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Incorporation of Genomic Information into Selection Tools

Incorporation of Genomic Information into Selection Tools. Mike Tess Montana State University. Outline. Where we are Where we need to go Efforts to get there. Where are we?. Types of markers. Parentage Determination Validation Traceability Genetic ID tag Management tools

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Incorporation of Genomic Information into Selection Tools

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

  2. Outline • Where we are • Where we need to go • Efforts to get there

  3. Where are we?

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

  5. U.S. Genomic Companies • Bovigen • Igenity • MMI

  6. DNA markers are evolving • Single locus • Multiple loci • Panels of many loci • Whole genome scans

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

  8. Where we need to go

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

  10. We need a common currency

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

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

  13. Some traits Phenotypes DNA Markers

  14. Some traits Phenotypes DNA Markers

  15. Genetic Evaluation (GE) A common currency Pedigrees Phenotypes EPD

  16. A common currency Phenotypes DNA Markers

  17. Some traits Phenotypes DNA Markers

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

  19. A common currency Phenotypes DNA Markers

  20. Some traits Phenotypes DNA Markers

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

  22. A common currency for selection Phenotypes DNA Markers Same units. Same measure of accuracy.

  23. Yearling Bulls • How much would increased accuracy be worth?

  24. A suggested roadmap . . .

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

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

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

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

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

  30. Challenges • QTL/DNA marker discovery • 50k – 300k loci chip • Statistical procedures • Data required

  31. Challenges • Database • Location(s) • Access • Data • 50-300k genotypes?

  32. Challenges • Validation • Definitions • Standards • Responsible organization • International scope • Assessment • Responsible organization • Relationship to discovery

  33. Challenges • Education • Changing technology • Exploding terminology • Variation in understanding • Multiple industry voices • Credibility at risk

  34. Challenges • Decision Support • Designing breeding plans • Bio-economic objectives • Speed versus direction • Choosing selection tools

  35. Efforts to get there

  36. A Team Approach • Genomic companies • Breed associations • USDA-ARS • State Experiment Stations • NBCEC • BIF • Commission

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

  38. Roles for the Commission • Facilitate meetings • Encourage action • Conduit for communication

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

  40. Education and Decision Support • BIF Commission • Assess needs • Encourage development of materials and tools

  41. Reference Populations • Breeds • Environments • Production systems • Traits measured • USDA-ARS • Breed Associations

  42. Reference Populations • Phenotypes • Pedigrees • DNA testing • Validation • Validated markers • Key sires • Critical link in national cattle evaluation

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

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

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

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

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

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

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

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