Premium
Stability of genetic parameter estimates for production traits in pigs
Author(s) -
Wolf J.,
Peškovičová D.,
Groeneveld E.
Publication year - 2001
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1046/j.1439-0388.2001.00288.x
Subject(s) - heritability , statistics , breed , zoology , trait , biology , large white , covariance , variance components , residual , mathematics , genetics , algorithm , computer science , programming language
Changes in variance component estimates in growing sets of performance data in two pig breeds were investigated. Data was used from the field and station test of Czech Landrace (LA: 75 099 observations) and the Slovakian breed, White Meaty swine (WM: 32 203 observations). In LA the traits analysed were estimated lean meat content (LM) and average daily gain (ADGF) on field test and average daily gain (ADGS) and weight of valuable cuts (VCW) on station test. In WM the traits analysed were backfat thickness on field and station test (BFF, BFS, respectively), proportion of valuable cuts (VCP) on station test, ADGF and ADGS. Covariance components were estimated from four‐ and five‐trait animal models using the VCE software. Omitting data from factor levels with a low number of records led to 4.2% of LA records and 21.7% of WM records being deleted. Changes in genetic and residual variance estimates were less than 5% for all traits in LA and less than 12% for all traits except ADGS in WM. The changes in estimated genetic variances caused by 18 months (LA) or 24 months (WM) of new data were 2–25% and the changes in estimated residual variances were less than 5% in LA and less than 20% in WM. In both breeds, changes in heritability estimates did not exceed 0.06 in absolute value. In LA, it is reasonable to use genetic parameter estimates for 3 years before re‐estimation. In WM the time interval should be shorter because of changes in the estimates caused by their lower accuracy arising from the smaller size of the data‐set and smaller frequency of station testing.