Genetic selection tools in the genomics era
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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

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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


  • 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

  • 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






BV Milk








Year of Birth

Dairy Cattle Genetics Success

Dairy Cattle Genetics Industry Cooperation

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


“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

  • 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

Compare Genetic


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 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

DGAT1 – Average Differencesin Daughters of Bulls

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$



Integrating Genomic Data: The DGAT1 Fat Situation!

Bull PTA Fat

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$

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


  • 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

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