Abstract
In population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand the evolutionary forces that drive genetic diversity using statistical inference. However, the era of population genomics presents new challenges in analysing the massive amounts of genomes and variants. Deep learning has demonstrated state-of-the-art performance for numerous applications involving large-scale data. Recently, deep learning approaches have gained popularity in population genetics; facilitated by the advent of massive genomic data sets, powerful computational hardware and complex deep learning architectures, they have been used to identify population structure, infer demographic history and investigate natural selection. Here, we introduce common deep learning architectures and provide comprehensive guidelines for implementing deep learning models for population genetic inference. We also discuss current challenges and future directions for applying deep learning in population genetics, focusing on efficiency, robustness and interpretability.
Original language | English |
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Pages (from-to) | 61–78 |
Number of pages | 18 |
Journal | Nature Reviews. Genetics |
Volume | 25 |
Issue number | 1 |
Early online date | 4 Sept 2023 |
DOIs | |
Publication status | Published - Jan 2024 |
Austrian Fields of Science 2012
- 106014 Genomics
- 106036 Population genetics
- 102019 Machine learning