Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-26T04:09:50.398Z Has data issue: false hasContentIssue false
  • English
  • Français

Bayesian prediction of breeding values for multivariate binary and continuous traits in simulated horse populations using threshold–linear models with Gibbs sampling

Published online by Cambridge University Press:  01 January 2008

K. F. Stock ,
O. Distl  and
I. Hoeschele
K. F. Stock*
Affiliation:
Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover (Foundation), Bünteweg 17p, D-30559 Hannover, Germany
O. Distl
Affiliation:
Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover (Foundation), Bünteweg 17p, D-30559 Hannover, Germany
I. Hoeschele
Affiliation:
Virginia Bioinformatics Institute and Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
*
E-mail: [email protected]
Get access

Abstract

Simulated data were used to determine the properties of multivariate prediction of breeding values for categorical and continuous traits using phenotypic, molecular genetic and pedigree information by mixed linear–threshold animal models via Gibbs sampling. Simulation parameters were chosen such that the data resembled situations encountered in Warmblood horse populations. Genetic evaluation was performed in the context of the radiographic findings in the equine limbs. The simulated pedigree comprised seven generations and 40 000 animals per generation. The simulated data included additive genetic values, residuals and fixed effects for one continuous trait and liabilities of four binary traits. For one of the binary traits, quantitative trait locus (QTL) effects and genetic markers were simulated, with three different scenarios with respect to recombination rate (r) between genetic markers and QTL and polymorphism information content (PIC) of genetic markers being studied: r = 0.00 and PIC = 0.90 (r0p9), r = 0.01 and PIC = 0.90 (r1p9), and r = 0.00 and PIC = 0.70 (r0p7). For each scenario, 10 replicates were sampled from the simulated horse population, and six different data sets were generated per replicate. Data sets differed in number and distribution of animals with trait records and the availability of genetic marker information. Breeding values were predicted via Gibbs sampling using a Bayesian mixed linear–threshold animal model with residual covariances fixed to zero and a proper prior for the genetic covariance matrix. Relative breeding values were used to investigate expected response to multi- and single-trait selection. In the sires with 10 or more offspring with trait information, correlations between true and predicted breeding values ranged between 0.89 and 0.94 for the continuous traits and between 0.39 and 0.77 for the binary traits. Proportions of successful identification of sires of average, favourable and unfavourable genetic value were 81% to 86% for the continuous trait and 57% to 74% for the binary traits in these sires. Expected decrease of prevalence of the QTL trait was 3% to 12% after multi-trait selection for all binary traits and 9% to 17% after single-trait selection for the QTL trait. The combined use of phenotype and genotype data was superior to the use of phenotype data alone. It was concluded that information on phenotypes and highly informative genetic markers should be used for prediction of breeding values in mixed linear–threshold animal models via Gibbs sampling to achieve maximum reduction in prevalences of binary traits.

