The imputation of unmeasured genotypes is essential in human genetic research, particularly in enhancing the power of genome-wide association studies and conducting subsequent fine-mapping. Recently, several deep learning-based genotype imputation methods for genome-wide variants with the capability of learning complex linkage disequilibrium patterns have been developed. Additionally, deep learning-based imputation has been applied to a distinct genomic region known as the major histocompatibility complex, referred to as HLA imputation. Despite their various advantages, the current deep learning-based genotype imputation methods do have certain limitations and have not yet become standard. These limitations include the modest accuracy improvement over statistical and conventional machine learning-based methods. However, their benefits include other aspects, such as their “reference-free” nature, which ensures complete privacy protection, and their higher computational efficiency. Furthermore, the continuing evolution of deep learning technologies is expected to contribute to further improvements in prediction accuracy and usability in the future.

未测量基因型的估算在人类遗传研究中至关重要，特别是在增强全基因组关联研究和进行后续精细绘图的能力方面。最近，开发了几种基于深度学习的全基因组变异基因型插补方法，具有学习复杂连锁不平衡模式的能力。此外，基于深度学习的插补已应用于称为主要组织相容性复合体的独特基因组区域，称为 HLA 插补。尽管具有各种优点，但当前基于深度学习的基因型插补方法确实存在一定的局限性，并且尚未成为标准。这些限制包括相对于统计和传统的基于机器学习的方法的准确性的适度提高。然而，它们的好处还包括其他方面，例如它们的“无引用”性质，这确保了完整的隐私保护，以及它们更高的计算效率。此外，深度学习技术的持续发展预计将有助于未来预测准确性和可用性的进一步提高。