Hecker Julian, Prokopenko Dmitry, Lange Christoph, Fier Heide Löhlein
Institute of Genomic Mathematics, University of Bonn, Bonn, Germany.
Department of Biostatistics, Harvard School of Public Health, Boston, United States of America.
PLoS One. 2015 Dec 30;10(12):e0145152. doi: 10.1371/journal.pone.0145152. eCollection 2015.
As recombination events are not uniformly distributed along the human genome, the estimation of fine-scale recombination maps, e.g. HapMap Project, has been one of the major research endeavors over the last couple of years. For simulation studies, these estimates provide realistic reference scenarios to design future study and to develop novel methodology. To achieve a feasible framework for the estimation of such recombination maps, existing methodology uses sample probabilities for a two-locus model with recombination, with recent advances allowing for computationally fast implementations. In this work, we extend the existing theoretical framework for the recombination rate estimation to the presence of population substructure. We show under which assumptions the existing methodology can still be applied. We illustrate our extension of the methodology by an extensive simulation study.
由于重组事件并非均匀分布于人类基因组，因此精细尺度重组图谱的估计，例如国际人类基因组单体型图计划（HapMap Project），在过去几年一直是主要的研究工作之一。对于模拟研究而言，这些估计为设计未来研究和开发新方法提供了现实的参考方案。为了实现估计此类重组图谱的可行框架，现有方法使用了具有重组的双位点模型的样本概率，并且最近的进展使得能够进行计算快速的实现。在这项工作中，我们将重组率估计的现有理论框架扩展到存在群体亚结构的情况。我们展示了在哪些假设下现有方法仍然可以应用。我们通过广泛的模拟研究说明了我们对该方法的扩展。
