Transcript National and International Genomic Evaluations for Dairy Cattle Paul VanRaden

National and International
Genomic Evaluations
for Dairy Cattle
Paul VanRaden
Animal Improvement Programs Lab, USDA, Beltsville, MD, USA
Pete Sullivan
Canadian Dairy Network, Guelph, ON, Canada
[email protected]
2009
2007
CAN, USA Combined Phenotypes
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Joint evaluations tested and reported at
1991 ADSA meeting
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Banos and Wiggans, Robinson and
Wiggans, Powell et al, Wiggans et al
Both countries used Cornell computer
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Animal models applied to yield data of
Jerseys and Ayrshires
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Correlations .98 and .96 between
combined vs. converted evaluation
ADSA / ASAS annual meeting, July 2009 (2)
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2009
International Evaluation
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Traditional genetic evaluations
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Small benefits from merging phenotypes
MACE used instead to merge EBV files
Proven bulls only, not cows or young bulls
Genomics: what role for Interbull?
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Large benefits from sharing genotypes
Brown Swiss genotype sharing project
Less benefits from combining only GEBV
using G-MACE
ADSA / ASAS annual meeting, July 2009 (3)
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2009
Topics
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National genomic evaluation
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International genomic evaluation
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Value of cows and old bulls as predictors
Deregression, blending, polygenic effects
Reliability approximation
Simple conversion formulas
Exchange of genomic EBVs via G-MACE
Multi-country exchange of genotypes
Update on US and world analyses
ADSA / ASAS annual meeting, July 2009 (4)
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2009
Bulls and Cows as Predictors
Holstein, Jersey, and Brown Swiss breeds
Historic test:1
Bulls born <2000
Cows with data
June predictions:
Proven bulls
Cows with data
1Data
HOL
JER
BSW
4,422
947
1,149
212
472
40
7,883
3,049
1,843
351
721
49
from 2004 used to predict independent data from 2009
ADSA / ASAS annual meeting, July 2009 (5)
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2009
Do Cows and Old Bulls Help?
Research by Marcos da Silva, BFGL
REL Gain Add Cows Add Bulls <85
Trait
HO
JE
HO
JE
HO
JE
NM$
24
8
+1
-1
0
0
Milk
26
6
-1
-1
+2
+2
PL
32
7
+1
+1
0
+1
SCS
33
3
+1
-3
+1
0
Type
20
2
0
+1
+1
+1
ADSA / ASAS annual meeting, July 2009 (6)
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Deregression Methods
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Simple (remove parent average)
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Partial (remove parents and genotyped
progeny)
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Method used in US = PA + (PTA – PA) / REL
Method includes cows without double-counting
information from progeny
[PTA – w1 PA – w3 ∑(2PTAprog – PTAmate)/DEprog] / w2
Predictions very similar to simple deregression
Decided not to implement at this time
Matrix (remove sire, MGS, and all sons)
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Method used in Canada = D-1 (D + A-1k) a^
ADSA / ASAS annual meeting, July 2009 (7)
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Genomic Methods
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Direct genomic value (DGV)
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Combined genomic evaluation (code 1)
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Sum of effects for 38,416 genetic markers
Now displayed for NM$ with chromosome query
Include phenotypes not used in estimating DGV
Selection index includes 3 PTAs per animal
Traditional, direct genomic, and subset PTA
Transferred genomic evaluation (code 2)
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Propagate from genotyped animals to nongenotyped descendants by selection index
Propagation to ancestors being developed
ADSA / ASAS annual meeting, July 2009 (8)
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Calculation of Reliability
for individual animals
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Inversion and discounting
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Diagonals of (D + G-1 k)-1 and (D + A-1 k)-1
Gain in daughter equivalents times .6
Simple approximation used in USA
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Gain in DE = ∑(REL – RELpa) k / 2000 for all
genotyped animals
Could adjust for Ne of breed or for number
of close relatives
Used in April 2009 to beat deadline
ADSA / ASAS annual meeting, July 2009 (9)
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2009
Genomic Daughter Equivalents
Holstein bulls, June 2009
Trait
Age Inverse Discount1 Approx
NM$
Yng
52
31
32
Old
73
44
32
Yng
40
24
23
Old
62
37
23
Fertility Yng
190
114
116
Old
248
149
116
Yield
1DE
ADSA / ASAS annual meeting, July 2009 (10)
from inverse * .6
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2009
Include Polygenic Effect?
