USDA Genetic Evaluation Program for Dairy Goats

1. USDA Genetic Evaluation Program for Dairy Goats

2. Why Genetic Evaluations? • A valuable tool for genetic selection • Allows for comparison of animals in different environments • Can include all of the information available for each animal • Greatest impact on progress is from selection for males

3. Why Genetic Selection? • Genetic selection can improve fitness, utility, and profitability • Females must be bred to provide replacements and initiate milk production • Mate selection is an opportunity to make genetic change

4. Selection is a Continuous Process • Decisions • Which females to breed • Which males to use • Which specific matings to make • Which progeny to raise • Which females to keep and breed • Goals • Improve production and efficiency • Avoiding inbreeding • Correct faults

5. Genetic Improvement Program Phenotype = Genotype + Environment • Genetic improvement programs only change genotype • Rate of genetic improvement determined by: • Generation interval • Selection intensity • Heritability • Heritability is the portion of total variation due to genetics

6. Steps in Genetic Evaluation • Define a breeding goal • Measure traits related to the goal • Record pedigree to allow detection of relationships across generations • Identify non-genetic factors that affect records and could bias evaluations • Make adjustments • Include in the model • Define an evaluation model

7. Examples of Breeding Goals • Increased milk, fat, or protein yield • Increased longevity • Optimal number of kids born • Improved conformation score (overall and linear) • Increased profitability

8. Examples of Non-genetic Factors • Age • Lactation • Season • Litter size • Milking frequency • Herd

9. Data Flow Milk Data collected monthly COMPONENT TEST LAB FARM DHIA DRPC ADGA INTERNET AIPL

10. Does on Test at Last Test in 2005By Processing Center Source: DHI Report K-6, 2006 Table 6 Available: http://aipl.arsusda.gov/publish/dhi/current/drpcx.html

11. Data Validation • Incoming data is checked against database for verification • Birth date is checked against kidding date • Sire and dam are checked against breeding records and ADGA • Cross-references are assigned when identification changes

12. Data Validation (Cont.) • Cross-references are determined based on control number • Abnormal yields are detected and reported to DRPC • Test dates and testing characteristics are compared with herd data

13. Lactation 1 Lactation 2 Lactation 3 Alpine Milk ProductionLactation Curve

14. Lactation 1 Lactation 2 Lactation 3 Alpine Fat PercentageLactation Curve

15. Lactation 1 Lactation 2 Lactation 3 Alpine Protein PercentageLactation Curve

16. Alpine and Nubian Milk Production Second Lactation Alpine Nubian

17. Nubian Fat and Protein PercentageSecond Lactation Fat Protein

18. Evaluation Calculation • Goal • Predict productivity of progeny • Method • Separate genetic component from other factors influencing evaluated traits • All relationships are considered • Bucks receive evaluations from the records on their female relatives

19. Evaluation model • An equation that indicates what factors contribute to an observation • Separates the genetic component from other factors • Solutions used to predict the genetic potential of progeny

20. Yield Model: y = hys + hs + pe + a + e y = yield of milk, fat, or protein during a lactation hys = herd-year-season Environmental effects common to lactations in the same season, within a herd hs = herd-sire Effects common to daughters of the same sire, within a herd pe = permanent environment Non-genetic effect common to all of a doe’s lactations a = animal genetic effect (breeding value) e = unexplained residual

21. Indexes • An index combines evaluations for a group of traits based on their contribution to a selection goal • Milk-Fat-Protein Dollars • Combines yield evaluations into a single number MFP$ = 0.01(PTAMilk) + 1.15(PTAFat) + 2.55(PTAProtein)

22. Type Traits • Describe physical characteristics of animal • Final Score (overall assessment) • Scored  50-99 • Linear traits (13 defined traits) • Scored 1-50

23. Type Evaluation Model MODEL: y = h + a + p + e y = Adjusted type record h = Herd appraisal date a = Animal genetic effect (breeding value) p = Permanent environment - Effect common to all a doe's lactations that is not genetic e = Unexplained residual Multi-trait - Scores of one trait affect evaluations of other traits.

