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ARS Home » Plains Area » Clay Center, Nebraska » U.S. Meat Animal Research Center » Genetics and Animal Breeding » Research » Publications at this Location » Publication #367513

Research Project: Developing a Systems Biology Approach to Enhance Efficiency and Sustainability of Beef and Lamb Production

Location: Genetics and Animal Breeding

Title: Assessment of alternative models for genetic analysis of worm and tick infestation in nellore cattle

item PASSAFARO, TIAGO - University Of Wisconsin
item LOPES, FERNANDO - Cobb-Vantress, Inc
item Murphy, Thomas - Tom
item VALENTE, BRUNO - Pig Improvement Company
item LEITE, ROMARIO - Federal University Of Minas Gerais
item ROSA, GUILHERME - University Of Wisconsin
item TORAL, FABIO - Federal University Of Minas Gerais

Submitted to: Livestock Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/28/2020
Publication Date: 2/1/2021
Citation: Passafaro, T.L., Lopes, F.B., Murphy Jr, T.W., Valente, B.D., Leite, R.C., Rosa, G.J., Toral, F.L. 2021. Assessment of alternative models for genetic analysis of worm and tick infestation in nellore cattle. Livestock Science. 244. Article 104276.

Interpretive Summary: Internal and external parasites are associated with reduced productivity and can pose animal welfare concerns especially in cattle populations reared in tropical environments. Traditionally, worms and ticks have been controlled through routine treatment of chemical compounds administered either orally or as a topical spray. However, inappropriate use or overuse of these chemicals have led many parasite populations to become genetically resistant to these treatments. There is mounting evidence that host resistance/resilience to parasite infection is under genetic control and selecting replacement bulls and heifers based on predictions of their genetic merit for these traits can reduce reliance on chemical treatments in future generations. From a theoretical standpoint, these traits can pose problems because they are counts measured on a discrete scale (i.e., 1, 2, 3, etc.) rather than a continuous scale and are often not normally distributed. This research compared the use of Gaussian (i.e., normal distribution) and non-Gaussian models in their ability to accurately predict individual genetic value for worm and tick resistance in a population of Nellore cattle in Brazil. Results suggested that non-Gaussian models are better suited for the genetic analysis of parasite infections, and that genetic gains in host parasite resistance can be realized.

Technical Abstract: Worms and ticks are important parasites in beef cattle, especially in tropical areas, causing significant economic and production losses. Understanding animal-to-animal variation on infestation for these parasites might guide genetic selection and improvement of management practices to attenuate its detrimental effects. Statistical models used to analyze such traits usually assume a Gaussian distribution for the observed data. However, this assumption is quite often inappropriate for counting data. Therefore, the objective of this study was to compare the performance of six data analysis approaches for worm and tick infestation in Nellore cattle, using different specifications of generalized linear mixed models (GLMM) and response variables. Data consisted of presence/absence of parasites as well as counting observations for both worms and ticks in a Nellore herd in Brazil. The binary data were analyzed with both Gaussian and Threshold models, whereas the counting data were studied using Gaussian models on the original and logarithmic scales, as well as Poisson and Zero-Inflated Poisson (ZIP) models. All models included the systematic effects of contemporary group and age, as well as the random additive genetic and residual effects. Models were compared using four criteria: Deviance Information Criterion (DIC), Spearman’s correlation between predicted breeding values from different models, the agreement on the 5 and 50% top ranked animals across models, and the Mean Squared Error of Prediction (MSEP) assessed via Monte Carlo Cross-Validation. The estimates of heritability ranged from 0.15 to 0.40 for worms and from 0.08 to 0.25 for ticks. According to the DIC, non-Gaussian models displayed the best goodness of fit. Spearman’s correlation and the percentage of agreement on the 5% and 50% top ranked animals suggested some re-ranking of animals depending upon the model used. Monte Carlos Cross-Validation showed that all models presented similar predictive ability. The estimates of heritability indicate that response to selection can be effective in long-term and should be performed concomitantly with traditional parasite control approaches. Overall, non-Gaussian models seem to be better suitable for genetic analysis of worm and tick infestation in beef cattle.