Multibreed herd, growth, feed efficiency, ultrasound, carcass, genetic-genomic research

Multibreed herd, growth, feed efficiency, ultrasound, carcass, genetic-genomic research M. A. Elzo University of Florida The UF Multibreed Herd Data Recording System Mating System Genetics Research Genomics Research 1

The UF Multibreed Herd Data Recording System Mating System Genetics Research Genomics Research Data Recording: Types of Data Data Recording: Database Files Pedigree: Animal, Sire, Dam, Mgs, Mgd, breed fractions Mating Data: AI & NS sires, AI Dates, NS Dates Phenotypic Data: {Growth, Reproduction, Survival, Feed Intake, Ultrasound, Carcass} {Dates and Traits} Genomic Data: Alleles for all SNP in a chip {3K, 5K, 77K, Complete Sequence} Calves (yearly, accumulated) Dams (yearly, accumulated) Sires (yearly, accumulated) Genotypes (accumulated) Pedigree, Breed Fractions, Survival, Growth, Ultrasound, Feed Efficiency, Carcass, Meat Palatability Pedigree, Breed Fractions, Reproduction, Weights, Condition Scores, ELISA ParaTBC Pedigree, Breed Fractions, Semen Dosages, Sire Usage Illumina3K, Illumina5K, IlluminaHD, Complete DNA Sequence Calf File 211: Pedigree Data Calf File 211: PreWean Growth Data 2

Calf File 211: Feed Efficiency Data Calf File 211: Ultrasound Data Calf File 211: Carcass Data Calf File 211: Meat Palatability Data Cow File 211: Ped & Mating Data Cow File 211: NS & Calving Data 3

Cow File 211: WT, CS, ELISA Data Sire File 212 MAB File 26-21: Genotypic Data MAB 26-21: FE & Genotypic Data The UF Multibreed Herd Data Recording System Mating System Genetics Research Genomics Research Mating System: Diallel Sires are mated to dams of all breed compositions Computer Program Assigns AI and NS sires to heifers and dams separately Aims at producing enough replacements for purebred groups (Angus, Brangus, Brahman) 4

Matings 212 Matings 212 22 Sires Angus.75 A Brang.5A.25A Brah 4 3 4 3 3 5 322 Dams Angus.75 A Brang.5A.25A Brah 47 67 45 69 39 55 Breed Group of Sire BGDam A.75 A Br.5A.25A B All A 22 5 8 4 3 5 47.75 A 13 9 14 8 9 14 67 Br 5 5 25 3 4 3 45.5A 12 11 14 1 9 13 69.25A 8 6 1 5 5 5 39 B 2 2 2 2 4 43 55 All 62 38 73 32 34 83 322 The UF Multibreed Herd Data Recording System Mating System Genetics Research Genomics Research Research: Statistical Genetics 1 Genetic evaluation methodology to evaluate animals in multibreed populations Statistical Models (Multiple Trait, Additive, Non- Additive, Direct, Maternal Genetic Effects) Estimation of variance and covariance components and genetic parameters Computational Procedures (Direct, Iterative) Computer Programs (Fortran, SAS) Research: Statistical Genetics 2 Research: Statistical Genetics 3 Angus-Brahman Multibreed Herd Test and Validate Multiple-Trait Multibreed Statistical Models Computational Procedures (REML, GEM Algorithms) MREMLEM Program (Data Editing, Predictions, Estimation of VarCov Components & GenPar) Growth Traits Carcass Traits Romosinuano-Zebu (Colombia) Sanmartinero-Zebu (Colombia) Holstein-Other Breeds (Thailand) Holstein-Chilean Friesian (Chile) Multiple-Trait Multibreed Statistical Models Dedicated Versions of MREMLEM Growth Traits (Colombia) Dairy Traits (Thailand, Chile) 5

Research: Feed Efficiency & Postweaning Growth Objectives HATCH & TSTAR Projects NFREC GrowSafe FE Facility Marianna, FL 24 pens 16-2 calves/pen Effect of breed composition and temperament (chute score, exit velocity) on RFI, DFI, FCR, PWG Estimate genetic parameters for RFI, DFI, FCR, PWG 6

