14.1- Predicting Breeding Value 2
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14.1- Predicting Breeding Value 2

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  • Originally, genetic evaluation programs were based on within-herd comparisons only. Increased use of A.I. And more sophisticated computer programs allowed expansion to across-herd evaluation. Across-herd genetic evaluation programs are usually done separately by breed.
  • STA (dairy) - PTA are standardized such that different traits have a similar range of values (-3 to +3 for most animals). Used for linear type traits. ?STA relates to number of standard deviations a particular individual is below or above the population mean.
  • 1) is accounted for by using sires in multiple herds simultaneously. See next slide. 2) relates to the fact that a sire used in a good herd will look better than when used in a bad herd because of the females he’s being mated to. Current statistical procedure account for this since all available records are used.
  • 1) is accounted for by using sires in multiple herds simultaneously. See next slide. 2) relates to the fact that a sire used in a good herd will look better than when used in a bad herd because of the females he’s being mated to. Current statistical procedure account for this since all available records are used.
  • Originally, in beef cattle, each breed with an across-herd evaluation program designated specific sires as reference sires. In order to have across-herd EPDs for animals in your herd, some of your calves had to be sired by reference sires. At that time, EPDs were calculated only for sires. Now they can be calculated for virtually every animal in the breed as long as the necessary trait data is available.
  • Originally, in beef cattle, each breed with an across-herd evaluation program designated specific sires as reference sires. In order to have across-herd EPDs for animals in your herd, some of your calves had to be sired by reference sires. At that time, EPDs were calculated only for sires. Now they can be calculated for virtually every animal in the breed as long as the necessary trait data is available.
  • Bull A is siring calves in both herds and so serves as a benchmark for comparisons. The column at the right compares each bull to Bull A in terms of progeny weaning weight. If we used the WW column, we’d rank the bulls A - D - B,E - C. This would not be correct because Herd 2 has a better environment and (or) better cows. Using the column at right, we correctly rank the bulls A - B - D - E - C. This concept applies for other species as well.
  • Bull A is siring calves in both herds and so serves as a benchmark for comparisons. The column at the right compares each bull to Bull A in terms of progeny weaning weight. If we used the WW column, we’d rank the bulls A - D - B,E - C. This would not be correct because Herd 2 has a better environment and (or) better cows. Using the column at right, we correctly rank the bulls A - B - D - E - C. This concept applies for other species as well.
  • Some species/breeds might still have designated reference sires for all traits, especially for carcass data. Not sure. Angus WWW site has the following: “ While advance technology in recent years has made possible National Cattle Evaluation for birth weight/calving ease, growth and maternal value from Angus Herd Improvement Records field data, it is still necessary to structure guidelines for evaluation seed stock for carcass merit.” HTTP://www.angus.org/sireeval/sirestru.htm
  • Originally, in beef cattle, each breed with an across-herd evaluation program designated specific sires as reference sires. In order to have across-herd EPDs for animals in your herd, some of your calves had to be sired by reference sires. At that time, EPDs were calculated only for sires. Now they can be calculated for virtually every animal in the breed as long as the necessary trait data is available.
  • Originally, in beef cattle, each breed with an across-herd evaluation program designated specific sires as reference sires. In order to have across-herd EPDs for animals in your herd, some of your calves had to be sired by reference sires. At that time, EPDs were calculated only for sires. Now they can be calculated for virtually every animal in the breed as long as the necessary trait data is available.
  • Breed associations usually hire university people (UG, CSU, ISU, Cornell). National sire: only sires were included. National cattle: virtually all animals in breed can be included if trait data available (because each animal is related to another animal with a record). Traits included vary by breed. Most include growth and maternal. Carcass trait EPDs are relatively new. Available for a limited number of breeds. Other: scrotal circumference, mature weight, calving ease.
  • Of course, this interpretation assumes that the two bulls would be mated to cows of similar merit for YW and that their calves would be raised in similar Environments.
