Evidence for archaic adaptive introgression in humans

Journal name:
Nature Reviews Genetics
Volume:
16,
Pages:
359–371
Year published:
DOI:
doi:10.1038/nrg3936
Published online

Abstract

As modern and ancient DNA sequence data from diverse human populations accumulate, evidence is increasing in support of the existence of beneficial variants acquired from archaic humans that may have accelerated adaptation and improved survival in new environments — a process known as adaptive introgression. Within the past few years, a series of studies have identified genomic regions that show strong evidence for archaic adaptive introgression. Here, we provide an overview of the statistical methods developed to identify archaic introgressed fragments in the genome sequences of modern humans and to determine whether positive selection has acted on these fragments. We review recently reported examples of adaptive introgression, grouped by selection pressure, and consider the level of supporting evidence for each. Finally, we discuss challenges and recommendations for inferring selection on introgressed regions.

At a glance

Figures

  1. Expected length of archaic tracts under admixture and ancestral population structure scenarios.
    Figure 1: Expected length of archaic tracts under admixture and ancestral population structure scenarios.

    Expected lengths of archaic-like tracts for a scenario of admixture (left panel) and a scenario of ancestral population structure (right panel) are shown. Because there is less time for recombination to break down the migrant tracts (red) in the admixed population, the expected tract length in this case will be longer than in the case of ancestral population structure.

  2. Example coalescent genealogy of uniquely shared mutations.
    Figure 2: Example coalescent genealogy of uniquely shared mutations.

    Several DNA fragments from two modern populations (pink and blue chromosomes) are sequenced. A diploid sequence is also obtained from an extinct archaic population (yellow chromosomes) that split from the population tree more anciently than the two modern populations split from each other. Uniquely shared mutations (red stars) occur in the archaic population but are passed on to the ancestors of the blue modern population via admixture (dashed line). These are then swept to high frequency by selection, producing a shallow local coalescent genealogy. This process results in sites with high-frequency derived alleles in the blue samples that are present in the archaic sample but not in the pink samples from the other modern population. Mutations in the genealogy that are not uniquely shared are shown as green stars.

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Affiliations

  1. Department of Integrative Biology, University of California, Berkeley, Berkeley, California 97420, USA.

    • Fernando Racimo &
    • Rasmus Nielsen
  2. Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA.

    • Sriram Sankararaman
  3. Department of Statistics, University of California, Berkeley, Berkeley, California 97420, USA.

    • Rasmus Nielsen
  4. Molecular and Cell Biology Unit, School of Natural Sciences, University of California, Merced, Merced, California 95343, USA.

    • Emilia Huerta-Sánchez

Competing interests statement

The authors declare no competing interests.

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Author details

  • Fernando Racimo

    Fernando Racimo is a graduate student at the Department of Integrative Biology at the University of California, Berkeley, USA. He is interested in human paleogenomics and works on developing methods that use archaic genomes to infer demographic models and to detect signals of positive selection in modern humans.

  • Sriram Sankararaman

    Sriram Sankararaman is a postdoctoral fellow at the Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA. He completed his Ph.D. in computer science at the University of California, Berkeley, USA. As a postdoctoral fellow, he has developed methods to estimate archaic ancestry and admixture times in modern humans. His interests lie at the interface of computational biology, statistical genomics and statistical machine learning.

  • Rasmus Nielsen

    Rasmus Nielsen is a professor at the Department of Integrative Biology and the Department of Statistics at the University of California, Berkeley, USA. His laboratory's research encompasses population genetics, statistical genetics, medical genetics, evolutionary biology and phylogenetics.

  • Emilia Huerta-Sánchez

    Emilia Huerta-Sánchez is an assistant professor at the School of Natural Sciences at the University of California, Merced, USA. She was previously a postdoctoral researcher in the Departments of Statistics and Integrative Biology at the University of California, Berkeley. Her laboratory focuses on modelling and characterizing the effects of natural selection and demography in natural populations. Emilia Huerta-Sánchez's homepage.

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