Data Recording at FEF Calves: Bulls, Heifers, Steers AdjPeriod: 21 d; Trial: 7 d Pens: 24; Calves/pen: 14-16 Intake: Feed, Water (Real time) Growth: Dates, weights, Hip Ht (2 wk) Temperament: Chute Score, Exit Vel (2 wk) Ultrasound: UREA, UIMF, UBF Data Calves 26-27 BKV-GNV-MAR 3 Herds: Brooksville, Gainesville, Marianna 2 Years: 26 27 Number Calves: 581 Brooksville (1 calves) Gainesville (388 calves) Marianna (93 calves) n = 581 Breed Group of Sire BGDam A.75 A Br.5A.25A B A 8 7 42 7 8 21.75 A 18 9 7 9 12 11 Br 16 2 7 2 3 4.5A 18 17 24 11 16 2.25A 8 6 6 8 6 5 B 14 84 7

Computation of RFI Definition of RFI groups Daily feed intake = Avge daily gain + Metabolic Mid-wt + Residual feed intake High = RFI >.9 kg DM/d Med = -.9 kg DM/d RFI.9 kg DM/d Low = RFI < -.9 kg DM/d Model for RFI Residual Feed Intake RFI = herd-year-pen + age of dam + sex of calf + age calf + BFcalf (sex)+ Het calf (sex) + mean chute score + mean exit velocity + sire + residual Sex Bulls similar RFI to Steers Heifers less efficient (higher RFI) than steers (1.24 ±.36 kg DM/d) Breed Bulls and Steers: Similar RFI from A to B Heifers: RFI decreased as B % increased (-1.29 ±.28 kg DM/d; more efficient) Model for FCR, DFI, PWG Feed Conversion Ratio FCR, DFI, PWG = herd-year-pen + age of dam + sex of calf + age calf + RFI group + BF calf (rfigrp) + Het calf (rfigrp) + mean chute score + mean exit velocity + sire + residual Breed FCR increased as B % increased (less efficient) High RFI Group = 1.41 ±.52 kg DM*d -1 /kg gain*d -1 Med RFI Group = 1.29 ±.47 kg DM*d -1 /kg gain*d -1 Heterosis FCR increased as Het % increased (less efficient) High RFI Group =.92 ±.51 kg DM*d -1 /kg gain*d -1 8

Daily Feed Intake Postweaning Gain (7 d) Breed DFI decreased as B % increased (more efficient) High RFI Group = -.97 ±.38 kg DM/d Med RFI Group = -.9 ±.33 kg DM/d Low RFI Group = -.99 ±.31 kg DM/d Heterosis DFI increased as Het % increased (less efficient) High RFI Group = 1.1 ±.35 kg DM/d Med RFI Group = 1.9 ±.32 kg DM/d Breed PWG decreased as B % increased (A better) High RFI Group = -18.81 ± 5.98 kg Med RFI Group = -16.18 ± 5.28 kg Low RFI Group = -14.82 ± 4.85 kg Heterosis PWG increased as Het % increased (favorable) Med RFI Group = 1.59 ± 5.88 kg Temperament Mean Chute Score No effect on any trait Mean Exit Velocity No effect on RFI, FCR, PWG DFI (-.29 ±.9 kg DM*d -1 /m*sec -1 ) Higher feed intake => lower EV Genetic Parameters (REML) Research: Feed Efficiency & Carcass and Meat Palatability RFI DFI FCR PWG RFI.19 ±.11.73 ±.13.9 ±.38.58 ±.28 HATCH & TSTAR Projects DFI.89 ±.1.42 ±.13 -.5 ±.31.88 ±.12 FCR.55 ±.3.37 ±.4.24 ±.11 -.5 ±.23 UF FE Facility, NFREC, Marianna, FL Suwannee Farms UF Meats Lab PWG.15 ±.4.41 ±.4 -.57 ±.3.4 ±.13 9

