Evidence map›Paper›PMID 34663213›Full record

ArticleBMC bioinformatics2021

AdmixSim 2: a forward-time simulator for modeling complex population admixture.

Rui Zhang, Chang Liu, Kai Yuan, Xumin Ni, Yuwen Pan, Shuhua Xu

Abstract read
In one paragraph

Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Haptools: a toolkit for admixture and haplotype analysis.Bioinformatics (Oxford, England) · 2023
    Article
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

6 authors.

Rui Zhang *Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
Chang Liu *Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
Kai YuanKey Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
Xumin NiDepartment of Mathematics, School of Science, Beijing Jiaotong University, Beijing, 100044, China.
Yuwen PanKey Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
Shuhua XuKey Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China. xushua@fudan.edu.cn.ORCID http://orcid.org/0000-0002-1975-1002

Funding

Fundamental Research Funds for the Central Universities 2020RC001National Key Research and Development Program 2016YFC0906403National Natural Science Foundation of China (NSFC) grant 11801027National Natural Science Foundation of China (NSFC) grant 31525014National Natural Science Foundation of China (NSFC) grant 31771388National Natural Science Foundation of China (NSFC) grant 31900418National Natural Science Foundation of China (NSFC) grant 31961130380National Natural Science Foundation of China (NSFC) grant 32030020National Natural Science Foundation of China (NSFC) grant 32041008Shanghai Municipal Science and Technology Major Project 2017SHZDZX01Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) XDB38000000Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) XDPB17UK Royal Society-Newton Advanced Fellowship NAF\R1\191094
6 · The paper itself

Abstract

backgroundComputer simulations have been widely applied in population genetics and evolutionary studies. A great deal of effort has been made over the past two decades in developing simulation tools. However, there are not many simulation tools suitable for studying population admixture.

resultsWe here developed a forward-time simulator, AdmixSim 2, an individual-based tool that can flexibly and efficiently simulate population genomics data under complex evolutionary scenarios. Unlike its previous version, AdmixSim 2 is based on the extended Wright-Fisher model, and it implements many common evolutionary parameters to involve gene flow, natural selection, recombination, and mutation, which allow users to freely design and simulate any complex scenario involving population admixture. AdmixSim 2 can be used to simulate data of dioecious or monoecious populations, autosomes, or sex chromosomes. To our best knowledge, there are no similar tools available for the purpose of simulation of complex population admixture. Using empirical or previously simulated genomic data as input, AdmixSim 2 provides phased haplotype data for the convenience of further admixture-related analyses such as local ancestry inference, association studies, and other applications. We here evaluate the performance of AdmixSim 2 based on simulated data and validated functions via comparative analysis of simulated data and empirical data of African American, Mexican, and Uyghur populations.

conclusionsAdmixSim 2 is a flexible simulation tool expected to facilitate the study of complex population admixture in various situations.

Indexed as

Gene FlowGenetics, PopulationComputer SimulationHaplotypesSelection, GeneticAdmixture modelsEvolutionary forcesForward-time simulationMultiple-wave admixturePopulation admixture

Identifiers

PMID34663213
PMCPMC8522168

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