Progress 07/01/00 to 09/30/11
Outputs
OUTPUTS: Our work this year focused largely on incorporation of genomic data resulting from dense single nucleotide polymorphism (SNP) genotyping into genetic evaluations of dairy cattle. One aspect was development and testing of advanced statistical methods and machine learning algorithms. We showed that neural networks, Bayesian regression methods, boosting algorithms, and other novel approaches can often provide superior predictive ability, with respect to conventional linear models. In addition, we adapted genotype imputation algorithms that were developed for applications in human biomedical research to the situation of imputing (i.e., deriving) high-density SNP genotypes of cattle from inexpensive, low-density genotyping platforms. We also showed that genomic breeding values based on imputed genotypes had predictive ability that was nearly as good as from actual high-density genotypes. PARTICIPANTS: Nanye Long - PhD student; Ana Vazquez - MS student; Gustavo de los Campos - PhD student; Oscar Gonzalez-Recio - Post-doc TARGET AUDIENCES: Other scientists, dairy cattle breeding (artificial insemination) companies, pedigree breeders, commercial dairy farmers, extension specialists and agents PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
Impacts
The novel statistical methods and machine learning algorithms that were developed and tested for the purpose of whole genome selection of dairy cattle and other livestock will help address the challenge of how to derive useful information (for genetic selection decisions) from massive amounts of genomic data. This is an increasingly important problem, as the cost of genomic technologies decrease, and as available computing power increases. Our work on genotype imputation and breeding value estimation from inexpensive, low-density genotyping chips is of immediate importance. High-density chips are too expensive for routine use on commercial dairy farms, and as such their use is limited to high-value breeding stock on a handful of farms or at breeding companies. We showed how to capture 95% of the gain for less than 25% of the price, so now farmers can genotype cows and heifers on their farms and use these data in day-to-day selection and management decisions.
Publications
- Gianola, D., H. Okut, K. A. Weigel, and G. J. M. Rosa. 2010. Predicting complex quantitative traits with neural networks: a case study with Jersey cows. Proc. Royal Society, Series B (in review).
- Weigel, K. A., G. de los Campos, A. I. Vazquez, G. J. M. Rosa, D. Gianola, and C. P. Van Tassell. 2010. Accuracy of direct genomic values derived from imputed single nucleotide polymorphism genotypes in Jersey cattle. Journal of Dairy Science 93:5423-5435.
- Vazquez, A. I., G. J. M. Rosa, K. A. Weigel, G. de los Campos, D. Gianola, and D. B. Allison. 2010. Predictive ability of subsets of single nucleotide polymorphisms with and without parent averages in US Holsteins. Journal of Dairy Science 93:5942-5949.
- Gonzalez-Recio, O., K. A. Weigel, D. Gianola, H. Naya, and G. J. M. Rosa. 2010. L2-Boosting algorithm applied to high dimensional problems in genomic selection. Genetics Research (in press).
- Lopez de Maturana, E., G. de los Campos, X. L. Wu, D. Gianola, K. A. Weigel, and G. J. M. Rosa. 2010. Modeling relationships between calving traits: A comparison between standard and recursive models. Genetics, Selection, and Evolution (in press).
- de los Campos, G., D. Gianola, G. J. M. Rosa, K. A. Weigel, and J. Crossa. 2010. Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genetics Research Cambridge 92:295-308.
- Weigel, K. A., J. R. O Connell, G. R. Wiggans, P. M. VanRaden, and C. P. Van Tassell. 2010. Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms. Journal of Dairy Science 93:2229-2238.
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Progress 01/01/09 to 12/31/09
Outputs
OUTPUTS: Activity in this area has focused heavily on genetic evaluation of health, fertility, and calving ability in dairy cattle. With respect to health, we carried out Poisson, linear, and logit model analyses of clinical mastitis rate in two populations of dairy cattle, one from Norway and one from the US. Also, we developed an R software package that allows generalized linear models to be used while including the additive genetic relationship matrix. We also carried out a separate analysis in which incidence rate of clinical mastitis and costs of clinical mastitis were evaluated in Spanish dairy cattle. With respect to calving ability, we developed a structural threshold model in which the recursive effects of calving difficulty, gestation length, and stillbirth rate were evaluated in terms of direct and maternal effects. Predictive ability of this model was compared with traditional threshold models. Lastly, we carried out an analysis of management factors associated with reproductive performance on US dairy farms. PARTICIPANTS: Kent Weigel (professor and extension specialist), Evangelina Lopez de Maturana (postdoctoral research associate), Marigel Angelez Perez Cabal (postdoctoral research associate), Ana Ines Vazquez (graduate student), Jon Schefers (graduate student) TARGET AUDIENCES: Other academic scientists, staff of commercial breeding companies, scientists at government research laboratories PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
Impacts
The most important impact of our work was the finding that the genetic relationship between calving difficulty and stillbirth varied as a function of gestation length. Our work will be key with respect to current and future efforts toward development of genetic and genomic evaluations for gestation length, which if shortened by 2 or 3 days in Holstein cattle could lead to significantly fewer calving problems and fewer stillborn calves. Our work on software development will allow many other scientists to fit appropriate models to health and fertility traits when analyzing experimental or field data. Lastly, our work on reproductive performance will lead to management recommendations regarding housing, facilities, and reproductive management tools.
