Detecting Recombination Hotspots from Patterns of Linkage Disequilibrium.
Detecting Recombination Hotspots from Patterns of Linkage Disequilibrium.
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DOI：
10.1534/g3.116.029587
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发表时间：
2016-08-09
期刊：
影响因子：
--
通讯作者：
Stevison LS
中科院分区：
文献类型：
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作者：
Wall JD;Stevison LS
随着DNA测序技术的最新进展，利用无关个体的全基因组测序来分析基因组中的连锁不平衡(LD)模式变得越来越容易。通常进行的一种类型的分析是估计局部重组率并从LD的模式中识别重组热点。一种检测重组热点的方法LDhot已经在少数物种中使用，以加深我们对重组的基本生物学的理解。在很大程度上，这种方法的有效性(例如，功率和假阳性率)是未知的。在这项研究中，我们运行了大量的模拟来比较三种不同的LDhot型实现的有效性。我们发现这些不同方法的能量和假阳性率有很大差异，并且对所使用的窗口大小有很强的敏感性(较小的窗口大小导致对热点位置的更准确估计)。我们还将LDhott模拟结果与贝叶斯最大似然方法识别热点的可比模拟结果进行了比较。令人惊讶的是，我们发现后一种计算密集的方法比我们的模拟中考虑的参数值具有更低的功率。
With recent advances in DNA sequencing technologies, it has become increasingly easy to use whole-genome sequencing of unrelated individuals to assay patterns of linkage disequilibrium (LD) across the genome. One type of analysis that is commonly performed is to estimate local recombination rates and identify recombination hotspots from patterns of LD. One method for detecting recombination hotspots, LDhot, has been used in a handful of species to further our understanding of the basic biology of recombination. For the most part, the effectiveness of this method (e.g., power and false positive rate) is unknown. In this study, we run extensive simulations to compare the effectiveness of three different implementations of LDhot. We find large differences in the power and false positive rates of these different approaches, as well as a strong sensitivity to the window size used (with smaller window sizes leading to more accurate estimation of hotspot locations). We also compared our LDhot simulation results with comparable simulation results obtained from a Bayesian maximum-likelihood approach for identifying hotspots. Surprisingly, we found that the latter computationally intensive approach had substantially lower power over the parameter values considered in our simulations.