  • Of course, this interpretation assumes that the two bulls would be mated to cows of similar merit for YW and that their calves would be raised in similar Environments. Note: ACC usually quite low for carcass traits, because of lack of data.
  • Units for all 3 of these are lb of weaning weight. However, there is a difference in what the lb of weaning weight refer to. I.e., own progeny versus daughters’ progeny versus daughters’ progeny, but only the portion of WW due to milk.
  • Units for all 3 of these are lb of weaning weight. We’ll distinguish on next slide. Bull A has best genes for milk. Bull B has best genes for growth (to weaning). Bull B is best choice for terminal sire. Bull A is best choice if replacement heifers are to be retained and you want to increase milk.
  • Until recent years, EPDs were available only on sires with progeny data. Thus, commercial producers using natural service did not have access to EPDs on bull they were buying (could look at EPDs of sires of bulls). Now, EPDs are available on purebred non-parents. Commercial producers purchasing several young bulls will likely be more satisfied with EPDs than a producer using only 1 or 2 bulls. Can expect considerable variation among progeny regardless of ACC.
  • The across-breed adjustments are controversial.
  • The across-breed adjustments are controversial.
  • Virginia Tech University is doing calculations at present time. NSIP used to calculated within-flock EPDs (FEPDs), but no longer offers that service. Not sure of plans.
  • For across-flock sire summary, the units are %. E.g. a difference of 15% between two rams indicates that daughters of one ram are expected to average 15% more lambs per daughter exposed to breeding. Milk + growth is similar to the maternal WW EPD in beef cattle. It is based on 60-day weight of daughters’ lambs which may or not reflect weaning age. Milk + Growth EPD = milk EPD + 1/2 (60-day wt EPD).
  • Fleece wt - normally, heavier considered better. Fiber length - normally, longer considered better. Fiber diameter - normally, finer considered better. (Sometimes called fleece grade).
  • Based was changed in August, 2000. The old base was animals born in 1990. The PTA of an individual usually goes down when the base is updated, because of overall genetic trend. For Holsteins, the average change was 668 lb for milk PTA, representing the average increase in PTA between cows born in 1990 and 1995. So, genetic improvement has been very evident.
  • Over 50,000,000 equations solved simultaneously for the Holstein breed. STA is expressed as a deviation in standard deviation units. Thus most animals will have values ranging between –3 and +3. Anything outside that range is extreme. STA makes it easier to select for a balance of type traits since each trait is on a similar scale. Yield records usually adjusted to 305-day lactation, 2x milking/day, and mature cow equivalent.
  • Usually STAs for these (standard deviation units).
  • Non-return rate: based on expected % of cows that do not return to heat following breeding (higher better?).
  • Bull C’s daughters should produce the most protein per lactation. Bull D’s daughters should produce the most protein per gallon of milk. PTAs for fat would be interpreted similarly.
  • The top three are measures of lifetime profitability. Each is an index based on both production (milk, fat, protein, productive life, and SCS) and type traits (udder comp, F&L comp, and body size comp). Net Merit is based on future expected milk price overall for USA. Fluid Merit is for producers who do not receive any direct payment for extra protein (and has a negative weighting on PTA Protein because it costs extra feed to produce extra protein). Cheese merit may be useful to producers selling milk directly to a cheese plant.

14.1- Predicting Breeding Value 2 14.1- Predicting Breeding Value 2 Presentation Transcript

  • Estimating Breeding Value
  • Breeding Value (BV)
    • Genetic merit of an animal for a given trait.
    • Often expressed as a deviation from herd or group average.
  • Breeding Value (BV)
    • In real life we observe the phenotype but want to estimate the breeding value (or its genetic additive effect)
  • Breeding Value (BV)
    • We observed that the phenotype of a given animal is 630 lbs at Weaning
    • But what is its breeding value (i.e. values of its genes to its offspring)?
  • Some Definitions Predicting Genetic Gain
    • Breeding Value (BV): The value of an animal as a (genetic) parent.
