Genetic Selection Tools in the Genomics Era Curt Van Tassell, PhD Bovine Functional Genomics...
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Transcript of Genetic Selection Tools in the Genomics Era Curt Van Tassell, PhD Bovine Functional Genomics...
Genetic Selection Tools in the Genomics Era
Curt Van Tassell, PhDBovine Functional Genomics Laboratory & Animal Improvement Programs LaboratoryBeltsville, MD
Outline
Background– Genetic Evaluations– Quantitative Genetics– Genomics
Integrating Genetics and Genomics Case Study: DGAT1 Tangent: Animal Identification Crystal Ball Conclusions
Background
Bovine Functional Genomics Laboratory (BFGL)– Structural and functional genomics of cattle– Emphasis on health and productivity– Bioinformatics (storage and use of genomic data)
Animal Improvement Programs Laboratory (AIPL)– “Traditional” genetic improvement of dairy cattle– Increasing emphasis on animal health and reproduction
Traditional Selection Programs
Estimate genetic merit for animals in a population
Select superior animals as parents of future generations
Genetic Evaluation System
Traditional selection has been very effective for many economically important traits
Example: Milk yield– Moderately heritable– ~30 million animals evaluated 4x/yr– Uses ~70 million lactation records– Includes ~300 million test-day records– Genetic improvement is near theoretical
expectation
Dairy Cattle Genetics Success
-6000
-4000
-2000
0
2000
1960 1970 1980 1990 2000
Year of Birth
BV
Milk
Cows Bulls
Dairy Cattle Genetics Industry Cooperation
Producer
DHIA/DRPC PDCA
NAAB
AIPL
Producer
DHIA/DRPC PDCA
NAAB
AIPL
Genomics - Introduction
Traditional dairy cattle breeding has assumed that an infinite number of genes each with very small effect control most traits of interest
Logical to expect some “major” genes with large effect; these genes are usually called quantitative trait loci (QTL)
The QTL locations are unknown! Genetic markers can provide information about
QTL
Genetic Markers Allow inheritance of a region
of the genome to be followed across generations
Single nucleotide polymorphisms (SNiP) are the markers of the future!
Need lots!– 3 million in the genome– 10,000 initial goal
Polymorphism“poly” = many “morph” = form
General
population 94%
6%
Single nucleotide
polymorphism
(SNP)
Application of Genetic Markers
1. Identify genetic markers or polymorphisms in genes that are associated with changes in genetic merit
2. Use marker assisted selection (MAS) or gene assisted selection (GAS) to make selection decisions before phenotypes are available
3. Adjust genetic merit for markers or genes in the genetic evaluation system
QTL Identification
Genetic
Merit
DNA
Data
Compare GeneticMerit
QTL Identification and Marker Assisted Selection
3.51.7 -0.1 -2.5 -6.20.7
Gene Assisted Selection
Marker or Gene Assisted Selection
Largest benefits are for traits that:– have low heritability, i.e., traits where genetics contribute a
small fraction of observed variation (e.g., disease resistance and fertility)
– are difficult or expensive to measure (e.g., parasite resistance )
– cannot be measured selection decision needs to be made (e.g., milk yield and carcass characteristics)
Evolution in traditional selection program by improving estimation of genetic merit
Example: DGAT1
DGAT1: diacylglycerol acyltransferase– Enzyme involved in fat sythesis– Identified using
Genetic marker dataModel organism (mouse) gene function
information Cattle sequence verified candidate gene
DGAT1
Two forms of the gene in cattle– M = high milk (low fat) form of gene– F = high fat (low milk) form gene
BFGL scientists decided to characterize the gene in North American population– Over 3300 animals genotyped for DGAT1 SNP– Approximately 2900 genotypes verified and used
in these analyses
DGAT1 – Average Differencesin Daughters of Bulls
Trait MM-FF Trait MM-FFMilk lbs 361 Fat% 0.13
Fat lbs 16.5 Protein% 0.02
Protein lbs 5.0 NM$ $24
SCS 0.05 CM$ $35
PL 0.07 FM$ $4
DPR 0.21
DGAT1 Genotypic Frequencies
Integrating Genomics Results
Genes will likely account for a fraction of the total genetic variation
Cannot select solely on gene tests!!