Objective Carcass (26-27) UF Angus-Brahman Herd Effect of breed composition, RFI, and temperament (chute score, exit velocity) on carcass and meat palatability traits n = 17 Breed Group of Sire BGDam A.75 A Br.5A.25A B A 17 2 2 2 3 4.75 A 6 4 8 5 3 4 Br 2 1 18 1 1 2.5A 5 1 1 6 5 5.25A 5 3 2 4 1 2 B 27 Model Regression of Carcass & Meat Quality traits on Brahman Fraction HCW, REA, BFAT, MAR, SF, TEND = year-pen + age calf + RFI group + BF calf (rfigrp) + Het calf + mean exit velocity + sire + residual Trait P > F High RFI Medium RFI Low RFI HCW.6-65.7 ± 22.1 kg -42.4 ± 23.5 kg -43.3 ± 18.2 kg REA.1-8.8 ± 5. cm 2-14.6 ± 5.3 cm 2-17.9 ± 4.1 cm 2 BFAT.27-1.8 ±.4 cm -.4 ±.4 cm -.1 ±.3 cm MAR.1-17.7 ± 44.1 units -182.2 ± 46.7 units -38.7 ± 36.3 units SF.3 1.3 ±.3 kg.6 ±.3 kg.2 ±.3 kg TEND.1 -.9 ±.3 units -1.7 ±.3 units -.9 ±.3 units Carcass Traits by Feed Efficiency Group In general High RFI smaller REA than Low RFI (-11. ± 3.8 cm 2 ) High RFI higher MAR than Low RFI (116. ± 34. units) Medium RFI higher MAR than Low RFI (18. ± 29.9 units) Less efficient steers (High RFI) had smaller REA than more efficient steers (Low RFI) HCW, REA, BFAT, MAR, and TEND decreased as Brahman fraction increased REA increased as heterozygosity increased EV had no effect on carcass and meat quality traits 1

Genomic-Polygenic Model Genomic Model RFI, FCR, DFI, PWG = year-pen + age of dam + sex of calf + age calf + BF calf + Het calf + additive animal polygenic + additive SNP genomic + residual RFI, FCR, DFI, PWG = year-pen + age of dam + sex of calf + age calf + BF calf + Het calf + additive SNP genomic + residual Polygenic Model Genomic-Polygenic Predictions RFI, FCR, DFI, PWG = year-pen + age of dam + sex of calf + age calf + BF calf + Het calf + additive animal polygenic + residual Prediction = Breed Solution + Sum SNP predictions EBV animal = Prob (Alleles Brahman) *(Brahman - Angus ) + Polygenic prediction + Sum [(# Alleles 2 at locus i) * (SNP i )], i = 1 to 2,899 + â animal Genomic Predictions Polygenic Predictions Prediction = Breed Solution + Sum SNP predictions Prediction = Breed Solution + Polygenic prediction EBV animal = Prob (Alleles Brahman) *(Brahman - Angus ) EBV animal = Prob (Alleles Brahman) *(Brahman - Angus ) + Sum [(# Alleles 2 at locus i) * (SNP i )], i = 1 to 2,899 + â animal 12

