An overview of population genetic data simulation

Xiguo Yuan, David Jonathan Miller, Junying Zhang, David Herrington, Yue Wang

    Research output: Contribution to journalArticle

    31 Scopus citations

    Abstract

    Simulation studies in population genetics play an important role in helping to better understand the impact of various evolutionary and demographic scenarios on sequence variation and sequence patterns, and they also permit investigators to better assess and design analytical methods in the study of disease-associated genetic factors. To facilitate these studies, it is imperative to develop simulators with the capability to accurately generate complex genomic data under various genetic models. Currently, a number of efficient simulation software packages for large-scale genomic data are available, and new simulation programs with more sophisticated capabilities and features continue to emerge. In this article, we review the three basic simulation frameworks-coalescent, forward, and resampling-and some of the existing simulators that fall under these frameworks, comparing them with respect to their evolutionary and demographic scenarios, their computational complexity, and their specific applications. Additionally, we address some limitations in current simulation algorithms and discuss future challenges in the development of more powerful simulation tools.

    Original languageEnglish (US)
    Pages (from-to)42-54
    Number of pages13
    JournalJournal of Computational Biology
    Volume19
    Issue number1
    DOIs
    StatePublished - Jan 1 2012

    All Science Journal Classification (ASJC) codes

    • Modeling and Simulation
    • Molecular Biology
    • Genetics
    • Computational Mathematics
    • Computational Theory and Mathematics

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