The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference
The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference
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DOI：
10.1093/molbev/msy224
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发表时间：
2019-02-01
影响因子：
10.7
通讯作者：
Schrider, Daniel R.
中科院分区：
文献类型：
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作者：
Flagel, Lex;Brandvain, Yaniv;Schrider, Daniel R.
群体规模的基因组数据集为研究人员提供了令人难以置信的大量信息，可以从中推断进化历史。伴随着大量数据的出现，理论和方法论的进步试图从基因组序列中提取信息，以推断人口统计事件，例如人口规模变化和密切相关的人口/物种之间的基因流动，构建重组图谱，并揭示近期适应的基因座。迄今为止，大多数方法仅使用输入序列的一个或几个摘要，因此忽略了数据中编码的潜在有用信息。这些方法中最复杂的涉及似然计算，这需要每个新问题的理论进步，并且通常关注数据的单个方面（例如，仅等位基因频率信息），以利于数学和计算的易处理性。因此，以无似然方式直接询问整个输入序列数据将提供一种富有成效的替代方案。在这里，我们通过将 DNA 序列比对表示为图像并使用一类称为卷积神经网络 (CNN) 的深度学习方法从这些图像进行群体遗传推断来实现这一目标。我们将 CNN 应用于许多进化问题，发现它们经常达到或超过当前方法的准确性。重要的是，我们证明了 CNN 能够执行准确的进化模型选择和参数估计，即使是在尚未接受详细理论处理的问题上也是如此。因此，当应用于群体遗传比对时，CNN 能够超越专家得出的统计方法，并在不存在似然方法的情况下提供新的前进道路。
Population-scale genomic data sets have given researchers incredible amounts of information from which to infer evolutionary histories. Concomitant with this flood of data, theoretical and methodological advances have sought to extract information from genomic sequences to infer demographic events such as population size changes and gene flow among closely related populations/species, construct recombination maps, and uncover loci underlying recent adaptation. To date, most methods make use of only one or a few summaries of the input sequences and therefore ignore potentially useful information encoded in the data. The most sophisticated of these approaches involve likelihood calculations, which require theoretical advances for each new problem, and often focus on a single aspect of the data (e.g., only allele frequency information) in the interest of mathematical and computational tractability. Directly interrogating the entirety of the input sequence data in a likelihood-free manner would thus offer a fruitful alternative. Here, we accomplish this by representing DNA sequence alignments as images and using a class of deep learning methods called convolutional neural networks (CNNs) to make population genetic inferences from these images. We apply CNNs to a number of evolutionary questions and find that they frequently match or exceed the accuracy of current methods. Importantly, we show that CNNs perform accurate evolutionary model selection and parameter estimation, even on problems that have not received detailed theoretical treatments. Thus, when applied to population genetic alignments, CNNs are capable of outperforming expert-derived statistical methods and offer a new path forward in cases where no likelihood approach exists.