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Current and Future Role of DNA Technology in the Livestock Industry

Current and Future Role of DNA Technology in the Livestock Industry. ARS, U.S. Meat Animal Research Center Clay Center, NE. Mark Allan, PhD Beef Cattle Geneticist. Seed Stock. Commercial Cow/calf. Finishing Phase. Young and Changing Technology. Information Recorded on Individual Animal.

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Current and Future Role of DNA Technology in the Livestock Industry

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  1. Current and Future Role of DNA Technology in the Livestock Industry ARS, U.S. Meat Animal Research Center Clay Center, NE Mark Allan, PhD Beef Cattle Geneticist

  2. Seed Stock Commercial Cow/calf Finishing Phase Young and Changing Technology

  3. Information Recorded on Individual Animal Family pedigree Birth date Birth weight Individual calving difficulty score Weaning weight Yearling weight Ultrasound - ~yearling Hip height Mature wt body condition scores Udder and teat - Ames Breeding records Carcass data Scrotal Docility

  4. Traditional Selection Works Well • Selection Practices • Visual • Performance Data *** • EPDs**** • Pedigree • DNA Marker Information • Modeling • Economic Indexes

  5. Selection Index • $VALUE INDEXES • Weaned Calf Value ($W) • Grid Value ($G) • Quality Grade ($QG) • Yield Grade ($YG) • Beef Value ($B) Different indexes for different phases of production!

  6. How Do We Collect DNA? • Blood • Hair Roots • Saliva • Skin • Semen • Fecal Samples • Other Tissues

  7. C G Bases A T Gene A Allele 1, Allele 2 Chromosomes

  8. Marker-Assisted Selection (MAS) -Inherited Diseases -Coat Color -Embryo Sexing -Horned/Polled -Quantitative Traits -Feed Efficiency, Growth, Reproduction, Carcass Traits Marker-Assisted Management (MAM)

  9. Populations QTL Scans • Nellore (Indicus)/Hereford sire n=547 • Brahman/Angus sire n=620 • Belgian Blue/MARCIII sire n=246 • Piedmontese/Angus sire n=209 x Sires were mated to Angus, Hereford and MARCIII females x MARCIII x x

  10. Carcass Traits – Minor Success

  11. Discovery of QTL Phenotypes Carcass Traits hot carcass wt fat depth marbling score est. k & p fat, heart fat rib bone ribfat ribmus USDA yield grade shear force Predicted Carcass Traits retail product yield fat yield whole sale rib-fat yield Growth Traits birth wt weaning wt yearling wt average daily gain Reproductive Traits FSH -males testicular weight testicular volume twinning rate ovulation rate

  12. Strategy to Identify Genes/Markers Candidate Position Fine Mapping Quantitative Trait Locus (QTL) Positional Candidate Gene/Marker Validation Industry Application Limiting Need additional laboratory tools

  13. Development of DNA Markers Fine mapping Progeny testing Mutant models Comparative maps Genetically modified animals Genome sequence DNA Marker Gene candidates Differential gene expression Biology Proteome analysis Metanomics Bioinformatics

  14. CAPN1 Story • QTL found on BTA29 for shear force in the Piedmontese/Angus sired population • CAPN1 mapped to region Q T L x CAPN1

  15. Parentage Verification Sire 1 Sire 2 m 2 P ? Progeny (P)

  16. Animal Identification Youth Livestock Shows

  17. Tracing Products

  18. Washington BSE First Case BSE Announced by the USDA on December 23, 2003 First recorded case in the U.S. APHIS requested our assistance on the DNA-based traceback

  19. Sequencing the Bovine Genome • PHASE1 - $53 million • NHGRI - $25 million • New Zealand - $1 million • Texas - $5 to10 million • National Cattlemen’s Beef Association, • Texas and South Dakota cattle producers - $820,000 Baylor College of Medicine-Human Genome Sequencing Center Hereford Cow from Miles City

  20. SNPs- Single Nucleotide Polymorphisms -Occur much more frequently throughout the genome -2 alleles possible ATGCAATTGCCACGTTGCAAT ATGCAATTGCTACGTTGCAAT ATGCAATTGCC/TACGTTGCAAT

  21. SNP Genotyping Allele 1 Allele 2 Primer

  22. * Linkage Disequilibrium- LD * * * * * * * * * * * * *

  23. Illumina Infinium Bovine BeadChip • ~ 54,008 SNP markers across the bovine genome • On average SNP every <67,000 base pair • Discovery SNP includes • many breeds (Van Tassell et al., 2008 Nature Methods) BARC USMARC University of Missouri University of Alberta

  24. Genotyping of Animals GPEVII • 2,020 F12 animals with feed intake record for ~52,156 SNP/animal • 152 GPEVII AI sires • 73 GPEVII F1 sires • 580 GPEVII F1 steers • 150 GPEVII F1 dams • ~155,164,000 genotypes 52,156 call rate >= 0.99 41,264 minor allele >= 0.1 31,466 minor allele >= 0.2

  25. WGA vs. WGS Bonferroni correction 1.03 e-6 = LOD 6.0

  26. WGS? • Using a large panel of markers to estimate genetic merit (MBV) marker breeding value. • Using the marker information from across the whole-genome to estimate the sum of effects. • How - Uses foundation information for the estimation process derived from training data sets (Equations). • Animals are genotyped that may or may not have phenotypic information and genetic merit is estimated.

