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Current and Future Role of DNA Technology in the Livestock Industry Mark Allan, PhD Beef Cattle Geneticist ARS, U.S. Meat Animal Research Center Clay Center, NE Young and Changing Technology Seed Stock Commercial Cow/calf Finishing Phase 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 Traditional Selection Works Well Selection Practices – – – – – – – Visual Performance Data *** EPDs**** Pedigree DNA Marker Information Modeling Economic Indexes 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! How Do We Collect DNA? • • • • • • • Blood Hair Roots Saliva Skin Semen Fecal Samples Other Tissues Bases Gene A Allele 1, Allele 2 Chromosomes G C A T Marker-Assisted Selection (MAS) -Inherited Diseases -Coat Color -Embryo Sexing -Horned/Polled -Quantitative Traits -Feed Efficiency, Growth, Reproduction, Carcass Traits Marker-Assisted Management (MAM) Populations QTL Scans • Nellore (Indicus)/Hereford sire n=547 x • Brahman/Angus sire n=620 Sires were mated to Angus, Hereford and MARCIII females x • Belgian Blue/MARCIII sire n=246 x • Piedmontese/Angus sire MARCIII n=209 x Carcass Traits – Minor Success Traitsa QTLb Chromosomesc TEND 8 2 MAR 24 2 3 4 5 6 7 8 9 10 PCHOICE 7 12 5 FATD 24 123 5678 14 BFAT EBV 12 56 14 AFAT 2 EEFAT 1 FATTYD 3 RIBFAT 5 KPH 7 FAa 5 LMA 7 2 REA 6 2 RIBB 1 SWT 10 123 RPYD 13 123 CW 27 12 DP 8 1 5 YG 10 12 5 45 7 8 9 10 11 15 12 13 14 11 18 19 20 16 17 18 14 29 20 21 23 26 27 19 16 1 29 26 19 21 23 19 21 23 19 19 1 5 13 23 11 18 26 18 26 15 16 17 19 4 6 12 14 12 14 12 14 26 19 21 5 5 4567 9 16 17 12 13 18 19 10 12 13 14 16 10 13 16 11 12 24 14 16 26 18 19 21 29 22 23 24 29 24 29 26 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 Strategy to Identify Genes/Markers Quantitative Trait Locus (QTL) Candidate Position Fine Mapping Positional Candidate Gene/Marker Limiting Need additional laboratory tools Validation Industry Application Development of DNA Markers Fine mapping Progeny testing Genetically modified animals Mutant models Comparative maps Genome sequence DNA Marker Gene candidates Differential gene expression Biology Metanomics Proteome analysis Bioinformatics CAPN1 Story • QTL found on BTA29 for shear force in the Piedmontese/Angus sired population x CAPN1 • CAPN1 mapped to region Q T L Parentage Verification m2P Sire 1 Sire 2 ? Progeny (P) Animal Identification Youth Livestock Shows Tracing Products 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 Washington BSE Sequencing the Bovine Genome PHASE1 - $53 million • NHGRI - $25 million • New Zealand - $1 million Baylor College of MedicineHuman Genome Sequencing Center • Texas - $5 to10 million • National Cattlemen’s Beef Association, Texas and South Dakota cattle producers - $820,000 Hereford Cow from Miles City SNPs- Single Nucleotide Polymorphisms -Occur much more frequently throughout the genome -2 alleles possible ATGCAATTGCCACGTTGCAAT ATGCAATTGCTACGTTGCAAT ATGCAATTGCC/TACGTTGCAAT SNP Genotyping Allele 1 Allele 2 Primer * Linkage Disequilibrium- LD * * ** * * ** * * * ** 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 BARC USMARC University of Missouri University of Alberta (Van Tassell et al., 2008 Nature Methods) 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 BFGL-NGS-119315 1.80 52,156 call rate >= 0.99 1.60 1.40 41,264 minor allele >= 0.1 Norm R 1.20 1 0.80 31,466 minor allele >= 0.2 0.60 0.40 0.20 0 79 346 472 -0.20 0 0.20 0.40 0.60 Norm Theta 0.80 1 WGA vs. WGS Bonferroni correction 1.03 e-6 = LOD 6.0 WGS? • Using a large panel of markers to estimate genetic merit (MBV) marker breeding value. • Using the marker information from across the wholegenome 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. WGS – Whole-Genome Selection • “There is no doubt that whole genomeenabled selection has the potential for being the most revolutionary technology since artificial insemination and performancebased index selection to change the nature of livestock improvement in the foreseeable future” Dorrian Garrick, Iowa State University Future Genetic Improvement of Beef Cattle? Grandsires U.S. Beef Cattle Gene Flow Information Flow USMARC Granddams F1 Parents Accurate Multi-trait Selection SNP Genotypes Phenotypes 3rd Generation Progeny 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. Players Changing Genetic Visions (WI) Infigen (WI) Celera AgGen (California, Maryland) Frontier Beef Systems Genaissance Genmark Pfizer (Bovigen- Catapult) Igenity MMI Maxxam SCR Genmark Genetic Solutions Pyxis ImmGen Geneseek Viagen Identigen 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 Tools for Selection • - Growth • - Feed efficiency • - Carcass composition - quality • - Reproduction • - Disease resistance Will markers replace “traditional” selection? 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 Implementation to the Industry 2000 – Industry sires Marker data- added to the databases to contribute to our national genetic evaluation system already in place Feed efficiency EBV through WGS Additional tool to be used in making genetic progress Where has animal genetic improvement lagged the most? Animal Health - all species BRD – Bovine Respiratory Disease • 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 NAHMS, 1999 – Cattle treated for BRD are expected to return at least $40 less than untreated Fulton et al., 2002 calves 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 Dystocia Live Calves Death Survival Embryonic/fetal death Weaning Survives to Endpoint Open Cow 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 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 Dry matter intake x RFI Day DMI kg n=1032 RFI kg/day Where has animal genetic improvement lagged the most? Stage of production - Diet x Genetic interaction Growing Cow production Avoid single-trait selection Finishing Heat Production of Mature Hereford and Simmental Cows Pooled Over Physiological States Fed at Varying Dry Matter Intakes 0.16 0.14 0.12 0.1 0.08 Hereford Simmental 0.06 0.04 0.02 0 0 0.005 0.01 0.015 0.02 0.025 Daily Dry Matter Intake/ unit cow weight Where has animal genetic improvement lagged the most? Matching genetic potential to the Climatic Environment Ability of animals to adapt to various environments “Adaptability” 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 Managing breed composition When breed composition is unknown 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 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) Marbling Known Disease Mature Wt Feed Intake • Collect tissues for DNA analysis Populations with phenotypes • Breed Association responsibilities database, education • Build database structure and become proactive in the implementation of the new technology Udder/teat Feet/legs B Wt W Wt Y Wt R e p r o Tenderness But... another valuable tool for the breeder’s tool box Vision • Larger panels of markers that explain greater portions of the genetic variation for traits. MAS MAM • WGS - ? Example - BW EPD 1.2 acc .75 on yearling bull – Validation, implementation • Change in costs? – Technology driven Questions