Article series: Applications of next-generation sequencing

The impact of whole-genome sequencing on the reconstruction of human population history

Journal name:
Nature Reviews Genetics
Volume:
15,
Pages:
149–162
Year published:
DOI:
doi:10.1038/nrg3625
Published online

Abstract

Examining patterns of molecular genetic variation in both modern-day and ancient humans has proved to be a powerful approach to learn about our origins. Rapid advances in DNA sequencing technology have allowed us to characterize increasing amounts of genomic information. Although this clearly provides unprecedented power for inference, it also introduces more complexity into the way we use and interpret such data. Here, we review ongoing debates that have been influenced by improvements in our ability to sequence DNA and discuss some of the analytical challenges that need to be overcome in order to fully exploit the rich historical information that is contained in the entirety of the human genome.

At a glance

Figures

  1. A possible model of archaic introgression based on the latest analysis using second-generation sequencing.
    Figure 1: A possible model of archaic introgression based on the latest analysis using second-generation sequencing.

    Red arrows indicate initial colonization events across the Old World after the origination of anatomically modern humans (AMHs) in Africa, including two movements into Asia. Approximate positions of introgression events are represented by coloured circles and are not intended to be accurate. This model portrays the hypothesis that portions of the Denisovan genome entered the human gene pool through hybridization with more widespread populations of archaic hominins (such as Homo erectus), which also interbred with the Denisovan population. Models that involve interbreeding directly between Denisovans and AMHs can be found in Ref. 46. The black arrow shows a more recent expansion of Asian farming populations (that is, <10,000 years ago) that did not carry introgressed Denisovan alleles and that replaced much of the indigenous resident population up to Wallace's phenotypic boundary (shown by the dashed line), which lies just east of Wallace's biogeographical line154. This hypothesis may explain the lack of evidence for Denisovan introgression outside islands in Southeast Asia and Oceania.

  2. Alternative human origin models that fit existing fossil evidence on the basis of either phylogenetic or pedigree-based mutation rates.
    Figure 2: Alternative human origin models that fit existing fossil evidence on the basis of either phylogenetic or pedigree-based mutation rates.

    Key fossil evidence for nearly or fully anatomically modern humans (AMHs) is described on the left, and approximate date range estimates are indicated by the grey shading. West Africans are relatively genetically homogenous modern-day Niger–Kordofanian- and Nilo-Saharan-speaking populations that are often represented by the Yoruba of Nigeria. Eurasians encompass all modern-day non-African populations. Divergence times that are estimated using the faster phylogenetic mutation rate under the assumption of relatively instantaneous population splitting are mostly consistent with the fossil evidence. To preserve the correspondence between fossil dates and population divergence times under the slower pedigree-based mutation rate, this model assumes long-term gene flow among subdivided ancestral populations (represented by the gradient of blue shading), which leads to older divergence time estimates.

  3. Second-generation sequencing in ancient Europeans.
    Figure 3: Second-generation sequencing in ancient Europeans.

    a | The map of Europe shows the major prehistoric migration events that may have influenced modern-day European genetic diversity. These events, in chronological order, are the initial colonization of Europe ~40,000 years ago from the Middle East (blue oval and black arrows); the contraction of humans into four major refugia during the Last Glacial Maximum ~18,000 years ago, followed by the subsequent recolonization of more northern and central parts of Europe (grey ovals and arrows); and the movement of Neolithic famers ~9,000 years ago from Anatolia (red oval and arrows). Also shown are the geographical locations of the Neolithic Scandinavian farmer108 (red circle) and the Late Neolithic or Early Copper Age Ötzi sample105 (red diamond), as well as two Mesolithic107 (blue triangles) and three Neolithic108 (blue circles) hunter-gatherer samples, from which second-generation sequencing data are obtained. The amount of sequence generated for each specimen in megabases (Mb) or the coverage for whole-genome sequencing (WGS) is shown in parentheses. b | A single-nucleotide polymorphism (SNP)-based principal component analysis (PCA) of ancient DNA samples (shown in part a) and 375 reference European samples with WGS data from the 1000 Genomes Project172 is shown. Details on how this plot was generated can be found in Supplementary information S2 (box). Despite being geographically disparate, samples from hunter-gatherers cluster together, as are those from putative farmers, but these two clusters are away from each other. Unlike the samples from putative farmers that show a similarity to southern Europeans, the five hunter-gatherer samples do not overlap with any samples in the reference data set, which suggests that they have at least some ancestry component that is not represented in modern-day populations and that might have been lost when Neolithic farmers moved into Europe. CEU, northern Europeans from Utah. Part a is modified, with permission, from Ref. 170 © (2004) Annual Reviews and Ref. 171 © (2010) Elsevier.

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Affiliations

  1. Arizona Research Laboratories Division of Biotechnology, Room 231, Life Sciences South, 1007 East Lowell Street, University of Arizona, Tucson, Arizona 85721, USA.

    • Krishna R. Veeramah &
    • Michael F. Hammer
  2. Present address: 650 Life Sciences Building, Stony Brook University, Stony Brook, New York 11794–5245, USA.

    • Krishna R. Veeramah

Competing interests statement

The authors declare no competing interests.

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  • Krishna R. Veeramah

    Krishna R. Veeramah has been a postdoctoral researcher in Michael F. Hammer's laboratory at the University of Arizona, Tucson, USA, since 2010 and is now Assistant Professor at the Department of Ecology and Evolution at Stony Brook University, New York, USA. He obtained his Ph.D. in Human Genetics at University College London, UK, in 2008 under Mark Thomas and received postdoctoral training at the University of California, Los Angeles, USA, as part of John Novembre's laboratory from 2008 to 2010. His primary research interest is the population and evolutionary genetics of sub-Saharan Africans and non-human primates. He is also involved in projects that examine ancient DNA samples from Medieval Europe and the genetics of epilepsy. Krishna R Veeramah's homepage.

  • Michael F. Hammer

    Michael F. Hammer is a research scientist in the Arizona Research Laboratories Division of Biotechnology at the University of Arizona, Tuscon, USA, with joint appointments in Ecology and Evolutionary Biology, and in Anthropology. He received his Ph.D. with Allan C. Wilson at the University of California, Berkeley, USA, and carried out postdoctoral work with Lee M. Silver at Princeton University, New Jersey, USA, and with Richard C. Lewontin at Harvard University, Cambridge, Massachusetts, USA. He moved to Arizona in 1991. His laboratory currently examines genomic diversity in apes with a particular emphasis on contrasting the X chromosome and autosomes, and characterizing the genetic basis of neurodevelopmental disorders using second-generation sequencing. He also leads a variety of projects that aim to infer population history and natural selection in modern populations using genome-wide data. Michael F. Hammer's homepage.

Supplementary information

PDF files

  1. Supplementary information S1 (box) (221 KB)

    Inferring changes in effective population size (Ne).

  2. Supplementary information S2 (box) (186 KB)

    Construction of the PCA plot of aDNA samples (Figure 3B).

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