24. Type Trait Genetic Correlations

25. Combining type and production Production-Type index (PTI) • Combines yield and type evaluations into a single value • There are 2 versions: • PTI 2:1, weights 2 production : 1 type • PTI 1:2, weights 2 type : 1 production

26. How Accurate are Evaluations? • Reliability measures the amount of information contributing to an evaluation • Increases as daughters are added (at decreasing rate) • Also affected by: • Number of contemporaries • Reliability of parents’ evaluations • Heritability

27. Accuracy of Evaluations • Does kidding in same season • More records  better estimate of herd-year-season (hys) effect • Bucks with daughters having records in same hys • More direct comparisons  better ranking of bucks • Number of lactation records • Number of daughters • Completeness of pedigree data

28. Methods of Expressing Evaluations • Estimated breeding value (EBV) • Animal’s own genetic value • Predicted transmitting ability (PTA) • ½ EBV • Expected contribution to progeny

29. Heritability • Portion of total variation due to genetics • Milk, Fat, Protein: 25% • Range for Type: 19% (r. udder arch) — 52% (stature)

30. USDA Dairy Goat Evaluations • Evaluations for milk, fat, protein, and type • Yield evaluations in July Type evaluations in November • Evaluations provided to ADGA, DRPC, and public via the Internet (aipl.arsusda.gov)

31. What Do the Numbers Mean? • Evaluations are predictions • The true value is unknown • The predictions rank animals relative to one another using a defined base • The base is the zero- or center-point for evaluations • For example: the performance of animals born in a given year

32. Trend in Breeding Value for Milk Available: http://aipl.arsusda.gov/eval/summary/goats.cfm?trnd_tbl=AIm

33. Ways to Increase Rate of Improvement • Use artificial insemination (AI) to use better males in more herds • Identify promising young males for progeny testing (PT) • Use on a representative group of does and observe the actual success of progeny • Focus on larger herds to improve accuracy

34. Factors Affecting Value of Data • Completeness of ID and parentage reporting • Years herd on test • Size of herd • Frequency of testing and component determination

35. Why Evaluations Go Wrong • Important factors ignored • Litter size • Milking Frequency • Preferential treatment • Unlucky • Current data not representative of future data • Traits with low heritability require large numbers to be accurate • Recording errors • Wrong daughters assigned to a sire

36. Dairy Cattle Program for Genetic Improvement • Artificial insemination (AI) • Allows for many progeny from superior males • Allows semen to be used in geographically diverse locations • Progeny testing (PT) • Use young males to get a representative group of daughters • Wait until those daughters are milking • Based on the evaluations, return the best males to heavy use

37. Dairy Cattle Program for Genetic Improvement (Cont.) • Pre-select only promising bulls for PT • Select only the best of the PT bulls for widespread use • Only about 1 in 10 PT bulls enter active service • Remove bulls from active service as better new bulls become available • Bulls remain active only a few years

38. Alternative to Waiting for PT • Use young bucks for most breedings • Replace bucks quickly • Bank semen of young bucks • Use frozen semen from superior proven bucks as sires of next generation of young bucks

39. Recent Changes to System • Web query for accessing data by animal name • Yield data since 1998 extracted from the master file each run • Incorporates corrections, deletions, and ID changes • Standardized yields back to 1974 available

40. Recent Changes to System (Cont.) • Added Breed codes • CC – Sable • ND – Nigerian Dwarf • ID simplified by removing G and 18 prefixes when not required for uniqueness • More complete breeding information stored

41. Possible Enhancements • Add evaluations for more traits • Productive Life • Somatic Cell Score • Daughter Pregnancy Rate • Switch to test day model • Provides better accounting for environment • Accounts for genetic differences in shape of lactation curve

42. Future • DNA analysis • Parentage verification • Genetic evaluation • Genomic information may enable reasonably accurate evaluation at birth • National Animal Identification System (NAIS) • May cause changes in ID

43. Genomic Data • Single Nucleotide Polymorphisms (SNP) • Large number of markers with 2 alleles • Tags segments of chromosomes • Parentage verification • Marker alleles must match those of a parent • Often can infer unknown parent ID • EBV calculated for chromosome segments • Sum the value of segments to approximate evaluation • Accuracy may approach progeny test

44. Conclusions • Genetic evaluations are available for type and production • Traits can be improved through selection • Rate of improvement increases with accuracy of evaluations • AI enables widespread use of superior bucks and enables PT bucks to be used across herds

45. Conclusions (cont.) • Genetic evaluations improve selection accuracy • Accurate evaluations also require adequate data and an appropriate model • Evaluations are based on comparisons • Differences for non-genetic reasons must be removed • DNA technology is of great interest • Still requires reliable evaluations

46. AIPL web services http://aipl.arsusda.gov/query/public/tdb.shtml#GoatsTBL • Queries provide display of: • Pedigree information • Yield records • Herd test characteristics • Genetic evaluations of does & bucks • Yield • Type • Access information using: • ID number • Animal name • Herd code