrichment and p-values were calculated from a one-sided permutation test based on an empirical null distribution generated from 10,000 shuffles of maximum ∆ across the entire dataset (Methods). Dotted and dashed lines represent false-discovery rate (FDR) corrected p-value thresholds at FDR = 0.05 and 0.1, respectively. At least one example phenotype with a p-value ≤ the stricter FDR threshold (0.05) is annotated per system. (B) Phenotypes enriched among genes with archaic-specific shared SAVs based on annotations from the Human Phenotype Ontology (HPO). Data were generated and visualized as in A. See Supplementary Data 2 for all phenotype enrichment results.

Source data

Extended Data Fig. 2 Altai phenotype enrichment.

(A) Phenotype associations enriched among genes with archaic-specific Altai SAVs based on annotations from the 2019 GWAS Catalog. Phenotypes are ordered by increasing enrichment within manually curated systems. Circle size indicates enrichment magnitude. Enrichment and p-values were calculated from a one-sided permutation test based on an empirical null distribution generated from 10,000 shuffles of maximum ∆ across the entire dataset (Methods). Dotted and dashed lines represent false-discovery rate (FDR) corrected p-value thresholds at FDR = 0.05 and 0.1, respectively. At least one example phenotype with a p-value ≤ the stricter FDR threshold (0.05) is annotated per system. (B) Phenotypes enriched among genes with archaic-specific Altai SAVs based on annotations from the Human Phenotype Ontology (HPO). Data were generated and visualized as in A. See Supplementary Data 2 for all phenotype enrichment results.

Source data

Extended Data Fig. 3 Chagyrskaya phenotype enrichment.

(A) Phenotype associations enriched among genes with archaic-specific Chagyrskaya SAVs based on annotations from the 2019 GWAS Catalog. Phenotypes are ordered by increasing enrichment within manually curated systems. Circle size indicates enrichment magnitude. Enrichment and p-values were calculated from a one-sided permutation test based on an empirical null distribution generated from 10,000 shuffles of maximum ∆ across the entire dataset (Methods). Dotted and dashed lines represent false-discovery rate (FDR) corrected p-value thresholds at FDR = 0.05 and 0.1, respectively. At least one example phenotype with a p-value ≤ the stricter FDR threshold (0.05) is annotated per system. (B) Phenotypes enriched among genes with archaic-specific Chagyrskaya SAVs based on annotations from the Human Phenotype Ontology (HPO). Data were generated and visualized as in A. See Supplementary Data 2 for all phenotype enrichment results.

Source data

Extended Data Fig. 4 Denisovan phenotype enrichment.

(A) Phenotype associations enriched among genes with archaic-specific Denisovan SAVs based on annotations from the 2019 GWAS Catalog. Phenotypes are ordered by increasing enrichment within manually curated systems. Circle size indicates enrichment magnitude. Enrichment and p-values were calculated from a one-sided permutation test based on an empirical null distribution generated from 10,000 shuffles of maximum ∆ across the entire dataset (Methods). Dotted and dashed lines represent false-discovery rate (FDR) corrected p-value thresholds at FDR = 0.05 and 0.1, respectively. At least one example phenotype with a p-value ≤ the stricter FDR threshold (0.05) is annotated per system. (B) Phenotypes enriched among genes with archaic-specific Denisovan SAVs based on annotations from the Human Phenotype Ontology (HPO). Data were generated and visualized as in A. See Supplementary Data 2 for all phenotype enrichment results.

Source data

Extended Data Fig. 5 Neanderthal phenotype enrichment.

(A) Phenotype associations enriched among genes with archaic-specific Neanderthal SAVs based on annotations from the 2019 GWAS Catalog. Phenotypes are ordered by increasing enrichment within manually curated systems. Circle size indicates enrichment magnitude. Enrichment and p-values were calculated from a one-sided permutation test based on an empirical null distribution generated from 10,000 shuffles of maximum ∆ across the entire dataset (Methods). Dotted and dashed lines represent false-discovery rate (FDR) corrected p-value thresholds at FDR = 0.05 and 0.1, respectively. At least one example phenotype with a p-value ≤ the stricter FDR threshold (0.05) is annotated per system. CVD = cardiovascular disease, Lp-PLA2 = Lipoprotein phospholipase A2. (B) Phenotypes enriched among genes with archaic-specific Neanderthal SAVs based on annotations from the Human Phenotype Ontology (HPO). Data were generated and visualized as in A. See Supplementary Data 2 for all phenotype enrichment results.

Source data

Extended Data Fig. 6 Vindija phenotype enrichment.