Publications
- Vazquez, A., D. Bates, D. Gianola, K. A. Weigel, and G. J. M. Rosa. 2009. Technical note: An R package for fitting generalized mixed linear models in animal breeding. Journal of Animal Science doi:10.2527,jas.2009-1952.
- Perez-Cabal, M. A., G. de los Camps, A. Vazquez, D. Gianola, G. J. M. Rosa, K. A. Weigel, and R. Alenda. 2009. Genetic evaluation of susceptibility to clinical mastitis in Spanish Holstein cows. Journal of Dairy Science 92:3472-3480.
- Vazquez, A., K. A. Weigel, D. Gianola, G. Rosa, and M. A. Perez-Cabal. 2009. Poisson versus Threshold Models for Genetic Analysis of Clinical Mastitis in US Holsteins. Journal of Dairy Science 92:5239-5247.
- Lopez de Maturana, E., D. Gianola, G. J. M. Rosa, and K. A. Weigel. 2009. Predictive ability of models for calving difficulty in US Holsteins. Journal of Animal Breeding and Genetics 126:179-188.
- Vazquez, A., D. Gianola, D. Bates, K. A. Weigel, and B. Heringstad. 2009. Assessment of Poisson, logit, and linear models for genetic analysis of clinical mastitis in Norwegian Red cows. Journal of Dairy Science 92:739-748.
- Lopez de Maturana, E., X. L. Wu, D. Gianola, K. A. Weigel, and G. J. M. Rosa. 2009. Exploring biological relationships between calving traits in primiparous cattle with a Bayesian recursive model. Genetics 181:277-287.
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Progress 01/01/08 to 12/31/08
Outputs
OUTPUTS: Our work focused on three main areas: genetic relationships among calving traits, genetic models for disease traits, and possibilities for genomic selection. With respect to the first topic area, structural equation models were used to explore biological relationships between gestation length, calving difficulty and perinatal mortality, also known as stillbirth, in cattle. An acyclic model was assumed, where recursive effects existed from gestation length phenotype to liabilities to calving difficulty and stillbirth rate and from liability to calving difficulty to stillbirth rate considering four periods regarding gestation length. Low genetic correlations between gestation length and the other calving traits were found, whereas the liabilities to calving difficulty and stillbirth were high and positively correlated, genetically. Next, we showed how structural equation models can be applied in animal breeding, specifically for modeling relationships between gestation length, calving difficulty and stillbirth rate. With respect to the second topic area, we assessed the properties of Poisson, logit, and linear models for genetic analysis of clinical mastitis in Norwegian Red cows. The main goal of this study was to compare linear (Gaussian), Bernoulli (with logit link), and Poisson models for the purpose of genetic evaluation of sires for mastitis in dairy cattle. The response variables were presence or absence of clinical mastitis and number of clinical mastitis cases. Predictive ability of models was assessed via a 3-fold cross-validation using mean squared error of prediction as the end-point. With respect to the third subject area, we considered the problem of estimating genomic values using dense single nucleotide polymorphism (SNP) data, as well as the problem of assessing genotype by environment interaction, using data from broiler chickens. Two non-parametric methods (kernel and reproducing kernel Hilbert space (RKHS) regression) fitted the data better than conventional methods. In addition, accuracy of prediction of yet-to-be observed phenotypes for food conversion rate in broilers was studied. A Bayesian regression model (Bayes A) and a semi-parametric approach (RKHS) using all available SNPs were compared with a standard linear model. A large improvement of genome-assisted prediction was found relative to pedigree index, and RKHS with pre-selected informative SNPs was more accurate than Bayes A with all SNPs. Next, we carried out a comparison of classification methods for detecting SNP-mortality associations in broilers. Use of extreme samples seems to enhance the ability of selecting influential SNPs in a genetic association study. Last, we did a marker-assisted assessment of genotype by environment interaction: SNP-mortality association in broilers in two hygiene environments. For both early and late mortality, it was found that the SNP subsets selected were not consistent across hygiene environments, in terms of either across-environment predictive ability or extent of linkage disequilibrium (LD) between the subsets. PARTICIPANTS: Not relevant to this project. TARGET AUDIENCES: Not relevant to this project. PROJECT MODIFICATIONS: Not relevant to this project.
