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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #308398
Title: Genomic prediction of disease occurrence using producer-recorded health data: A comparison of methods

Author
item PARKER GADDIS, KRISTEN - North Carolina State University
item TIEZZI, FRANCESCO - North Carolina State University
item Cole, John
item CLAY, JOHN - Dairy Records Management Systems(DRMS)
item MALTECCA, CHRISTIAN - North Carolina State University

Submitted to: Genetic Selection Evolution
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/14/2015
Publication Date: 5/8/2015
Publication URL: https://handle.nal.usda.gov/10113/60889
Citation: Parker Gaddis, K.L., Tiezzi, F., Cole, J.B., Clay, J.S., Maltecca, C. 2015. Genomic prediction of disease occurrence using producer-recorded health data: A comparison of methods. Genetic Selection Evolution. 47:41.

Interpretive Summary: There is growing demand for genetic tools to improve dairy cow health. However, far fewer data are available for disease incidence traits than for yield and fertility traits. In this study, several different statistical models were used to calculate predictions of genetic merit for mastitis (an infection of the mammary gland) and somatic cell score (an indirect measure of mammary gland health). Results showed that single-step methods, which combine pedigree, performance, and DNA marker information in a single analysis, had advantages over traditional models that include only pedigree and performance information, with DNA marker information added in a later step.

Technical Abstract: Background: Genetic selection has been successful in achieving increased production in dairy cattle; however, corresponding declines in fitness traits have been documented. Fitness traits are more difficult to select for, as they have low heritabilities and are influenced by a multitude of non-genetic factors. The objective of this paper was to investigate predictive ability of two-stage and single-step genomic selection methods applied to health data collected from on-farm computer systems in the U.S. Methods: Implementation of single-trait and two-trait models was investigated using BayesA and single-step methods for mastitis and somatic cell score. Variance components were estimated. The complete dataset was divided into training and validation to perform model comparison. Estimated sire breeding values were used to estimate number of daughters expected to experience mastitis. Predictive ability of each model was assessed using sum of chi-squared and proportion of wrong predictions. Results: Depending on model implemented, heritability of liability to mastitis ranged from 0.05 (SD = 0:02) to 0.11 (SD = 0:03) and heritability of somatic cell score ranged from 0.08 (SD = 0:01) to 0.18 (SD = 0:03). Posterior mean of genetic correlation between mastitis and somatic cell score was 0.63 (SD = 0:17). The single-step method had the best predictive ability among univariate analyses of mastitis. Conversely, the BayesA univariate model had the smallest number of wrong predictions. Best model fit was found for single-step and pedigree-based models. Bivariate single-step analysis had a better predictive ability than bivariate BayesA; however, bivariate BayesA analysis had the smallest number of wrong predictions. Conclusions: Genomic data improved our ability to predict animal breeding values. Performance of genomic selection methods will depend on a multitude of factors. Heritability of traits and reliability of genotyped individuals will have a large impact on performance of genomic evaluation methods. Single-step methodology provided several advantages compared to two-stage methods given the current characteristics of producer-recorded health data.