Abstract
Now a days, 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 × 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.
Highlights
In the tropics, cattle can be afflicted by various tick species and the diseases they transmit, possibly leading to significant loss in production systems
Regarding losses caused by Boophilus microplus, Frisch et al (2000) estimated that an animal with an average of 40 ticks per day could lose weight equivalent to 20 kg/year, whereas Furlong et al (1996) calculated a reduction of 23% in the daily milk yield, when crossbred Holstein-Zebu cows were infested by 105 ticks, on an average
The QTL mapping methodologies discussed here, besides being based on the regression approach described by Haley et al (1994), were further extended to the context of generalized linear models (GLM) for count data (McCullagh and Nelder, 1989), to Poisson, Zero-Inflated Poisson (ZIP) and Generalized zero-inflated Poisson distribution (ZIP) (GZIP) regression models
Summary
Cattle can be afflicted by various tick species and the diseases they transmit, possibly leading to significant loss in production systems. As opposed to most QTL studies, which consider continuous phenotypes and normal assumptions, the number of ticks/animal is characterized as a discrete trait, as a counting variable, which could potentially follow Poisson distribution.
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