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Bayesian interval mapping of count trait loci based on zero-inflated generalized Poisson regression model
Jinling Chi1,2, Ying Zhou1,2, Lili Chen1,2
1Department of Statistics, School of Mathematical Sciences, Heilongjiang University, Harbin, P. R. China.
Biometrical Journal. Biometrische Zeitschrift
|May 14, 2020
Summary
This study introduces a novel Bayesian interval mapping method for quantitative trait loci (QTLs) in zero-inflated count data. The approach accurately identifies QTLs influencing complex traits, demonstrated in mouse cholesterol gallstone formation.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Count phenotypes with excessive zeros are common in biological research.
- Existing quantitative trait loci (QTL) mapping methods often approximate QTL positions and use the EM algorithm.
Purpose of the Study:
- To propose a Bayesian interval mapping scheme for QTLs in zero-inflated count data.
- To leverage a zero-inflated generalized Poisson (ZIGP) regression model for improved QTL analysis.
Main Methods:
- Developed a Bayesian interval mapping approach for zero-inflated count data.
- Utilized a zero-inflated generalized Poisson (ZIGP) regression model.
- Employed Markov Chain Monte Carlo (MCMC) for parameter estimation and the Haldane map function for genetic distance conversion.
Main Results:
- Monte Carlo simulations confirmed the method's applicability and advantages.
- Successfully demonstrated the influence of QTLs on mouse cholesterol gallstone formation using real data.
Conclusions:
- The proposed Bayesian interval mapping method offers a robust approach for analyzing zero-inflated count phenotypes.
- This method enhances the understanding of genetic architectures underlying complex traits.
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