<p>A key goal of human genetics research is to understand how the effects of genetic variants combine to produce the risk of complex disease. Here we discuss and contrast three conceptual models developed to explain how multigenic risk is generated. The polygenic model, derived from the century-old infinitesimal model, has been the dominant framework for understanding the genetic inheritance of complex traits. More recently, two mechanistic models have been proposed: the omnigenic model, which hypothesizes core genes with direct effects on disease and peripheral genes with regulatory, indirect effects, and what we call the ‘stratagenic’ model, in which the genetic risk of disease is stratified across genomic pathways of functional relevance. There are key differences in the implications of these models for research, drug development and precision medicine. Therefore, it is essential to determine which model is most accurate for each disease or whether a single model is broadly optimal across complex diseases.</p>

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The polygenic, omnigenic and stratagenic models of complex disease risk

  • Judit García-González,
  • Paul F. O’Reilly

摘要

A key goal of human genetics research is to understand how the effects of genetic variants combine to produce the risk of complex disease. Here we discuss and contrast three conceptual models developed to explain how multigenic risk is generated. The polygenic model, derived from the century-old infinitesimal model, has been the dominant framework for understanding the genetic inheritance of complex traits. More recently, two mechanistic models have been proposed: the omnigenic model, which hypothesizes core genes with direct effects on disease and peripheral genes with regulatory, indirect effects, and what we call the ‘stratagenic’ model, in which the genetic risk of disease is stratified across genomic pathways of functional relevance. There are key differences in the implications of these models for research, drug development and precision medicine. Therefore, it is essential to determine which model is most accurate for each disease or whether a single model is broadly optimal across complex diseases.