Rapid, Reference-Free human genotype imputation with denoising autoencoders.
Rapid, Reference-Free human genotype imputation with denoising autoencoders.
复制标题
DOI：
10.7554/elife.75600
复制
发表时间：
2022-09-23
期刊：
影响因子：
7.7
通讯作者：
Torkamani A
中科院分区：
文献类型：
--
作者：
Dias R;Evans D;Chen SF;Chen KY;Loguercio S;Chan L;Torkamani A
基因型插补是群体遗传学的基本工具。标准的统计插补方法依赖于大型全基因组测序参考面板的协同定位、强大的计算环境和潜在敏感的遗传研究数据。这导致计算资源和隐私风险阻碍了尖端插补技术的获取。此外，目前的统计方法的准确性是已知的，以降低低和复杂的连锁不平衡的地区。基于人工神经网络的插补方法可以通过在易于移植的推理模型中编码复杂的基因型关系来克服这些限制。在这里，我们展示了一种基于自动编码器的基因型插补方法，使用一个大的，常用的参考面板，并跨越整个人类22号染色体。我们的基于自动编码器的基因型插补策略在等位基因频谱和不同祖先的基因组中实现了上级插补准确性，同时相对于标准插补工具提供至少四倍的推理运行时间。
Genotype imputation is a foundational tool for population genetics. Standard statistical imputation approaches rely on the co-location of large whole-genome sequencing-based reference panels, powerful computing environments, and potentially sensitive genetic study data. This results in computational resource and privacy-risk barriers to access to cutting-edge imputation techniques. Moreover, the accuracy of current statistical approaches is known to degrade in regions of low and complex linkage disequilibrium. Artificial neural network-based imputation approaches may overcome these limitations by encoding complex genotype relationships in easily portable inference models. Here, we demonstrate an autoencoder-based approach for genotype imputation, using a large, commonly used reference panel, and spanning the entirety of human chromosome 22. Our autoencoder-based genotype imputation strategy achieved superior imputation accuracy across the allele-frequency spectrum and across genomes of diverse ancestry, while delivering at least fourfold faster inference run time relative to standard imputation tools.