    • Breeding Value: The part of an individual genotypic value that is due to additive effect and therefore transmittable. (Breed true)
    • Independent Gene Effect: The effect of an allele is independent of the effect of the other allele at the same locus (dominance) and the effects of alleles at other loci (epistasis). ADDITIVE EFFECT.
    • Estimated Breeding Value (EBV): An estimation of a breeding Value.
  • Some Definitions Predicting Genetic Gain
    • Independent Gene Effect: The effect of an allele is independent of the effect of the other allele at the same locus (dominance) and the effects of alleles at other loci (epistasis). ADDITIVE EFFECT.
    • Estimated Breeding Value (EBV): An estimation of a breeding Value.
    • Additive Genetic Value = Breeding Value.
    • “ Breed True" (i.e., average offspring performance closely approximates average parent performance assuming constant environment)
  • Genotypic Value is not the same as Breeding Value
    • Genotypic Value of an animal is the value of its genes on itself and includes Additive, Dominant and Epistatic Effects.
    • Breeding Value is the value of its genes on the progeny and is related to the Additive Effects (Breed True and narrow sense heritability)
  • Progeny Differences
    • Progeny Difference (PD) or Transmitting Ability (TA): Half of an individual’s breeding value. The expected difference of the individual’s progeny and the mean performance of all progenies.
    • Expected Progeny Difference (EPD) or Predicted Transmitting Ability (PTA): A prediction of a progeny difference.
  • Expected Progeny Difference (EPD) or Predicted Transmitting Ability (PTA): The expected difference of the individual’s progeny and the mean performance of all progenies.
    • Its called prediction because its an estimation of the future performance of the animal’s offspring in relation to all progenies
  • EPD or PTA: Half of an individual’s breeding value (BV).
    • A parent passes 1/2 of its BV to an offspring.
    • The other half comes from the other parent
    • On phenotypic selection the gain is determined by selection differential averaged for males and females
  • Estimated Breeding Value (EBV)
    • Actual BV is unknown for most traits.
    • We can estimate BV of an animal based on performance of the animal itself and its relatives.
    • Similar to EPD, PTA, etc.
  • Estimating Breeding Value Within Herd – Contemporary Group Breeding Value Estimation
    • Animal of Interest
      • Animal whose BV is being estimated.
    • Animal(s) of Record
      • Animal(s) being evaluated or measured. Can be the animal of interest and(or) relatives.
  • Predicting Breeding Value
    • Phenotypic deviation from a contemporary mean!!
    • Population mean
    • Herd or flock mean
    • Mean of animal born in same management group
    • It’s a way to correct for non- genetic effects
  • Predicting Breeding Value Within Herd Genetic Evaluation Standardization of Performance Records (WW205, YW365, SC365, REA480) Adjustments (Age of the Cow, Age at weight data collection)
  • Predicting Breeding Value Within Herd Genetic Evaluation Population Farms Contemporary Groups Within CG Adjustments AOD, Age… Between CG Reference Sires – Half Sibs
  • Predicting Breeding Value Within Herd Genetic Evaluation Table: Birth weight adjustment factor for age of the dam. Table: Weaning weight adjustment factor for age of the dam. 18 20 11+ 0 0 .5-10 18 20 4 36 40 3 54 60 2 Heifers Bulls   BIF AOD (yr) 3 3 11+ 0 0 .5-10 2 2 4 5 5 3 8 8 2 Heifers Bulls   BIF AOD (yr)
