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Genetic Selection Tools in the Genomics Era. Curt Van Tassell, PhD Bovine Functional Genomics Laboratory & Animal Improvement Programs Laboratory Beltsville, MD. Outline. Background Genetic Evaluations Quantitative Genetics Genomics Integrating Genetics and Genomics Case Study: DGAT1

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genetic selection tools in the genomics era

Genetic Selection Tools in the Genomics Era

Curt Van Tassell, PhD

Bovine Functional Genomics Laboratory & Animal Improvement Programs Laboratory

Beltsville, MD

outline
Outline
  • Background
    • Genetic Evaluations
    • Quantitative Genetics
    • Genomics
  • Integrating Genetics and Genomics
  • Case Study: DGAT1
  • Tangent: Animal Identification
  • Crystal Ball
  • Conclusions
background
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
Traditional Selection Programs
  • Estimate genetic merit for animals in a population
  • Select superior animals as parents of future generations
genetic evaluation system
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

2000

Cows

Bulls

0

-2000

BV Milk

-4000

-6000

1960

1970

1980

1990

2000

Year of Birth

Dairy Cattle Genetics Success
genomics introduction
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

Polymorphism

“poly”= many“morph”= form

General

population

94%

Single nucleotide

polymorphism

(SNP)

6%

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
application of genetic markers
Application of Genetic Markers
  • Identify genetic markers or polymorphisms in genes that are associated with changes in genetic merit
  • Use marker assisted selection (MAS) or gene assisted selection (GAS) to make selection decisions before phenotypes are available
  • Adjust genetic merit for markers or genes in the genetic evaluation system
slide11

QTL Identification

DNA

Genetic

Merit

Data

qtl identification and marker assisted selection

1.7

3.5

-0.1

0.7

-2.5

-6.2

QTL Identification and Marker Assisted Selection

Compare Genetic

Merit

marker or 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
Example: DGAT1
  • DGAT1: diacylglycerol acyltransferase
    • Enzyme involved in fat sythesis
    • Identified using
      • Genetic marker data
      • Model organism (mouse) gene function information
      • Cattle sequence verified candidate gene
dgat1
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
integrating genomics results
Integrating Genomics Results
  • Genes will likely account for a fraction of the total genetic variation
  • Cannot select solely on gene tests!!
integrating genomics results1
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!
and it really works
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
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
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
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 sequence1
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 sequence2
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
Genomic Tools for Parentage Verification
  • Low-cost high-throughput SNP marker tests would facilitate parentage verification and traceability
  • $10 to $20 per sample seems to be a common break point
  • Progeny test herds would likely be early adopters
    • Support from studs?
  • Results in increased stability on first proofs?
    • Nearly impossible to make mistake on parentage
    • Punished on second crop proofs?
  • With widespread implementation
    • Increase effective heritability
    • Decrease evaluation variability
    • Enhanced genetic improvement
crystal ball wishful thinking
Crystal Ball (Wishful Thinking?)
  • Large number of validated genetic tests available
  • Large amounts of marker and gene data publicly available
  • Genomic data incorporated into genetic evaluation
  • Management decisions facilitated by genomics data
considerations in genomic tests
Considerations in Genomic Tests
  • How big is the effect?
    • Traits of interest, economic index (NM$, 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
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