1 / 34

An Introduction to Sire Proofs and Modeling

An Introduction to Sire Proofs and Modeling. Genetic Evaluation Advances and increases in genetic progress. Traits Evaluated by AIPL. 1 Sire calving ease evaluated by Iowa State U. 1978-1999 2 Estimated relative conception rate evaluated by DRMS@Raleigh 1986-2005. Animal Model 1989-present.

hamiltonj
Download Presentation

An Introduction to Sire Proofs and Modeling

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. An Introduction to Sire Proofs and Modeling

  2. Genetic Evaluation Advancesand increases in genetic progress

  3. Traits Evaluated by AIPL 1Sire calving ease evaluated by Iowa State U. 1978-1999 2Estimated relative conception rate evaluated by DRMS@Raleigh 1986-2005

  4. Animal Model1989-present • Introduced by: • George Wiggans and Paul VanRaden • Advantages: • Use of all relatives • Adjusts for merit of mates • Uniform procedures for males and females • Best prediction given the model (BLUP) • Revised to include crossbreds (2007)

  5. Models Borrowed from Others • Calving ease threshold model • Berger and Freeman (Iowa State, 1978) • Somatic cell score • Shook (U. WI, 1980), Boettcher et al (U. MN, 1992) • Multi-trait productive life • Weigel et al (Holstein USA, 1994) • Multi-trait linear type • Programs by Gengler (Belgium)

  6. Animal Model Terms • Predicted Transmitting Ability (PTA) is an estimate of genetic merit and is the part of an animal’s genetic makeup that is transmitted to offspring.

  7. Animal Model Terms • The amount of information about the genetic merit of dairy animals is measured by reliability. This measure of the accuracy of a genetic evaluation varies from animal to animal and ranges from 0 percent for unevaluated animals to 99 percent.

  8. Animal Model Terms • PTAs are computed relative to a zero point or genetic base. For evaluating dairy cattle, a new genetic base is calculated every five years to account for genetic change. • The current genetic base used for the underlying, unpublished all-breed PTAs sets the average PTA for each trait to zero for all cows born in 2000, regardless of breed.

  9. All Breed Animal Model • Evaluate crossbred animals without biasing purebred evaluations. • Accurately estimate breed differences. • Compute national evaluations and examine changes. • Display results without confusion.

  10. All-Breed Analyses • Crossbred animals • Will have PTAs, only 3% did before if in breed association grading-up programs. • Reliable PTAs from both parents. • Purebred animals • Information from crossbred relatives. • More herdmates (other breeds, crossbreds) • Routinely used in other populations • New Zealand (1994), Netherlands (1997) • USA goats (1989), calving ease (2005)

  11. Methods • All-breed animal model • Purebreds and crossbreds together. • Relationship matrix among all animals. • Unknown parents grouped by breed. • Data set to breed base after animal model completes. • Jersey would have poor PTA milk when compared to Holstein • Jersey would have better calving ability than other breeds

  12. Unknown Parent Groups • Look up PTAs of known parents • Estimate averages for unknowns • Group unknown parents by • Birth year • Breed • Path (dams of cows, sires of cows, parents of bulls) • Origin (domestic vs other countries)

  13. Data Extraction • Genetic Evaluations • Applying new data stopped • All required data extracted • Pedigree • Herd data • All trait specific data extracted • Genetic Evaluations started • Minimal use of database • Performance improvement • Stabilizes data connections • Applying new data resumed

  14. Animal Model Repeatability animal model: yijkl = hysi + lacj + ak + pek + β(dojk) + eijkl yijkl = persistency of milk, fat, protein, or SCS hysi = fixed effect of herd-year-season of calving I lacj = fixed effect of lactation j ak = random additive genetic effect of animal k pek = random permanent environmental effect of animal k dojk = days open for lactation j of animal k eijkl = random residual error

  15. Using DHI Data • Not all cows have the same number of test days in a lactation. • Best Prediction is one way of turning different test days into 305-day lactation totals. • Lactations longer than 305 days.

  16. Using DHI data (cont.) • All records are standardized to a twice-a-day milking frequency basis, and are adjusted to 36 months. • (2009) All lactation information is used up to 999 days. • The animal model includes up to five lactation records on cows. • Records from 1970 to present.

  17. Using DHI data (cont.) • Information for daughter pregnancy rate and somatic cell score also comes from the first five lactation records. • Daughter pregnancy rate is measured from calving intervals. • Productive life is unique in that it is expressed only once at the end of the life of a cow.

  18. Using DHI data (cont.) • The greatest challenge to any genetic evaluation system is to separate environmental effects from genetic ones. • Use contemporary groups. • Herd • Calving groups • Registered status

  19. Genetic relationships • Pedigree data is vital. • Pedigree information helps separate favorable or unfavorable permanent environmental effects. • A cow with favorable parentage may not produce very well because of permanent effects of mastitis as a young cow.

  20. Genetic relationships (cont.) • The most important genetic relationship in dairy cattle breeding is between a daughter of an AI bull and her many half-sisters distributed across many different herd environments. • Used to evaluate the bull himself and contributes to the accuracy of the PTA on each of the half-sisters as well.

  21. Genetic relationships (cont.) • It would be difficult to find a U.S. Holstein cow that is not related to Elevation or Chief, or a U.S. Jersey cow that didn’t have Duncan or Generator in her pedigree somewhere. • This means Holstein or Jersey cows are related in some way to almost all other cows in their breed.

  22. Special Procedures • Cow must have a first lactation for her to influence her relatives. • Cows may change herds or stop testing. • Bulls need to have daughters in 10 different herds to be official.

  23. Sire Proofs • Animal Model programs are run by Leigh Walton. • Domestic proofs are produced. • Bull data sent to Interbull. • Returned 2 weeks later with Multiple Across Country Evaluations (MACE).

  24. Published Proofs • Interbull data combined with US domestic data. • Highest reliability determines which evaluation is official.

  25. Data Distribution • All data distributed electronically • Web queries • Public and restricted • Computer optimized files • Bull evaluations – format 38 • Cow evaluation – format 105 • Public and restricted • Password-protected zip files

  26. Data Distribution (cont.) • Formatted reports • Complete Sire List • Elite Cow List • Active/Foreign/Genomic Lists • XML files • Schema • Style sheet

  27. Trends • AIPL Website • http://aipl.arsusda.gov/

  28. Milk (kg)Genetic trend on all-breed base

  29. Fat (kg)Genetic trend on all-breed base

  30. Protein (kg)Genetic trend on all-breed base

  31. Somatic Cell ScoreGenetic trend on all-breed base

  32. Productive LifeGenetic trend on all-breed base

  33. Daughter Pregnancy RateGenetic trend on all-breed base

  34. Questions or Comments

More Related