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Related Concept Videos

Limits to Natural Selection01:38

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Organisms that are well-adapted to their environment are more likely to survive and reproduce. However, natural selection does not lead to perfectly adapted organisms. Several factors constrain natural selection.
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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Related Experiment Video

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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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ImaGene: a convolutional neural network to quantify natural selection from genomic data.

Luis Torada1, Lucrezia Lorenzon1,2, Alice Beddis1

  • 1Department of Life Sciences, Silwood Park campus, Imperial College London, Buckhurst Road, Ascot, SL5 7PY, UK.

BMC Bioinformatics
|November 24, 2019
PubMed
Summary
This summary is machine-generated.

Deep learning, via the ImaGene program, analyzes population genomic data to detect and quantify natural selection. This approach visualizes genetic information as images, improving the understanding of complex human phenotypes.

Keywords:
Convolutional neural networksNatural selectionPopulation geneticsSupervised machine learning

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Area of Science:

  • Evolutionary biology
  • Genomics
  • Bioinformatics

Background:

  • Complex human phenotypes often have unknown genetic bases due to polygenic inheritance and small mutation effects.
  • Traditional association studies have limitations in identifying these genetic underpinnings.
  • Evolutionary frameworks, identifying natural selection signatures, offer a promising alternative to uncover genetic mechanisms.

Purpose of the Study:

  • To explore the application of deep learning in evolutionary biology for detecting and quantifying natural selection.
  • To develop a user-friendly program, ImaGene, for analyzing population genomic data using convolutional neural networks.
  • To address limitations of existing methods in information loss and quantifying selection strength.

Main Methods:

  • Genomic data from multiple individuals are transformed into abstract images by stacking aligned data and color-encoding alleles.
  • Convolutional neural networks are trained using simulations to detect and quantify signatures of positive selection.
  • The study investigates the impact of data manipulation (e.g., image sorting) and demographic model misspecification on prediction accuracy and selection quantification.

Main Results:

  • ImaGene successfully represents genomic information as images for deep learning analysis.
  • Image sorting by row and column enhances prediction accuracy.
  • Misspecification of demographic models can influence the quantification of positive selection.
  • A method for estimating the selection coefficient using multiclass classification was demonstrated.

Conclusions:

  • Deep learning shows significant potential for detecting patterns in large-scale genomic data within evolutionary genomics.
  • The ImaGene program provides a user-friendly tool for processing genomic data for deep learning applications.
  • This approach facilitates mapping studies and offers new insights into the molecular mechanisms of human phenotypes by jointly inferring evolutionary history and functional impact.