Predicting the Landscape of Recombination Using Deep Learning
Predicting the Landscape of Recombination Using Deep Learning
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DOI：
10.1093/molbev/msaa038
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发表时间：
2020-06-01
影响因子：
10.7
通讯作者：
Kern, Andrew D.
中科院分区：
文献类型：
--
作者：
Adrion, Jeffrey R.;Galloway, Jared G.;Kern, Andrew D.
准确推断自然群体中重组率的全基因组景观是基因组学的核心目标，因为连锁模式影响着从遗传作图到理解进化历史的一切。在这里，我们描述了使用递归神经网络（ReLERNN）的重组景观估计，这是一种用于估计全基因组重组图的深度学习方法，即使有少量的合并或单独测序的基因组也是准确的。ReLERNN不是使用连锁不平衡的摘要作为输入，而是从基因型比对中提取列，然后使用递归神经网络将其建模为整个基因组的序列。我们证明了ReLERNN相对于现有方法提高了准确性并减少了偏倚，并且在人口统计模型错误指定，缺失基因型调用和基因组不可访问性方面保持了高准确性。我们将ReLERNN应用于非洲黑腹果蝇的自然种群，并表明全基因组重组景观尽管在种群之间很大程度上相关，但表现出重要的种群特异性差异。最后，我们连接推断的重组模式与自然果蝇种群中分离的主要倒位的频率。
Accurately inferring the genome-wide landscape of recombination rates in natural populations is a central aim in genomics, as patterns of linkage influence everything from genetic mapping to understanding evolutionary history. Here, we describe recombination landscape estimation using recurrent neural networks (ReLERNN), a deep learning method for estimating a genome-wide recombination map that is accurate even with small numbers of pooled or individually sequenced genomes. Rather than use summaries of linkage disequilibrium as its input, ReLERNN takes columns from a genotype alignment, which are then modeled as a sequence across the genome using a recurrent neural network. We demonstrate that ReLERNN improves accuracy and reduces bias relative to existing methods and maintains high accuracy in the face of demographic model misspecification, missing genotype calls, and genome inaccessibility. We apply ReLERNN to natural populations of African Drosophila melanogaster and show that genome-wide recombination landscapes, although largely correlated among populations, exhibit important population-specific differences. Lastly, we connect the inferred patterns of recombination with the frequencies of major inversions segregating in natural Drosophila populations.