2001 ASAS/ADSA 2001 Conference (1) Simultaneous accounting for heterogeneity of (co)variance components in genetic evaluation of type traits N. Gengler 1,2, G.R. Wiggans *,3, J.R. Wright 3, and T. Druet 1,2 1 Gembloux Agricultural University, Belgium 2 National Fund for Scientific Research, Brussels, Belgium 3 Agricultural Research Service, USDA, Beltsville, MD

2001 ASAS/ADSA 2001 Conference (2) USDA Type evaluation  Breeds:  Ayrshire  Brown Swiss  Guernsey  Jersey  Milking Shorthorn  Red and White  15 Linear traits and Final Score  Multi-trait Model  Canonical Transformation  Estimation of missing values

2001 ASAS/ADSA 2001 Conference (3) Heterogeneity of (co)variance  Variances and (co)variances assumed constant across herds and time  Holstein Association accounts for heterogeneity in final score  Variance tends to decrease with increasing herd average final score  Changes in appraisal program over time can be a cause

2001 ASAS/ADSA 2001 Conference (4) Methods of HV estimation  Holstein Association estimates phenotypic variance using combination  observed variance  predicted variance from model including – mean final score – registry status – number of appraisals for herd-classification date  Meuwissen proposed simultaneous estimation of variances and breeding values  expected to improve accuracy of both estimates

2001 ASAS/ADSA 2001 Conference (5) USDA HV adjustment system  Apply Canonical transformation to linear traits  Prepare data for 2 models  score = herd-sire + herd-year-season-parity + parity-time_period-age + parity-time_period-stage  var = mean + parity-group_size + parity- herd_mean_final_score + parity-year-season + herd-year-month

2001 ASAS/ADSA 2001 Conference (6) Variance model  Herd-year-month effect in variance model random  Regression of variances within parity toward population mean (fixed effects)  Method R estimation of variance ratios within each EBV iteration

2001 ASAS/ADSA 2001 Conference (7) Computational requirements February 2001 Jersey Evaluation Run number of rounds time required (hr) no HV4213 with HV4326.5

2001 ASAS/ADSA 2001 Conference (8) Iteration  Time increased due to  iteration for variance model  estimation of variance ratios  Convergence sensitive to  quality of the starting values  size of unknown parent groups  Ways of reducing processing time  parallel processing  using a faster computer  limiting iteration for variances

2001 ASAS/ADSA 2001 Conference (9) Correlations between HV- adjusted official evaluations  February 2001 official evaluations  2497 Jersey AI bulls born  All correlations.989 or higher

2001 ASAS/ADSA 2001 Conference (10) Change in slope of PTA for Jersey AI bulls Trait slope official slope HV adjdiff pct diff Dairy form Rear udder height Front teat place Strength Fore udder attach Teat length Udder Depth

2001 ASAS/ADSA 2001 Conference (11) Differences between HV-adjusted and official evaluation  Standard deviations & mean absolute values  increased as reliabilities increased to 80%  decreased slightly for reliabilities of > 90%  Mean differences largest for bulls  born from 1985 through 1994  with lowest mean daughters final scores

2001 ASAS/ADSA 2001 Conference (12) Mendelian sampling  Mendelian sampling  evaluation minus mean of parent evaluations  Jersey cows born from 1984 through 1998  regression of SD on birth year

2001 ASAS/ADSA 2001 Conference (13) Mendelian sampling Trait slope official slope HV adjdiff pct diff Foot angle Rump angle Rear udder height Teat placement Stature

2001 ASAS/ADSA 2001 Conference (14) Interbull trend validation  Trend tests conducted for  Stature  Udder support  Comparison  first parity v. all parities  without recent years v. all data  Trend differences within tolerance

2001 ASAS/ADSA 2001 Conference (15) HV model implementation plans  Jersey - May 2001  Other breeds by Feb 2002

2001 ASAS/ADSA 2001 Conference (16) Conclusions  Simultaneous estimation of BV & Variances possible  Requires substantially more computer time  Improves stability of Mendelian Sampling Variance over time  HV adjusted EBV enable more accurate selection decisions