1. Genomic tools to improve progress and preserve variation for future generations
EAAP abstract 26496
Paul VanRaden
Animal Genomics and Improvement Laboratory
Agricultural Research Service, USDA, Beltsville, MD, USA

2. Topics
Evaluations adjusted for pedigree inbreeding and expected future inbreeding (EFI) in USA since 2005
Average relationships across time
Average relationships across countries
Genomic mating programs
Rare favorable alleles
Crossbreeding and gene flow
Genetic progress

3. Pedigree inbreeding trends by breed

4. Average relationships across time
Future generations are more related than past
The animal’s own inbreeding coefficient (F) is constant
Average progeny F remains constant if no new progeny
EBVs and PTAs are affected by inbreeding depression
What progeny merit should EBV or PTA include?
Average F of past progeny?
Average F of current progeny?
Expected F for future progeny (EFI)? YES!
Examine progeny F and EFI of famous Holstein bulls

5. Daughter inbreeding (famous Holstein bulls)

6. Daughter EFI (famous Holstein bulls)

7. U.S. PTAs are adjusted for inbreeding
Trait
Inbreeding depression/1%
Trait value
in NM$
$ Value
/1% F
Milk
–63.9
–0.004
0.3
Fat
–2.37
3.56
–8.4
Protein
–1.89
3.81
–7.2
Productive life
–0.26 21
–5.5
Somatic cell score
0.004
–117
–0.5
Daughter pregnancy rate
–0.13 11
–1.4
Cow conception rate
–0.16
2.2
–0.4
Heifer conception rate
–0.08
–0.2
Cow livability 12
–1.0
Net merit $
–25 1

8. Example EFI adjustment for OMan
Difference of EFI – daughter F = 9.0 – 5.4 = 3.6%
Economic loss (future – past daughters) = 3.6 ($25/1%F) = $90
OMan’s initial NM$ = +$426 before adjustment
OMan’s official NM$ = +$336 after adjustment
As the population becomes more related to an animal, its evaluations decrease
Progeny, grandprogeny, etc., also adjusted because their EFIs tend to be higher than breed average

9. Foreign bull–U.S. Holstein cow relationships
Country
Bulls
EFI
NM$
USA
4,140
7.0
+308
GBR
279
6.5
+145
NLD
1,143
6.4
+179
AUS
271
5.9
+27
DEU
999
6.6
+175
ESP
234
6.8
+98
FRA
940
+180
IRL
186
3.9
–44
ITA
870
6.7
+127
ISR
130
4.5
+118
NZL
762
3.2
–34
EST 89
5.1
–26
DFS
744
5.8
+232
CHE 84
6.3
–18
CAN
719
7.1
+210
CZE 72
POL
646
6.1
+74
KOR 61
+41
JPN
470
+101
BEL 43
+211
Correlation (EFI, NM$) = 0.72
(proven bulls born since 2009)

10. Bull inbreeding vs. relationship to population (Holstein)

11. Bull inbreeding vs. relationship to population (Jersey)

12. Mating programs Pedigree and “corrective” mating programs popular
Since 2015, the G matrix is provided in a bulk file for >1.5 million females  3,000 marketed males
Relationships (G and A) are displayed on web for about 100,000 animal requests per year via free public query:
Reports economic loss and inbreeding depression per trait

13. Recessive defects
Lethal recessives contribute only a small fraction of the additive genetic variance for fertility
Many were detected by lack of homozygous haplotypes
Further DNA sequencing revealed the exact mutations for many of these
Defects common in one breed are often absent or rare in other breeds (recent mutations within a breed)

14. Causal variants for recently discovered haplotypes
Breed
Chromosome
Location
Gene
AH1
Ayrshire 17
65,921,497
UBE3B
AH2
Ayrshire2 3
51,267,548
RPAP2
BH1
Brown Swiss 7 ?
BH2 19
11,063,520
TUBD1
HH0
Holstein 21
21,184,869 – 21,188,198
FANCI
HH1 5
63,150,400
APAF1
HH2 1
HH3 8
95,410,507
SMC2
HH4
1,277,227
GART
HH5 9
93,223,651 – 93,370,998
TFB1M
HCD 11
77,958,995
APOB
JH1
Jersey 15
15,707,169
CWC15
JH2 26
Causal variant found in 10 of 13 haplotypes
BH1, HH2 and JH2 still left to find
2AH2 is not yet officially released

15. Selection for rare favorable alleles
How to increase genetic variance?
Genetic variance increases as allele frequency increases to 0.5; variance declines after that
Rare favorable alleles can be accidentally lost via drift
Genomic selection can increase both genetic mean and variance by placing more weight on rare, favorable alleles

