Incorporation of Genomic Information into Selection Tools Mike

Incorporation of Genomic Information into Selection Tools Mike Tess Montana State University

Outline Where we are p Where we need to go p Efforts to get there p

Where are we?

Types of markers p Parentage n n p Traceability n p Genetic ID tag Management tools n p Determination Validation Predict a future phenotype Selection tools n n Predict progeny performance Produce genetic change

U. S. Genomic Companies Bovigen p Igenity p MMI p

DNA markers are evolving p Single locus p Multiple loci p Panels of many loci p Whole genome scans

The language of DNA markers Genotypes Units? p Haplotypes p Scores Accuracy? p Scans p “Stars” Confusion! p “Profiles” p “Molecular Genetic Values” p

Where we need to go

DNA Technologies and Genetic Improvement p How can we use DNA markers to achieve: n n Maximum speed? Minimum cost? Maximum control? Maximum choice?

We need a common currency

Multiple sources of information True Breeding Value Pedigree and Phenotypes DNA Score(s) DNA Scores, Pedigree, and Phenotypes

A common currency p SINGLE estimate of breeding value based on all information available n n n p DNA scores Pedigree Phenotypes SINGLE measure of accuracy Higher accuracy earlier in life

Some traits Phenotypes NO NO DNA Markers YES ---- YES

Some traits Phenotypes NO NO DNA Markers YES ---- YES

A common currency Pedigrees Phenotypes Genetic Evaluation (GE) EPD

A common currency Phenotypes NO NO DNA Markers YES ---- EPD

Some traits Phenotypes NO NO DNA Markers YES ---- EPD

A common currency Bovigen Score Igenity Score MMI Score Molecular Translation (MT) M-EPD

A common currency Phenotypes NO YES NO ---- EPD YES M-EPD DNA Markers

Some traits Phenotypes NO YES NO ---- EPD YES M-EPD DNA Markers

A common currency Pedigrees Phenotypes DNA Scores Molecular Translation (MT) MA-EPD Genetic Evaluation (GE)

A common currency for selection Phenotypes NO YES NO ---- EPD YES M-EPD MA-EPD DNA Markers Same units. Same measure of accuracy.

Yearling Bulls p How much would increased accuracy be worth?

A suggested roadmap. . .

Reference Populations DNA Companies Validation & Assessment Public Website Pedigrees & Phenotypes DNA Scores Samples Information

Validation and Assessment p Independent verification Does the test work? p What else might change if I select based on this test? p

Reference Populations p Data = tissue (DNA), pedigrees, and phenotypes n n p Existing data Herds optimally designed and managed for current and future use Representative of: n n Different breeds Different production environments

DNA Companies Reference Populations Validation & Assessment Breeders & Producers Pedigrees & Phenotypes DNA Scores Samples Information Breed Associations Database DNA companies Breed Assoc. Consultants Breeders Producers Extension Decision Support Education Multi-breed GE and MT

Challenges p Multiple companies marketing markers for the same traits. n n Overlapping information Dynamic individual DNA tests Increasing number of loci p Increasing accuracy p p Animals evaluated for the same traits at different points in time

Challenges p QTL/DNA marker discovery n n n 50 k – 300 k loci chip Statistical procedures Data required

Challenges p Database n n n Location(s) Access Data p 50 -300 k genotypes?

Challenges p Validation n n p Definitions Standards Responsible organization International scope Assessment n n Responsible organization Relationship to discovery

Challenges p Education n n Changing technology Exploding terminology Variation in understanding Multiple industry voices Credibility at risk

Challenges p Decision Support n Designing breeding plans Bio-economic objectives p Speed versus direction p n Choosing selection tools

Efforts to get there

A Team Approach Genomic companies p Breed associations p USDA-ARS p State Experiment Stations p NBCEC p BIF p n Commission

BIF Commission Ronnie Green – USDA-ARS p Ronnie Silcox – University of Georgia p Darrell Wilkes – ABS Global p Jim Wilton – University of Guelph p Mike Tess – Montana State University p p Bill Bowman – American Angus n Chair BIF Emerging Technologies Committee

Roles for the Commission Facilitate meetings p Encourage action p Conduit for communication p

Validation and Assessment p BIF Commission p Responsible organization = NBCEC? p Recommended standards and procedures n n n p p Definitions Multiple sources of information Populations and production/marketing systems BIF Recommendations International collaborations

Education and Decision Support p BIF Commission n n Assess needs Encourage development of materials and tools

Reference Populations Breeds p Environments p Production systems p Traits measured p USDA-ARS p Breed Associations p

Reference Populations Phenotypes p Pedigrees p DNA testing p n n Validation Validated markers p p Key sires Critical link in national cattle evaluation

Statistics – Commission/NBCEC Rohan Fernando – Iowa State Univ. p Steve Kachman – Univ. Nebraska p Rob Templeman – Michigan State Univ. p Mark Thallman – USMARC p Dick Quass – Cornell Univ. p

Statistics and Software p Recommended standards for estimating and reporting: n DNA marker effects Units of the trait p Additive genetic value p n p DNA marker accuracy Procedures for mining information from large SNP panels

Statistics and Software p Procedures for melding information from multiple markers into a single M-EPD with a corresponding accuracy n p Molecular translation Procedures for incorporating M-EPD into national genetic evaluation systems n DNA-markers, phenotypes, pedigree

Goal p Delivery of M-EPD or MA-EPD to producers soon after completion of DNA tests p Updated as new information is added to the database n n n DNA scores of relatives Individual performance Performance of relatives

DNA Companies Reference Populations Validation & Assessment Breeders & Producers Pedigrees & Phenotypes Scores Samples Information Breed Associations Database DNA companies Breed Assoc. Consultants Breeders Producers Extension Decision Support Education Multi-breed GE and MT

In closing. . . …. . p DNA technologies have always offered great promise. . . p DNA is always more complicated than it seems at the time p Confusion within industry is high. . .

In closing. . . …. . p Credibility of genetic research and genetic tools at risk p Lines between research, technology transfer and education need to blur p We must walk before we can run p Good things are on the horizon