Impacts
With respect to genetic relationships among calving traits, our work indicated that gestations of around 274 days of length (3 days shorter than the average) would lead to the lowest calving difficulty and stillbirth, and confirmed the existence of an intermediate optimum of gestation length with respect to these traits. We also confirmed the presence of heterogenous correlations between gestation length and the liabilities to calving difficulty and stillbirth, and homogeneous correlations between the liabilities to calving difficulty and stillbirth, and suggested that the phenotypic recursion is the only cause of correlation, because the most restrictive model, a recursive mixed model that postulated that the correlations between genetic effects, contemporary groups and residuals are due to recursive relationships between phenotypes, performed as well as more parameterized models. With respect to modeling of disease traits, we showed that models for count variables had better performance when predicting diseased animals, and we also showed that logit and linear models for presence or absence of clinical mastitis had better predictive ability. With respect to genomic selection, our work provided independent confirmation of the gains in accuracy that can be achieved by dense genotyping. With regard to dairy cattle, our work showed that among 54,000 markers that were genotyped on nearly 5,000 dairy bulls, roughly 32,500 markers could be used for selection (after editing). When effects of these markers were estimated in a population of 3,500 older bulls (i.e., the "training set"), these estimates could be used to predict genomic breeding values that had a correlation of 0.61 with future breeding values of 1,400 younger bulls (i.e., the "testing set"). Furthermore, our work showed that a low-cost assay with 300 SNP markers could provide a correlation of 0.42 with future breeding values. In other words, it could provide about half of the benefit (in terms of reliability, or the squared correlation) for one-tenth of the cost. The primary product resulting from our work was to assist the USDA-ARS Bovine Functional Genomics Laboratory project regarding development of a low-cost, low-density assay for genotyping single nucleotide polymorphisms affecting lifetime net profit in dairy cattle. Previous work by the ARS scientists confirmed the usefulness of genotypic information from dense (54,000) SNP assays in predicting the breeding values of young male and female cattle, long before these animals had phenotypic data or progeny of their own. However, at a cost of nearly $250 per animal, the usage of dense genotyping assays is limited to high-value, potentially elite bulls and cows, and the technology is not cost effective for routine use on commercial farms. We played a key role in identifying the most informative SNPs associated with profitability in dairy cattle, and the resulting list was provided to ARS scientists and their collaborators at Illumina, Corp. (San Diego, CA) for development of a commercial 384-SNP assay that can be used to genotype cows, heifers, and calves at a cost of less than $25 per animal.
Publications
- Long, N., D. Gianola, G. J. M. Rosa, K. A. Weigel, and S. Avendano. 2008. Marker-assisted assessment of genotype by environment interaction: A case study of single nucleotide polymorphism-mortality association in broilers in two hygiene environments. Journal of Animal Science 86:3358-3366.
- Vazquez, A., D. Gianola, D. Bates, K. A. Weigel, and B. Heringstad. 2008. Assessment of Poisson, logit, and linear models for genetic analysis of clinical mastitis in Norwegian Red cows. Journal of Dairy Science (in press).
- Wu, X. L., D. Gianola, and K. A. Weigel. 2008. Bayesian joint mapping of quantitative trait loci for Gaussian and categorical characters in line crosses. Genetica (in press).
- Gonzalez-Recio, O., D. Gianola, N. Long, K. A. Weigel, G. J. M. Rosa, and S. Avendano. 2008. Non-parametric methods for incorporating genomic information into genetic evaluations: An application to mortality in broilers. Genetics 178:2305-2313.
- Quintans, G., A. Vazquez, and K. A. Weigel. 2008. Effect of suckling restriction with nose plates and premature weaning on postpartum anoestrous interval in primiparous cows under range conditions. Animal Reproduction Science (in press).
- Gonzalez-Recio, O., D. Gianola, G. J. M. Rosa, K. A. Weigel, and A. Kranis. 2008. Genome-assisted prediction of a quantitative trait measured in parents and progeny: Application to food conversion rate in chickens. Genetics, Selection, and Evolution (in press).
- Long, N., D. Gianola, G. J. M. Rosa, K. A. Weigel, and S. Avendano. 2008. Comparison of classification methods for detecting associations between SNPs and chick mortality. Genetics, Selection, and Evolution (in press).
- Lopez de Maturana, E., X. L. Wu, D. Gianola, K. A. Weigel, and G. J. M. Rosa. 2008. Exploring biological relationships between calving traits in primiparous cattle with a Bayesian recursive model. Genetics (in press).
- Lopez de Maturana, E., D. Gianola, G. J. M. Rosa, and K. A. Weigel. 2008. Predictive ability of models for calving difficulty in US Holsteins. Journal of Animal Breeding and Genetics (in press).
- Norman, H. D., J. R. Wright, and K. A. Weigel. 2008. Our Industry Today: Alternatives for examining daughter performance of progeny test bulls between official evaluations. Journal of Dairy Science (in press).
