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Sire Fertility & Genomics

Learn about the history and benefits of sire fertility evaluations and genomic testing in the dairy industry. Discover how these advancements can help improve breeding decisions and increase conception rates.

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Sire Fertility & Genomics

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  1. Sire Fertility & Genomics

  2. Service-sire fertility – history • Estimated relative conception rate (ERCR) • 70-day nonreturn rate (NRR) • Source: • Dairy Records Management Systems, 1986−2005 • USDA, 2006−2008 • Western Bull Fertility Analysis • 75-d confirmed conception rate (CR) • Source: AgriTech Analytics, 2003−present

  3. Sire conception rate (SCR) • New USDA fertility evaluation • Initiated Aug. 2008 • Based on confirmed Conception Rate • Why is SCR most accurate? • Inseminations from ~80% of DHI herds in US • Most services (not just first) • More effects accounted for

  4. Data included • Only AI inseminations with confirmed pregnancy • Service numbers 1–7 for cows in lactations 1–5 • Inseminations between 30 and 365 days in milk • Minimum standardized (ME) milk yield • >10,000 lb for Holsteins • >8,000 lb for Brown Swiss • >6,000 lb for all other breeds

  5. Data included (cont.) • Most recent 4 yr. of inseminations • The 6 traditional dairy breeds • Ayrshire • Brown Swiss • Guernsey • Holstein • Jersey • Milking Shorthorn

  6. Data excluded • Embryo-transfer donors • Services with sexed semen • Heifer services • If services within 10 d of each other, only the later of the 2 used

  7. Data excluded (cont.) • Herd with ≥50% of milking cows without recorded breeding • Herd with average CR <10% or >90% • Service sire <0.8 yr old • Services just prior to submission deadline (<70 d)

  8. Data sources • 3 dairy records processing centers contributed >99% of data • AgriTech Analytics: California • AgSource Cooperative Service: Wisconsin • Dairy Records Management Systems: North Carolina • 46 States and Puerto Rico

  9. Development of SCR • 4-year research effort prior to implementation – primarily by Dr. Melvin Kuhn • Bull variables (expanded service-sire effects) • Cow variables (nuisance variables)

  10. Bull variables • Inbreeding coefficient of: • Service sire • Embryo • Bull age • Combined AI organization x mating year • Bull

  11. Cow variables • Combined herd, mating year, cow parity, and cow registry status • Combined mating month, year, and State • Cow parity • Service number • Short interval between matings • Cow age • Cow standardized milk yield • Cow’s permanent environment • Cow’s genetics

  12. SCR model Hoard’s Dairyman “The most complex model that I know of to evaluate animal performance” — Bennet Cassell, VPISU, 2008

  13. Accuracy of SCR • Reliability (Rel.) = n/(n + 260) • n = number of inseminations • Constant 260 was needed based of variance components estimated for this model • Confidence interval (CI) = • 0.02313 = true standard deviation • 1.282 = standard normal variate from normal distribution for an 80% CI

  14. Relationship of Rel. and 80% CI

  15. SCR published • Released 3 times a year with USDA genetic evaluation runs • January • April • August • Eligible AI bulls • Active AI • Progeny test

  16. SCR published (cont.) • Overall matings • Holstein ≥300 in ≥10 herds • Ayrshire, Brown Swiss, ≥200 in ≥5 herds Guernsey, Jersey • Matings during current 12 mo • Holsteins, Jersey ≥100 • Ayrshires, Brown Swiss, ≥30 Guernsey

  17. Interpretation of SCR • Predictor of bull fertility • Indicates Conception Rate • Reported as a percentage • Average bulls SCR is 0.0% • Standard deviation for SCR is 2.4%, 2/3 of bulls between -2.4% and +2.4%

  18. Examples • Bull with SCR of +3.0% should provide a 3% higher CR than an average bull (SCR of 0.0), and 6% higher CR than a bull with SCR of −3.0% • Bull with SCR of +3.0% expected to provide a CR of 33% in herd that has been averaging 30% and has been using “average” fertility bulls

