学術雑誌論文

学術雑誌論文

text

Journal of Human Genetics. 2024, 69(10), p. 481-486

Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology

Springer Nature

英語

text

2024-01-15

https://doi.org/10.1038/s10038-023-01213-6

Naito T., Okada Y.. Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology. Journal of Human Genetics , (2024); https://doi.org/10.1038/s10038-023-01213-6.

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.

This article is licensed under a Creative Commons Attribution 4.0 International License.

94637

Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology

Springer Nature

英語

2024-01-15

Journal of Human Genetics

69

10

481

486

https://doi.org/10.1038/s10038-023-01213-6

https://hdl.handle.net/11094/94637