Abstract:

Genotype imputation can compensate for the missing due to technical limitations by estimating the sample regions that are not covered in gene sequencing data with imputation， but the existing deep learning-based imputation methods cannot effectively capture the linkage among complete sequence loci， resulting in low overall imputation accuracy and high dispersion of batch sequence imputation accuracy. Therefore， FCSA （Fusing Convolution and Self-Attention）， an imputation method that fuses convolution and self-attention mechanism， was proposed to address the above problems， and two fusion modules were used to form encoder and decoder to construct network model. In the encoder fusion module， a self-attention layer was used to obtain the correlation among complete sequence loci， and the local features were extracted through the convolutional layer after fusing the correlation to global loci. In the decoder fusion module， the local features of the encoded low-dimensional vector were reconstructed by convolution， and the complete sequence was modeled and fused by self-attention layer. The genetic data of multiple species of animals were used for model training， and the comparison and validation were carried out on Dog， Pig and Chicken datasets. The results show that compared to SCDA （Sparse Convolutional Denoising Autoencoders）， AGIC （Autoencoder Genome Imputation and Compression） and U-net， FCSA achieves the highest average imputation accuracy at 10%， 20% and 30% missing rate. Ablation experimental results also show that the design of the two fusion modules is effective in improving the accuracy of genotype imputation.

Key words: genotype imputation, convolution, self-attention, fusion module, full sequence modeling

摘要：

基因型填补可以通过填补估算出在基因测序数据中未覆盖的样本区域弥补因技术限制导致的缺失，但现有的基于深度学习的填补方法不能有效捕捉到全序列位点间的连锁关系，造成整体填补准确率低、批量序列填补准确率分散等问题。针对这些问题提出一种融合卷积与自注意力机制的填补方法——FCSA，使用两种融合模块构成编解码器组建网络模型。编码器融合模块使用自注意力层得到全序列位点间的关联度，将该关联度融合到全局位点后再通过卷积层提取局部特征；解码器融合模块使用卷积对编码后的低维向量进行局部特征重建，应用自注意力层对全序列建模并融合。使用多物种的动物基因数据进行模型训练，并在Dog、Pig和Chicken数据集上进行比较验证，结果表明，与SCDA（Sparse Convolutional Denoising Autoencoders）、AGIC（Autoencoder Genome Imputation and Compression）和U-net相比，FCSA在10%、20%和30%缺失率下的平均填补准确率均取得了最高值，且批量序列填补准确率的分散程度较小；消融实验的结果也表明，这两种融合模块的设计能够有效提升基因型填补的准确率。

关键词: 基因型填补, 卷积, 自注意力, 融合模块, 全序列建模