Evidence map›Paper›PMID 26818889›Full record

ArticleScientific reports2016

Length Distribution of Ancestral Tracks under a General Admixture Model and Its Applications in Population History Inference.

Xumin Ni, Xiong Yang, Wei Guo, Kai Yuan, Ying Zhou, Zhiming Ma, Shuhua Xu

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Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers.

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0cells of the map it votes in
14citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Review
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  4. Human genetic admixture through the lens of population genomics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022
    Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Xumin NiDepartment of Mathematics, School of Science, Beijing Jiaotong University, Beijing 100044, China.
Xiong YangChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.
Wei GuoInstitute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
Kai YuanChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.
Ying ZhouChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.
Zhiming MaInstitute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
Shuhua XuChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The length of ancestral tracks decays with the passing of generations which can be used to infer population admixture histories. Previous studies have shown the power in recovering the histories of admixed populations via the length distributions of ancestral tracks even under simple models. We believe that the deduction of length distributions under a general model will greatly elevate the power. Here we first deduced the length distributions under a general model and proposed general principles in parameter estimation and model selection with the deduced length distributions. Next, we focused on studying the length distributions and its applications under three typical special cases. Extensive simulations showed that the length distributions of ancestral tracks were well predicted by our theoretical framework. We further developed a new method, AdmixInfer, based on the length distributions and good performance was observed when it was applied to infer population histories under the three typical models. Notably, our method was insensitive to demographic history, sample size and threshold to discard short tracks. Finally, good performance was also observed when applied to some real datasets of African Americans, Mexicans and South Asian populations from the HapMap project and the Human Genome Diversity Project.

Indexed as

Genetics, PopulationModels, TheoreticalPopulation GroupsAlgorithmsComputer SimulationHumans

Identifiers

PMID26818889
PMCPMC4730239

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