Hometop nav spacerAbout ARStop nav spacerHelptop nav spacerContact Ustop nav spacerEn Espanoltop nav spacer
Printable VersionPrintable Version     E-mail this pageE-mail this page
United States Department of Agriculture Agricultural Research Service
Search
 
 
 
National Programs
International Programs
Find Research Projects
The Research Enterprise
Office of Scientific Quality Review
Research Initiatives
 

Title: Estimates of (co) variance components using random regression models for gastrointestinal helminthoid data in Angus cattle.

Authors
item Silva, Marcos
item Van Tassell, Curtis
item Sonstegard, Tad
item Cobuci, Jaime - EMBRAPA
item Gasbarre, Louis

Submitted to: Brazilian Animal Science Society
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 27, 2008
Publication Date: July 24, 2008
Citation: Silva, M.V., Van Tassell, C.P., Sonstegard, T.S., Cobuci, J.A., Gasbarre, L.C. 2008. Estimates of (co) variance components using random regression models for gastrointestinal helminthoid data in Angus cattle. Brazilian Animal Science Society. CD-ROM MEL036. p. 105.

Technical Abstract: A total of 6,375 FEC measures were determined for 409 animals between 1992 and 2003 from Beltsville Agricultural Research Center Wye Angus herd. Original data were transformed using an extension of the Box-Cox transformation to approach normality and to estimate (co)variance components. The Random Regression Models (RRM) may be used as a new tool for genetic and non-genetic studies of FEC. Phenotypes were analyzed using RRM, by the Restricted Maximum Likelihood method. Within the different orders of Legendre polynomials used, those with more parameters (order 4) adjusted FEC data best. Results indicated that the Box-Cox transformation has direct influence on the (co)variances and genetic parameters and the lambda value, estimated by ML, is the more accurate.

   
 
 
Last Modified: 05/24/2013
ARS Home | USDA.gov | Site Map | Policies and Links 
FOIA | Accessibility Statement | Privacy Policy | Nondiscrimination Statement | Information Quality | USA.gov | White House