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The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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Related Experiment Video

Updated: Jun 15, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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(Epi)mutation Rates and the Evolution of Composite Trait Architectures.

Bastien Polizzi, Vincent Calvez, Sylvain Charlat

    The American Naturalist
    |August 23, 2024
    PubMed
    Summary

    Mutation rate heterogeneity can influence complex trait architecture, especially in fluctuating environments. Mathematical modeling reveals that while composite architectures are often selected against, epistatic interactions and population size can alter evolutionary trajectories.

    Keywords:
    adaptive dynamicsfluctuating environmentgenetic architecturemutation ratenongenetic inheritancesign inversion

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    Area of Science:

    • Evolutionary biology
    • Genetics
    • Mathematical modeling

    Background:

    • Mutation rates vary significantly across genomes and inheritance systems.
    • Complex traits result from multiple genetic determinants, potentially with heterogeneous mutation rates.
    • Phenotypic instability can be advantageous in fluctuating environments.

    Purpose of the Study:

    • To investigate if mutation rate heterogeneity drives changes in trait architecture using mathematical modeling.
    • To explore the conditions under which composite trait architectures are adaptive.
    • To understand the role of epistatic interactions and population size in trait evolution.

    Main Methods:

    • Mathematical modeling of trait evolution.
    • Convexity principle analysis of fitness functions.
    • Simulations incorporating epistatic interactions.
    • Application of the adaptive dynamics framework.
    • Analysis under varying population sizes.

    Main Results:

    • A convexity principle was identified, generally selecting against composite architectures.
    • Epistatic interactions significantly influence the fate of mutations affecting trait architecture.
    • Evolutionary trajectories are often dependent on the initial trait architecture (historical contingency).
    • Population size affects both the strength and direction of selection on trait architecture, demonstrating 'sign inversion.'

    Conclusions:

    • Composite trait architectures are typically selected against, but historical contingencies and epistatic interactions complicate this.
    • Population size is a crucial factor influencing the evolution of trait architecture.
    • The study reveals a novel instance of 'sign inversion' related to population size effects on selection.