3. 2. 1.. -1. -2. -3. y = -.634x + 1.2375 4 8 12 16 2 24 28 32 8. 6. 4. 2.. -2. -4. -6. -8. 4 8 12 16 2 24 28 32 25. 2. 15. 1. 5.. -5. -1. -15. -2. -25. y = -.664x +.9724 4 8 12 16 2 24 28 32 Number of calves by breed group of sire x breed group of dam combination Breed group of dam Breed group of sire Angus ¾ A ¼ B Brangus ½ A ½ B ¼ A ¾ B Brahman All Angus 46 1 18 7 7 17 15 ¾ A ¼ B 24 21 31 26 14 16 132 Brangus 4 1 6 9 1 7 1 ½ A ½ B 3 27 21 26 22 2 146 ¼ A ¾ B 13 17 11 9 11 4 65 Brahman 1 2 1 68 72 All 118 87 142 77 64 132 62 Additive Genetic and Genomic Variation for RFI, DFI, FCR and PWG Trait Parameter AGVar PhenVar Heritability AGOVar/AGVar RFI Mean.37 1.79.21.14 (kg/d) SD.15.11.8.11 DFI Mean.8 2.42.33.1 (kg/d) SD.24.15.9.8 FCR Mean 1.32 6.5.2.26 (kfd/kgd) SD.56.4.8.17 PWG Mean 89.74 24.97.37.16 (kg) SD 25.85 15.9.1.11 Rank correlations of animals evaluated for RFI, DFI, FCR, and PWG using genomic-polygenic, genomic, and polygenic models Linear regression coefficients of genomicpolygenic, genomic, and polygenic EBV for RFI, DFI, FCR, and PWG on Brahman fraction of calf Trait Correlation RFI DFI FCR PWG GP Model, G Model.65.62.66.74 GP Model, P Model.98.99.95.99 G Model, P Model.52.51.42.65 Trait Effect RFI DFI FCR PWG Genomic-Polygenic -.3 -.66 -.2 -.634 P <.311 P <.7 P <.4812 P <.274 Genomic -.16 -.3 -.15 -.86 P <.1 P <. 1 P <.1529 P <.2825 Polygenic -.15 -.4 -.7 -.664 P <.2395 P <.1 P <.7772 P <.122 RFI and PWG RFI and PWG RFI, Genomic-Polygenic EBV 1.5 Genomic-Polygenic Linear (Genomic-Polygenic) 1..5. -.5-1. y = -.3x +.249-1.5 4 8 12 16 2 24 28 32 RFI, Genomic EBV.3 Genomic Linear (Genomic).2.1. -.1 -.2 y = -.16x +.27 -.3 4 8 12 16 2 24 28 32 RFI, Polygenic EBV 1.5 Polygenic Linear (Polygenic) 1..5. -.5-1. y = -.15x +.89-1.5 4 8 12 16 2 24 28 32 RFI,.4.3.2.1 -.1 -.2 -.3 -.4 5 1 15 2 25 SNP Number (Illumina3k) RFI,.4.3.2.1 -.1 -.2 -.3 -.4 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 16 17 18 19 2 21 22 23 24 25 26 27 28 29 3 Chromosome PWG, Genomic-Polygenic EBV Genomic-Polygenic Linear (Genomic-Polygenic) PWG, Genomic EBV Genomic Linear (Genomic) y = -.86x +.446 PWG, Polygenic EBV Polygenic Linear (Polygenic) PWG,.5.4.3.2.1 -.1 -.2 -.3 -.4 -.5 5 1 15 2 25 SNP Number (Illumina3k) PWG,.5.4.3.2.1 -.1 -.2 -.3 -.4 -.5 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 16 17 18 19 2 21 22 23 24 25 26 27 28 29 3 Chromosome 13

Number and percentage of standardized predicted SNP values from the genomic-polygenic model Summary for RFI, DFI, FCR and PWG Trait RFI DFI FCR PWG SDSNP Range N % N % N % N % -.4 to -.5 1.3 -.3 to -.4 1.3 4.14 1.3 -.2 to -.3 8.28 19.66 6 2.7 66 2.28 -.1 to -.2 187 6.45 244 8.42 393 13.55 371 12.8 to -.1 124 41.53 1171 4.39 17 34.74 998 34.43 to.1 1289 44.46 1169 4.32 14 34.63 11 34.84.1 to.2 22 6.97 277 9.56 379 13.7 376 12.97.2 to.3 9.31 18.62 48 1.66 72 2.48.3 to.4 4.14 4.14 Fraction AddGenomVar/AddGenVar Low:.1 to.26 Corr (GP Model, Genomic Model) Medium:.62 to.74 Corr (GP Model, Polygenic Model) Highest:.95 to.99 Corr (G Model, Polygenic Model) Lowest:.42 to.65 Regr (EBV, B fraction of animal) Low and Negative as B increased Influencial SNP Throughout the genome Objectives Additive Genetic and Genomic Variation for UREA, UBF, UPIMF, and UW Trait Parameter AGVar PhenVar Heritability AGOVar/AGVar UREA Mean 2.14 56.3.39.9 Fraction of additive genetic variation explained by the Illumina Bovine3K Chip (29 SNP) Ranking of animals for UREA, UBF, UPIMF, and UW using genomic-polygenic, genomic, and polygenic models Genetic trends from Angus to Brahman for UREA, UBF, UPIMF, and UW with the 3 models (cm 4 ) SD 6. 3.58.1.7 UBF Mean.4.22.25.38 (cm 2 ) SD.2.1.8.17 UPIMF Mean.29.59.53.6 (%) SD.8.4.12.5 UW Mean.6.12.54.8 (kg 2 ) SD.1.1.11.6 Rank correlations of animals evaluated for UREA, UBF, UPIMF, and UW using genomic-polygenic, genomic, and polygenic models Linear regression coefficients of genomicpolygenic, genomic, and polygenic EBV for UREA, UBF, UPIMF, and UW on Brahman fraction of calf Trait Correlation UREA UBF UPIMF UW GP Model, G Model.99.89.99.99 GP Model, P Model.58.51.6.65 G Model, P Model.65.79.64.7 Trait Effect UREA UBF UPIMF UW Genomic-Polygenic -.198 -.11.24 -.23 P =.1778 P <.1 P =.2222 P =.133 Genomic -.127 -.15 -.8 -.17 P <.1 P <.1 P =.17 P <.1 Polygenic -.136 -.7.19 -.2 P =.3321 P <.1 P =.3256 P =.252 14

