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Inference of multiple-wave admixtures by length distribution of ancestral tracks
Ni, Xumin1; Yuan, Kai2,3; Yang, Xiong2; Feng, Qidi2,3; Guo, Wei4; Ma, Zhiming1,4; Xu, Shuhua2,3,5,6
2018-07-01
Source PublicationHEREDITY
ISSN0018-067X
Volume121Issue:1Pages:52-63
AbstractThe ancestral tracks in admixed genomes are valuable for population history inference. While a few methods have been developed to infer admixture history based on ancestral tracks, these methods suffer the same flaw: only population admixture history under some specific models can be inferred. In addition, the inference of history might be biased or even unreliable if the specific model deviates from the real situation. To address this problem, we firstly proposed a general discrete admixture model to describe the admixture history with multiple ancestral populations and multiple-wave admixtures. We next deduced the length distribution of ancestral tracks under the general discrete admixture model. We further developed a new method, MultiWaver, to explore multiple-wave admixture histories. Our method could automatically determine an optimal admixture model based on the length distribution of ancestral tracks, and estimate the corresponding parameters under this optimal model. Specifically, we used a likelihood ratio test (LRT) to determine the number of admixture waves, and implemented an expectation-maximization (EM) algorithm to estimate parameters. We used simulation studies to validate the reliability and effectiveness of our method. Finally, good performance was observed when our method was applied to real data sets of African Americans and Mexicans, and new insights were gained into the admixture history of Uyghurs and Hazaras.
DOI10.1038/s41437-017-0041-2
Language英语
Funding ProjectStrategic Priority Research Program[XDB13040100] ; Key Research Program of Frontier Sciences of the Chinese Academy of Sciences (CAS)[QYZDJ-SSW-SYS009] ; National Natural Science Foundation of China (NSFC)[91331204] ; National Natural Science Foundation of China (NSFC)[91731303] ; National Natural Science Foundation of China (NSFC)[31771388] ; National Natural Science Foundation of China (NSFC)[11426237] ; National Natural Science Foundation of China (NSFC)[31711530221] ; National Science Fund for Distinguished Young Scholars[31525014] ; Program of Shanghai Academic Research Leader[16XD1404700] ; 973 Project[2011CB808000] ; Fundamental Research Funds for the Central Universities[2017JBM071] ; Fundamental Research Funds for the Central Universities[2015IBM099] ; National Excellent Doctoral Dissertation Foundation of PR China[201213] ; National Center for Mathematics and Interdisciplinary Sciences of CAS ; Key Laboratory of Random Complex Structures and Data Science, CAS[2008DP173182] ; National Program for Top-Notch Young Innovative Talents of the "Wanren Jihua" Project
WOS Research AreaEnvironmental Sciences & Ecology ; Evolutionary Biology ; Genetics & Heredity
WOS SubjectEcology ; Evolutionary Biology ; Genetics & Heredity
WOS IDWOS:000434984600005
PublisherNATURE PUBLISHING GROUP
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/30515
Collection应用数学研究所
Affiliation1.Beijing Jiaotong Univ, Sch Sci, Dept Math, Beijing, Peoples R China
2.Chinese Acad Sci, Shanghai Inst Biol Sci, Key Lab Computat Biol,PICB, Max Planck Independent Res Grp Populat Genom,MPG, Shanghai, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing, Peoples R China
5.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai, Peoples R China
6.Collaborat Innovat Ctr Genet & Dev, Shanghai, Peoples R China
Recommended Citation
GB/T 7714
Ni, Xumin,Yuan, Kai,Yang, Xiong,et al. Inference of multiple-wave admixtures by length distribution of ancestral tracks[J]. HEREDITY,2018,121(1):52-63.
APA Ni, Xumin.,Yuan, Kai.,Yang, Xiong.,Feng, Qidi.,Guo, Wei.,...&Xu, Shuhua.(2018).Inference of multiple-wave admixtures by length distribution of ancestral tracks.HEREDITY,121(1),52-63.
MLA Ni, Xumin,et al."Inference of multiple-wave admixtures by length distribution of ancestral tracks".HEREDITY 121.1(2018):52-63.
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