Distinguishing between recent balancing selection and incomplete sweep using deep neural networks
Distinguishing between recent balancing selection and incomplete sweep using deep neural networks
复制标题
DOI：
10.1111/1755-0998.13379
复制
发表时间：
2021-04-05
影响因子：
7.7
通讯作者：
Fumagalli, Matteo
中科院分区：
文献类型：
--
作者：
Isildak, Ulas;Stella, Alessandro;Fumagalli, Matteo
平衡选择是一种重要的适应机制，支撑着广泛的表型。尽管其相关性，但从基因组数据中检测最近的平衡选择是具有挑战性的，因为其特征在定性上与正在进行的正选择留下的特征相似。在这项研究中，我们开发并实现了两个深度神经网络，并测试了它们的性能，以预测最近选择下的基因座，无论是由于平衡选择还是不完全扫描，从群体基因组数据。具体来说，我们生成了时间向前模拟来训练和测试人工神经网络（ANN）和卷积神经网络（CNN）。ANN接收在感兴趣的基因座上计算的多个汇总统计量作为输入，而CNN直接应用于单倍型矩阵。我们发现，这两种架构具有较高的准确性，以确定基因座下最近的选择。CNN在区分平衡选择信号和不完整扫描信号方面的表现通常优于ANN，并且受不正确训练数据的影响较小。我们在欧洲人群的中性基因组区域部署了两个训练过的网络，并证明CNN的假阳性率低于ANN。我们最终在MEFV基因区域内部署了CNN，并确定了几个预测在欧洲人群中不完全扫描的常见变异。值得注意的是，这些变异中有两个是功能性变化，可以调节家族性地中海热的易感性，这可能是过去适应病原体的结果。总之，深度神经网络能够表征中频变体的选择信号，这是目前常用策略无法实现的分析。
Balancing selection is an important adaptive mechanism underpinning a wide range of phenotypes. Despite its relevance, the detection of recent balancing selection from genomic data is challenging as its signatures are qualitatively similar to those left by ongoing positive selection. In this study, we developed and implemented two deep neural networks and tested their performance to predict loci under recent selection, either due to balancing selection or incomplete sweep, from population genomic data. Specifically, we generated forward-in-time simulations to train and test an artificial neural network (ANN) and a convolutional neural network (CNN). ANN received as input multiple summary statistics calculated on the locus of interest, while CNN was applied directly on the matrix of haplotypes. We found that both architectures have high accuracy to identify loci under recent selection. CNN generally outperformed ANN to distinguish between signals of balancing selection and incomplete sweep and was less affected by incorrect training data. We deployed both trained networks on neutral genomic regions in European populations and demonstrated a lower false-positive rate for CNN than ANN. We finally deployed CNN within the MEFV gene region and identified several common variants predicted to be under incomplete sweep in a European population. Notably, two of these variants are functional changes and could modulate susceptibility to familial Mediterranean fever, possibly as a consequence of past adaptation to pathogens. In conclusion, deep neural networks were able to characterize signals of selection on intermediate frequency variants, an analysis currently inaccessible by commonly used strategies.