Keywords

genetic markersGibbs samplingmultivariate polygenic breeding valuesselectionthreshold model
Type
Full Paper
Information
animal , Volume 2 , Issue 1 , January 2008 , pp. 9 - 18
Copyright
Copyright © The Animal Consortium 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdel-Azim, GA, Berger, PJ 1999. Properties of threshold model predictions. Journal of Animal Science 77, 582–590.Google Scholar
Abdel-Azim, G, Freeman, AE 2002. Superiority of QTL-assisted selection in dairy cattle breeding schemes. Journal of Dairy Science 85, 1869–1880.Google Scholar
Abdel-Azim, G, Freeman, AE 2003. Effects of including quantitative trait locus in selection under different waiting plans of young bulls. Journal of Dairy Science 86, 667–676.Google Scholar
Dekkers, JCM 2004. Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons. Journal of Animal Science 82 (E Suppl), E313–E328.Google Scholar
Dierks C, Löhring K, Lampe V, Wittwer C, Drögemüller C and Distl O 2007. Genome-wide search for markers associated with osteochondrosis in Hanoverian Warmblood horses. Mammalian Genome 18, early online (doi:10.1007/s00335-9058-9).Google Scholar
Dik, KJ, Enzerink, E, Van Weeren, PR 1999. Radiographic development of osteochondral abnormalities, in the hock and stifle of Dutch Warmblood foals, from age 1 to 11 months. Equine Veterinary Journal Supplements 31, 9–15.Google Scholar
Foulley, JL 1992. Prediction of selection response for threshold dichotomous traits. Genetics 132, 1187–1194.Google Scholar
Harville, DA, Mee, RW 1984. A mixed-model procedure for analyzing ordered categorical data. Biometrics 40, 393–408.Google Scholar
Janss, LLG, Foulley, JL 1993. Bivariate analysis for one continuous and one threshold dichotomous trait with unequal design matrices and an application to birth weight and calving difficulty. Livestock Production Science 33, 183–198.Google Scholar
Jensen J 1994. Bayesian analysis of bivariate mixed models with one continuous and one binary trait using the Gibbs Sampler. In Proceedings of the Fifth World Congress on Genetics Applied Livestock Production, 7–12 August 1994, Guelph, vol. 18, pp. 33–336.Google Scholar
Kadarmideen, HN, Janss, LLG 2005. Evidence of a major gene from Bayesian segregation analyses of liability to osteochondral diseases in pigs. Genetics 171, 1195–1206.Google Scholar
Kroll, A, Hertsch, B, Höppner, S 2001. Entwicklung osteochondraler Veränderungen in den Fessel- und Talokruralgelenken im Röntgenbild beim Fohlen. Pferdeheilkunde 17, 489–500.Google Scholar
Lande, R, Thompson, R 1990. Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124, 743–756.CrossRefGoogle ScholarPubMed
Luo, MF, Boettcher, PJ, Schaeffer, LR, Dekkers, JCM 2001. Bayesian inference for categorical traits with an application to variance component estimation. Journal of Dairy Science 84, 694–704.Google Scholar
Mäntyssari, EA, Quaas, RL, Gröhn, YT 1991. Simulation study on covariance component estimation for two binary traits in an underlying continuous scale. Journal of Dairy Science 74, 580–591.Google Scholar
Matos, CAP, Thomas, DL, Gianola, D, Tempelman, RJ, Young, LD 1997. Genetic analysis of discrete reproductive traits in sheep using linear and nonlinear models. I. Estimation of genetic parameters. Journal of Animal Science 75, 76–87.Google Scholar
Meijering, A, Gianola, D 1985. Linear versus nonlinear methods of sire evaluation for categorical traits: a simulation study. Genetic Selection Evolution 17, 115–132.Google Scholar
Meuwissen, THE, Van Arendonk, JAM 1992. Potential improvement in rate of genetic gain from marker-assisted selection in dairy cattle breeding schemes. Journal of Dairy Science 75, 1651–1659.Google Scholar
Phocas, F, Laloë, D 2003. Evaluation models and genetic parameters for calving difficulty in beef cattle. Journal of Animal Science 81, 933–938.Google Scholar
Raftery, AE, Lewis, SM 1992. How many iterations in the Gibbs sampler? In Bayesian Statistics (ed. JM Bernardo, JO Berger, AP Dawid and AFM Smith), vol. 4, pp. 763–773. Oxford University Press, Oxford, UK.Google Scholar
Statistical Analysis Systems Institute 2006. SAS/STAT user’s guide, version 9.1.3. Cary, NC, USA.Google Scholar
Stock, KF, Distl, O 2005a. Prediction of breeding values for osseous fragments in fetlock and hock joints, deforming arthropathy in hock joints and pathologic changes in navicular bones of Hanoverian Warmblood horses. Livestock Production Science 92, 77–94.Google Scholar
Stock, KF, Distl, O 2005b. Expected response to selection when accounting for orthopedic health traits in a population of Warmblood riding horses. American Journal of Veterinary Research 66, 1371–1379.Google Scholar
Stock, KF, Distl, O 2006a. Correlations between sport performance and different radiographic findings in the limbs of Hanoverian Warmblood horses. Animal Science 82, 83–93.Google Scholar
Stock, KF, Distl, O 2006b. Genetic correlations between osseous fragments in fetlock and hock joints, deforming arthropathy in hock joints and pathologic changes in the navicular bones of Warmblood riding horses. Livestock Production Science 104 (available online, doi:10.1016/j.livsci.2006.04.027).Google Scholar
Stock, KF, Distl, O 2006c. Genetic analyses of radiographic appearance of the navicular bones in the Warmblood horse. American Journal of Veterinary Research 67, 1013–1019.Google Scholar
Stock, KF, Distl, O, Hoeschele, I 2007. Bayesian estimation of genetic parameters for multivariate threshold and continuous phenotypes and molecular genetic data in simulated horse populations using Gibbs sampling. BMC Genetics 8, 19.CrossRefGoogle ScholarPubMed
Van Tassell, CP, Van Vleck, LD 1996. Multiple-trait Gibbs sampler for animal models: flexible programs for Bayesian and likelihood-based (co)variance component inference. Journal of Animal Science 74, 2586–2597.CrossRefGoogle ScholarPubMed
Van Vleck, LD 1972. Estimation of heritability of threshold characters. Journal of Dairy Science 55, 218–225.CrossRefGoogle Scholar
Van Vleck, LD, Gregory, KE 1992. Multiple-trait restricted maximum likelihood for simulated measures of ovulation rate with underlying multivariate normal distributions. Journal of Animal Science 70, 57–61.Google Scholar
Varona, L, Misztal, I, Bertrand, JK 1999. Threshold-linear versus linear-linear analysis of birth weight and calving ease using an animal model: II. Comparison of models. Journal of Animal Science 77, 2003–2007.Google Scholar
Wang, CS, Quaas, RL, Pollak, EJ 1997. Bayesian analysis of calving ease scores and birth weights. Genetic Selection Evolution 29, 117–143.Google Scholar
Willms, F, Röhe, R, Kalm, E 1999. Genetische Analyse von Merkmalskomplexen in der Reitpferdezucht unter Berücksichtigung von Gliedmaßenveränderungen. 1. Mitteilung: Züchterische Bedeutung von Gliedmaßenveränderungen. Züchtungskunde 71, 330–345.Google Scholar
Winter, D, Bruns, E, Glodek, P, Hertsch, B 1996. Genetische Disposition von Gliedmaßenerkrankungen bei Reitpferden. Züchtungskunde 68, 92–108.Google Scholar