Integrating Genomic Data:An Ideal Situation!
Bull PTA
Integrating Genomic Data: The DGAT1 NM$ Situation!
Bull PTA NM$
MM
FF
Integrating Genomic Data: The DGAT1 Fat Situation!
Bull PTA Fat
MM
FF
Integrating Genomics Results
Combine information– Ideally would incorporate genomic data into
genetic evaluation system Adjust PTA??
– Don’t adjust well proven animals (it’s in there!!)– Adjust parent average for flush mates
– Progeny have identical parent averages– Adjusting other PTA is non-trivial!
Integrating Genomic Data: Another view of DGAT1 NM$!
Bull PTA NM$
MM
FF
And it Really Works! Recent German study evaluated impact on adjusting
historic parent averages (PA) for DGAT1 and evaluated impact of predictability of future evaluations
Correlations of original PA with eventual PTA for milk were 45%
Correlations of adjusted PA with eventual PTA for milk were 55% (10% gain)
Incorporation of genomic data will result in increased stability of evaluations
Genetic Evaluations - Limitations
Slow!– Progeny testing for production traits take 3 to 4
years from insemination– A bull will be at least 5 years old before his first
evaluation is available Expensive!
– Progeny testing costs $25,000 per bull– Only 1 in 8 to 10 bulls graduate from progeny test– At least $200,000 invested in each active bull!!
Genetic Evaluations:Genomics Enhancements
Faster– Use of gene and marker tests allow preliminary
selection decisions beyond parent average before performance and progeny test data are available
Cheaper– Improved selection decisions should result in
higher graduation rates or enhanced genetic improvement
How do we get there
Increase number of genetic markers Continue QTL discovery for MAS/GAS Better characterize the genome
– Compare genome to well characterized human and mouse genome
Bovine Genome Sequence
Bovine Genome Sequence Inbred Hereford is primary animal being
sequenced Genome size is similar to humans Sequencing about half completed First assembly released yesterday!!
– 2.3 of 2.8 billion base pairs– 84% coverage
L1 Dominette 01449
Bovine Genome Sequence Six breeds selected for low
level sequencing Holstein and Jersey cows
represent dairy breeds Useful for SNP marker
development Expect 3 million SNPs in the
genome Preliminary goal is to
characterize 10,000
Wa-Del RC Blckstr Martha-ET
Mason Berretta Jenetta
Genomic Tools for Parentage Verification
Low-cost high-throughput SNP marker tests would facilitate parentage verification and traceability
$10 to $20 per sample seems to be a common break point Progeny test herds would likely be early adopters
– Support from studs? Results in increased stability on first proofs?
– Nearly impossible to make mistake on parentage– Punished on second crop proofs?
With widespread implementation– Increase effective heritability– Decrease evaluation variability– Enhanced genetic improvement
Crystal Ball (Wishful Thinking?)
Large number of validated genetic tests available
Large amounts of marker and gene data publicly available
Genomic data incorporated into genetic evaluation
Management decisions facilitated by genomics data
Considerations in Genomic Tests
How big is the effect?– Traits of interest, economic index (NM$, TPI, PTI)– How many genetic standard deviation units?
Has this been validated by a sufficiently large independent study?
What correlated response is expected & observed? What are allele frequencies? What is the value of this test?
– not simple to answer
Conclusions
Genomics is enhancing genetic improvement DGAT1 has large impacts on milk, fat,
protein, SCS Genetic tests need to be weighted
appropriately for optimal selection decisions Genomic tools will be extremely powerful for
parentage verification and traceability– Could impact genetic evaluations