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The Future of Genomics in the
Beef industry
Prof Mike Goddard
Future developments
More accurate EBVs
Genotype x environment interactions
Payment based on genotype
Multiple uses of DNA
Structural change
More accurate EBVs
Current prediction equations are useful but
just a start
Dairy already has accuracy = 0.8
Can we achieve this for beef?
More accurate EBVs
More animals in the training data
Genome sequence data
Biological knowledge about genes and mutations
Find causative mutations
Better statistical methods
More accurate EBVs
More animals in the training data
Genome sequence data
Biological knowledge about genes and mutations
Find causative mutations
Better statistical methods
Effect of number of animals on
accuracy of prediction equation
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000
Number of individuals in reference population
Accura
cy o
f G
EB
V in u
n-p
henotp
yed indiv
iduals
h2=0.1
h2=0.3
h2=0.5
h2=0.8
More accurate EBVs
More animals in the training data
Holstein experience of accuracy of EBV
3500 bulls +5000 cows
(14,000)
Milk 0.73 0.78
Longevity 0.55 0.60
More accurate EBVs
More animals in the training data
More Australian data BINs
Commercial use of tests
Data from other countries
Canada, USA …
Collaborate with dairy industry
More accurate EBVs
More animals in the training data
Need to use multiple breeds
But
Problem combining data across breeds
More accurate EBVs
Problem combining data across breeds
Training Validation
Within breed prediction
Jersey Holstein
Jersey 0.45 0.14
Holstein 0.17 0.62
Across breed prediction
Jersey + 0.49 0.62
Holstein
Feed conversion efficiency
Chr 1 in Beef (▲) and Dairy (♦)
More accurate EBVs
Breed 1 Breed 2
common + T rare
rare + C common
rare - T common
common - C rare
More accurate EBVs
Genome sequence data
Cost decreased from $1B to $1000
International reference panel
1000 cattle genome project
Why sequence data?
• The causative mutations are in the data set!
• Genomic EBVs – No longer have to rely on LD with SNP
– Higher accuracy of prediction (rare variants)?
– Better persistence of accuracy across generations
– Better prediction across breeds?
• Causal mutations have consistent effects across breeds
1000 Bull genomes project • Sequencing still more expensive than SNP chip genotyping
• Alternative strategy
– Sequence key ancestors and impute genotypes from sequenced
animals into all animals genotyped with SNP chips
• Common need for reference file of sequence
• 1000 bull genomes project
Provide a database of sequenced bulls
Global effort!
10,000 cow genomes project
ATTCTGGGGGCCTTACTCCC
ATTGTGGGGGCCATACGCCC
ATTCTGGGGGCCTTACGCCC
ATTGTGGGGGCCATACTCCC
10,000 cow genomes project
ATTCTGGGGGCCTTACTCCC
ATTGTGGGGGCCATACGCCC
ATTCTGGGGGCCTTACGCCC
ATTGTGGGGGCCATACTCCC
ATTCTGGGGGCCTTACTCCC ATTGTGGGGGCCATACGCCC
ATTGTGGGGGCCATACTCCC
10,000 cow genomes project
ATTCTGGGGGCCTTACTCCC
ATTGTGGGGGCCATACGCCC
ATTCTGGGGGCCTTACGCCC
ATTGTGGGGGCCATACTCCC
ATTCTGGGGGCCTTACTCCC ATTGTGGGGGCCATACGCCC
ATTGTGGGGGCCATACTCCC
More accurate EBVs
Genome sequence
1000 bull genome sequence project
133 bulls in first run (Holstein and Simmental)
11 fold coverage each
Found
15.8M SNP
1.6M insertions and deletions
Error rate = Disagree with 700k SNP chip = 0.0031
More accurate EBVs
Genome sequence data
Cost decreased from $1B to $1000
International reference panel
1000 cattle genome project
32 Angus and 24 Brahman
Impute sequence from SNP genotypes
Mutations causing variations in the sequence
more accurate and stable EBVs
All your cattle could have their own genome sequence!
More accurate EBVs
Biological information
Which genes affect each trait
eg calpatstatin affects tenderness
Eg SNPs associated with human height are in genes – Known to cause skeletal abnormalities
– Growth hormone pathway
– TGFB pathway (Marfan syndrome)
Which mutations affect the function of the gene
eg mutations that make a protein that doesn’t function
Incorporate into prediction equations
More accurate EBVs
More animals in the training data
Genome sequence data
Biological knowledge about genes and mutations
Find causative mutations
Better statistical methods
Better statistical methods
Accuracy*
BLUP Bayes R
Holstein 0.57 0.62
Jersey 0.43 0.49
* Correlation (GEBV, DYD)
Better statistical methods
Accuracy
BLUP Bayes R
Weight 0.34 0.36
Tenderness 0.19 0.35
Fat depth 0.27 0.26
Genotype by environment interactions
Eg FTO x exercise on fatness in humans
Possible uses
Which Charolais bulls to sell to northern producers?
Which steers to long feed for B3 market?
Payment based on DNA
DNA can predict tenderness
Genotype a mob of cattle and pay on average genotype
predict growth rate, marbling, tenderness, breed
Multiple uses of DNA
Pedigree discovery
multiple sire mate
Detect carriers of abnormalities
eg Pompe’s disease
Genotype for single genes
eg polled
Diagnose breed composition
Calculate EBVs
Polled test
Test Actual
Horned Scurred Polled
PP 1 1 84
PH 24 119 96
PA 2 2 9
HH 235 15 4
HA 9 7 10
AA 2 0 4
(more from John Henshall at Belmont)
Multiple uses of DNA
Pedigree discovery
multiple sire mate
Detect carriers of abnormalities
eg Pompe’s disease
Genotype for single genes
eg polled
Diagnose breed composition
Calculate EBVs
Can we distinguish breeds by SNP genotypes
Multiple uses of DNA
Pedigree discovery
multiple sire mate
Detect carriers of abnormalities
eg Pompe’s disease
Genotype for single genes
eg polled
Diagnose breed composition
Calculate EBVs
Change in Beef Industry
DNA technology could change the structure of the Beef stud industry
Reduce cost of breeding herd bulls
don’t stop recording
New players in stud industry
poultry example?
?????
Big change in technology
change in structure
Problem = who will pay to update data in training database?
Conclusions
We are only at the beginning of the use of DNA in breeding
cattle
EBVs can become more accurate with addition of data and
continued research
There will be multiple uses of DNA data
Opportunity to select cattle with better FCE, fertility, carcase
and meat quality
The structure of beef cattle genetic improvement may
change
