ࡱ> FHEyE@$wbjbj$x````l!l!l!l!"\6"####%%%5555555$7Rd9v5E*%"%**5``##6,,,*`8##5,*5,,--#" l! ,|-1,60\6-9,X9-````-9-%d',(($%%%55$ ,ESTIMATION OF GENETIC PARAMETERS FOR NUMBER OF PIGLETS BORN ALIVE USING RANDOM REGRESSION MODEL LUKOVI Z., , GORJANC G., MALOVRH `., KOVA  M. University of Ljubljana, Biotechnical Faculty, Ljubljana, Slovenia University of Zagreb, Faculty of Agriculture, Zagreb, Croatia Keywords: random regression model, genetic parameters, litter size, pigs INTRODUCTION In conventional animal breeding, both repeatability and multitrait (MT) models have been applied to analyze traits that are measured more than once in lifetime. More appropriate way of dealing with these traits is to fit a set of random regression (RR) coefficients describing production over time for each animal to allow individual variation in the course of trajectory (1). In pigs, RR models were mainly used for growth and feed intake. Although litter size differs from fattening traits, some authors suggest that RR models could be applied in selection on this trait (2, 3). Possible genetic differences in litter size along the trajectory could be identified using RR models and something like persistency in litter size could be used to select breeding sows (3). The aim of this paper was to estimate and compare dispersion parameters from MT and RR models with Legendre (LG) polynomials of different order for number of piglets born alive (NBA). MATERIAL AND METHODS Data from 98,359 litters from first to sixth parity, collected between years 1990 and 2002 at farm Nema ak, were analyzed. The pedigree file contained 28,055 sows with records and 6,733 ancestors. Fixed part of the models was developed with the SAS package (4). In MT, model 1 was used for gilts and model 2 for sows after first parity. Model 3 was used in RR analysis for sows together.  EMBED Equation.3 (1) EMBED Equation.3 (2) EMBED Equation.3 (3)where  EMBED Equation.3  is NBA,  EMBED Equation.3 ,  EMBED Equation.3 ,  EMBED Equation.3  and  EMBED Equation.3 are fixed effects of breed, mating season, parity and weaning to conception interval, respectively. For MT models age at farrowing ( EMBED Equation.3 ) was adjusted by quadratic regression for each trait separately, while it was nested within parity in RR model. Previous lactation length ( EMBED Equation.3 ) was fitted as linear regression. Random part of the MT models consisted of direct additive genetic ( EMBED Equation.3 ) and common litter effect ( EMBED Equation.3 ). RRs described by LG polynomials of standardized parity ( EMBED Equation.3 ) were included for direct additive genetic, permanent environmental and common litter effect. LG polynomials from the first (LG(1)) to third order (LG(3)) were fitted. Estimation of (co)variance components from MT and RR models was based on REML method using the VCE5 software package (5). Modul SAS/IML was used for computation of eigenvalues for covariance matrices of regression coefficients to quantify contribution of higher order of LG polynomials. RESULTS AND DISCUSSION Estimated phenotypic variances and ratios were approximately the same between models. Eigenvalues of genetic covariance function show that the constant term accounted for around 93-95% of total genetic variability for NBA. This means that 5-7% of variability in our study is covered by different production curves of sows. Computing time needed for MT analyze was about five times longer than for RR model with LG3. Table1. Estimated variance and ratios in multitrait and random regression models Multitrait modelRandom regression with LG3ParityVar(ph)hlVar(ph)hlp17.850.1020.0217.870.1010.0210.05427.380.1300.0307.390.1250.0130.07037.990.1120.0078.030.1230.0050.07948.190.1110.0268.200.1180.0050.09158.250.1230.0398.270.1180.0100.09968.540.1180.0348.560.1190.0200.099 Table 2. Eigenvalues of estimated covariance matrices of RR coefficients as proportion (%) in the total variability for random effects Direct AdditivePerm. EnvironmentalCommon litterEigenvaluesLG(1)LG(2)LG(3)LG(1)LG(2)LG(3)LG(1)LG(2)LG(3)0th95.0494.6193.6091.4591.1390.8565.8063.2559.711st4.964.455.088.558.869.1434.2036.7534.402nd0.931.310.010.010.005.883rd0.010.000.01 REFERENCES 1. Meyer, K., 1998. Modeling repeated records: covariance functions and random regression models to analyse animal breeding data. In: Proc. 6th WCGALP 25:517-520, Armidale, Australia. 2. Schaeffer, L.R., 1994. Application of Random Regression Models in Animal Breeding.  HYPERLINK "http://www.aps.uoguelph.ca~lrs/ANSC637/LRS14/" http://www.aps.uoguelph.ca~lrs/ANSC637/LRS14/ (25.10.2002.) 3. Huisman, A.E., 2002. Genetic analysis of growth and feed intake patterns in pigs. Ph.D. Thesis, Wageningen Institute of Animal Sciences, The Netherlands. 4. SAS Inst. Inc. (2001). The SAS System for Windows, Release 8.02. Cary, NC. 5. 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