  • Predicting Breeding Value Within Herd Genetic Evaluation Example 1: Rank animals based on BW, WW205 and YW365 374 1094 207 614 5 M 75 H 371 1070 204 536 8 F 68 G 360 1164 193 610 12 M 71 F 356 1194 189 635 4 M 81 E 390 1035 223 580 8 F 74 D 381 1038 214 537 6 F 63 C 366 1134 199 610 7 M 80 B 368 1050 201 540 3 F 65 A YW365 AGE YW WW205 AGE WW AdBW AOD SEX BW Animal
  • Predicting Breeding Value Within Herd Genetic Evaluation Example 1: Rank animals based on BW, WW205 and YW365 Rank for Males: F>H>B>E Rank for Females: C>G>A>D Rank for BW 374 1094 207 614 75 5 M 75 H 371 1070 204 536 68 8 F 68 G 360 1164 193 610 74 12 M 71 F 356 1194 189 635 83 4 M 81 E 390 1035 223 580 74 8 F 74 D 381 1038 214 537 63 6 F 63 C 366 1134 199 610 80 7 M 80 B 368 1050 201 540 70 3 F 65 A YW365 AGE YW WW205 AGE WW AdBW AOD SEX BW Animal
  • Predicting Breeding Value Within Herd Genetic Evaluation Example 1: Rank animals based on BW, WW205 and YW365 Rank for Males: F>H>B>E Rank for Females: C>G>A>D Rank for BW Rank for Males: E>F>B>H Rank for Females: A>D>G>C Rank for WW205 374 1094 608.8 207 614 75 5 M 75 H 371 1070 538.3 204 536 68 8 F 68 G 360 1164 679.5 193 610 74 12 M 71 F 356 1194 699.9 189 635 83 4 M 81 E 390 1035 539.2 223 580 74 8 F 74 D 381 1038 517.1 214 537 63 6 F 63 C 366 1134 626.0 199 610 80 7 M 80 B 368 1050 585.5 201 540 70 3 F 65 A YW365 AGE YW WW205 AGE WW AdBW AOD SEX BW Animal
  • Predicting Breeding Value Within Herd Genetic Evaluation Example 1: Rank animals based on BW, WW205 and YW365 Rank for Males: F>H>B>E Rank for Females: C>G>A>D Rank for BW Rank for Males: E>F>B>H Rank for Females: A>D>G>C Rank for WW205 Rank for Males: E>F>B>H Rank for Females: A>G>C>D Rank for YW365 1068.7 374 1094 608.8 207 614 75 5 M 75 H 1049.9 371 1070 538.3 204 536 68 8 F 68 G 1210.3 360 1164 679.5 193 610 74 12 M 71 F 1235.5 356 1194 699.9 189 635 83 4 M 81 E 975.1 390 1035 539.2 223 580 74 8 F 74 D 997.1 381 1038 517.1 214 537 63 6 F 63 C 1128.0 366 1134 626.0 199 610 80 7 M 80 B 1074.1 368 1050 585.5 201 540 70 3 F 65 A YW365 AGE YW WW205 AGE WW AdBW AOD SEX BW Animal
  • Breeding Value (BV)
    • The contribution of each effect is proportional to the variance explained by effect
    Additive Effect Dominance Environment or Breeding Value
    • Concepts discussed on Phenotypic Selection still valid!!
  • Estimated Breeding Value (EBV)
    • Notice that the Breeding Value of an animal is the sum of its genes Additive Effects
    Additive Effect Breeding Value Genetic Gain When estimated from Phenotypes Phenot. Selection Phenotype expressed as a deviation from the mean
    • Concepts discussed on Phenotypic Selection still valid!!
  • General Formulas for BV and ACC
    • P = trait mean of the animal(s) of record.
    • trait mean of contemporary group.
    • b = regression factor.
    • Phenotype expressed as a deviation from the mean
  • Estimated Breeding Value x Expected Progeny Difference
    • EPD = PTA = 1/2 EBV = the portion of an animal’s BV that is expected to be passed on to its progeny for a given trait.
  • Estimated Breeding Value x Expected Progeny Difference What is the expected average Phenotype on the progeny (change on the distribution mean)
  • Accuracy (ACC) of EBV
    • Mathematical expression of the degree of confidence that the EBV accurately predicts true BV.
    • Ranges between 0 and 1.
