1. Use of DNA information in Genetic Programs.

2. Next Four Seminars John Pollak – DNA Tests and genetic Evaluations and sorting on genotypes. John Pollak – Parent Identification With DNA Rob Templeman – Parent Uncertainty Models Bob Weaber – Application to Commercial Bull Evaluations

3. Outline 1.DNA Information in Genetic Evaluation: DNA Tests Inclusion in Genetic Evaluations 2.Commercial Ranch Genetic Evaluations Sorting Bulls on DNA Genotyping DNA Parent identification

4. DNA Tests One use of DNA test information is to incorporate that information into genetic evaluation systems. We view ourselves as the gate keepers to what information should go into evaluations. The process of validation is a means to insure DNA test results going into our genetic evaluations are reproducible.

5. Terminology Discovery, Validation, Assessment and Application Discovery: Process of identifying QTL Validation: Process of replicating results in independent data through blind testing Assessment: Process of evaluating the effect of the QTL in a broader context (other traits and environments) Application: Process of using the DNA information in genetic decisions

6. DNA Tests for Carcass Merit Traits Thyroglobulin Calpain (MARC Discovery) Calpistatin Leptin Three QTL from NCBA Carcass Merit Project (genes unknown) DGAT1

7. SNPs in Calpain1 Gene CAPN1 gene –  -Calpain enzyme  post-mortem tenderness MARC: 2 SNP that alter amino acid at positions (codons) 316 and 530 of μ-calpain Public domain marker Genotyping performed as a service by GeneSeek Incorporated (Lincoln, NE)

8. Calpain Commercial Tests Frontier Beef Systems  Merial –Igenity TenderGENE Calpain codons/SNPs/markers 316 & 530 Bovigen Solutions (Genetic Solutions products) –GeneStar Tenderness II Calpain1 (exon 9=codon316) + Calpastatin MMI Genomics –Calpain codons 316 & 530

9. NBCEC Taurus Data 14d post-mortem WBSF measurements on 362 AI-sired cattle 23 Simmental sires Predominately commercial Angus dams 19 CG = same source, sex, days on feed and harvest date

10. Initial MARC Results Marker Favorable Allele Unfavorable Allele 316CG 530GA

11. Calpain Marker Genotype Counts SNP 316 CCCGGG SNP 530 AA0426 AG34081 GG63774

12. Frequency at SNP 316 GenotypeCCCGGG Count981181 Frequency.033.299.669 f(C allele) =.18 f(G allele) =.82 Equilibrium Genotype Frequencies: CC =.032 CG =.296 GG =.672

13. Frequency at SNP 530 GenotypeAAAGGG Count30124117 Frequency.110.458.432 f(A allele) =.23 f(G allele) =.77 Equilibrium Genotype Frequencies: AA =.053 AG =.354 GG =.593

14. Calpain: 2 Additive Genotypes SNPGenotype WBSF (lbs) SE (lbs) 316 CC-1.110.64 CG-0.390.22 GG0- 530 AA0.680.34 AG0.030.22 GG0-

15. Indicus-influenced Cattle 297 King Ranch Santa Gertrudis carcasses 226 Simbrah carcasses from CMP (10 sires) Separate analyses by breed; similar results –Highly significant genotype effect, either individually or jointly –No interaction between SNP316 & SNP530 –SNP530 NOT significant after fitting SNP316, i.e., SNP 530 provides no additional information if you know the SNP316 genotype.

16. Indicus-influenced Cattle Contrast (vs GG)  SE 316 genotype Santa GertrudisSimbrah CC -.84  0.60 N = 18 -- N = 0 CG -.71  0.29 N = 113 -1.47 .39 N = 41 GG 0 N =166 0 N = 185

17. Outline 1.DNA Information in Genetic Evaluation: DNA Tests Inclusion in Genetic Evaluations 2.Commercial Ranch Genetic Evaluations Sorting Bulls on DNA Genotyping DNA Parent identification

18. Marker Assisted EPD’s The evolution of the use of marker data for traits where EPD’s are available will be to include that DNA data in genetic evaluation.

