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 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 Year of Birth BV Milk CowsBulls

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

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 Genetic Merit QTL Identification and Marker Assisted Selection

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

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 Differences in Daughters of Bulls TraitMM-FFTraitMM-FF Milk lbs361Fat%  0.13 Fat lbs  16.5Protein%  0.02 Protein lbs5.0NM$  $24 SCS0.05CM$  $35 PL  0.07FM$  $4 DPR0.21

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

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