ArticleMolecular biology and evolution2023
Uncovering Footprints of Natural Selection Through Spectral Analysis of Genomic Summary Statistics.
Article in Molecular biology and evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Allelic gene conversion softens selective sweeps.Genetics · 2026Article
- Dissecting Fluctuating Selection: A Unified Population and Quantitative Genetics Framework.Genome biology and evolution · 2026Article
- Identifying Adaptive Footprints in the Presence of Demographic Uncertainty.Genome biology and evolution · 2026Article
- Soft Selective Sweeps Predominate in the Yellow Fever Mosquito Aedes aegypti.Molecular biology and evolution · 2026Article
- Detecting Positive Selection by Modeling Structure Within Images of Genetic Variation.Genome biology and evolution · 2026Article
- Summary statistics and approximate bayesian computation are comparable to convolutional neural networks for inferring times to fixation.bioRxiv : the preprint server for biology · 2026Article
- A Systematic Review: Deep Learning for Analyzing Genomic Data to Discover Evolutionary Patterns.Scientifica · 2026Review
- Comprehensive Annotation of Olfactory and Gustatory Receptor Genes and Transposable Elements Revealed Their Evolutionary Dynamics in Aphids.Molecular biology and evolution · 2025Article
- Fast and Memory-Efficient Dynamic Programming Approach for Large-Scale EHH-Based Selection Scans.Molecular biology and evolution · 2025Article
- Fast and Memory-Efficient Dynamic Programming Approach for Large-Scale EHH-Based Selection Scans.bioRxiv : the preprint server for biology · 2025Article
- Signatures of soft selective sweeps predominate in the yellow fever mosquitobioRxiv : the preprint server for biology · 2025Article
- Article
- Sweeps in Space: Leveraging Geographic Data to Identify Beneficial Alleles in Anopheles gambiae.Molecular biology and evolution · 2025Article
- Efficient Detection and Characterization of Targets of Natural Selection Using Transfer Learning.Molecular biology and evolution · 2025Article
- Efficient detection and characterization of targets of natural selection using transfer learning.bioRxiv : the preprint server for biology · 2025Article
- Digital Image Processing to Detect Adaptive Evolution.Molecular biology and evolution · 2024Article
- Tree Sequences as a General-Purpose Tool for Population Genetic Inference.Molecular biology and evolution · 2024Article
- Tree sequences as a general-purpose tool for population genetic inference.bioRxiv : the preprint server for biology · 2024Article
- iHDSel software: The price equation and the population stability index to detect genomic patterns compatible with selective sweeps. An example with SARS-CoV-2.Biology methods & protocols · 2024Article
- Harnessing deep learning for population genetic inference.Nature reviews. Genetics · 2024Review
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3 authors.
Funding
Abstract
Natural selection leaves a spatial pattern along the genome, with a haplotype distribution distortion near the selected locus that fades with distance. Evaluating the spatial signal of a population-genetic summary statistic across the genome allows for patterns of natural selection to be distinguished from neutrality. Considering the genomic spatial distribution of multiple summary statistics is expected to aid in uncovering subtle signatures of selection. In recent years, numerous methods have been devised that consider genomic spatial distributions across summary statistics, utilizing both classical machine learning and deep learning architectures. However, better predictions may be attainable by improving the way in which features are extracted from these summary statistics. We apply wavelet transform, multitaper spectral analysis, and S-transform to summary statistic arrays to achieve this goal. Each analysis method converts one-dimensional summary statistic arrays to two-dimensional images of spectral analysis, allowing simultaneous temporal and spectral assessment. We feed these images into convolutional neural networks and consider combining models using ensemble stacking. Our modeling framework achieves high accuracy and power across a diverse set of evolutionary settings, including population size changes and test sets of varying sweep strength, softness, and timing. A scan of central European whole-genome sequences recapitulated well-established sweep candidates and predicted novel cancer-associated genes as sweeps with high support. Given that this modeling framework is also robust to missing genomic segments, we believe that it will represent a welcome addition to the population-genomic toolkit for learning about adaptive processes from genomic data.
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