(A) Phenotype associations enriched among genes with archaic-specific Vindija SAVs based on annotations from the 2019 GWAS Catalog. Phenotypes are ordered by increasing enrichment within manually curated systems. Circle size indicates enrichment magnitude. Enrichment and p-values were calculated from a one-sided permutation test based on an empirical null distribution generated from 10,000 shuffles of maximum ∆ across the entire dataset (Methods). Dotted and dashed lines represent false-discovery rate (FDR) corrected p-value thresholds at FDR = 0.05 and 0.1, respectively. At least one example phenotype with a p-value ≤ the stricter FDR threshold (0.05) is annotated per system. (B) Phenotypes enriched among genes with archaic-specific Vindija SAVs based on annotations from the Human Phenotype Ontology (HPO). Data were generated and visualized as in A. See Supplementary Data 2 for all phenotype enrichment results.

Source data

Extended Data Fig. 7 Modelling SAV effects on the canonical transcript.

We used the SpliceAI output to construct a novel transcript per SAV by modifying the canonical transcript for that gene. We considered only one effect per SAV (for example, either an acceptor gain, acceptor loss, donor gain or donor loss) based on the effect with the largest ∆. Therefore, we did not model multiple effects for a single SAV (for example, an acceptor gain and acceptor loss). Here, we illustrate all the possible consequences of a SAV for each of the four classes. We indicate the variant position with a red ‘X’ and the position of the effect with a red vertical line (sequence deletion) or box (sequence addition). Each scenario includes a two exon gene (boxes) with a single intron (horizontal line).

Extended Data Fig. 8 ∆ max exhibits a variable relationship to 1KG allele frequency.

(A) 1KG allele frequency and ∆ max for all ancient variants per48. Allele frequencies are from 1KG. Dashed lines reflect both ∆ thresholds. (B) 1KG allele frequency and ∆ max for all introgressed variants per48. Allele frequencies are from 1KG. If the introgressed allele was the reference allele, we subtracted the 1KG allele frequency from 1. (C) 1KG allele frequency and ∆ max for all ancient variants per47. Allele frequencies are from 1KG. (D) 1KG allele frequency and ∆ max for all introgressed variants per47. Allele frequencies represent the mean from the AFR, AMR, EAS, EUR, SAS frequencies from the47 metadata.

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Extended Data Fig. 9 Vernot et al. 2016 introgressed phenotype enrichment.

(A) Phenotype associations enriched among genes with47 introgressed SAVs based on annotations from the 2019 GWAS Catalog. Phenotypes are ordered by increasing enrichment within manually curated systems. Circle size indicates enrichment magnitude. Enrichment and p-values were calculated from a one-sided permutation test based on an empirical null distribution generated from 10,000 shuffles of maximum ∆ across the entire dataset (Methods). Dotted and dashed lines represent false-discovery rate (FDR) corrected p-value thresholds at FDR = 0.05 and 0.1, respectively. At least one example phenotype with a p-value ≤ the stricter FDR threshold (0.05) is annotated per system. (B) Phenotypes enriched among genes with47 introgressed SAVs based on annotations from the Human Phenotype Ontology (HPO). Data were generated and visualized as in A. See Supplementary Data 2 for all phenotype enrichment results.

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Extended Data Fig. 10 Browning et al. 2018 introgressed phenotype enrichment.

(A) Phenotype associations enriched among genes with48 introgressed SAVs based on annotations from the 2019 GWAS Catalog. Phenotypes are ordered by increasing enrichment within manually curated systems. Circle size indicates enrichment magnitude. Enrichment and p-values were calculated from a one-sided permutation test based on an empirical null distribution generated from 10,000 shuffles of maximum ∆ across the entire dataset (Methods). Dotted and dashed lines represent false-discovery rate (FDR) corrected p-value thresholds at FDR = 0.05 and 0.1, respectively. At least one example phenotype with a p-value ≤ the stricter FDR threshold (0.05) is annotated per system. CEA = carcinoembryonic antigen, FGF = fibroblast growth factor. (B) Phenotypes enriched among genes with48 introgressed SAVs based on annotations from the Human Phenotype Ontology (HPO). Data were generated and visualized as in A. See Supplementary Data 2 for all phenotype enrichment results.

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Supplementary information

Supplementary Information

Supplementary Text, Figs. 1–27 and Tables 1–15.

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Supplementary Data

Supplementary Data 1, 2 and 3. Ensembl VEP predictions for archaic-specific spliceosome variants, phenotype enrichment results and splice variant predicted effects on the resulting transcript and protein.

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Brand, C.M., Colbran, L.L. & Capra, J.A. Resurrecting the alternative splicing landscape of archaic hominins using machine learning. Nat Ecol Evol 7, 939–953 (2023). https://doi.org/10.1038/s41559-023-02053-5

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