UREA and UPIMF UREA and UPIMF UREA, Genomic-Polygenic EBV 1. Genomic-Polygenic Linear (Genomic-Polygenic) 8. 6. 4. 2.. -2. -4. -6. -8. y = -.198x +.144-1. 4 8 12 16 2 24 28 32 UREA, Genomic EBV 2.5 Genomic Linear (Genomic) 2. 1.5 1..5. -.5-1. -1.5-2. y = -.127x +.1714-2.5 4 8 12 16 2 24 28 32 UREA, Polygenic EBV 1. Polygenic Linear (Polygenic) 8. 6. 4. 2.. -2. -4. -6. -8. y = -.136x +.484-1. 4 8 12 16 2 24 28 32 UREA,.4.3.2.1 -.1 -.2 -.3 -.4 5 1 15 2 25 SNP Number (Illumina3k) UREA,.4.3.2.1 -.1 -.2 -.3 -.4 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 16 17 18 19 2 21 22 23 24 25 26 27 28 29 3 Chromosome UPIMF, Genomic-Polygenic EBV 2.5.3 Genomic-Polygenic Linear (Genomic-Polygenic) Genomic Linear (Genomic) 2..2 1.5 UPIMF, Genomic EBV 1..1.5.. -.5-1. -.1-1.5 -.2-2. y =.24x -.15 y = -.8x +.67-2.5 -.3 4 8 12 16 2 24 28 32 4 8 12 16 2 24 28 32 UPIMF, Polygenic EBV 2. Polygenic Linear (Polygenic) 1.5 1..5. -.5-1. -1.5 y =.19x -.18-2. 4 8 12 16 2 24 28 32 UPIMF,.4.3.2.1 -.1 -.2 -.3 -.4 5 1 15 2 25 SNP Number (Illumina3k) UPIMF,.4.3.2.1 -.1 -.2 -.3 -.4 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 16 17 18 19 2 21 22 23 24 25 26 27 28 29 3 Chromosome Number and percentage of standardized predicted SNP values from the genomic-polygenic model Summary for UREA, UBF, UPIMF, and UW Trait UREA UBF UPIMF UW SDSNP Range N % N % N % N % -.3 to -.4 2.7 11.38 6.21 -.2 to -.3 14.48 78 2.69 15.52 65 2.24 -.1 to -.2 276 9.52 419 14.45 245 8.45 359 12.38 to -.1 198 37.88 954 32.91 1217 41.98 118 35.12 to.1 119 41.5 92 31.74 117 4.36 16 34.7.1 to.2 296 1.21 415 14.32 234 8.7 37 12.76.2 to.3 2.69 83 2.86 18.62 7 2.41.3 to.4 3.1 17.59 5.17.4 to.5 2.7 Fraction AddGenomVar/AddGenVar Low:.9 to.38 Corr (GP Model, Genomic Model) Highest:.89 to.99 Corr (GP Model, Polygenic Model) Lowest:.51 to.65 Corr (G Model, Polygenic Model) Medium:.64 to.79 Regr (EBV, B fraction of animal) Near Zero (Pos or Neg) as B increased Influencial SNP Throughout the genome Objectives Fraction of additive genetic variation explained by the Illumina Bovine3K Chip (29 SNP) Ranking of animals for HCW, DP, REA, FOE, and MAB using genomic-polygenic, genomic, and polygenic models Genetic trends from Angus to Brahman for HCW, DP, REA, FOE, and MAB with the 3 models Number of calves by breed group of sire x breed group of dam combination Breed group of dam Breed group of sire Angus ¾ A ¼ B Brangus ½ A ½ B ¼ A ¾ B Brahman All Angus 19 3 6 1 2 4 35 ¾ A ¼ B 6 2 9 11 4 3 35 Brangus 1 2 22 3 4 2 34 ½ A ½ B 8 12 9 7 3 1 4 ¼ A ¾ B 6 8 8 1 4 2 29 Brahman 2 27 29 All 4 29 54 23 17 39 22 15

