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Genome-wide scans for selective sweeps using convolutional neural networks.

Hanqing Zhao1, Matthijs Souilljee1, Pavlos Pavlidis2

  • 1Faculty of EEMCS, University of Twente, Enschede, The Netherlands.

Bioinformatics (Oxford, England)
|June 30, 2023
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Summary

A new neural network framework, ASDEC, efficiently detects positive selection across whole genomes. It surpasses existing methods in sensitivity and accuracy, aiding in identifying genes under selection.

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Area of Science:

  • Genomics
  • Computational Biology
  • Evolutionary Genetics

Background:

  • Selective sweep detection methods often rely on summary statistics, limiting whole-genome scans and accurate estimation of selection.
  • Existing approaches are sensitive to confounding factors and not optimized for identifying candidate genes or selection parameters.

Purpose of the Study:

  • To introduce ASDEC, a novel neural network framework for whole-genome selective sweep detection.
  • To improve the speed, sensitivity, and accuracy of identifying genomic regions affected by positive selection.

Main Methods:

  • ASDEC utilizes a neural network to infer genomic region characteristics directly from raw sequence data.
  • The framework is designed for efficient whole-genome scanning, avoiding the need for pre-computed summary statistics.

Main Results:

  • ASDEC demonstrates comparable classification performance to existing methods but trains 10x faster and classifies 5x faster.
  • Genomic scans using ASDEC showed up to 15.2x higher sensitivity, 19.4x higher success rates, and 4x higher detection accuracy.
  • ASDEC successfully identified nine known candidate genes on human chromosome 1 in the Yoruba population.

Conclusions:

  • ASDEC offers a significant advancement in selective sweep detection, enabling comprehensive whole-genome analyses.
  • The framework's efficiency and accuracy facilitate the identification of genes under positive selection and the characterization of selection events.