Xiong Yang1, Kai Yuan1, Xumin Ni2, Ying Zhou1, Wei Guo3, Shuhua Xu1,4,5,6,7. 1. Key Laboratory of Computational Biology, Chinese Academy of Sciences (CAS) and Max Planck Society (MPG) Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. 2. Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, China. 3. Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China. 4. School of Life Science and Technology, ShanghaiTech University, Shanghai, China. 5. Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China. 6. Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China. 7. Collaborative Innovation Center of Genetics and Development, Fudan University, Shanghai, China.
Abstract
Background: Population admixture is a common phenomenon in humans, animals, and plants, and it plays a very important role in shaping individual genetic architecture and population genetic diversity. Inference of population admixture, however, is very challenging and typically relies on in silico simulation. We are aware of the lack of a computerized tool for such a purpose. A simulator capable of generating data under various complex admixture scenarios would facilitate the study of recombination, linkage disequilibrium, ancestry tracing, and admixture dynamics in admixed populations. We described such a simulator here. Results: We developed a forward-time simulator (AdmixSim) under the standard Wright Fisher model. It can simulate the following admixed populations: (1) multiple ancestral populations; (2) multiple waves of admixture events; (3) fluctuating population size; and (4) admixtures of fluctuating proportions. Analysis of the simulated data by AdmixSim showed that our simulator can quickly and accurately generate data resembling real-world values. We included in AdmixSim all possible parameters that would allow users to modify and simulate any kind of admixture scenario easily, so it is very flexible. AdmixSim records recombination break points and traces of each chromosomal segment from different ancestral populations, with which users can easily perform further analysis and comparative studies with empirical data. Conclusions: AdmixSim facilitates the study of population admixture by providing a simulation framework with the flexible implementation of various admixture models and parameters.
Background: Population admixture is a common phenomenon in humans, animals, and plants, and it plays a very important role in shaping individual genetic architecture and population genetic diversity. Inference of population admixture, however, is very challenging and typically relies on in silico simulation. We are aware of the lack of a computerized tool for such a purpose. A simulator capable of generating data under various complex admixture scenarios would facilitate the study of recombination, linkage disequilibrium, ancestry tracing, and admixture dynamics in admixed populations. We described such a simulator here. Results: We developed a forward-time simulator (AdmixSim) under the standard Wright Fisher model. It can simulate the following admixed populations: (1) multiple ancestral populations; (2) multiple waves of admixture events; (3) fluctuating population size; and (4) admixtures of fluctuating proportions. Analysis of the simulated data by AdmixSim showed that our simulator can quickly and accurately generate data resembling real-world values. We included in AdmixSim all possible parameters that would allow users to modify and simulate any kind of admixture scenario easily, so it is very flexible. AdmixSim records recombination break points and traces of each chromosomal segment from different ancestral populations, with which users can easily perform further analysis and comparative studies with empirical data. Conclusions: AdmixSim facilitates the study of population admixture by providing a simulation framework with the flexible implementation of various admixture models and parameters.
Authors: Alkes L Price; Nick J Patterson; Robert M Plenge; Michael E Weinblatt; Nancy A Shadick; David Reich Journal: Nat Genet Date: 2006-07-23 Impact factor: 38.330