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Pedigree analysis package (PAP) vs. MORGAN: model selection and hypothesis testing on a large pedigree
1Department of Biostatistics, University of Washington, Seattle 98195-7720, USA.
Genetic Epidemiology
|July 22, 1998
Summary
The MORGAN package, using Monte Carlo Markov chain (MCMC) methods, shows comparable model selection to PAP for quantitative trait segregation analysis. MCMC offers a viable approach for complex genetic models.
Area of Science:
- Biostatistics
- Statistical Genetics
- Computational Biology
Background:
- Segregation analysis is crucial for understanding the genetic architecture of quantitative traits.
- Accurate model selection is essential for identifying major genes and polygenic effects.
- Existing software packages like PAP use approximations, potentially limiting analysis of complex models.
Purpose of the Study:
- To compare the MORGAN package, utilizing Monte Carlo Markov chain (MCMC) methods, with the PAP package for model selection in quantitative trait segregation analysis.
- To evaluate the performance of MCMC-based likelihood estimation against approximated likelihoods in mixed models.
- To assess the utility of MORGAN for analyzing complex genetic models intractable by direct computational methods.
Main Methods:
- Simulated data from a 232-member pedigree were analyzed.
- Models included various combinations of environmental, polygenic, and major gene effects.
- MORGAN's MCMC approach was compared against two versions of the PAP package (versions 3.0 and 4.0) employing approximated likelihoods.
Main Results:
- MORGAN and PAP version 4.0 yielded similar model selection results across most simulated scenarios.
- PAP version 3.0 produced more divergent results compared to MORGAN and PAP version 4.0.
- All tested packages (MORGAN, PAP v3.0, PAP v4.0) detected major gene components more readily with dominance.
- A frequent finding was the false acceptance of the polygenic model when residual heritability was high.
Conclusions:
- Monte Carlo Markov chain (MCMC) methods, as implemented in MORGAN, provide a computationally tractable and reliable approach for complex segregation analysis.
- MORGAN offers a robust alternative for genetic model selection, especially for scenarios beyond the scope of approximated likelihood methods.
- The presence of dominance significantly enhances the detectability of major gene effects in segregation analyses.