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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

  • Background
    • Genetic Evaluations
    • Quantitative Genetics
    • Genomics
  • Integrating Genetics and Genomics
  • Case Study: DGAT1
  • Tangent: Animal Identification
  • Crystal Ball
  • Conclusions
  • 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






BV Milk








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


“poly”= many“morph”= form




Single nucleotide




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

QTL Identification





qtl identification and marker assisted selection







QTL Identification and Marker Assisted Selection

Compare Genetic


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
  • 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
  • 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