ArticleGenetics2006
On the generalized poisson regression mixture model for mapping quantitative trait loci with count data.
Article in Genetics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 46 citations in OpenAlex.
- Bayesian modeling of Escherichia coli contamination in household drinking water in Bangladesh: evidence from the Multiple Indicator Cluster Survey 2019.International health · 2026Article
- Socio-environmental predictors of diabetes incidence disparities in Tanzania mainland: a comparison of regression models for count data.BMC medical research methodology · 2024Article
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- Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways.Genetics, selection, evolution : GSE · 2016Article
- Bayesian GWAS and network analysis revealed new candidate genes for number of teats in pigs.Journal of applied genetics · 2015Article
- Generalized linear model for mapping discrete trait loci implemented with LASSO algorithm.PloS one · 2014Article
- Utilizing prospective sequence analysis of SHH, ZIC2, SIX3 and TGIF in holoprosencephaly probands to describe the parameters limiting the observed frequency of mutant gene×gene interactions.Molecular genetics and metabolism · 2012Article
- Zero-inflated Poisson regression models for QTL mapping applied to tick-resistance in a Gyr × Holstein F2 population.Genetics and molecular biology · 2011Article
- Generalized linear model for interval mapping of quantitative trait loci.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2010Article
- A statistical model for testing the pleiotropic control of phenotypic plasticity for a count trait.Genetics · 2008Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Statistical methods for mapping quantitative trait loci (QTL) have been extensively studied. While most existing methods assume normal distribution of the phenotype, the normality assumption could be easily violated when phenotypes are measured in counts. One natural choice to deal with count traits is to apply the classical Poisson regression model. However, conditional on covariates, the Poisson assumption of mean-variance equality may not be valid when data are potentially under- or overdispersed. In this article, we propose an interval-mapping approach for phenotypes measured in counts. We model the effects of QTL through a generalized Poisson regression model and develop efficient likelihood-based inference procedures. This approach, implemented with the EM algorithm, allows for a genomewide scan for the existence of QTL throughout the entire genome. The performance of the proposed method is evaluated through extensive simulation studies along with comparisons with existing approaches such as the Poisson regression and the generalized estimating equation approach. An application to a rice tiller number data set is given. Our approach provides a standard procedure for mapping QTL involved in the genetic control of complex traits measured in counts.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.