Cited by 116Forensic and Genetic ResearchPleistocene-Era Hominins and ArchaeologyGenetic diversity and population structure

Detecting Ancient Admixture in Humans Using Sequence Polymorphism Data

Wall, Jeffrey D·2000·Genetics

Abstract

A debate of long-standing interest in human evolution centers around whether archaic human populations (such as the Neanderthals) have contributed to the modern gene pool. A model of ancient population structure with recent mixing is introduced, and it is determined how much information (i.e., sequence data from how many unlinked nuclear loci) would be necessary to distinguish between different demographic scenarios. It is found that ~50–100 loci are necessary if plausible parameter estimates are used. There are not enough data available at the present to support either the “single origin” or the “multiregional” model of modern human evolution. However, this information should be available in a few years.

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Crossref · 10.1093/genetics/154.3.1271

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Authors
Wall, J. D.
Affiliations
University of Chicago (US)
Corresponding author
Wall, Jeffrey D
Venue
Genetics
Published
2000-03-01
Volume
154
Issue
3
Pages
1271-1279
ISSN
  • 0016-6731 (ISSN-L)
  • 1943-2631
OpenAlex
W2107737723
Cited by
116
References
62
Citations by year
2025: 2 · 2024: 1 · 2023: 2 · 2022: 1 · 2021: 7 · 2020: 1 · 2019: 3 · 2018: 1 · 2017: 1 · 2016: 3 · 2015: 2 · 2014: 3 · 2013: 5 · 2012: 4
FWCI
4.88
Type
journal article
Language
en
Open access
closed
Locations
  • Genetics (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
  • Social Sciences › Anthropology › Pleistocene-Era Hominins and Archaeology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
Keywords
Neanderthal admixture, human evolution, population structure, demographic models, sequence variation, sample size requirements, multiregional model
MeSH terms
Humans, Models, Genetic, Polymorphism, Genetic, Models, Statistical

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