Zero-inflated Poisson regression models for QTL mapping applied to tick-resistance in a Gyr x Holstein F2 population

AUTOR(ES)

Silva, Fabyano Fonseca, Tunin, Karen P., Rosa, Guilherme J.M., Silva, Marcos V.B. da, Azevedo, Ana Luisa Souza, Verneque, Rui da Silva, Machado, Marco Antonio, Packer, Irineu Umberto

FONTE

Genetics and Molecular Biology

DATA DE PUBLICAÇÃO

28/10/2011

RESUMO

Nowadays, an important and interesting alternative in the control of tick-infestation in cattle is to select resistant animals, and identify the respective quantitative trait loci (QTLs) and DNA markers, for posterior use in breeding programs. The number of ticks/animal is characterized as a discrete-counting trait, which could potentially follow Poisson distribution. However, in the case of an excess of zeros, due to the occurrence of several noninfected animals, zero-inflated Poisson and generalized zero-inflated distribution (GZIP) may provide a better description of the data. Thus, the objective here was to compare through simulation, Poisson and ZIP models (simple and generalized) with classical approaches, for QTL mapping with counting phenotypes under different scenarios, and to apply these approaches to a QTL study of tick resistance in an F2 cattle (Gyr x Holstein) population. It was concluded that, when working with zero-inflated data, it is recommendable to use the generalized and simple ZIP model for analysis. On the other hand, when working with data with zeros, but not zero-inflated, the Poisson model or a data-transformation-approach, such as square-root or Box-Cox transformation, are applicable.

Documentos Relacionados

• Quantitative trait locus affecting birth weight on bovine chromosome 5 in a F2 Gyr x Holstein population
• Inference and diagnostic in zero-inflated count data models
• Detection of a QTL associated with tick resistance on bovine chromossome 7 (BTA7) using A F2 experimental population.
• Models for zero-inflated and overdispersed proportion data - a bayesian approach.
• Multinomial model and zero-inflated gamma model to study time spent on leisure time physical activity: an example of ELSA-Brasil