Genetic Selection Tools in the Genomics Era

1. Genetic Selection Tools in the Genomics Era Curt Van Tassell, PhD Bovine Functional Genomics Laboratory & Animal Improvement Programs Laboratory Beltsville, MD

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

3. 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

4. Traditional Selection Programs • Estimate genetic merit for animals in a population • Select superior animals as parents of future generations

5. 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

6. 2000 Cows Bulls 0 -2000 BV Milk -4000 -6000 1960 1970 1980 1990 2000 Year of Birth Dairy Cattle Genetics Success

7. Dairy Cattle Genetics Industry Cooperation

8. 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

9. 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

10. 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

11. QTL Identification DNA Genetic Merit Data

12. 1.7 3.5 -0.1 0.7 -2.5 -6.2 QTL Identification and Marker Assisted Selection Compare Genetic Merit

13. Gene Assisted Selection

14. 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

15. 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

16. 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

17. DGAT1 – Average Differencesin Daughters of Bulls

18. DGAT1 Genotypic Frequencies

19. Integrating Genomics Results • Genes will likely account for a fraction of the total genetic variation • Cannot select solely on gene tests!!

20. Integrating Genomic Data:An Ideal Situation! Bull PTA

21. MM FF Integrating Genomic Data: The DGAT1 NM$ Situation! Bull PTA NM$

22. MM FF Integrating Genomic Data: The DGAT1 Fat Situation! Bull PTA Fat

23. 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!

24. Integrating Genomic Data: Another view of DGAT1 NM$! MM FF Bull PTA NM$

25. 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

26. 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!!

27. 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

28. 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

29. Bovine Genome Sequence

30. 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

31. 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

32. 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

33. 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

34. 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

35. 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