  • General Formulas for EBV and ACC
    • g = relationship weighting factor.
    • b = regression factor.
    Correlation between real breeding value and estimated breeding value i.e. the closest the estimation to real BV more accurate is the EBV
  • ACCURACY Expected Variation on Progeny Difference What is the expected average Phenotype on the progeny for high and low accuracy EPDs (change on the distribution mean)
  • Predicting Breeding Value Across-Herd Genetic Evaluation Allows comparisons of breeding value estimates of animals in different herds or contemporary groups.
  • Predicting Progeny Performance
    • EBV = estimated breeding value (all species).
    • EPD = expected progeny difference (beef, swine, and sheep).
    • PTA = predicted transmitting ability (dairy).
  • To compare animals from different herds:
    • Must account for between-herd differences in:
    • 1) environment
    • 2) overall herd genetic potential (genetic potential of mates)
    • Variation on mean contemporary group may be due environmental and genetic differences
    • Question: How to differentiate Environmental and Genetic effects on different CG?
  • To compare animals from different herds:
    • Must account for between-herd differences in:
    • 1) environment
    • 2) overall herd genetic potential(genetic potential of mates)
  • To compare animals from different herds:
    • Must account for between-herd differences in:
    • 1) environment
    • 2) overall herd genetic potential(genetic potential of mates)
    • 1) is accounted for by using sires in multiple herds simultaneously. See next slide.
    • 2) relates to the fact that a sire used in a good herd will look better than when used in a bad herd because of the females he’s being mated to. Current statistical procedure account for this since all available records are used.
  • Reference sire concept
    • Because of AI, a sire can produce progeny in multiple herds simultaneously.
    • Such sires serve as a base or reference point in order to adjust for differences in E.
  • Across-Herd Genetic Evaluation
    • Originally, genetic evaluation programs were based on within-herd comparisons only.
    • Increased use of A.I. And more sophisticated computer programs allowed expansion to across-herd evaluation.
    • Across-herd genetic evaluation programs are usually done separately by breed.
  • Reference sire concept
    • Originally, in beef cattle, each breed with an across-herd evaluation program designated specific sires as reference sires. In order to have across-herd EPDs for animals in your herd, some of your calves had to be sired by reference sires.
    • At that time, EPDs were calculated only for sires. Now they can be calculated for virtually every animal in the breed as long as the necessary trait data is available.
  • Reference Sire concept
  • Reference Sire concept Bull A is siring calves in both herds and so serves as a benchmark for comparisons. The column at the right compares each bull to Bull A in terms of progeny weaning weight. If we used the WW column, we’d rank the bulls A - D - B,E - C. This would not be correct because Herd 2 has a better environment and (or) better cows. Using the column at right, we correctly rank the bulls A - B - D - E - C. This concept applies for other species as well.
  • Predicting Breeding Value Across-Herd Genetic Evaluation Population Farms Contemporary Groups Within CG Adjustments AOD, Age… Between CG Reference Sires – Half Sibs
  • Predicting Breeding Value Animals Compared within Contemporary Group. It’s a way to correct for non- genetic effects. Reference Sires = Animal used in different contemporary groups or different farms. Mean production of Half Sibs from Reference Sires allows the estimation of the effect of the contemporay group. Once the contemporary group effect is calculated is possible to compare animals born in different farms. Within Contemporary Group Animals have performance adjusted for non-genetics effects such as age of the Dam Population Farms Contemporary Groups
  • Reference Sire Concept
    • Today, designated reference sires are not usually needed.
    • Many sires serve as references without being designated as such.
    • Other relationships between herds also serve as ties to adjust for differences in E.
