1. 2006 George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD wiggans@aipl.arsusda.gov USDA Genetic Evaluation Program for Dairy Goats

2. ADGA 2006 (2) G.R. Wiggans 2006 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. ADGA 2006 (3) G.R. Wiggans 2006 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. ADGA 2006 (4) G.R. Wiggans 2006 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. ADGA 2006 (5) G.R. Wiggans 2006 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. ADGA 2006 (6) G.R. Wiggans 2006 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. ADGA 2006 (7) G.R. Wiggans 2006 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. ADGA 2006 (8) G.R. Wiggans 2006 Examples of Non-genetic Factors  Age  Lactation  Season  Litter size  Milking frequency  Herd

9. ADGA 2006 (9) G.R. Wiggans 2006 Data Flow FARM COMPONENT TEST LAB DRPC Center Data Sent to AIPL DRMS – NCDaily DHI-Provo – UTWeekly Agri-Tech – CA2/week AgSource – WIWeekly Langston – OK2/month AIPL ADGA INTERNET Milk Data collected monthly DHIA

10. ADGA 2006 (10) G.R. Wiggans 2006 Does on Test at Last Test in 2005 Does on Test at Last Test in 2005 By Processing Center CenterHerdsDoesPercent of Does DRMS1574,54137.0 DHI-Provo1583,78530.8 AgSource372,24618.3 Agri-Tech201,0998.9 Langston556185.0 Total42712,289 Source: DHI Report K-6, 2006 Table 6 Available: http://aipl.arsusda.gov/publish/dhi/current/drpcx.htmlhttp://aipl.arsusda.gov/publish/dhi/current/drpcx.html

11. ADGA 2006 (11) G.R. Wiggans 2006 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. ADGA 2006 (12) G.R. Wiggans 2006 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. ADGA 2006 (13) G.R. Wiggans 2006 Alpine Milk Production Lactation Curve Lactation 1 Lactation 2 Lactation 3

14. ADGA 2006 (14) G.R. Wiggans 2006 Alpine Fat Percentage Lactation Curve Lactation 1 Lactation 2 Lactation 3

15. ADGA 2006 (15) G.R. Wiggans 2006 Alpine Protein Percentage Lactation Curve Lactation 1 Lactation 2 Lactation 3

16. ADGA 2006 (16) G.R. Wiggans 2006 Alpine and Nubian Milk Production Second Lactation AlpineNubian

17. ADGA 2006 (17) G.R. Wiggans 2006 Nubian Fat and Protein Percentage Second Lactation FatProtein

18. ADGA 2006 (18) G.R. Wiggans 2006 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. ADGA 2006 (19) G.R. Wiggans 2006 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. ADGA 2006 (20) G.R. Wiggans 2006 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. ADGA 2006 (21) G.R. Wiggans 2006 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(PTA Milk ) + 1.15(PTA Fat ) + 2.55(PTA Protein )

22. ADGA 2006 (22) G.R. Wiggans 2006 Type Traits  Describe physical characteristics of animal  Final Score (overall assessment)  Scored 50-99  Linear traits (13 defined traits)  Scored 1-50

23. ADGA 2006 (23) G.R. Wiggans 2006 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. ADGA 2006 (24) G.R. Wiggans 2006 Type Trait Genetic Correlations Final ScoreStrengthDairyness Fore Udder Attachment Final Score1.00.30-.15.66 Strength1.00-.51.15 Dairyness1.00-.16 F. Udder Att.1.00

25. ADGA 2006 (25) G.R. Wiggans 2006 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. ADGA 2006 (26) G.R. Wiggans 2006 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. ADGA 2006 (27) G.R. Wiggans 2006 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. ADGA 2006 (28) G.R. Wiggans 2006 Methods of Expressing Evaluations  Estimated breeding value (EBV)  Animal’s own genetic value  Predicted transmitting ability (PTA)  ½ EBV  Expected contribution to progeny

29. ADGA 2006 (29) G.R. Wiggans 2006 Heritability  Portion of total variation due to genetics  Milk, Fat, Protein: 25%  Range for Type: 19% (r. udder arch) — 52% (stature)

30. ADGA 2006 (30) G.R. Wiggans 2006 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. ADGA 2006 (31) G.R. Wiggans 2006 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. ADGA 2006 (32) G.R. Wiggans 2006 Trend in Breeding Value for Milk Available: http://aipl.arsusda.gov/eval/summary/goats.cfm?trnd_tbl=AIm

33. ADGA 2006 (33) G.R. Wiggans 2006 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. ADGA 2006 (34) G.R. Wiggans 2006 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. ADGA 2006 (35) G.R. Wiggans 2006 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. ADGA 2006 (36) G.R. Wiggans 2006 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. ADGA 2006 (37) G.R. Wiggans 2006 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. ADGA 2006 (38) G.R. Wiggans 2006 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. ADGA 2006 (39) G.R. Wiggans 2006 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. ADGA 2006 (40) G.R. Wiggans 2006 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. ADGA 2006 (41) G.R. Wiggans 2006 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. ADGA 2006 (42) G.R. Wiggans 2006 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. ADGA 2006 (43) G.R. Wiggans 2006 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. ADGA 2006 (44) G.R. Wiggans 2006 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. ADGA 2006 (45) G.R. Wiggans 2006 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. ADGA 2006 (46) G.R. Wiggans 2006 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