Optimizing Strategies for
Marker-Assisted Selection

January 25, 1999

Table of Contents

  1. Title Page
  2. Past and Current Selection for Quantitative Traits
  3. Genetic Improvement indays to reach 230 lbs
  4. Genetic Improvement in # born alive/litter
  5. Molecular Genetics Enables Identification of Genes Affecting Quantitative Traits
  6. Genes Identified in Swine (Rothschild, 1998)
  7. Repeated QTL ‘hits’ in swine (Rothschild, 1998)
  8. Use of Identified Genes in Genetic Improvement
  9. Use of Molecular Genetic Data in Marker-Assisted Selection
  10. Combining Phenotypic and Genotypic Data into an Overall EBV
  11. Overall EBV with a Known QTL (Example)
  12. Factors Affecting Extra Response from MAS
  13. Possible gains using MAS
  14. Possible gains using MAS - Effect of Heritability
  15. Possible gains using MAS - Effect of QTL size
  16. Benefit of MAS decreases over generations
  17. Long-term effects of Gene-Assisted Selection
  18. Gene-Assisted vs Phenotypic selection (1)
  19. Gene-Assisted vs Phenotypic selection (2)
  20. Why does Gene-Assisted Selection give less response in the longer-term?
  21. Relationship between frequencyand variance at the major gene
  22. Division of selection pressure between the QTL and polygenes for GAS
  23. MAS Strategies that maximize responseto the next generation may not maximize response over multiple generations
  24. Implications for the use of molecular genetic data in breeding programs?
  25. (A Catoon
  26. Implications for the use of molecular genetic data in breeding programs
  27. Infinite simal model for Polygenes
  28. 10 polygenic loci
  29. 5 polygenic loci
  30. "This will not be a problem when new QTL are found on a regular basis"?
  31. Hypothesized response when new QTL are identified on a regular basis?
  32. Current strategies for MAS do not maximize response in the longer-term
  33. Optimizing Selection on Identified QTL
  34. Use of Optimal Control Theory to optimize GAS over multiple generations
  35. Response to Optimal Gene-Assisted Selection on an Additive QTL (1)
  36. Response to Optimal Gene-Assisted Selection on an Additive QTL (2)
  37. Response to Optimal Gene-Assisted Selection on an Additive QTL (3)
  38. Major Gene Frequencies for Optimal and Standard GAS on an Additive QTL
  39. Optimal Weights on an Additive QTL
  40. Optimal Index Weights on an Additive QTL
  41. Extra response from optimal GAS over 5 generations with dominance
  42. Gene Frequencies for Optimal Selectionon an Over-Dominant QTL
  43. Application to selection for litter size using the ESR gene
  44. Extra response from standard Gene-Assisted Selection on ESR
  45. Extra response from Gene-Assisted Selection on ESR
  46. Extra response from GAS on ESR
  47. Extra response from GAS on ESR
  48. ESR frequency
  49. Conclusion: Strategies for MAS can be optimized
  50. Evaluation of optimal strategies: Extra response from optimal over standard GAS based on a stochastic model
  51. Additional Complications to Optimizing MAS
  52. Implementation of MAS within existing selection programs
  53. MAS in ‘extra’ selection stage(s)
  54. QTL pre-selection: WITH excess reproductive capacity
  55. QTL pre-selection: NO excess reproductive capacity (1)
  56. QTL pre-selection: NO excess reproductive capacity (2)
  57. MAS + Reproductive Technology: Velogenetics
  58. MAS in Crossbreeding Programs (1)
  59. MAS in Crossbreeding Programs (2)
  60. Gene Frequencies for Optimal Selection on an Over-Dominant QTL
  61. MAS or GAS inCrossbreeding Programs
  62. Conclusions/Summary
  63. Can molecular genetic information enhance selection programs?
  64. Acknowledgements

The Author:

Dr. Jack Dekkers jdekkers@iastate.edu

Dept. of Animal Science
Iowa State University





Abstract

Genetic improvement of livestock primarily focuses on selection for quantitative traits in outbred populations. To date, most genetic improvement has been achieved through selection on breeding values estimated from phenotype of the individual and/or its relatives. Molecular genetics is now providing tools to enhance rates of genetic improvement by being able to select on quantitative trait loci (QTL) or on linked markers. Sophisticated statistical methods have been developed to estimate the effects of QTL in complex pedigrees. The use of this QTL information in strategies for marker-assisted selection (MAS) has, however, received less attention. This is best illustrated by recent simulation studies (e.g. Gibson, 1994, Proc. Wld. Congr. Genet. Appl. Livest. 21:201), which showed that, although current strategies for MAS on a known QTL increase response to selection in the short term, they can lead to less response in the longer term than selection based on phenotype. Reduced longer term response is caused by the impact of increased emphasis on the known QTL on response in other genes that affect the trait. We (Dekkers and van Arendonk, 1998, Genetical Research 71:257) recently developed methods to optimize selection on a known QTL. Results show that QTL information can lead to greater responses to selection in both the short and longer term, in particular for QTL that exhibit dominance, provided selection on the QTL is optimally balanced with selection on phenotypic information. Implications for strategies for MAS and the benefits that can be expected from MAS in livestock breeding programs will be discussed.







This material was presented on Plant & Animal Genome VII held at St. Diego (January 18-22, 1999) and Dr. Jack Dekkers was an invited speaker
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