  • Reference sire concept
    • 1- Same animal in different herds (impossible for many traits)
    • 2- Clones (not available)
    • 3- Full Sibs (ET not very effective)
    • 4- Half Sibs (AI – Improve connection between CG)
    • 5- Any related animal (Connect different CGs)
  • Reference sire concept
    • 1- Same animal in different herds (impossible for many traits)
    • 2- Clones (not available)
    • 3- Full Sibs (ET not very effective)
    • 4- Half Sibs (AI – Improve connection between CG)
    • 5- Any related animal (Connect different CG)
    • Within family variation – number of progenies
    • _____
    • Mean of HS in different CG tend to be similar
    • n#HS < n# animals with more distant relationship.
    Coefficient of Relationship
  • Beef Cattle EPDs
    • Different programs for different breeds.
    • National Sire Evaluation - previous.
    • National Cattle Evaluation - today.
    • Typical traits
      • Growth: BW, WW direct, YW, others.
      • Maternal: Milk, WW maternal.
      • Carcass: wt, external fat, REA, marbling.
      • Others: vary by breed
  • Breed Average EPD and $Values Genetic Base: 1979 Distributions 25.8 13.2 12 0.04 0.003 0.11 0.06 Non-Parent Cows 25.6 12.8 12.6 0.04 0.003 0.11 0.06 Non-Parent Bulls 18.2 12.2 4.21 0 0 0.01 0 Current Dams 1 23.8 12.2 11.7 0.01 0.002 0.08 0.02 Current Sires 1 $B $G $F %RP Fat RE %IMF   $VALUES BODY COMPOSITION                   1 66 18 35 2.6 Non-Parent Cows           0.3     1 67 18 36 2.6 Non-Parent Bulls           0.1     1 55 15 30 2.7 Current Dams 1 0.07 0.001 0.120 0.11 4 0.2 0.8 4 1 66 18 36 2.6 Current Sires 1 %RP Fat RE Marb CW SC MH MW YH YW Milk WW BW   CARCASS PRODUCTION  
  • Interpretation
    • Yearling weight _
    • EPD, lb ACC
    • Bull A - 5.0 .56
    • Bull B +25.0 .72
    • Future offspring of B are expected to weigh 30 lb more than those of A at one year, on average.
  • Interpretation
    • Fat thickness _
    • EPD, in ACC
    • Bull A - .20 .41
    • Bull B + .08 .38
    • Offspring of A are expected, on average, to produce carcasses with .28 in less fat than those of B at the same slaughter age.
  • Beef Cattle EPDs
    • Direct WW EPD: predicts WW difference of the animal’s own offspring (growth potential).
    • Maternal WW EPD: predicts WW difference of calves of the animal’s daughters (milk and growth potential).
    • Milk EPD: predicts the portion of WW difference of calves of the animal’s daughters due to milk (milk potential).
  • Beef Cattle EPDs Maternal WW EPD = Milk EPD + 1/2 Direct WW EPD
  • Accuracy of EPD
    • Similar to ACC of EBV.
    • Level of confidence that the EPD closely approximates true PD.
    • Is not a measure of expected variation among progeny.
    • Use EPDs to select or rank breeding animals. Use ACC to determine how extensively an animal is used.
  • Accuracy and Associated Possible Change
    • The following table lists the possible change values associated with each EPD trait at the various accuracy levels.
    • Possible change is expressed as &quot;+&quot; or &quot;-&quot; pounds of EPD and can be described as a measure of expected change or potential deviation between the EPD and the &quot;true&quot; progeny difference.
    • This confidence range depends on the standard error of prediction for an EPD. For a given accuracy, about two-thirds of the time an animal should have a &quot;true&quot; progeny difference within the range of the EPD plus or minus the possible change value.
    More info higher Acc. (.3-.4 for young animals and .99 for sires with more than 500 offspring
  • Accuracy and Associated Possible Change
    • For example, a sire with an accuracy of .7 and birth weight EPD of +1.0 is expected to have his &quot;true&quot; progeny value falling within ±0.86 pounds for birth weight EPD (ranging between 0.14 and +1.86) about two-thirds of the time.
    • With the conservative approach taken with respect to heritabilities in the Angus evaluation, actual EPD changes of animals within the population are much less than statistics would indicate.
