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Evolutionary models of quantitative disease risk factors

A Connor1, K M Weiss, S C Weeks

  • 1Department of Anthropology, Pennsylvania State University, University Park 16802.

Human Biology
|December 1, 1993
PubMed
Summary

Genetic heterogeneity in disease risk factors is complex. Evolutionary models suggest nearly neutral evolution may explain genetic effects on human quantitative chronic disease risks, aiding in predicting disease causes.

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Area of Science:

  • Evolutionary genetics
  • Population genetics
  • Human disease risk factors

Background:

  • Many mutations significantly impact phenotypes and disease risk factors.
  • Understanding genetic heterogeneity in allele effects (size, frequency, severity) is crucial.
  • Evolutionary models are explored to find patterns in genetic variation.

Purpose of the Study:

  • To examine the distribution of quantitative effects of new mutations on phenotypes.
  • To investigate the distribution of allelic effects in natural populations and their relation to fitness.
  • To apply these concepts to genetic effects on the cholesterol ratio, a coronary heart disease risk factor.

Main Methods:

  • Review of existing knowledge on mutation effects and allele distributions.

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  • Analysis of evolutionary models proposed for genetic variation.
  • Examination of data related to genetic effects on human quantitative traits, specifically cholesterol ratio.

    • Main Results:

    • Complexities of quantitative traits and data limitations hinder definitive modeling.
    • Nearly neutral models of allelic evolution at single loci appear applicable to human chronic disease risk factors.
    • Available data for risk factors align with expectations, potentially aiding prediction of etiological heterogeneity.

    Conclusions:

    • While definitive models are challenging, nearly neutral evolution provides a useful framework for understanding genetic variation in human disease risk factors.
    • The findings suggest that evolutionary principles can help predict the sources of genetic variation contributing to complex diseases.
    • Further research is needed to refine models and overcome limitations posed by population history and data availability.