Additive Genetic and Genomic Variation for HCW, DP, REA, FOE, and MAB Trait Parameter AGVar PhenVar Heritability AGOVar/AGVar Rank correlations of animals evaluated for HCW, DP, REA, FOE, and MAB using genomic-polygenic, genomic, and polygenic models HCW Mean 895.88 1232.48.72.8 (kg) 2 SD 27.57 15.59.18.9 DP Mean 5.3 19.24.25.47 (%) 2 SD 3.89 2.39.17.26 REA Mean 39.2 71.73.53.19 (cm) 4 SD 18.74 9.17.22.16 FOE Mean.11.23.44.27 (cm) 2 SD.6.3.2.21 MAB Mean 358.57 4739.93.75.23 (unit) 2 SD 98.23 572.93.16.16 Trait Correlation HCW DP REA FOE MAB GP Model, G Model.85.95.84.84.9 GP Model, P Model.99.94.99.99.99 G Model, P Model.84.78.79.78.85 Linear regression coefficients of genomicpolygenic, genomic, and polygenic EBV for HCW, DP, REA, FOE, and MAB on Brahman fraction of calf Trait Effect HCW DP REA FOE MAB Genomic-Polyg.1643.5.144..2714 P <.3869 P <.5414 P <.6643 P <.7554 P <.494 Genomic -.43.16.98..2212 P <.9226 P <.7556 P <. 386 P <.229 P <.3326 Polygenic.1655.57.46.144.71 P <.3742 P <.2286 P <.8798 P <.6643 P <.8457 REA, Genomic-Polygenic EBV HCW, Genomic-Polygenic EBV 1 Genomic-Polygenic Linear (Genomic-Polygenic) 8 6 4 2-2 -4-6 -8 y =.1643x - 5.1677-1 4 8 12 16 2 24 28 32 15 Genomic-Polygenic Linear (Genomic-Polygenic) 1 5-5 -1 y =.144x -.411-15 4 8 12 16 2 24 28 32 REA, Genomic EBV HCW, Genomic EBV HCW and REA 25 1 Genomic Linear (Genomic) Polygenic Linear (Polygenic) 2 8 15 6 1 4 5 2-5 -2-1 -4-15 -6-2 -8 y = -.43x -.8431 y =.1655x - 4.92-25 -1 4 8 12 16 2 24 28 32 4 8 12 16 2 24 28 32 5 15 Genomic Linear (Genomic) Polygenic Linear (Polygenic) 4 1 3 2 5 1-1 -5-2 -3-1 -4 y =.46x -.1644 y =.98x -.474-5 -15 4 8 12 16 2 24 28 32 4 8 12 16 2 24 28 32 HCW, Polygenic EBV REA, Polygenic EBV HCW and REA Number and percentage of standardized predicted SNP values from the genomic-polygenic model.15.15.1.1 Trait HCW, REA,.5 -.5 -.1 -.15 5 1 15 2 25 SNP Number (Illumina3k).6.4.2 -.2 -.4 -.6 5 1 15 2 25 SNP Number (Illumina3k) HCW, REA,.5 -.5 -.1 -.15 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 16 17 18 19 2 21 22 23 24 25 26 27 28 29 3 Chromosome.6.4.2 -.2 -.4 -.6 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 16 17 18 19 2 21 22 23 24 25 26 27 28 29 3 Chromosome HCW DP REA FOE MAB SD Range N % N % N % N % N % -.4 to -.5 2.7 -.3 to -.4 2.7 23.79 -.2 to -.3 37 1.28 124 4.28 -.1 to -.2 38 1.62 474 16.35 to -.1 1437 49.57 18 37.25 837 28.87 1446 49.88 154 51.88 to.1 1462 5.43 183 37.36 798 27.53 1453 5.12 1395 48.12.1 to.2 346 11.94 464 16.1.2 to.3 42 1.45 143 4.93.3 to.4 1.3 3 1.3.4 to.5 4.14 16

