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Update on All-breed Model, Fertility, Genomics. Test Day Model - Potential Benefits. Increased accuracy of evaluations Account for lactation curve differences Account for genetic differences by parity Evaluate persistency, rate of maturity Include milk-only records if multi-trait
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Test Day Model - Potential Benefits • Increased accuracy of evaluations • Account for lactation curve differences • Account for genetic differences by parity • Evaluate persistency, rate of maturity • Include milk-only records if multi-trait • Possible earlier selection of bull dams • Promote as state-of-the-art system • Management effects more accurate • Could provide to DRPCs and herd owners
All-Breed PTAs – March Test Run • Genetic correlations mostly same • JE increase .02 for PL and .01 for SCS • BS decrease .01 for fat and SCS • AY increase .01 for PL • USA bulls in top 100 differ little • Numbers are averages across all scales • JE improve for SCS, fat (26 vs 25) • JE decline for milk, protein (59 vs 62) • BS decline for yield (10 vs 15) • HO improve for yield (17 vs 16)
Jersey and Swiss PTAs • Base cow means changed little • Base cow SD changed little • Top bulls for protein dropped by ~9 lbs, bottom bulls dropped by ~4 lbs in both breeds • Unknown parent grouping, heterosis may be responsible
All-breed Trend Validation • 85 tests, 6 were significant (.05) • None significant for milk or SCS • 1 of 15 for fat and for protein • 2 of 15 for PL and for DPR • Increase in DPR repeatability made trend more negative, helped tests
DPR Results – March Test Run Holstein genetic correlations March model also included an increase in repeatability
DPR - Top 100 bullsBorn in last 12 years, March 2007 test run
Bulls to Genotype60,000 SNP Project • Choose HO bulls with semen at BFGL • Genotype 1777 proven bulls • Born 1994-1996 with >75% REL NM • Plus 172 ancestor bulls born 1952-1993 • Predict 500 bulls sampled later • Born 2001 with >75% REL NM • Include other bulls in gap years? • Born 1997-2000 (proven) or >2002 (waiting)
Birth Years of Bulls to Genotype Data cutoff
Potential ResultsSimulation of 10,000 SNPs • QTLs normally distributed, n = 100 • Reliability vs parent average REL • 58% vs 36% if QTLs are between SNPs • 71% vs 36% if QTLs are located at SNPs • Higher REL if major loci and Bayesian methods used, lower if many loci (>100) affect trait
Reliability from Full SibsMarker and QTL positions identical, sib REL = 99% A = traditional additive relationships, G = genomic relationships