spline; legendre polynomials; random regression test-day model
Abstract :
[en] Random regression test-day models using Legendre polynomials are commonly used for the estimation of genetic parameters and genetic evaluation for test-day milk production traits. However, some researchers have reported that these models present some undesirable properties such as the overestimation of variances at the edges of lactation. Describing genetic variation of saturated fatty acids expressed in milk fat might require the testing of different models. Therefore, 3 different functions were used and compared to take into account the lactation curve: (1) Legendre polynomials with the same order as currently applied for genetic model for production traits; 2) linear splines with 10 knots; and 3) linear splines with the same 10 knots reduced to 3 parameters. The criteria used were Akaike’s information and Bayesian information criteria, percentage square biases, and log-likelihood function. These criteria indentified Legendre polynomials and linear splines with 10 knots reduced to 3 parameters models as the most useful. Reducing more complex models using eigenvalues seemed appealing because the resulting models are less time demanding and can reduce convergence difficulties, because convergence properties also seemed to be improved. Finally, the results showed that the reduced spline model was very similar to the Legendre polynomials model.
Disciplines :
Genetics & genetic processes Animal production & animal husbandry
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
Akaike H. Information theory and an extension of the maximum likelihood principle. 2nd Int. Symp. Information Theory 1973, 267-281. Akademiai Kiado, Budapest, Hungary. B.N. Petrov, F. Csaki (Eds.).
Ali T.E., Schaeffer L.R. Accounting for covariances among test day milk yields in dairy cows. Can. J. Anim. Sci. 1987, 67:637-644.
Arnould V.M.-R., Soyeurt H. Genetic variability of milk fatty acids. J. Appl. Genet. 2009, 50:29-39.
Bohmanova J., Miglior F., Jamrozik J., Misztal I., Sullivan P.G. Comparison of random regression models with Legendre polynomials and linear splines for production traits and somatic cell score of Canadian Holstein cows. J. Dairy Sci. 2008, 91:3627-3638.
Druet T., Jaffrezic F., Boichard D., Ducrocq V. Modeling lactation curves and estimation of genetic parameters for first lactation test-day records of French Holstein cows. J. Dairy Sci. 2003, 86:2480-2490.
Gengler N., Wiggans G. Heterogeneity in (co)variances structures of test-day yields. Interbull Bull. 2001, 27:179-184.
Jamrozik J., Bohmanova J., Schaeffer L.R. Selection of locations of knots for linear splines in random regression test-day models. J. Anim. Breed. Genet. 2010, 127:87-92.
Jamrozik, J., and L. R. Schaeffer. 2002. Bayesian comparison of random regression models for test-days yield in dairy cattle; Session 01, Breeding ruminants for milk production. Commun. No. 01-03 in 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France.
López-Romero P., Rekaya R., Carabano M.J. Bayesian comparison of test-day models under different assumptions of heterogeneity for the residual variance: The change point technique versus arbitrary intervals. J. Anim. Breed. Genet. 2004, 121:14-25.
Meyer K. Random regression analyses using B-splines to model growth of Australian Angus cattle. Genet. Sel. Evol. 2005, 37:473-500.
Misztal I. Properties of random regression models using linear splines. J. Anim. Breed. Genet. 2006, 123:74-80.
Misztal, I. 2007. BLUPF90 family of programs. University of Georgia. Accessed Jan. 2, 2007. http://nce.ads.uga.edu/~ignacy/numpub/blupf90/.
Soyeurt H., Dardenne P., Dehareng F., Bastin C., Gengler N. Genetic parameters of saturated and monounsaturated fatty acid content and the ratio of saturated to unsaturated fatty acids in bovine milk. J. Dairy Sci. 2008, 91:3611-3626.
Stoop W.M., Bovenhuis H., Heck J.M.L., van Arendonk J.A.M. Effect of lactation stage and energy status on milk fat composition of Holstein-Friesian cows. J. Dairy Sci. 2009, 92:1469-1478.
Torres R.A.A., Quaas R.L. Determination of covariance functions for lactation traits on dairy cattle using random-coefficient regressions on B-splines. J. Anim. Sci. 2001, 79(Suppl. 1):112. (Abstr.).
Wilmink J.B.M. Adjustment of test-day milk, fat and protein yields for age, season and stage of lactation. Livest. Prod. Sci. 1987, 16:335-348.
Similar publications
Sorry the service is unavailable at the moment. Please try again later.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.