1. Genomic Evaluations

2. Structure of genomic evaluations
All genotypes shared with Canada
Holstein, Jersey, Brown Swiss included
Participants
3 Breed Associations
7 major studs and 5 small ones
4 genotyping laboratories
Monthly evaluations

3. Bull genomic evaluations
Designated studs have exclusive right to receive genomic evaluations of bulls until May 2013

4. Genotyped Holstein by run
Run Date
Old*
Young**
Total
Male
Female
0904
7600
2711
9690
1943
21944
0906
7883
3049
11459
2974
25365
0908
8512
3728
12137
3670
28047
0910
8568
3965
13288
4797
30618
1001
8974
4348
14061
6031
33414
1002
9378
5086
15328
7620
37412
1004
9770
7415
16007
8630
41822
1005
9958
7940
16594
9772
44264
1006
8122
17507
10713
46300
* Animals with traditional evaluation
** Animals with no traditional evaluation

5. How the system works
Studs and breeds nominate animals through web site
Hair, blood, semen, or extracted DNA sent to Labs
Genotypes sent to AIPL monthly
Evaluation updates provided to studs and breeds monthly
All evaluations updated at tri-annual traditional runs

6. Conflict checking Parent-progeny conflicts detected
Sex and breed checked
Conflicts reported to lab and requester for resolution

7. Recent changes Genotypes for some dams imputed from progeny
Traditional evaluations of genotyped cows adjusted to improve accuracy of genomic evaluations

8. Imputation
Determine an animal’s genotype from genotypes of its parents and progeny
Genotype separated into sire and dam contributions. Identifies the allele on each member of a chromosome pair

9. O-Style Haplotypes chromosome 15

10. Imputation (cont.) Inheritance of haplotypes tracked
Accuracy of imputation improves with number of progeny
Crossovers during meiosis contribute to uncertainty

11. Cow Adjustment Evaluations of elite cows biased upward
Cutoff studies showed little benefit from including cows as predictors
Reducing heritability would reduce the problem but industry is reluctant to do so
Adjustment of cow evaluations implemented

12. Effect of Adjustment on Holstein
Bias
Regression
Gain REL No
Yes
Diff
Milk (lb)
-75.3
-27.9
47.4
.93
.90
-.03
29.5
32.5
3.0
Fat (lb)
-5.7
-2.9
2.8
.98
.97
-.01
34.0
37.1
3.1
Protein (lb)
-0.2
0.8
1.0
.07
25.0
27.1
2.1
Fat (%)
0.0
.99
.02
49.8
52.4
2.6
Protein (%)
.87
.88
.01
38.8
41.5
2.7

13. Cow Adjustment Summary
Increased reliability of genomic predictions
Genomic evaluations of the top cows, top young bulls, and top heifers decreased
Among bulls, foreign bulls with a high proportion of genotyped daughters had largest changes

14. Reliability for young HO Bulls
500
1000
1500
2000
2500
3000
3500
4000 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79
Milk REL
Number of Bulls
N = 15,226

15. Chip diversity Low cost chip with 3072 (3K) SNP expected this summer
High density chip (860K) in development
Version 2 of BovineSNP50 announced with changes in SNP list

16. Accommodating chip diversity
Impute to higher density
Calculate effects for all high density SNP
Mechanism for accounting for loss in accuracy due to imputation error needed
Percent missing genotypes
Only observed genotypes stored in database
Evaluations labeled as to source of genotype

17. Illumina 3K chip SNP chosen 3072, evenly spaced Some Y specific SNP
90 SNP for breed determination
Expect to impute genotypes for 43,382 SNP with high accuracy
Expect breeds to use 3K chip to replace microsatellites for parentage verification
Breeds allowed to genotype bulls for parentage only

18. Proposed stud use of 3K chip
Accuracy adequate for first stage screening
Higher density genotyping reserved for bulls acquired.
Confirm ID
Second stage selection
Maximize accuracy of evaluation used for marketing
Genotype more candidates

19. Current research
Prepare for Interbull validation of genomic evaluations
Improve accuracy of imputation
Refine cow adjustment for use with 3K chip
Evaluate how to restore comparability between evaluations of genotyped and non-genotyped cows
Investigate ways to increase accuracy by increasing predictor population