Summary for HCW, DP, REA, FOE, and MAB Remarks Fraction AddGenomVar/AddGenVar Low:.8 to.47 Corr (GP Model, Genomic Model) Medium:.84 to.9 Corr (GP Model, Polygenic Model) Highest:.94 to.99 Corr (G Model, Polygenic Model) Lowest:.78 to.85 Regr (EBV, B fraction of animal) Near Zero (Pos or Neg) as B increased Influencial SNP Throughout the genome The low fraction of Add Genetic Variation accounted for by the Illumina Bovine3K Chip indicates that this chip should be used in combination with higher density chips for genomic evaluation If genotypes from the Illumina Bovine3K Chip were available, then a genomic-polygenic model should be used If imputation from a low density chip (e.g., 3K, 7K) to a higher density chip (e.g., 5K) were used in a multibreed population, animals from various breed groups would need to be genotyped with the higher density chip Genomic results here should be taken with caution due to the small number of animals and the large number of predicted SNP effects (even with a low density chip) Next Steps Research Team Continue to collect multibreed phenotypic information Collaborate with researchers at institutions with similar goals and available phenotypes and genotypes Genotype animals in the multibreed population with a combination of Illumina Bovine7K, Bovine5K and BovineHD (77K) Chips Strengthen genetic links with straightbred and multibreed cattle herds where Brahman is one of the component breeds Collaborate with researchers working in physiological genomics University of Florida Animal Sciences Large Animal Clinical Sciences North Florida Research & Education Center Beef Research Unit Pine Acres New Mexico State University University of Georgia National University of Colombia, ICA, & CORPOICA (Colombia) Kasetsart University (Thailand) University of Chile (Chile) Illumina Bovine3k BeadChip Illumina BovineSNP5 v2 BeadChip Number of Markers 2,9 Number of Markers 54,69 Samples per BeadChip 32 Samples per BeadChip 24 DNA Requirement 25ng DNA Requirement 2ng Assay Infinium HD Assay GoldgenGate Instrument iscan, HiScanSQ, or BeadArray Reader http://www.illumina.com/products/bovine_snp5_whole-genome_genotyping_kits.ilmn Instrument iscan or HiScanSQ http://www.illumina.com/products/bovine_snp5_whole-genome_genotyping_kits.ilmn 17

Illumina BovineSNPHD BeadChip Number of Markers 777,962 Samples per BeadChip 8 DNA Requirement 2ng Assay Infinium HD Instrument iscan or HiScanSQ http://www.illumina.com/products/bovinehd_whole-genome_genotyping_kits.ilmn People University of Florida Don Wakeman, Jerry Wasdin, Paul Dixon, Danny Driver Roger West, Dwain Johnson, Lee McDowell, Owen Rae, Gary Hansen, Cliff Lamb, Tim Olson New Mexico State University: Milton Thomas University of Georgia: Ignacy Misztal Colombia: Carlos Manrique, German Martinez, Gustavo Ossa Thailand: Skorn Koonawootrittriron, Sornthep Tumwasorn, Thanathip Suwanasopee Chile: Nelson Barria, Alejandro Jara Percent of calves 14 12 1 8 6 4 2 Residual Feed Intake Low Medium High 16.7 16.3 21.5 33.3 29.6 51.6 5. 55.1 38.3 59.8 46.4 25.3 33.3 32.2 28.6 2.3 23.1 18.7 Angus 3/4 A 1/4 B Brangus 1/2 A 1/2 B 1/4 A 3/4 B Brahman Breed group of calf Trait Effect RFI DFI FCR PWG Herd-year-pen <.1 <.2 <.1 <.1 Age of dam.41.12.65.28 Sex of calf.3 <.1 <.1 <.1 Age of calf.6.1.4.4 RFI group <.1.78.9 Brahman fraction nested within sex of calf <.1 Heterosis nested within sex of calf.24 Brahman fraction nested within RFI group.9.73.4 Heterosis nested within RFI group.2.22.2 Mean Chute Score.39.42.11.33 Mean Exit Velocity.89.12.34.31 18

