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Updated: Jun 28, 2025

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Mapping QTL controlling count traits with excess zeros and ones using a zero-and-one-inflated generalized Poisson

Jinling Chi1, Jimin Ye1, Ying Zhou2

  • 1School of Mathematics and Statistics, Xidian University, Xi'an, China.

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|April 15, 2024
PubMed
Summary

Researchers developed a new zero-and-one-inflated generalized Poisson (ZOIGP) model to address overdispersion and excess zeros/ones in quantitative trait locus (QTL) mapping for count data. This model improves the analysis of count phenotypes in genetic studies.

Keywords:
expectation–maximization algorithmquantitative trait locus mappingscore testzero‐and‐one‐inflated count datazero‐and‐one‐inflated generalized Poisson model

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Area of Science:

  • Genetics
  • Statistical modeling
  • Bioinformatics

Background:

  • Quantitative trait locus (QTL) mapping is crucial for understanding genetic contributions to complex traits.
  • Conventional Poisson models struggle with count data exhibiting overdispersion and excess zeros/ones, limiting their application in genetic analysis.
  • Addressing these data limitations is essential for accurate QTL detection.

Purpose of the Study:

  • To propose a novel zero-and-one-inflated generalized Poisson (ZOIGP) model for count data analysis in QTL mapping.
  • To develop a score test for the inflation parameter to compare the ZOIGP model with standard generalized Poisson models.
  • To extend the ZOIGP model for QTL interval mapping of count phenotypes with excess zeros and ones.

Main Methods:

  • Development of the zero-and-one-inflated generalized Poisson (ZOIGP) model.
  • Implementation of a score test for model comparison.
  • Application of the expectation-maximization (EM) algorithm with Newton-Raphson for genetic effect estimation.
  • Genome-wide scan and likelihood ratio tests for QTL mapping.

Main Results:

  • The proposed ZOIGP model effectively handles count data with overdispersion and excess zeros/ones.
  • Simulation studies demonstrate favorable statistical properties of the new method.
  • The model's utility is validated through a real data analysis example for QTL mapping.

Conclusions:

  • The ZOIGP model offers a robust solution for analyzing count phenotypes with excess zeros and ones in QTL mapping.
  • This novel approach enhances the accuracy and applicability of genetic studies involving count data.
  • The method provides a practical tool for identifying potential QTLs in complex genetic traits.