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Progress 01/01/07 to 12/31/07
Outputs
OUTPUTS: Our efforts in the area of genetic evaluation of dairy cattle using statistical methods during the past year have focused on several key areas. Our work on development of databases for genetic evaluation of health traits, including mastitis, ketosis, lameness, displaced abomasum, and retained placenta continues, with emphasis on proper handling of traits that are recorded as binary (yes/no) responses or measured as count data. In addition, the effects of inbreeding on animal health and fertility continue to be of interest, and we used alternative methodologies (i.e., nonparametric regression models) to examine the effects of inbreeding on the performance of Jersey cattle, the dairy breed currently facing the most challenging genetic diversity issues. Lastly, the development of high-throughput systems for genotyping single nucleotide polymorphism (SNP) markers in cattle has opened many interesting avenues for research on statistical methods and genetic evaluation methodology,
particularly with regard to approaches for identifying optimal sets of SNP markers that can explain genetic variation in traits of economic importance.
PARTICIPANTS: Kent Weigel, Department of Dairy Science, University of Wisconsin - Madison. Guilherme Rosa, Department of Dairy Science, University of Wisconsin - Madison. Daniel Gianola, Department of Animal Sciences, University of Wisconsin - Madison. Nanye Long, Department of Animal Sciences, University of Wisconsin - Madison. Oscar Gonzalez-Recio, Department of Dairy Science, University of Wisconsin - Madison.
TARGET AUDIENCES: The target audiences for this work include other academic scientists, as well as staff of national genetic evaluation centers who have responsibility for routine estimation of breeding values of dairy sires and cows.
Impacts
Our work on analysis of SNP marker data led to identification of a very useful machine learning algorithm that can effectively and efficiently find a subset of 20 to 30 markers (from thousands of potential markers) that explains variation in quantitative traits, and cross-validation techniques were used to confirm these results. In addition, our work on methodology for genetic evaluation of binary and count data led to a very thorough comparison of the advantages and disadvantages of Poisson, logit, and sequential threshold models for analysis of such traits. Lastly, our work on inbreeding showed that inbreeding depression may be nonlinear for some traits, and that differences in the extent of inbreeding depression exist between sire families, perhaps reflecting previous purging of deleterious recessive alleles in these families.
Publications
- Long, N., D. Gianola, K. A. Weigel, G. J. M. Rosa, and S. Avendano. 2007. Association between SNPs and mortality in commercial broilers: a machine learning approach. J. Dairy Sci. 90(Suppl. 1):133.
- Coburn, A. D., K. A. Weigel, S. A. Schnell, and G. Abdel-Azim. 2007. Evaluation of factors affecting changes in the ranking of sires over time. J. Dairy Sci. 90(Suppl. 1):193.
- Vazquez, A. I., K. A. Weigel, D. Gianola, D. M. Bates, and B. Heringstad. 2007. Poisson versus logit models for genetic analysis of mastitis in Norwegian cattle. J. Dairy Sci. 90(Suppl. 1):195.
- Long, N., D. Gianola, G. J. M. Rosa, K. A. Weigel, and S. Avendano. 2007. Machine learning procedure for selecting single nucleotide polymorphisms in genomic selection: Application to early mortality in broilers. J. Anim. Breed. Genet. (accepted).
- Maltecca, C., A. Rossoni, C. Nicoletti, E. Santus, K. A. Weigel, and A. Bagnato. 2007. Genetic parameter estimation for perinatal drinking behavior of Italian Brown Swiss calves. J. Dairy Sci. (submitted).
- Chang, Y. M., O. Gonzalez-Recio, K. A. Weigel, and P. M. Fricke. 2007. Genetic analysis of 21-day pregnancy rate in US Holsteins using an ordinal censored threshold model with unknown voluntary waiting period. J. Dairy Sci. 90:1987-1997.
- Gulisija, D., D. Gianola, and K. A. Weigel. 2007. Nonparametric analysis of the impact of inbreeding depression on production in Jersey cows. J. Dairy Sci. 90:493-500.
- Gulisija, D., D. Gianola, K. A. Weigel, and M. A. Toro. 2007. Between-founder heterogeneity in inbreeding depression for production in Jersey cows. Livest. Prod. Sci. (in press).
- Chang, Y. M., O. Gonzalez-Recio, D. Gianola, and K. A. Weigel. 2007. Genetic analysis of count data using threshold models. Genet. Sel. Evol. (accepted).
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Progress 01/01/06 to 12/31/06
Outputs
Genetic improvement programs in dairy cattle have evolved from selection for high milk production (i.e., considering income only) to selection for improved animal health, fertility, and longevity (i.e., considering both income and expenses). However, data collection and statistical analysis for health-related traits can be challenging, because trait definitions and diagnostic criteria vary between farms and practitioners, and because these traits are highly influenced by various herd management practices. We have focused on developing standards and guidelines for data collection, editing, and standardization based on extensive discussions with veterinarians, farmers, and management software providers. We have developed extensive databases consisting of millions of health, fertility, and management events, and we continue to develop and apply the most advanced statistical models available for the purpose of genetic evaluation of dairy cows and bulls for these key
traits.
