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.
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Acknowledgements
This work was supported by the Strategic Priority Research Program (XDB13040100) and Key Research Program of Frontier Sciences (QYZDJ-SSW-SYS009) of the Chinese Academy of Sciences (CAS), the Fundamental Research Funds for the Central Universities (2017JBM071, 2017YJS197), the National Natural Science Foundation of China (NSFC) (91731303, 31771388, 11426237, and 31711530221), the National Science Fund for Distinguished Young Scholars (31525014), the Program of Shanghai Academic Research Leader (16XD1404700), the National Key Research and Development Program (2016YFC0906403), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01), the China Postdoctoral Science Foundation (2017M620595), the National Center for Mathematics and Interdisciplinary Sciences of CAS. SX also gratefully acknowledges the support of the National Program for Top-Notch Young Innovative Talents of the “Wanren Jihua” Project.
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Ni, X., Yuan, K., Liu, C. et al. MultiWaver 2.0: modeling discrete and continuous gene flow to reconstruct complex population admixtures. Eur J Hum Genet 27, 133–139 (2019). https://doi.org/10.1038/s41431-018-0259-3
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DOI: https://doi.org/10.1038/s41431-018-0259-3
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