19. Test Case: Marker Assisted EPD WBSF measurements Calpain genotypes Small data set Relatively large fraction of WBSF measurements on progeny of genotyped sires

20. Progeny Genotype vs. Sire Genotype Progeny Genotype Progeny Phenotype Progeny Genotype Progeny Phenotype Sire Haplotype Sire Genotype Dam Haplotype

21. Haplotype Marker allele make-up of a sperm or egg Examples: (316 alleles = C & G, 530 alleles = A & G) –CCGG  only CG gametes –CCGA  CG & CA gametes –CGGA  CG, CA, GA & GG gametes (without knowing phase)

22. EPD –Expected Haplotype Effect given sire genotype –Polygenic effect Marker Assisted EPD’s

23. EPD data SF data in current WBSF sire evaluation –1833 WBSF records –120 Simmental sires –93 Contemporary Groups Genotypes (only sires’ used in EPD analysis) –~1/2 of sires were genotyped –~ 2/3 of animals had genotyped sire

24. ASA Simmental Sire Genotype 316Frequency 530CCCGGGGenoAllele AA02120.20.5 AG08310.6 GG0370.20.5 Geno Freq. 0.00.20.8 Allele Freq. 0.1 0.9

25. Four Gametes

26. WBSF: EPD vs MA-EPD Genotype

27. WBSF: EPD vs MA-EPD

28. Outline 1.DNA Information in Genetic Evaluation: DNA Tests Inclusion in Genetic Evaluations 2.Commercial Ranch Genetic Evaluations Sorting Bulls on DNA Genotyping DNA Parent identification

29. Progeny Testing Commercial Bulls The commercial ranch project centers on the progeny test of yearling bulls brought into a commercial ranch each year.

30. Economic Genetic Programs We can treat genetic programs as economic enterprises with costs and returns. Process: Define current genetic program then assess changes to that program relative to costs and returns.

31. Progeny Test Costs Individual identification Data recording Multiple sire pastures (calf sire identification)

32. Progeny Test Revenues Increased revenue that results from increase “product” generated by bull selection.

33. Progeny Test Costs Multiple sire pastures (Tool = DNA)

34. DNA Panels Typically use microsatellites: Anomalies in the genome where DNA sequences of two (or more) base pairs are repeated. Alleles at the microsatellite loci are the number of repeats. Example of a genotype at one microsatellite locus = 110/116

35. Exclusions A mismatch between the genotype of the putative sire and the calf in question. Sire = 110/110 Calf = 112/114

36. Panel Exclusion Rate Measure of the effectiveness of a DNA panel to exclude an animal as a parent. Probability of excluding as the parent any animal drawn at random from the population.

37. The probability of uniquely identifying the sire in a group of “N” bulls is: ( Exclusion rate ) N Sire Identification

38. Bulls0.900.950.98 20.810.900.96 30.730.860.94 40.660.810.92 50.590.770.90 60.530.740.89 70.480.700.87 80.430.660.85 90.390.630.83 100.350.600.82

39. Bull Sorting We use the DNA genotypes to create the breeding groups of bulls.

40. Create genetically diverse groups. Objective: is to maximize the probability of uniquely identifying one sire to a calf.

41. Pasture 1 Pasture 2 Criteria: Minimize the probability that both bulls would qualify as the sire of a calf produced by either bull. Sire Sorting

42. Pasture 1 Pasture 2 Sire Sorting Randomly assign one bull to each pasture. N*(N-1) 2

43. Pasture 1 Sire Sorting 112/114 110/110 112/116

44. Pasture 1 Sire Sorting 112/114 110/110 112/116 Damsf(110)f(112)f(114)f(116) Sire 0.50.2 0.1 112 110/112112/112112/114112/116 114110/114 114/114114/116

45. Pasture 1 Sire Sorting 112/114 112/116 Damsf(110)f(112)f(114)f(116) Sire 0.50.2 0.1 112110/112112/112112/114112/116 114110/114112/114114/114114/116 P(not excluded)=0.65 Not this one

46. P (Excluded) P(excluded) = 1 -  { P(not excluded) i } Across all marker loci

47. Pasture 1 Sire Sorting 112/114 110/110 112/116 Damsf(110)f(112)f(114)f(116) Sire 0.50.2 0.1 112110/112112/112112/114112/116 114110/114112/114114/114114/116 P(not excluded)=0.5 Produces calf

48. Pasture 1 Sire Sorting 112/114 110/110 112/116 Damsf(110)f(112)f(114)f(116) Sire 0.50.2 0.1 110110/110110/112110/114110/116 P(not excluded)=0.4 Produces calf