Impacts
Health, fertility, and longevity traits have increased from a negligible level two decades ago to more than 50% of the total economic value in our national genetic selection indices. As the marginal cost of producing more milk per cow increases, many farmers are looking to reduce the costs associated with replacement heifers, veterinary treatments, and other management interventions. Therefore, genetic selection for healthy, fertile cows that can produce milk efficiently without excessive caretaking is of greater interest than any time in the past century.
Publications
- Caraviello, D. Z., K. A. Weigel, M. Craven, D. Gianola, N. B. Cook, K. Nordlund, P. M. Fricke, and M. L. Wiltbank. 2006. Analysis of the reproductive performance of lactating Holstein cows on large dairy farms using machine learning algorithms. J. Dairy Sci. 89:4703-4722.
- Caraviello, D. Z., K. A. Weigel, P. M. Fricke, M. C. Wiltbank., M. J. Florent, N. B. Cook, K. Nordlund, N. R. Zwald, and C. L. Rawson. 2006. Survey of management practices on reproductive performance of dairy cattle on large commercial farms in the United States. J. Dairy Sci. 89:4723-4735.
- Gonzalez-Recio, O., R. Alenda, Y. M. Chang, K. A. Weigel, and D. Gianola. 2006. Selection for female fertility using censored fertility traits and investigation of the relationship with milk production. J. Dairy Sci. 89:4438-4444.
- Gonzalez-Recio, O., Y. M. Chang, D. Gianola, and K. A. Weigel. 2006. Comparisons between models using different censoring scenarios for days open in Spanish Holstein cows. An. Sci. 82:233-239.
- Weigel, K. A. 2006. Genetic progress: Has it helped or hindered reproductive function? Anim. Repro. Sci. 96:323-330.
- Chang, Y. M., O. Gonzalez-Recio, K. A. Weigel, and P. M. Fricke. 2006. Genetic analysis of pregnancy rate with unknown voluntary waiting period in US Holsteins.using a threshold model. Proc. 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Brazil (August 13-18).
- Gevrekci, Y., Y. M. Chang, K. Kizilkaya, D. Gianola, K. A. Weigel, and Y. Akbas. 2006. Bayesian inference for calving ease and stillbirth in Holsteins using a bivariate threshold sire-maternal grandsire model. Proc. 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Brazil (August 13-18).
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Progress 01/01/05 to 12/31/05
Outputs
This project involved statistical methods to evaluate longevity, health, and fertility in dairy cattle. With regard to longevity, we used state-of-the-art survival analysis methods to evaluate Holstein and Jersey sires for length of productive life of their daughtetrs. Advantages of the new methodology for estimating sire breeding values, as compared with traditional approaches, were quantified. In addition, we used similar methodology to evaluate the relationship between linear type traits and longevity in both the Jersey and Holstein breeds. With regard to fertility data, we developed, refined, and implemented threshold models for prediction of sire breeding values for male and female fertility. Our approach advanced the current state of knowledge by accommodating censored records, incorporating information from veterinary pregnancy examinations, and considering differences in the voluntary waiting period between herds. Lastly, our health data project involved
electronic collection of mastitis, ketosis, displaced abomasum, retained placenta/metritis, and lameness data on more than 400 commercial dairy farms. We estimated sire breeding values, for the first time in North America, for daughters' susceptibility to each of these traits using advanced multiple-trait threshold models. Furthermore, we considered differences in mastitis susceptibility within and between lactations. Lastly, we used electronically recorded milking times to develop a genetic evaluation system for milking speed in Holstein cattle.
Impacts
This project has had a tremendous impact on the US dairy genetics industry. Our research on the relationship between type traits and longevity has been presented at dozens of conferences and farmer meetings within the US, as well as numerous foreign countries. Each of the major dairy breeds has modified its selection index based on our results. Our work on fertility has initiated changes at the state and national level in the manner in which male and female fertility data are collected, analyzed, and interpreted. Lastly, our work on systems for collection and analysis of health data has led to development of new programs by the artifical inseminations studs and breed associations to begin collecting such data on a national basis.
Publications
- Zwald, N.R., K.A. Weigel, Y.M. Chang, R.D. Welper, and J.S. Clay. 2006. Genetic analysis of clinical mastitis data from on-farm management software using threshold models. J. Dairy Sci. 89:330-336.
- Gonzalez-Recio, O., Y.M. Chang, D. Gianola, and K.A. Weigel. 2005. Number of inseminations to conception in Holstein cows using censored records and time-dependent covariates. J. Dairy Sci. 88:3655-3662.