16. Rare favorable alleles
Source:
Sun & VanRaden, 2013, Interbull annual meeting presentation
Further information:
Sun & VanRaden, 2014, Increasing long-term response by selecting for favorable minor alleles, PLoS ONE 9:e88510

17. Rare favorable alleles
Source:
Sun & VanRaden, 2013, Interbull annual meeting presentation
Further information:
Sun & VanRaden, 2014, Increasing long-term response by selecting for favorable minor alleles, PLoS ONE 9:e88510

18. Genomic breed composition
Breed base representation (BBR) reported since 2016 for all 1.9 million genotyped animals, including >60,000 crossbreds, of which >35,000 are not yet evaluated
BBR estimates contributions of 5 breeds
Breed percentages forced to sum to 100%
Reported as purebred if any breed >94%
Ayrshires and Scandinavian Reds treated as 1 breed
Pedigree breed composition (reported since 2007) often less accurate and complete than genomic BBR

19. Top young Jersey bulls (before)
Name
Breed composition1
% Unknown
Ped
% Jersey
% Holstein
BBR
NM$
Cespedes 92 83 8 2 16
777
Familia 93 87 7 13
759
Marlo 89 75 11 25
744
Bauer 81 3
737
Tyrion
736
1BBR = Genomic breed base representation, Ped = Pedigree breed composition
After filling missing ancestors/breed codes in pedigree to match reported BBR
Ranking based on April 2017 NM$

20. Top young Jersey bulls (after)
Name
Breed composition1
% Unknown
Ped
% Jersey
% Holstein
BBR
NM$
Cespedes 92 91 8 1
777
Familia 93 94 7 6
759
Marlo 89 87 11 13
744
Bauer 88 9 3
737
Tyrion
736
1BBR = Genomic breed base representation, Ped = Pedigree breed composition
After filling missing ancestors/breed codes in pedigree to match reported BBR
Ranking based on April 2017 NM$

21. Nongenotyped cow* in top 50 Holsteins
Generation
Breed
Cow, Sire, MGS, MGGS, etc.
NM$ 1
HOL
VTF CABRIOLET
+821 2
CO-OP ROBUST CABRIOLET
+875 3
LADYS-MANOR PL SHAMROCK
+602 4
HIDDEN-VIEW POMEROY
+171 5
RDC
PETERSLUND
+524 6
PAULO-BRO CEL JASPER
–116 7
JER
MVF BOLD VENTURE DANIEL
+62 8
SOONER CENTURION
–114
*Cow HOUSA owned by Virginia Tech research herd        

22. Purebred Definition I was taught
All known ancestors are from the same breed
All pedigree paths trace back to importation
I am not purebred (many ancestors are unknown)
Human genotyping companies mainly report “breed composition” (where your genes are from)

23. Genomic evaluations on all-breed scale
Convert MACE and foreign dam data to all-breed scale
Estimate SNP effects for each breed on all-breed scale
Traits: Milk, fat, protein, productive life, somatic cell score, daughter pregnancy rate, cow conception rate, heifer conception rate, and livability
Conformation and calving traits still within breed
Jersey SNP effects used for crossbred conformation
To compute evaluations for crossbreds, SNP effects for each breed blended by BBR

24. Introgression, gene editing, transgenics
Move a few haplotypes from 1 breed into another
Editing
“Fast” introgression of easily predicted gene effects
Transgenics
Introgression of haplotypes or genes from another species
Breed preservation or replacement
Largest breeds benefit most from genomic tools

25. Reviews/summaries/other ideas
Howard, Pryce, Baes, & Maltecca Invited review: Inbreeding in the genomics era: Inbreeding, inbreeding depression, and management of genomic variability. JDS 100:6009–6024.
2017 ADSA symposium: Inbreeding in the genomics era
Optimal contribution theory
Balance progress and inbreeding depression in a closed population if 1 organization has full control
Other measures of homozygosity and inbreeding

26. Conclusions
Adjusting EBVs for differences between past and future inbreeding (EFI or GFI) selects for outcrosses
Selecting for rare alleles or against GFI give less short term but more long term response (>10 generations)
Choosing bulls that are less related to the population usually also reduces average net merit
Weighting SNP effects by breed composition can accurately evaluate crossbred animals
Using genomic tools can preserve variation while increasing progress

27. Acknowledgements
Mel Tooker (USDA), Gary Fok (USDA), Jay Megonigal (CDCB), and Chuanyu Sun (STgenetics) for assistance with computing and for slides
Council on Dairy Cattle Breeding (CDCB) for data
USDA-ARS project , “Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information”