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Markers explain <100% of genetic
variance
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y = Zg + a + e, and Var(u) = w G + (1-w) A
In simulation, w = .95 had highest accuracy,
and regressions were close to 1
Tested w = .60, .80, and .95 in real data
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Nov 2004 Holstein data
Linear instead of nonlinear SNP estimates
Polygenic effect now in nonlinear program
More important with low-density SNP chip
ADSA / ASAS annual meeting, July 2009 (11)
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R2 with Polygenic Effect
Trait
.60
.80
.95
Net Merit $
.262
.268
.267
Milk yield
.414
.426
.426
Productive Life
.237
.248
.252
SCS
.344
.351
.349
Fertility
.239
.251
.255
ADSA / ASAS annual meeting, July 2009 (12)
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Regressions with Polygenic Effect
Trait
.60
.80
.95
Net Merit $
.83
.80
.77
Milk yield
.90
.87
.84
1.27
1.23
1.18
.96
.92
.88
1.22
1.19
1.15
Productive Life
SCS
Fertility
ADSA / ASAS annual meeting, July 2009 (13)
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Blending of Interbull PTAs
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Order of national calculations
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Phenotypic animal model evaluation
Direct genomic evaluation (DGV), using
previous MACE for foreign bulls
Selection index combining DGV, animal
model PTA, and subset PTA
Redo last step, using new MACE PTA
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Plan to implement in August
Suggested by Brian Van Doormaal, CDN
ADSA / ASAS annual meeting, July 2009 (14)
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Interbull Evaluation (Plans)
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Convert genomic EBVs
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Young bulls from FRA, NLD, NZL
EU requires 50% REL for marketing
Combine using G-MACE (2010)
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Proven bulls next year (2010)
Countries must compute domestic and
genomic evaluations 1-2 weeks earlier to
meet Interbull deadline
Currently genomics, MACE at same time
ADSA / ASAS annual meeting, July 2009 (15)
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Genomic MACE
Interbull Genomics Task Force
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Residuals correlated across countries
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Repeated tests of the same major gene, or
SNP effects estimated from common bulls
Let cij = proportion of common bulls
Let gi = DEgen / (DEdau + DEgen)
Corr(ei, ej) = cij * Corr(ai, aj) * √(gi * gj)
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Avoids double counting genomic
information from multiple countries i, j
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New deregression formulas tested
ADSA / ASAS annual meeting, July 2009 (16)
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2009
Multi-Country Combined Genotypes
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Evaluation methods
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Foreign data included via MACE, then
single-trait genomic evaluation
Domestic and foreign data evaluated using
multi-country genomic model
Advantages of multi-trait model
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Phenotypic and genomic both multi-trait
Domestic data weighted more than foreign
More accurate ranking than G-MACE
ADSA / ASAS annual meeting, July 2009 (17)
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2009
Multi-Country Genotype Model
X’R1X 0
0 X’R2X
Z’R1X 0
0 Z’R2X
R1’X
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0
R2’X
X’R1Z
0
X’R1
0
0
X’R2Z
0
X’R2
Z’R1Z+Ik11 Ik12 Z’R1
0
Ik21 Z’R2Z+Ik22
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Z’R2
R1Z
0
R1+A-1λ11 A-1λ12
0
R2Z A-1λ21 R2+A-1λ22
b1
b2
g1
g2
a1
a2
=
X’R1y
X’R2y
Z’R1y
Z’R2y
R 1y
R2y
Trait genetic covariance matrix = T, and Var-1(error) = R
Marker variance ratio kij = (T-1)ij / [∑ 2p(1-p) * w]
Polygenic variance ratio λij = (T-1)ij / (1 – w)
ADSA / ASAS annual meeting, July 2009 (18)
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2009
Multi-Country Computation
with shared genotype files
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USA-CAN, 2 trait model
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10,129 HO with data, 11,815 without
Block-diagonal solver converged in
250 iterations (similar to single-trait)
11 hours using 2 processors
Global Brown Swiss, 9 countries
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All 8,073 proven bulls simulated
30 hours using 9 processors
ADSA / ASAS annual meeting, July 2009 (19)
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2009
Proven Bull Reliability
Simulated BS bulls on home country scale
Traditional
Country
Genomic
Nat’l MACE Nat’l Multi-trait
USA
80
81
83
88
CAN
71
86
72
90
CHE
91
91
91
92
NZL
58
65
60
78
ADSA / ASAS annual meeting, July 2009 (20)
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2009
Young Bull Reliability
120 simulated BS bulls sampled in USA
Traditional
Country
Genomic
Nat’l MACE Nat’l Multi-trait
USA
20
17
55
70
CAN
1
14
9
61
CHE
14
17
65
73
NZL
1
1
1
26
ADSA / ASAS annual meeting, July 2009 (21)
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2009
Holstein Simulation Results
World population, single-trait methods
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40,360 older bulls to predict 9,850
younger bulls in Interbull file
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50,000 or 100,000 SNP; 5,000 QTL
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Reliability vs. parent average REL
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Genomic REL = corr2 (EBV, true BV)
81% vs 30% observed using 50K
83% vs 30% observed using 100K
ADSA / ASAS annual meeting, July 2009 (22)
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2009
Country Borders
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Most phenotypic data collected and stored
within country
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Genomic data allows simple, accurate
prediction across borders
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Need traditional EBV or PA for foreign animals, but
not available for young bulls, cows, or heifers
May need full foreign pedigrees
Genomic evaluations rapidly becoming international
DEU, FRA, NLD, DFS Holstein cooperation
World Brown Swiss cooperation
Accuracy requires very many genotypes
ADSA / ASAS annual meeting, July 2009 (23)
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2009
Conclusions
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National evaluation options
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Include cows as predictors?
Include polygenic effect in model?
International evaluation options
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Conversion formulas for young bulls
G-MACE to exchange GEBVs
Direct multi-country genomic
evaluation works well
ADSA / ASAS annual meeting, July 2009 (24)
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2009
Acknowledgments -1
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Genotyping and DNA extraction:
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USDA Bovine Functional Genomics
Lab, U. Missouri, U. Alberta,
GeneSeek, Genetics & IVF Institute,
Genetic Visions, DNA Landmarks,
and Illumina
Computing:
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AIPL, CDN, and U. Guelph staff
ADSA / ASAS annual meeting, July 2009 (25)
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2009
Acknowledgments -2
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Interbull Genomics Task Force:
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Georgios Banos, Esa Mantysaari,
Mario Calus, Vincent Ducrocq,
Zengting Liu, Hossein Jorjani, and
João Dürr
Data subset research:
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Marcos da Silva
ADSA / ASAS annual meeting, July 2009 (26)
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2009