- Zwald, N.R., K.A. Weigel, Y.M. Chang, R.D. Welper, and J.S. Clay. 2005. Genetic evaluation of dairy sires for milking duration using electronically-recorded milking times of their daughters. J. Dairy Sci. 88:1192-1198.
- Caraviello, D. Z., K. A. Weigel, G.E. Shook, and P.L. Ruegg. 2005. Impact of somatic cell count on the functional longevity of Holstein and Jersey cattle. J. Dairy Sci. 88:804-811.
- Caraviello, D. Z., K. A. Weigel, and D. Gianola. 2004. Prediction of longevity breeding values for US Holstein sires using survival analysis methodology. J. Dairy Sci. 87:3518-3524.
- Caraviello, D. Z., K. A. Weigel, and D. Gianola. 2004. Analysis of the relationship between type traits and functional survival in US Holstein cattle using a Weibull proportional hazards model. J. Dairy Sci. 87:2677-2686.
- Caraviello, D.Z., K.A. Weigel, and D. Gianola. 2004. Comparison between a Weibull proportional hazards model and a linear model for predicting the genetic merit of US Jersey sires for daughter longevity. J. Dairy Sci. 87:1469-1476.
- Caraviello, D. Z., K. A. Weigel, and D. Gianola. 2003. Analysis of the relationship between type traits, inbreeding, and functional survival in Jersey cattle using a Weibull proportional hazards model. J. Dairy Sci. 86:2984-2989.
- Rekaya, R., D. Gianola, K. A. Weigel, and G. E. Shook. 2003. Longitudinal random effects models for genetic analysis of binary data with application to mastitis in dairy cattle. Genet. Sel. Evol. 35:457-468.
- Weigel, K. A., R. W. Palmer, and D. Z. Caraviello. 2003. Investigation of factors affecting voluntary and involuntary culling in expanding dairy herds in Wisconsin using survival analysis. J. Dairy Sci. 86:1482-1486.
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Progress 01/01/04 to 12/31/04
Outputs
At present, we are carrying out an assessment of factors that influence reproductive performance on commercial farms, including facilities, nutrition, management, labor, and health. We will develop models to predict cow fertility, with or without various management interventions, and these will assist breeding companies in providing reproductive management services to dairy farmers. This is an extension of our previous work on statistical models for evaluation of fertility.
Impacts
Dairy cow fertility has declined in recent years, primarly due to intense selection for high milk production and correlated increases in cow "stress". Reproductive traits are extremely difficult to analyze, because problems such as non-normality of the data, binary responses, incomplete (or selective) reporting, etc. are common. The advanced statistical models developed by our laboratory can accommodate these challenges and provide useful tools for genetic improvement of male and female fertility traits.
Publications
- Zwald, N.R., and K.A. Weigel. 2004. Estimation of genetic parameters for health traits in large commercial herds using data recorded in on-farm herd management software programs. Proc. American Dairy Science Assn. Annual Mtg., St. Louis, MO, July 25-29 (Vol. 87 (Suppl. 1), p. 86).
- Goodling, R.C., G.E. Shook, K.A. Weigel, and N.R. Zwald. 2004. Effect of synchronization protocols on days open and pregnancy rate at 120 days in dairy cattle. Proc. American Dairy Science Assn. Annual Mtg., St. Louis, MO, July 25-29 (Vol. 87 (Suppl. 1), p. 283).
- Zwald, N.R., K.A. Weigel, Y.M. Chang, R.D. Welper, and J.S. Clay. 2004. Genetic selection for health traits using producer-recorded data. I. Incidence rates, heritability estimates, and sire breeding values. J. Dairy Sci. 87:4287-4294.
- Zwald, N.R., K.A. Weigel, Y.M. Chang, R.D. Welper, and J.S. Clay. 2004. Genetic selection for health traits using producer-recorded data. II. Genetic correlations, disease probabilities, and relationships with existing traits. J. Dairy Sci. 87:4295-4302.
- Averill, T.A., R. Rekaya, and K.A. Weigel. 2004. Genetic evaluation of male and female fertility using longitudinal binary data. J. Dairy Sci. 87:3947-3952.
- Weigel, K.A. 2004. Improving the reproductive efficiency of dairy cattle through genetic selection. J. Dairy Sci. 87:(E. Suppl.):E86-E92.
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Progress 01/01/03 to 12/31/03
Outputs
Our primary work this year involved development of statistical models for the genetic analysis of male and female fertility traits. In particular, we looked at survival analysis (failure time) models for the analysis of days open. These models are particularly appealing for reproductive traits, because they can account for censoring of records from cows that never become pregnant or are culled. In addition, we considered longitudinal binary models, in which each (e.g., monthly) time period or each insemination can be considered as one of a series of repeated binary (yes/no) responses. Lastly, we developed and applied a threshold (binary) model for genetic evaluation of male fertility using data from veterinary-confirmed pregnancy diagnoses.