Research: Breed Composition & Carcass, Meat Palatability HATCH & TSTAR Projects Complete Angus-Brahman Multibreed Dataset (1989 to 29) 1,367 Steers 1% Angus to 1% Brahman Objective Breed & Heterosis Effects in the Angus-Brahman Multibreed Population Additive Angus Brahman Differences Angus x Brahman Heterosis Effects 6 Carcass Traits: HCW, DP, REA, FOE, KPH, MAB 6 Meat Palat Trt: WBSF, TEND, CTI, JUIC, FLAV, OFLAV Number of steers by breed group of sire x breed group of dam combination Breed group of dam Breed group of sire Angus ¾ A ¼ B Brangus ½ A ½ B ¼ A ¾ B Brahman All Angus 116 16 34 17 27 32 242 ¾ A ¼ B 47 23 3 26 29 32 14 Brangus 28 6 134 17 2 21 245 ½ A ½ B 54 5 61 46 49 46 28 ¼ A ¾ B 29 2 32 21 24 45 197 Brahman 28 15 26 11 1 144 235 All 32 13 137 138 16 32 1367 Model HCW, DP, MAR, REA,FOE, KPH WBSF, TEND, JUIC, FLAV, OFLAV = year + age at slaughter + BF calf + Het calf + sire + residual Breed differences and heterosis effects for carcass traits Trait n Effect Estimate SE Pr > t HCW, kg 1359 Brahman Angus 2.65 3.44.44 DP, % 1359 Brahman Angus 1.6.25 <.1 MAB, units 1357 Brahman Angus -15.97 7.68 <.1 REA, cm 2 1328 Brahman Angus -3.82.93 <.1 FOE, cm 1353 Brahman Angus -.38.5 <.1 KPH, % 1275 Brahman Angus -.8.5.15 HCW, kg 1359 Heterosis 35.1 3.95 <.1 DP, % 1357 Heterosis.69.29.17 MAB, units 1328 Heterosis.26 8.83.98 REA, cm 2 1353 Heterosis 5.31 1.8 <.1 FOE, cm 1275 Heterosis.26.5 <.1 KPH, % 1359 Heterosis.16.6.1 19

Breed differences and heterosis effects for meat palatability traits Trait n Effect Estimate SE Pr > t Carcass Traits WBSF, kg 662 Brahman Angus.7.11 <.1 TEND, units 352 Brahman Angus -1.18.15 <.1 CTI, units 352 Brahman Angus -.97.14 <.1 JUIC, units 352 Brahman Angus -.4.12.1 FLAV, units 352 Brahman Angus.5.9.56 OFLAV, units 352 Brahman Angus -.4.7.57 WBSF, kg 662 Heterosis -.6.14.68 TEND, units 352 Heterosis.26.17.13 CTI, units 352 Heterosis.29.16.62 JUIC, units 352 Heterosis -.9.14.54 FLAV, units 352 Heterosis.18.1.8 OFLAV, units 352 Heterosis -.1.8.22 Meat Palatability Traits In short Brahman carcasses had similar HCW and KPH, but higher DP, lower MAB, smaller REA, and thinner FOE than Angus carcasses DP and WBSF increased as Brahman fraction increased MAB, REA, FOE, TEND, CTI, and JUIC decreased as Brahman fraction increased HCW, DP, REA, and FOE increased as heterozygosity increased 2