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Forward-time simulations of human populations with complex diseases
Bo Peng1, Christopher I Amos, Marek Kimmel
1Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, United States of America. bpeng@mdanderson.org
Plos Genetics
|March 27, 2007
Summary
A new simulation framework enables forward-time modeling of complex human diseases, overcoming previous limitations. This tool facilitates the study of genetic and demographic factors influencing disease evolution and gene mapping.
Area of Science:
- Computational Biology
- Human Genetics
- Evolutionary Medicine
Background:
- Forward-time simulations offer advantages over coalescent approaches for modeling complex human diseases.
- Existing simulation tools lack comprehensive frameworks for introducing disease mutations and controlling allele frequencies.
Purpose of the Study:
- To introduce a novel forward-time simulation framework for complex disease evolution.
- To enable the generation of large, multi-generation populations with specified genetic and demographic properties for disease modeling.
Main Methods:
- Developed a flexible simulation framework compatible with programs like simuPOP.
- Integrated methods for introducing disease mutants under selection and controlling allele frequencies.
- Simulated complex diseases involving unlinked susceptibility loci, nonadditive fitness, population structure, and gene-gene interactions.
Main Results:
- Successfully generated large populations for simulating complex disease inheritance.
- Demonstrated framework utility through examples mapping genes for affection status, quantitative traits, and cancer age of onset.
- Facilitated drawing of diverse samples (case-control, sibpair, pedigrees) for gene-mapping studies.
Conclusions:
- The introduced framework effectively supports forward-time simulations of complex human diseases.
- This tool enhances the study of genetic architecture and evolutionary dynamics of diseases.
- Enables diverse gene-mapping strategies using simulated population data.
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