Impacts
Dairy cow fertility has declined in recent years, primarly due to intense selection for high milk production and correlated increases in cow "stress". Reproductive traits are extremely difficult to analyze, because problems such as non-normality of the data, binary responses, incomplete (or selective) reporting, etc. are common. The advanced statistical models developed by our laboratory can accomodate these challenges and provide useful tools for genetic improvement of male and female fertility traits.
Publications
- No publications reported this period
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Progress 01/01/02 to 12/31/02
Outputs
In the past year, we have made progress in several key areas under this objective. In the area of international dairy sire evaluations, we completed and published our work on border-less genetic evaluations, and we began to investigate the impact of preferential treatment on international sire evaluations. We used lactation records from several leading dairy countries to identify characteristics (e.g., size, production level) that were associated with bias in lactation data of daughters of expensive foreign sires. Along these lines, we also investigated ways to correct for preferential treatment in genetic evaluations of elite cows within the US. We also looked at ways to select an optimal subset of data for estimation of genetic correlation parameters within an international sire evaluation system based on daughter performance records. In the area of fertility, we continued our work on developing more advanced models for genetic evaluation of female fertility,
including models based on veterinarian-confirmed pregnancy examination results, rather than non-return rates. We looked at the impact of estrus synchronization, voluntary waiting period, and other factors on bull and cow fertility estimates as well. In a related (and ongoing) study, we began development of an interface between on-farm computer systems and central research databases. In this manner, detailed health and reproductive data that formerly resided only on-farm can be used to enhance our genetic evaluation programs for male and female fertility.
Impacts
Our work on international dairy sire evaluation methods has stimulated a great deal of new research on this topic in Canada and several European countries, as well as at the Interbull Centre. At the same time, our work on preferential treatment within the US genetic evaluation system has led to a potential modification that may be implemented in routine national evaluations sometime this year. Most importantly, the work our group has done on genetic improvement of fertility has greatly contributed to the introduction (in February 2003) of national genetic evaluations for daughter pregnancy rate of US dairy sires.
Publications
- Zwald, N. R., and K. A. Weigel. 2003. Application of a multiple-trait herd cluster model for genetic evaluation of dairy sires from seventeen countries. J. Dairy Sci. 86:376-382.
- Jamrozik, J., L. R. Schaeffer, and K. A. Weigel. 2002. Estimates of genetic parameters for single and multiple country test-day models. J. Dairy Sci. 85:3131-3141.
- Shook, G. E., K. A. Weigel, J. Sun, and J. S. Clay. 2002. Effect of data quality on genetic parameters for days to first insemination in dairy cattle. Proc. 4th World Congr. Genet. Appl. Livest. Prod., Montpellier, France, August 19-23.
- Weigel, K. A., and N. R. Zwald. 2002. Selection and grouping of herds in international genetic evaluation of daughter performance records. INTERBULL Bull. No. 29, pp. 7-11.
- Zwald, N. R., and K. A. Weigel. 2002. Examination of methods to correct for preferential treatment among AI bull dams. J. Dairy Sci. 85(Suppl. 1):32.
- Weigel, K. A. 2002. Possibilities for genetic improvement of fertility in US dairy cattle. J. Dairy Sci. (IN PRESS).
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Progress 01/01/01 to 12/31/01
Outputs
Progress in this area during the past year can be divided into several areas. In the first, international genetic evaluation of dairy sires, we conduted a very large project involving more than 16 million lactation records of Holstein cows in 17 countries. We applied a multiple-trait BLUP model, with each country considered as a different trait, and we subsequently estimated genetic correlations between countries for milk, fat, and protein. Next we calculated thirteen genetic, management, and climate variables corresponding to each herd, and we evaluated the role of each variable as an indicator of genotype by environment interaction. Herds were divided into quintiles based on each variable, and genetic covariances between quintiles were calculated. Covariance functions were applied to the resulting covariance matrices, and the difference in goodness of fit of the full model versus an intercept-only model served as a quantative measure of the usefulness of each trait.
Next we weighted each of the thirteen herd variables according to its importance, and we grouped herds into production environments (traits) without regard to country borders. Seven unique clusters of herds were formed, and we estimated genetic parameters and sire breeding values corresponding to each. In the second, inbreeding, we looked at methodology for constraining genetic relationships between young sires entering AI progeny test programs. We analyzed data from the five major dairy breeds, and determined the number of selected sires and dams that would give optimium genetic response adjusted for inbreeding. Next we evaluated the loss in response in milk yield associated with differing levels of the inbreeding constraints. In the third, we considered the application of survival analysis methods for evaluating dairy cow survival data. The relationship between type traits and survival was evaluated in Jersey cattle. In addition, the impact of herd expansions on culling in Holstein
cattle was evaluated, and a decrease in voluntary culling (due to poor milk production) over time was detected. Finally, we considered genetic evaluation of dairy cattle for male and female fertility traits. Data quality was assessed, and genetic parameters were estimated for non-return rate and days to first insemination.
