Evidence map›Paper›PMID 36517735›Full record

ArticleBMC genomics2022

eSMC: a statistical model to infer admixture events from individual genomics data.

Yonghui Wang, Zicheng Zhao, Xinyao Miao, Yinan Wang, Xiaobo Qian, Lingxi Chen, Changfa Wang, Shuaicheng Li

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Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Yonghui Wang *Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University, 252059, Liaocheng, People's Republic of China.
Zicheng Zhao *The Byoryn Technology Co., Ltd, 518122, Shenzhen, People's Republic of China.
Xinyao Miao *Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Hong Kong, People's Republic of China.
Yinan WangSchool of forensic and medicine, Xi'an Jiaotong University, Xi'an, 710004, Xi'an, Shaanxi, People's Republic of China.
Xiaobo QianThe BGI Education Center, University of Chinese Academy of Sciences, 518083, Shenzhen, People's Republic of China.
Lingxi ChenDepartment of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Hong Kong, People's Republic of China.
Changfa WangLiaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University, 252059, Liaocheng, People's Republic of China. wangcf1967@163.com.ORCID http://orcid.org/0000-0002-5186-1703
Shuaicheng LiDepartment of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Hong Kong, People's Republic of China. shuaicli@cityu.edu.hk.

Funding

National Natural Science Foundation of China 31671287Shandong Province Modern Agricultural Technology System Donkey Industrial Innovation Team SDAIT-27Taishan Leading Industry Talents-Agricultural Science of Shandong Province LJNY201713Well-bred Program of Shandong Province 2017LZGC020
6 · The paper itself

Abstract

backgroundInferring historical population admixture events yield essential insights in understanding a species demographic history. Methods are available to infer admixture events in demographic history with extant genetic data from multiple sources. Due to the deficiency in ancient population genetic data, there lacks a method for admixture inference from a single source. Pairwise Sequentially Markovian Coalescent (PSMC) estimates the historical effective population size from lineage genomes of a single individual, based on the distribution of the most recent common ancestor between the diploid's alleles. However, PSMC does not infer the admixture event.

resultsHere, we proposed eSMC, an extended PSMC model for admixture inference from a single source. We evaluated our model's performance on both in silico data and real data. We simulated population admixture events at an admixture time range from 5 kya to 100 kya (5 years/generation) with population admix ratio at 1:1, 2:1, 3:1, and 4:1, respectively. The root means the square error is [Formula: see text] kya for all experiments. Then we implemented our method to infer the historical admixture events in human, donkey and goat populations. The estimated admixture time for both Han and Tibetan individuals range from 60 kya to 80 kya (25 years/generation), while the estimated admixture time for the domesticated donkeys and the goats ranged from 40 kya to 60 kya (8 years/generation) and 40 kya to 100 kya (6 years/generation), respectively. The estimated admixture times were concordance to the time that domestication occurred in human history.

conclusionOur eSMC effectively infers the time of the most recent admixture event in history from a single individual's genomics data. The source code of eSMC is hosted at https://github.com/zachary-zzc/eSMC .

Indexed as

Genetics, PopulationGenomicsAllelesHumansModels, StatisticalPopulation DensityDemographic HistoryDomesticationPopulation AdmixturePSMCTMRCA

Identifiers

PMID36517735
PMCPMC9748406

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