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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.

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|May 14, 2020
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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.

Keywords:
MCMC algorithmQTL mappingzero-inflated count datazero-inflated generalized Poisson regression model

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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.