  27. WGS – Whole-Genome Selection • “There is no doubt that whole genome-enabled selection has the potential for being the most revolutionary technology since artificial insemination and performance-based index selection to change the nature of livestock improvement in the foreseeable future” Dorrian Garrick, Iowa State University

  28. USMARC Granddams Grandsires U.S. Beef Cattle F1 Parents 3rd Generation Progeny Future Genetic Improvement of Beef Cattle? Gene Flow Information Flow SNP Genotypes Accurate Multi-trait Selection Phenotypes

  29. Release the Data- Breed Association • The results of the DNA tests will be critical in the National Sire Evaluations in the future. • To estimate genetic effects for a trait all the data needs to be used (“good and bad alleles”). • Selective reporting is a long-term disadvantage.

  30. Players Changing Genetic Visions (WI) Infigen (WI) Celera AgGen (California, Maryland) Frontier Beef Systems Genaissance Genmark Pyxis ImmGen Geneseek Viagen Identigen Pfizer (Bovigen- Catapult) Igenity SCR MMI Genmark Maxxam Genetic Solutions

  31. Using DNA in Selection Programs • Just because animal is not carrying the favorable allele for a specific test does not mean the animal is not genetically superior for the trait. • Increase the accuracy of EPD • Hard (expensive) traits to measure • Sex-limited traits • Lowly heritable traits • Speed selection decisions • Merchandising genetics

  32. Tools for Selection • - Growth • - Feed efficiency • - Carcass composition - quality • - Reproduction • - Disease resistance Will markers replace “traditional” selection?

  33. Marker-Assisted Selection (MAS) - At the seed stock/multiplier level Marker-Assisted Management (MAM) - At the commercial level Seed Stock MAS Commercial Cow/calf MAM & MAS Finishing Phase MAM

  34. Implementation to the Industry Marker data- added to the databases to contribute to our national genetic evaluation system already in place 2000 – Industry sires Feed efficiency EBV through WGS Additional tool to be used in making genetic progress

  35. Where has animal genetic improvement lagged the most? • Most costly disease to the cattle industry • 97.6% of feedlots treat • 14.4% of cattle are treated for symptoms • Accounts for over 50% of feedlot deaths • Cattle treated for BRD are expected to return at least $40 less than untreated calves Animal Health - all species BRD – Bovine Respiratory Disease NAHMS, 1999 Fulton et al., 2002

  36. Where has animal genetic improvement lagged the most? Reproduction - Beef, Dairy cattle Low Heritability Multi-component Trait Ovulate one/two eggs Fertilization None Open Cow Pregnancy Twins/singles Embryonic/fetal death Dystocia Live Calves Open Cow Death Survival Weaning Survives to Endpoint

  37. Where has animal genetic improvement lagged the most? • Lifetime productivity – all species • Longevity of female production makes the system more profitable and is more environmentally friendly • Female production efficiency Dairy cows – 2.8-3.2 parities Sows – 3.6 parities

  38. Where has animal genetic improvement lagged the most? • Feed Efficiency – ? Hard to measure trait Expensive! Cattle Fax Issue 25, Vol. 40 June 20, 2008 • Little effort has been focused on the amount or causes of individual variation in efficiency of energy utilization by cattle, even though differences among individuals have long been recognized. (Johnson et al., 2002). USMARC & CSU

  39. Dry matter intake x RFI Day DMI kg RFI kg/day n=1032

  40. Where has animal genetic improvement lagged the most? Stage of production - Diet x Genetic interaction Cow production Finishing Growing Avoid single-trait selection

  41. Daily Dry Matter Intake/ unit cow weight Heat Production of Mature Hereford and Simmental Cows Pooled Over Physiological States Fed at Varying Dry Matter Intakes Daily Heat Production, Mcal/unit weight

  42. Where has animal genetic improvement lagged the most? Matching genetic potential to the Climatic Environment Ability of animals to adapt to various environments “Adaptability”

  43. Where has animal genetic improvement lagged the most? “On the radar” may become important Treatment x Genetic Interaction Implants & feed additives (muscle enhancement) Health interventions (antibiotics) Healthfulness of Product Omega3 FA content of protein products

  44. Managing breed composition When breed composition is unknown

  45. Using DNA in Selection Programs • Just because the animal is not carrying the favorable allele for a specific test does not mean the animal is not genetically superior for the trait. • Increase the accuracy of EPDs • Hard (expensive) traits to measure • Sex-limited traits • Lowly heritable traits • Speed selection decisions • Merchandising genetics

  46. Present/Future • Will DNA testing play a role in the future of beef cattle- yes Parental ID, Quantitative tests (panels), Simple genetic inheritance, WGS • Will implementation be tough- Maybe; Yes (implementation of EPDs 80s, acceptance of crossbreeding programs, ultrasound) • Collect tissues for DNA analysis Populations with phenotypes • Breed Association responsibilities database, education • Build database structure and become pro-active in the implementation of the new technology Marbling R e p r o Known Disease Mature Wt B Wt W Wt Y Wt Feed Intake Udder/teat Feet/legs Tenderness But... another valuable tool for the breeder’s tool box

  47. Vision • Larger panels of markers that explain greater portions of the genetic variation for traits. MASMAM • WGS - ? Example - BW EPD 1.2 acc .75 on yearling bull • Validation, implementation • Change in costs? • Technology driven Questions

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