综述

群体遗传学模拟软件应用现状

展开
  • 1. 中国科学院计算生物学重点实验室,中国科学院-德国马普学会计算生物学伙伴研究所,上海 200031;
    2. 中国科学院大学,北京100049
高峰,博士,专业方向:生物信息学。E-mail: gaofeng@picb.ac.cn

收稿日期: 2016-03-22

  修回日期: 2016-04-25

  网络出版日期: 2016-07-23

基金资助

中国科学院先导B项目(编号:XDB13040800)和国家自然科学基金项目(编号:91531306)资助

Application of computer simulators in population genetics

Expand
  • 1. CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai 200031, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2016-03-22

  Revised date: 2016-04-25

  Online published: 2016-07-23

Supported by

Supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (No; XDB13040800) and the National Natural Science Foundation of China (No; 91531306)

摘要

随着下一代测序技术的不断进步与测序价格的不断下降,越来越多物种的全基因组信息被公开。作为研究群体遗传变异模式工具之一的模拟软件必然将发挥越来越重要的作用。依据时间推演方向的不同,模拟软件可以分为依时间向前和向后推演,二者各有所长,功能上互相补充,分别适合于不同的模拟需求。这些软件在研究进化动力的影响、估计进化动力参数与验证不同进化假设以及新方法有效性等方面起着重要作用。本文简要介绍了群体遗传学相关理论知识,详细比较了近10年来发表的32款模拟软件,并对模拟软件的未来发展方向给出了建议。

本文引用格式

高峰, 李海鹏 . 群体遗传学模拟软件应用现状[J]. 遗传, 2016 , 38(8) : 707 -717 . DOI: 10.16288/j.yczz.16-100

Abstract

The genomes of more and more organisms have been sequenced due to the advances in next-generation sequencing technologies. As a powerful tool, computer simulators play a critical role in studying the genome-wide DNA polymorphism pattern. Simulations can be performed both forwards-in-time and backwards-in-time, which complement each other and are suitable for meeting different needs, such as studying the effect of evolutionary dynamics, the estimation of parameters, and the validation of evolutionary hypotheses as well as new methods. In this review, we briefly introduced population genetics related theoretical framework and provided a detailed comparison of 32 simulators published over the last ten years. The future development of new simulators was also discussed.

参考文献

[1] Hartl DL, Clark AG. Principles of population genetics. 4th ed. Sunderland, Mass: Sinauer Associates, 2007.
[2] Messer PW. SLiM: simulating evolution with selection and linkage. Genetics , 2013, 194(4): 1037-1039.
[3] Kessner D, Novembre J. Forqs: forward-in-time simulation of recombination, quantitative traits and selection. Bioinformatics , 2014, 30(4): 576-577.
[4] Shlyakhter I, Sabeti PC, Schaffner SF. Cosi2 : an efficient simulator of exact and approximate coalescent with selection. Bioinformatics , 2014, 30(23): 3427-3429.
[5] Servedio MR. The evolution of premating isolation: local adaptation and natural and sexual selection against hybrids. Evolution , 2004, 58(5): 913-924.
[6] Daleszczyk K, Bunevich AN. Population viability analysis of European bison populations in Polish and Belarusian parts of Białowieża Forest with and without gene exchange. Biol Conserv , 2009, 142(12): 3068-3075.
[7] Alves DA, Imperatriz-Fonseca VL, Francoy TM, Santos- Filho PS, Billen J, Wenseleers T. Successful maintenance of a stingless bee population despite a severe genetic bottleneck. Conserv Genet , 2011, 12(3): 647-658.
[8] Fu YX, Li WH. Estimating the age of the common ancestor of a sample of DNA sequences. Mol Biol Evol , 1997, 14(2): 195-199.
[9] Li HP, Stephan W. Inferring the demographic history and rate of adaptive substitution in Drosophila . PLoS Genet , 2006, 2(10): e166.
[10] Beaumont MA, Zhang WY, Balding DJ. Approximate Bayesian computation in population genetics. Genetics , 2002, 162(4): 2025-2035.
[11] Li HP. A new test for detecting recent positive selection that is free from the confounding impacts of demography. Mol Biol Evol , 2011, 28(1): 365-375.
[12] Lin K, Futschik A, Li HP. A fast estimate for the population recombination rate based on regression. Genetics , 2013, 194(2): 473-484.
[13] Gao F, Ming C, Hu WJ, Li HP. New software for the fast estimation of population recombination rates (FastEPRR) in the genomic era. G3 ( Bethesda ), 2016, 6(6): 1563-1571.
[14] Daetwyler HD, Villanueva B, Woolliams JA. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One , 2008, 3(10): e3395.
[15] Huang YZ. The application of computer simulation in teaching population genetics. Hereditas (Beijing) , 1998, 20(4): 26-27. 黄远樟. 计算机模拟在群体遗传教学中的应用. 遗传, 1998, 20(4): 26-27.
[16] Gao J, Pan SY, Cao J. Design and application of computer- assisted software for teaching and research of population genetics. Hereditas (Beijing) , 2008, 30(5): 642-648. 高婧, 潘沈元, 曹静. 群体遗传学教学与研究辅助软件的设计与应用. 遗传, 2008, 30(5): 642-648.
[17] Sved JA. Genetics computer teaching simulation programs: promise and problems. Genetics , 2010, 185(4): 1537-1540.
[18] Vähä JP, Primmer CR. Efficiency of model-based Bayesian methods for detecting hybrid individuals under different hybridization scenarios and with different numbers of loci. Mol Ecol , 2006, 15(1): 63-72.
[19] Ryman N, Palm S. POWSIM: a computer program for assessing statistical power when testing for genetic differentiation. Mol Ecol Notes , 2006, 6(3): 600-602.
[20] Peng B, Amos CI. Forward-time simulation of realistic samples for genome-wide association studies. BMC Bioinformatics , 2010, 11: 442.
[21] Vonholdt BM, Stahler DR, Smith DW, Earl DA, Pollinger JP, Wayne RK. The genealogy and genetic viability of reintroduced Yellowstone grey wolves. Mol Ecol , 2008, 17(1): 252-274.
[22] Peng B, Kimmel M. Simulations provide support for the common disease-common variant hypothesis. Genetics , 2007, 175(2): 763-776.
[23] Dickson SP, Wang K, Krantz I, Hakonarson H, Goldstein DB. Rare variants create synthetic genome-wide associations. PLoS Biol , 2010, 8(1): e1000294.
[24] Carvajal-Rodríguez A. Simulation of genomes: a review. Curr Genomics , 2008, 9(3): 155-159.
[25] Carvajal-Rodríguez A. Simulation of genes and genomes f
文章导航

/