Population genetic simulations of complex phenotypes with implications for rare variant association tests
Lawrence H Uricchio1, Raul Torres, John S Witte
1Graduate Program in Bioinformatics, University of California, San Francisco, California, United States of America.
Genetic Epidemiology
|November 25, 2014
Summary
Evolutionary forces like demography and natural selection impact genetic variation and disease risk. Ignoring these factors in association tests can reduce statistical power, especially for rare variants in specific genomic regions.
Area of Science:
- Population Genetics
- Evolutionary Biology
- Statistical Genetics
Background:
- Demographic events and natural selection shape genetic variation within populations.
- These evolutionary forces are crucial for understanding the genetic architecture of complex phenotypes and diseases.
- The joint impact of demography and selection is often overlooked in statistical association tests.
Purpose of the Study:
- To develop a simulation framework (sfs_coder) that integrates selection and demography for DNA sequence generation.
- To investigate the effects of evolutionary forces on genetic variation patterns.
- To assess the implications for statistical power in rare variant association tests.
Main Methods:
- Developed a simulation-based framework (sfs_coder) for DNA sequences incorporating selection and demography.
- Incorporated flexible models for simulating phenotypic variation.
- Enabled locus-specific simulations by querying genomic functional elements and genetic maps.
- Simulated human selection and demography models to demonstrate effects on genetic variation.
Main Results:
- Demographic models and locus-specific features (e.g., proportion of sites under selection) significantly impact statistical power for rare variant association tests.
- Power to detect rare variant associations may be higher in African populations for certain phenotype models but is reduced in regions with strong negative selection.
- Existing haplotype resampling methods fail to accurately simulate rare variant distributions under rapid population growth.
Conclusions:
- The sfs_coder framework provides a robust tool for simulating genetic variation under realistic evolutionary scenarios.
- Accurate modeling of demography and selection is essential for reliable power estimations in genetic association studies.
- Findings highlight the importance of considering population-specific demography and genomic context in association studies.
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