title: Length Distribution of Ancestral Tracks under a General Admixture Model and Its Applications in Population History Inference.
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.
authors.affiliation: Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Xumin
authors.initials: X
authors.lastname: Ni
authors.affiliation: Chinese 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.
authors.forename: Xiong
authors.initials: X
authors.lastname: Yang
authors.affiliation: Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Guo
authors.affiliation: Chinese 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.
authors.forename: Kai
authors.initials: K
authors.lastname: Yuan
authors.affiliation: Chinese 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.
authors.forename: Ying
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Zhiming
authors.initials: Z
authors.lastname: Ma
authors.affiliation: Chinese 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.|School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China.|Collaborative Innovation Center of Genetics and Development, Shanghai 200438, China.
authors.forename: Shuhua
authors.initials: S
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005828:Genetics, Population; D006801:Humans; D008962:Models, Theoretical; D044382:Population Groups
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