Evidence map›Paper›PMID 30206356›Full record

ArticleEuropean journal of human genetics : EJHG2019

MultiWaver 2.0: modeling discrete and continuous gene flow to reconstruct complex population admixtures.

Xumin Ni, Kai Yuan, Chang Liu, Qidi Feng, Lei Tian, Zhiming Ma, Shuhua Xu

Abstract read
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Article in European journal of human genetics : EJHG, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

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0cells of the map it votes in
26citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

26 citing papers in PubMed.

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

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Xumin NiDepartment of Mathematics, School of Science, Beijing Jiaotong University, Beijing, 100044, 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 Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China.
Chang LiuChinese 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 Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China.
Qidi FengChinese 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 Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China.
Lei TianChinese 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 Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China.
Zhiming MaDepartment of Mathematics, School of Science, Beijing Jiaotong University, Beijing, 100044, China. mazm@amt.ac.cn.
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 Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, CAS, Shanghai, 200031, China. xushua@picb.ac.cn.ORCID http://orcid.org/0000-0002-1975-1002

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Our goal in developing the MultiWaver software series was to be able to infer population admixture history under various complex scenarios. The earlier version of MultiWaver considered only discrete admixture models. Here, we report a newly developed version, MultiWaver 2.0, that implements a more flexible framework and is capable of inferring multiple-wave admixture histories under both discrete and continuous admixture models. MultiWaver 2.0 can automatically select an optimal admixture model based on the length distribution of ancestral tracks of chromosomes, and the program can estimate the corresponding parameters under the selected model. Specifically, for discrete admixture models, we used a likelihood ratio test (LRT) to determine the optimal discrete model and an expectation-maximization algorithm to estimate the parameters. In addition, according to the principles of the Bayesian Information Criterion (BIC), we compared the optimal discrete model with several continuous admixture models. In MultiWaver 2.0, we also applied a bootstrapping technique to provide levels of support for the chosen model and the confidence interval (CI) of the estimations of admixture time. Simulation studies validated the reliability and effectiveness of our method. Finally, the program performed well when applied to real datasets of typical admixed populations, such as African Americans, Uyghurs, and Hazaras.

Indexed as

Gene FlowSoftwareGenetics, PopulationHumans

Identifiers

PMID30206356
PMCPMC6303267

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.