Download Potential of promotion of alleles by genome editing for improving

Potential of promotion of alleles by genome
editing for improving quantitative traits in
livestock breeding programs
John M. Hickey, John Woolliams, Janez Jenko, Rajeev
Varshney, Bruce Whitelaw, Matthew Cleveland, Gregor
Gorjanc
Future plant breeding program design?
Population
improvement
Select
best F1’s
on GEBV
Select
best F1’s
on GEBV
Select
best F1’s
on GEBV
Recurrent
selection
Select
best F1’s
on GEBV
•
Select
best F1’s
on GEBV
Product
development
Select best F1’s
on GEBV to pass
to product
development
Could pepper
with genomic
information
All phenotype
information
passed back
Operated by a quantitative geneticist
(Bonus on basis of program wide ΔG per year!!)
•
•
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Multiple trait and environment selection index
Manage utilization of diversity
Entirely genomic selection
Winter nurseries
•
Operated by a traditional breeder
(Bonus on basis of new lines released!!)
•
Traditional method supplemented by GS
GS1.0 has been a major success
• Accurate breeding values possible
• Shorter generation interval
• Dynamics are now well understood
• Good systems in place
• Most importantly for the future
– Breeding programs can generate lots of genomic data
Overall hypothesis of GS2.0
• Sequence data has huge potential in livestock
and plant breeding
• Huge volumes of sequence needed to realize
potential
• Individual breeding programs with 1 million
animals with sequence information:
– Will be normal “by the end of the decade”
– We have project in place to do 325,000 individuals
• Industrial scale fine mapping
– Perhaps 50% of the variance mapped to QTN
How might we benefit?
• We can do better what we do today
– Operational simplicity
– Persistent predictions
• (e.g. multiplier layers, across breeds, train with commercial data)
– Cheaper
• Or we could be bolder
– Explicit utilization of de-novo mutations
– Higher recombination rate
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Rapid response to some disease outbreaks
Much greater biological understanding
Better monitoring and utilization of variation
?????
Genome editing
GE is the process of
precise editing genome
Nucleotides can be
• added
• deleted
• replaced
Pig 26
• African Swine Fever – major disease risk in pig
production
• Pig 26
– Used genome editing to make a single base deletion
in the gene that controls susceptibility to ASF
• Genome editing
– 10% to 15% success rate
– Some off target editing
– Expensive!
Genome editing
• Focus to date has been on simple traits
• Most traits of economic importance are complex
• When it will be cost effective to edit lots of alleles
in sires ……
• …. what will the benefit of GE be for complex
traits?
• PAGE – Promotion of Alleles by Genome Editing
Quantitative genetics perspective of GE
as a breeding tool
• Genome editing is basically highly controlled
recombination
• Enables variation be moved around a population
more freely
• Don’t have to wait for favorable permutations to
arise
• Don’t have to waste selection intensity on:
– Keeping permutations in place
– Selecting the bigger QTN
– Don’t need to have the bigger QTN dominating GEBV
Simulation – generic features
• 10 generations of selection
– 500 males and 500 females
– 25 sires selected / all females selected
• Select sires on basis of TBV
– GS with perfect accuracy (i.e., huge data)
• Traits
– Polygenic with 1,000 or 10,000 QTN
– Effects from Gaussian Distribution
• Selected sires were edited using different
strategies
Editing strategies
• Constrain the number of edits per selected sire
– Between 0 and 100 edits
• Constrain the number of edits per generation
– Between 0 and 500 edits
• Edit different portions of sires
– All, Top 5, Top 10, Bottom 5, Bottom 10
• QTN effects assumed known (big data!)
– Sire edited for largest n QTN that it was not already
fixed for the favorable allele
– Still only polygenic Gaussian effects!!!
Strategies
• GS only
• GS + PAGE with restriction on the number of edits per sire
• GS + PAGE with restriction on the number of edits per generation
• These numbers were not chosen randomly!
Comparison metrics
• Response to selection
• Change in the allele frequency
• Number of distinct QTN edited
• Inbreeding
Genetic gain – n edits per sire
1,000 QTN
10,000 QTN
Editing portions of sires
1,000 QTN
10,000 QTN
Fixed editing resources per generation
1,000 QTN
10,000 QTN
Changes in allele frequencies
1,000 QTN
10,000 QTN
Number of distinct QTN
being edited
Scenario details
- 10,000 QTN
- 20 edits per sire
- All sires edited
- Across the 10 generations 162.5 distinct QTN edited
- These 162.5 distinct QTN explain 12.5% of base population genic variance
- Mapping these is is within the scope of our planned data sets
Inbreeding
1,000 QTN
10,000 QTN
Conclusions
• PAGE is very effective for increasing genetic gain
– 162.5 QTN edited that explain 12.5% of genic variance
– Probably possible to find these with planned data sets
• Some risks if not managed properly
– Inbreeding
– Target identification
– Off-target editing
• Practical use
– Huge data sets needed
– Good targets
– Costs?
Acknowledgements
• My Co-authors
– Particularly Janez Jenko
• Genus/PIC
– Dave McLaren
– Alan Mileham
• Aviagen
• ICRISAT
Sum of the effects of the edited QTN
Changes in genic variances
All QTN
20 QTN with largest effect