Impacts
The primary impact of the international evaluation research will be improved modeling of genotype by environment interactions in international sire evaluations. This will increase the fairness and accuracy of sire evaluations and will make results more useful for dairy farmers who are interested in international genetics. The impact of the fertility and survival research will be an enhanced opportunity to breed for cattle that can survive, conceive, and prosper while simultaneously producing large quantities of milk. Improved health, animal welfare, and economic efficiency will result. Lastly, the impact of the inbreeding project will be to develop sustainable breeding programs that can yield fast genetic progress while ensuring that problems in future generations due to lack of genetic diversity will be limited.
Publications
- Weigel, K. A., R. Rekaya, N. R. Zwald, and W. F. Fikse. 2001. International genetic evaluation of dairy sires using a multiple-trait model with individual animal performance records. J. Dairy Sci. 84:2789-2795.
- Urioste, J., D. Gianola, R. Rekaya, W. F. Fikse, and K. A. Weigel. 2001. Evaluation of extent and amount of heterogeneous variance for milk yield in Uruguayan Holsteins. Anim. Sci. 72:259-268.
- Weigel, K. A. 2001. Controlling inbreeding in modern breeding programs. J. Dairy Sci. 84:E177.
- Rekaya, R., K. A. Weigel, and D. Gianola. 2001. Threshold model for misclassified binary responses: A Bayesian approach. Biometrics 57:1123-1129.
- Heringstad, B., R. Rekaya, D. Gianola, G. Klemetsdal, and K. A. Weigel. 2001. Bayesian analysis of heritability of liability to clinical mastitis in Norwegian cattle with a threshold model; Effects of data sampling method and model specification. J. Dairy Sci. 84:2337-2346.
- Zwald, N. R., K. A. Weigel, W. F. Fikse, and R. Rekaya. 2001. Characterization of dairy production systems in countries that participate in the International Bull Evaluation Service. J. Dairy Sci. 84:2530-2534.
- Rekaya, R., K. A. Weigel, and D. Gianola. 2001. Hierarchical nonlinear model for persistency of milk yield in the first three lactations of Holsteins. Livest. Prod. Sci. 68:181-187.
- Rekaya, R., K. A. Weigel, and D. Gianola. 2001. Application of a structural model for genetic covariances in international dairy sire evaluations. J. Dairy Sci. 84:1525-1530.
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Progress 01/01/00 to 12/31/00
Outputs
During the past year, we made significant progress in two areas. The first area was development of advanced models for international genetic evaluation of dairy cattle. We obtained milk production data from cows in seventeen countries, and we developed a multiple-trait herd cluster model for evaluation of this data. In this model, factors such as climate, management, and genetic background are used to group herds across country borders according to production systems. Specific management factors that cause genotype by environment can be identified, and dairy sires can receive a genetic evaluation for each type of production system that exists globally. The second area was genetic evaluation of dairy cow fertility. We developed an improved model for male fertility that accounted for previously unidentified sources of variation such as age of bull and age of bull by region interaction. We also conducted a quality control verification of the current procedure for
national genetic evaluation of bull fertility, and we investigated possible biases by time and geographical region. We also estimated genetic parameters for female fertility and published the first parameter estimates and breeding value statistics derived from US dairy cattle.
Impacts
The border-less model for international sire evaluation can have a great impact on international genetic comparisons. It is an improvement computationally, because the number of statistical parameters can be reduced dramatically as compared with the present procedure. It is also advantageous conceptually, as most people already believe that it is more appropriate to stratify herds by production system than by country borders. This procedure should result in more fair and accurate international breeding values of dairy sires. Fertility is a key topic in the dairy industry today. Although previously thought to be environmentally controlled, we showed that genetic variation exists between sire progeny groups in both male and female fertility. This research has lead to an improvement in the methods for male fertility evaluation in this country, and it has provided the framework for development of female (daughter) fertility breeding values.
Publications
- Weigel, K. A., and S. Lin. 2000. Use of computerized mating programs to control inbreeding of Holstein and Jersey cattle in the next generation. J. Dairy Sci. 83:822.
- Weigel, K. A., and R. Rekaya. 2000. Genetic parameters for reproductive traits in Holstein cattle in California and Minnesota. J. Dairy Sci. 83:1072-1080.
- Weigel, K. A., and R. L. Powell. 2000. Retrospective analysis of the accuracy of conversion equations and multiple-trait across country evaluations of Holstein bulls used internationally. J. Dairy Sci. 83:1081-1088.
- Weigel, K. A., and R. Rekaya. 2000. A multiple-trait herd cluster model for international dairy sire evaluation. J. Dairy Sci. 83:815.
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