Workshop "Animal genomics and breeding for sustainable production"
Precision animal breeding
Theo Meuwissen
Norwegian University of Life Sciences, Ås, Norway.
Workshop "Animal genomics and breeding for sustainable production"
Norwegian University of Life Sciences
1
Introduction
• Priorities for research needs in animal genomics and breeding?
• What are our future goals?
–Challenges to agriculture?
• Question is different from:
–How to more fully apply genomics in animal breeding
–(i.e. we choose the tool to address the challenges)
Tittel på presentasjon
Norwegian University of Life Sciences
2
Future challenges to
livestock production
–Global warming
• Direct problem in south; indirect problem in north (feed prices)
• Costs on GHG emissions
–Food/feed shortage (population growth, biofuels, etc)
• Feed prices surge (livestock products become elite products?)
• Animals robust to alternative feeds
–Energy, water, land and other shortages
• Efficient use of (feed) resources
–Reducing environmental footprint (idem)
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Institute for Animal and Aquacultural Sciences
Animal Breeding & the challenges
• AB is one of the tools to address these challenges
– can affect an improvement of 2% annually
–Important due to accumulation of these improvements
• 20% improvement in 10 years
• Challenges demand:
–Efficient use of critical resources
–Rapid genetic change/improvements
–Robustness:
• Efficient production under changing circumstances
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Institute for Animal and Aquacultural Sciences
Efficient use of critical resources
• is aim of precision agriculture
• In animal breeding context:
–Precision (animal) breeding
Tittel på presentasjon
Norwegian University of Life Sciences
5
Precision breeding (Flint & Woolliams, 2007)
1. Accurate GEBV
2. Avoid deleterious side effects from breeding
–Select for broad breeding goal
3. Manage genetic variation
–Maintain genetic resources
–Realize precision breeding also into the future
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Norwegian University of Life Sciences
6
Ad 1: Accurate GEBV
• Accuracy > 90%
–For broad spectrum of traits (see Ad 2)
• Large scale genotyping and phenotyping
–In the age of GS phenotype is king (Mike Coffey)
–Make GS work accross large genetic distances
• Rapid genetic improvement
• Efficient allocation of animals to environments/markets
Tittel på presentasjon
Norwegian University of Life Sciences
7
GEBV with 90+% accuracy
• Within breed genomic prediction
–Game of big numbers: many phenotyped and genotyped animals
• Big breeds win this game
–No need for high density genotyping / whole genome sequencing
–Methods for genomic prediction : G-BLUP or SNP-BLUP
–Problematic for traits with large-scale recording difficulties
• Limited number of records => low accuracy
• Not a broad breeding goal => no precision breeding
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Norwegian University of Life Sciences
8
GEBV with 90+% accuracy (cont.)
• Across breed genomic prediction
–Reference population: cooperation across breeds /breedcrosses
–High density genotyping / whole genome sequence data needed
–discover of causal variants or small segments
• Biological knowledge
–Non-linear methods for genomic prediction
–Small reference populations per breed
• Works for traits that are difficult to record on a large scale
• Broad breeding goal
• Utilises and nurtures AnGR
Tittel på presentasjon
Norwegian University of Life Sciences
9
Genomic selection (GS)
• Speeds up selection process dramatically
–When generation interval can be reduced
–Whith strong selection at young age
• Reproductive technology: parents produce many embryos at young age
–Especially if trait not recorded on selection candidates
• Can address GxE issues
–production under low-input/practical environment
–Traits not measured on elite animals: slaughter quality, disease resistance
–GS can select elite animals that are best under practical conditions
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Institute for Animal and Aquacultural Sciences
Ad 2: Broad breeding goal
• Phenomics to predict many traits
–Use of novel recording technologies
• On a large scale on practical data
• E.g: IR, CT, gene-expression data, automatic milking systems
– Combine with genotypes to get GEBV
• For broad spectrum of traits for all animals
• Robustness:
–Maintain production as circumstances change
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Norwegian University of Life Sciences
11
Ad 3: manage genetic variation
• Optimum Contribution Selection:
–Maximises genetic progress
–Manages the inbreeding
• At the level of the DNA (Sonesson et al, 2012)
• Using high density SNP data
• Maintain ability for fast genetic change
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Norwegian University of Life Sciences
12
Fast genetic change
• Breed or cross substitution
• Genomic selection (GS)
–High accuracy;
–short generation intervals (using novel reproductive techniques)
• GS-intogression (combination of GS and AnGR)
• Animal genetic resources (AnGR)
–Within breed
–Across breeds
• Widespread trait recording
–Phenomics
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Norwegian University of Life Sciences
13
GS-introgression
• Introgression of trait(s) from donor breed
• Donor breed better for e.g. disease resistance but inferior for Total Merit
Total Merit
Pure line
breeding
Cross with donor breed
__Tradit. Selection; _ _ _Genomic selection
Odegard et al. 2008
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Genetic Conservation
Widespread trait recording
• Calls for ‘phenomics’:
– large scale in-line recording of a large spectrum of traits
–e.g. infra-red spectroscopy, automatic milking systems, CT etc.
– payment-systems to farmers => incentive to select for traits
– traits may be indicator traits instead of actual trait:
• But genetic correlation should be high
• Low heritability may be compensated by large scale recording
–Assumes genotyping is widespread ie. recorded animals are
genotyped (or pedigree recorded)
Tittel på presentasjon
Norwegian University of Life Sciences
15
Take home messages…
• Livestock production is facing urgent challenges
-efficient use of critical (environmental) resources
–Robust production system => (genetically) robust animals
–Rapid adaptations => fast genetic improvements
• Precision animal breeding:
1. Accurate GEBV (>90%)
2. Selection for broad breeding goal
3. Maintain genetic variation / AnGR
Tittel på presentasjon
Norwegian University of Life Sciences
16
Take home messages (2)
• Fast genetic change
–Genomic selection
–Breed or crossbreed substitution (relying on AnGR)
–GS-introgression (introgression of few traits)
–Phenomics (large scale recording)
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Norwegian University of Life Sciences
17
Take home messages (3)
• within breed Genomic Selection (wbGS)
–Game of large numbers: many phenotypes and genotypes
–Problems with traits that cannot be recorded on large scale
• Problems with really broad breeding goal
• across breed Genomic Selection (abGS)
–Reference population consists of several breeds/crossbreds
• Handles traits with limited recording opportunities
–abGS focusses on causal variants
• WGS data
• Biological information
• Nonlinear methods for genomic prediction
Tittel på presentasjon
Norwegian University of Life Sciences
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