Whole genome selection and the 2000 bull project at USMARC

1. Whole genome selection and the 2000 bull project at USMARC Larry Kuehn Research Geneticist

2. DNA Technology • Genome is highly complex • Difficult to understand, manipulate • Historically, oversold to producers • Need patience, research support, partnerships

3. Definitions • Genome is the whole set of DNA • Genetic Markers track regions of the genome • May be linked/explain phenotypes we observe • A SNP is a type of genetic marker • Single Nucleotide Polymorphism • A Genotype is any pair of markers/marker groups

4. Whole Genome Selection (WGS) • Use genotypes of thousands of genetic markers, like SNP, to predict breeding values (EPDs) • Like marker sets for traits being offered now but with much more density • Genetic differences in DNA that cause phenotypic differences likely close to many markers • As of now unproven (but preliminary analyses look promising)

5. Problem: • Whole-genome selection seems to be big leap technology that needs to either be implemented in a big way or not at all. • Early adopters are likely to receive little benefit until a critical mass of influential and extensively phenotyped animals is tested and the data is analyzed jointly through NCE.

6. Whole Genome Selection • So is now the time? • We think so! • Multiple reasons why the time is right

7. Reason 1: Technology Illumina Infinium Bovine BeadChip • ~ 50,000 SNP markers across the bovine genome • On average SNP every >60,000 base pair • Discovery SNP includes many breeds BARC (ARS) USMARC University of Missouri University of Alberta

8. Reason 2: Data/Cattle • About 3,000 hd of pedigreed animals with extensive phenotypes at USMARC genotyped using the 50K chip • Primarily descendants of Germplasm Evaluation (GPE) Cycle VII • Angus, Hereford, Red Angus • Charolais, Gelbvieh, Simmental, Limousin

9. Reason 2: Data/Cattle • Some phenotypes difficult to measure in industry setting • Feed intake • Carcass data • Slice shear force • Structure • Puberty and productivity • Disease diagnostic records

10. Reason 3: People • Collaborative effort between researchers and breed representatives to implement WGS for the industry

11. 2000 Bull Project • Collaborative effort • DNA samples from influential or upcoming bulls in 16 most prominent breeds with a genetic evaluation • Running on 50K chip • Extensively phenotyped animals from USMARC used as training data set • Establish relationships of markers to phenotypes and use results to predict potential of 2000 industry bulls

12. 2000 Bull Project • Objectives: • Extend genetic predictions from USMARC phenotypes to industry bulls • EPDs developed and delivered to breed associations (they decide use) • Hopefully leads to further bull genotyping • Validate the effectiveness of WGS using EPDs from the 2000 bulls relative to USMARC data on common traits (e.g., weaning weight) • Improve accuracy of EPDs for common traits

13. 2000 Bull Project • Breed associations responsible for selecting sires • High accuracy sires (verify the process) • Influential sires (greater gain from novel traits) • Number of bulls from each breed dependent on breed size • Maximum of 400 sires from largest breed

14. Potential pitfalls • We don’t know the process of WGS will work • May need more than 50,000 markers • Need to develop complex computational methods • 1992-2006: 1.7 million genotypes at USMARC • 2007-2008: ~300 million genotypes expected from chip results

15. Potential looks good Simulated accuracy of genetic prediction:

16. SNP associations Birth Weight Weaning Weight Milk Postweaning gain Ribeye area Marbling score

17. Contributors USMARC -- Tim Smith -- John Keele -- Mark Allan -- Warren Snelling -- Mark Thallman -- Gary Bennett -- Larry Kuehn -- Tara McDaneld Bovine SNP Consortium -- ARS, BARC -- U. Missouri -- U. Alberta -- ARS, USMARC NBCEC Breed Associations ARS NPS -- Steve Kappes