  19. Herd & service sire fertility • Relationship between fertility of herd and bull SCR when examined together • Herd-years stratified into 3 equally sized groups by CR • ≤27.3% Low fertility • 27.4 to 33.9% Medium fertility • ≥34.0% High fertility • Bulls stratified into 3 equally sized groups by SCR • ≤−0.9% Low fertility • −0.8 to +1.0% Medium fertility • ≥+1.1% High fertility

  20. Herd CR (August 2008)

  21. Conclusions • SCR more accurate because it uses more inseminations • More DHI herds • Extra services (2–7) • More complete model

  22. Genomics, genomics,genomics You 9 What?

  23. Genomic evaluations – history • Illumina BovineSNP50 BeadChip developed (December 2007) • Unofficial genomic evaluations for Holsteins initiated (April 2008); computed every 2 mo. • Owner letters and computer files distributed by AIPL • Unofficial genomic evaluations for Jerseys (Oct. 2008)

  24. Genomic eval. – history(cont.) • Brown Swiss genomics tested; negotiations started to exchange Brown Swiss genotypes with Switzerland (Oct. 2008) • Unofficial genomic evaluations for Brown Swiss (Dec. 2008) • Over 22,000 animals genotyped (Feb. 2009) • Genomic evaluations become official (Jan. 2009) • Owner letters and computer files distributed by breed associations and NAAB

  25. Genomic evaluations: what’s it about? • DNA extracted from blood, hair, or semen • ~40,000 genetic markers (SNPs) tested simultaneously (about 1/2 cent per test) • Value of each SNP determined by examining how each SNP impacts “tested animal” performance • The payoff is to genotype young animals and apply the prediction equations • Genomic evaluation combines SNP effects with traditional PA or PTA

  26. Genomic vs. traditional PTA • Genotype is additional source of information (like parents, progeny, and animal records) • For each animal, the genomic test is used to calculate genomic evaluations for all 29 traits • Genomic evaluation interpreted the same as traditional PTA • Expected to increase genetic improvement by 50% with decreased generation interval. Also, genomics contributes ~15 daughter equivalents to a bull

  27. Genomic evaluations – Jan. 2009 • Genomic evaluations became official • Genotyped ancestors contribute their evaluations to descendents, not the reverse • Evaluations of all genotyped females are public • Evaluations of genotyped males either enrolled with NAAB or ≥24 mo. are public • Young-bull genomic evaluations may be shared among AI organizations or disclosed by owner

  28. Genomic eval. – producer impact • Young-bull genomic evaluations have accuracy approaching 1st-crop daughter evaluations (60-70% Reliability) • AI organizations have started marketing genomically evaluated 2-year-old bulls • AI organizations are requiring genotypes on potential bull dams • Progeny-test programs are changing

  29. Genomic evaluations – schedule • Genomic evaluations provided at each of the 3 annual traditional genetic runs: January, April, and August • Genomic evaluations on new bulls provided once or twice between traditional runs • We re-estimate SNP predictors when large numbers of predictor animals added

  30. Genomic eval. – increase accuracy? • Genotyping more predictor bulls (most active-AI bulls expected to be genotyped soon) • Should reach 1,500 Brown Swiss through foreign collaboration • Aggressive genotyped in domestic Jerseys underway, foreign collaboration is likely • Investigate across-breed analysis to see if Holstein data helps accuracy for Jerseys and Brown Swiss

  31. How animals get genotyped • Participating AI organizations have 5-year exclusive right to evaluate bulls genomically • Each AI organization genotypes 1st-choice flushes, thus avoiding duplicate genotyping • Web-based system being developed to show activity • Should help avoid expensive duplication • Breed associations developing cow genotyping service

  32. On horizon: Low-cost genotyping • Developing a new genomic test, inexpensive enough to use for most animals • 384 SNPs proposed for new genomic test • Will provide parentage verification/discovery • Will provides a genetic estimate accurate enough for 1st-stage screening

  33. Implications for dairy industry • Rapid acceptance and use of genomic evaluations • Young-bull acquisition and marketing now based on genomic evaluations • Diversity of bull dams needs considered • Industry groups taking responsibility for genotyping and validation

  34. International implications • All major dairy countries reviewing genomic • Interbull discussing how genomic evaluations should be integrated • Balance needed between treating genotypes as proprietary versus open sharing • Some importing countries might change rules to allow young genomic-tested bull use

  35. Fertility & genomics Over- load !

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