@article{gaddis_tiezzi_cole_clay_maltecca_2015, title={Genomic prediction of disease occurrence using producer-recorded health data: a comparison of methods}, volume={47}, ISSN={["1297-9686"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84928778631&partnerID=MN8TOARS}, DOI={10.1186/s12711-015-0093-9}, abstractNote={Genetic selection has been successful in achieving increased production in dairy cattle; however, corresponding declines in fitness traits have been documented. Selection for fitness traits is more difficult, since they have low heritabilities and are influenced by various non-genetic factors. The objective of this paper was to investigate the 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. Implementation of single-trait and two-trait sire 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 sets to perform model comparison. Estimated sire breeding values were used to estimate the number of daughters expected to develop mastitis. Predictive ability of each model was assessed by the sum of χ 2 values that compared predicted and observed numbers of daughters with mastitis and the proportion of wrong predictions. According to the model applied, estimated heritabilities of liability to mastitis ranged from 0.05 (S D=0.02) to 0.11 (S D=0.03) and estimated heritabilities of somatic cell score ranged from 0.08 (S D=0.01) to 0.18 (S D=0.03). Posterior mean of genetic correlation between mastitis and somatic cell score was equal to 0.63 (S D=0.17). The single-step method had the best predictive ability. Conversely, the smallest number of wrong predictions was obtained with the univariate BayesA model. The 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, the latter led to the smallest number of wrong predictions. Genomic data improved our ability to predict animal breeding values. Performance of genomic selection methods depends on a multitude of factors. Heritability of traits and reliability of genotyped individuals has a large impact on the performance of genomic evaluation methods. Given the current characteristics of producer-recorded health data, single-step methods have several advantages compared to two-step methods.}, number={1}, journal={GENETICS SELECTION EVOLUTION}, publisher={BioMed Central}, author={Gaddis, Kristen L. Parker and Tiezzi, Francesco and Cole, John B. and Clay, John S. and Maltecca, Christian}, year={2015}, month={May} }
 @article{gaddis_cole_clay_maltecca_2014, title={Genomic selection for producer-recorded health event data in US dairy cattle}, volume={97}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84899070158&partnerID=MN8TOARS}, DOI={10.3168/jds.2013-7543}, abstractNote={Emphasizing increased profit through increased dairy cow production has revealed a negative relationship of production with fitness and health traits. Decreased cow health can affect herd profitability through increased rates of involuntary culling and decreased or lost milk sales. The development of genomic selection methodologies, with accompanying substantial gains in reliability for low-heritability traits, may dramatically improve the feasibility of genetic improvement of dairy cow health. Producer-recorded health information may provide a wealth of information for improvement of dairy cow health, thus improving profitability. The principal objective of this study was to use health data collected from on-farm computer systems in the United States to estimate variance components and heritability for health traits commonly experienced by dairy cows. A single-step analysis was conducted to estimate genomic variance components and heritabilities for health events, including cystic ovaries, displaced abomasum, ketosis, lameness, mastitis, metritis, and retained placenta. A blended H matrix was constructed for a threshold model with fixed effects of parity and year-season and random effects of herd-year and sire. The single-step genomic analysis produced heritability estimates that ranged from 0.02 (standard deviation = 0.005) for lameness to 0.36 (standard deviation = 0.08) for retained placenta. Significant genetic correlations were found between lameness and cystic ovaries, displaced abomasum and ketosis, displaced abomasum and metritis, and retained placenta and metritis. Sire reliabilities increased, on average, approximately 30% with the incorporation of genomic data. From the results of these analyses, it was concluded that genetic selection for health traits using producer-recorded data are feasible in the United States, and that the inclusion of genomic data substantially improves reliabilities for these traits.}, number={5}, journal={JOURNAL OF DAIRY SCIENCE}, author={Gaddis, K. L. Parker and Cole, J. B. and Clay, J. S. and Maltecca, C.}, year={2014}, month={May}, pages={3190–3199} }
 @article{gaddis_cole_clay_maltecca_2012, title={Incidence validation and relationship analysis of producer-recorded health event data from on-farm computer systems in the United States}, volume={95}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84865338998&partnerID=MN8TOARS}, DOI={10.3168/jds.2012-5572}, abstractNote={The principal objective of this study was to analyze the plausibility of health data recorded through on-farm recording systems throughout the United States. Substantial progress has been made in the genetic improvement of production traits while health and fitness traits of dairy cattle have declined. Health traits are generally expensive and difficult to measure, but health event data collected from on-farm computer management systems may provide an effective and low-cost source of health information. To validate editing methods, incidence rates of on-farm recorded health event data were compared with incidence rates reported in the literature. Putative relationships among common health events were examined using logistic regression for each of 3 timeframes: 0 to 60, 61 to 90, and 91 to 150 d in milk. Health events occurring on average before the health event of interest were included in each model as predictors when significant. Calculated incidence rates ranged from 1.37% for respiratory problems to 12.32% for mastitis. Most health events reported had incidence rates lower than the average incidence rate found in the literature. This may partially represent underreporting by dairy farmers who record disease events only when a treatment or other intervention is required. Path diagrams developed using odds ratios calculated from logistic regression models for each of 13 common health events allowed putative relationships to be examined. The greatest odds ratios were estimated to be the influence of ketosis on displaced abomasum (15.5) and the influence of retained placenta on metritis (8.37), and were consistent with earlier reports. The results of this analysis provide evidence for the plausibility of on-farm recorded health information.}, number={9}, journal={JOURNAL OF DAIRY SCIENCE}, author={Gaddis, K. L. Parker and Cole, J. B. and Clay, J. S. and Maltecca, C.}, year={2012}, month={Sep}, pages={5422–5435} }