Bayesian interval mapping of count trait loci based on zero‐inflated generalized Poisson regression model

Chi, Jinling; Zhou, Ying; Chen, Lili; Zhou, Yajing

doi:10.1002/bimj.201900274

Cited by 2 publications

(3 citation statements)

References 34 publications

(59 reference statements)

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“…Cui and Yang (2009) and Moghimbeigi (2015) proposed the zero-inflated generalized Poisson regression model and the two-part zero-inflated negative binomial regression model for QTL mapping of count traits with excess zeros, respectively. More recently, Chi et al (2020) extended the QTL mapping method to the zero-inflated count trait based on the Bayesian framework.…”

Section: Introductionmentioning

confidence: 99%

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

Chi,

Ye,

Zhou

2024

Biometrical J

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The research on the quantitative trait locus (QTL) mapping of count data has aroused the wide attention of researchers. There are frequent problems in applied research that limit the application of the conventional Poisson model in the analysis of count phenotypes, which include the overdispersion and excess zeros and ones. In this article, a novel model, that is, the zero‐and‐one‐inflated generalized Poisson (ZOIGP) model, is proposed to deal with these problems. Based on the proposed model, a score test is performed for the inflation parameter, in which the ZOIGP model with a constant proportion of excess zeros and ones is compared with a standard generalized Poisson model. To illustrate the practicability of the ZOIGP model, we extend it to the QTL interval mapping application that underpins count phenotype with excess zeros and excess ones. The genetic effects are estimated utilizing the expectation–maximization algorithm embedded with the Newton–Raphson algorithm, and the genome‐wide scan and likelihood ratio test is performed to map and test the potential QTLs. The statistical properties exhibited by the proposed method are investigated through simulation. Finally, a real data analysis example is used to illustrate the utility of the proposed method for QTL mapping.

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Section: Introductionmentioning

confidence: 99%

“…More recently, Chi et al. (2020) extended the QTL mapping method to the zero‐inflated count trait based on the Bayesian framework.…”

Section: Introductionmentioning

confidence: 99%

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

Chi,

Ye,

Zhou

2024

Biometrical J

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“…It is a strong competitor to the Poisson and negative binomial regression model when the count data is over-dispersed. In addition, zero-inflated generalized Poisson and zero-inflated negative binomial regression models were used in QTL mapping studies for the count traits with excess zeros (Cui and Yang, 2009 ; Moghimbeigi, 2015 ; Chi et al, 2020 ). More recently, Tirozzi et al ( 2022 ) used zero-inflation models to assess long-term population trends and elucidate the effects of environmental bias, over-dispersion, and zero-inflation on the population trend estimates.…”

Section: Introductionmentioning

confidence: 99%

A GLM-based zero-inflated generalized Poisson factor model for analyzing microbiome data

Chi,

Ye,

Zhou

2024

Front. Microbiol.

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MotivationHigh-throughput sequencing technology facilitates the quantitative analysis of microbial communities, improving the capacity to investigate the associations between the human microbiome and diseases. Our primary motivating application is to explore the association between gut microbes and obesity. The complex characteristics of microbiome data, including high dimensionality, zero inflation, and over-dispersion, pose new statistical challenges for downstream analysis.ResultsWe propose a GLM-based zero-inflated generalized Poisson factor analysis (GZIGPFA) model to analyze microbiome data with complex characteristics. The GZIGPFA model is based on a zero-inflated generalized Poisson (ZIGP) distribution for modeling microbiome count data. A link function between the generalized Poisson rate and the probability of excess zeros is established within the generalized linear model (GLM) framework. The latent parameters of the GZIGPFA model constitute a low-rank matrix comprising a low-dimensional score matrix and a loading matrix. An alternating maximum likelihood algorithm is employed to estimate the unknown parameters, and cross-validation is utilized to determine the rank of the model in this study. The proposed GZIGPFA model demonstrates superior performance and advantages through comprehensive simulation studies and real data applications.

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Bayesian interval mapping of count trait loci based on zero‐inflated generalized Poisson regression model

Cited by 2 publications

References 34 publications

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

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

A GLM-based zero-inflated generalized Poisson factor model for analyzing microbiome data

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