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Negative frequency-dependent selection: a positive outlook with deep learning
Cindy Gilda Santander1,2, Andre Luiz Campelo Dos Santos1, Sandipan Paul Arnab1,2
1Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL33431, USA.
Summary
Negative frequency-dependent selection (NFDS) maintains genetic diversity but is hard to distinguish from other selection types. New deep learning models can now effectively detect NFDS using genomic data, aiding evolutionary studies.
Area of Science:
- Evolutionary genetics
- Population genetics
- Genomics
Background:
- Balancing selection preserves genetic diversity via mechanisms like negative frequency-dependent selection (NFDS).
- Distinguishing the genomic footprint of NFDS from other balancing selection modes (e.g., overdominance) is a significant challenge.
- Accurate identification of NFDS is crucial for understanding evolutionary processes and genetic diversity maintenance.
Purpose of the Study:
- To outline strategies for improving models of genomic patterns under NFDS.
- To enhance the differentiation of NFDS signatures from neutrality and other selection processes.
- To provide practical recommendations for detecting NFDS in genomic data.
Main Methods:
- Utilizing resource-efficient deep transfer learning.
- Implementing novel data preprocessing techniques.
- Modelling genomic autocovariation for pattern detection.
- Applying methods to phased/unphased genotypes and ancient DNA (aDNA) data.
Main Results:
- Demonstrated effective detection and characterization of NFDS using advanced deep learning.
- Showcased the utility of genomic autocovariation modelling.
- Validated the approach with diverse genotype data and temporal information from aDNA.
- Improved ability to distinguish NFDS from neutral and other selection signals.
Conclusions:
- Deep transfer learning offers a powerful approach for identifying NFDS signatures in genomic data.
- Improved modelling strategies are essential for resolving complex evolutionary selection patterns.
- Recommendations are provided for researchers to advance NFDS detection in empirical and computational studies.
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