  • Accuracy and Associated Possible Change Variation on progeny (Distributions) 0.02 0.001 0.02 0.01 0.03 0.002 0.01 0.01 0.81 0.04 0.04 2.07 0.85 0.48 0.58 0.14 0.95 0.04 0.002 0.03 0.02 0.06 0.004 0.03 0.03 1.62 0.07 0.08 4.14 1.70 0.97 1.16 0.29 0.90 0.06 0.003 0.05 0.03 0.08 0.005 0.04 0.04 2.43 0.11 0.12 6.21 2.55 1.45 1.74 0.43 0.85 0.07 0.004 0.06 0.04 0.11 0.007 0.06 0.05 3.25 0.15 0.16 8.28 3.40 1.94 2.32 0.57 0.80 0.09 0.005 0.08 0.05 0.14 0.009 0.07 0.06 4.06 0.18 0.19 10.35 4.26 2.42 2.90 0.72 0.75 0.11 0.006 0.10 0.06 0.17 0.011 0.09 0.08 4.87 0.22 0.23 12.42 5.11 2.91 3.48 0.86 0.70 0.17 0.010 0.14 0.09 0.25 0.016 0.13 0.12 7.30 0.33 0.35 18.62 7.66 4.36 5.22 1.29 0.55 0.19 0.011 0.16 0.10 0.28 0.018 0.14 0.13 8.12 0.37 0.39 20.69 8.51 4.85 5.80 1.44 0.50 0.22 0.013 0.19 0.12 0.34 0.021 0.17 0.16 9.74 0.44 0.47 24.83 11.21 5.82 6.96 1.72 0.40 0.24 0.014 0.21 0.13 0.36 0.023 0.19 0.17 10.55 0.48 0.51 26.90 11.06 6.30 7.54 1.87 0.35 0.26 0.015 0.22 0.14 0.39 0.025 0.20 0.18 11.36 0.51 0.54 28.97 11.92 6.79 8.12 2.01 0.30 0.32 0.018 0.27 0.16 0.48 0.030 0.25 0.22 13.80 0.62 0.66 35.18 14.47 8.24 9.85 2.44 0.15 0.35 0.020 0.30 0.18 0.53 0.034 0.270 0.25 15.42 0.70 0.74 39.32 16.17 9.21 11.0 2.73 0.05 %RP Fat RE %IMF %RP Fat RE Marb CW SC MH MW YW Milk WW BW Acc   Body Composition EPD Carcass EPD Production EPD
  • Accuracy and Associated Possible Change
    • For example, a sire with an accuracy of .7 and birth weight EPD of +1.0 is expected to have his &quot;true&quot; progeny value falling within ±0.86 pounds for birth weight EPD (ranging between 0.14 and +1.86) about two-thirds of the time.
    • With the conservative approach taken with respect to heritabilities in the Angus evaluation, actual EPD changes of animals within the population are much less than statistics would indicate.
  • Beef Cattle EPDs
    • Much information can be found on WWW.
      • Breed associations
        • Angus - http://www.angus.org/index.html
        • Limousin - http:// www.ansi.okstate.edu/breeds/cattle/limousin /
        • Hereford - http:// www.hereford.org/tailored.aspx
        • Simmental – http://www.simmgene.com/
      • A.I. Organizations
        • ABS Global - http://www.absglobal.com/home.html
  • EPDs for commercial beef producers
    • Unless using A.I., bulls will likely have low ACC values.
    • Progeny of low ACC bulls tend to perform as expected when averaged over several bulls. Some individual bulls will be over- or under-estimated.
    • The ACC of an EPD averaged over several bulls will be higher than the average of their individual ACCs.
  • Genetic Base
    • Base = zero-point. EPDs calculated as deviations from “genetic base”.
    • Fixed base example: all animals born 1979.
    • Some breeds now use “floating base”.
    • Implication: In general, an EPD of 0.0 does not equal current breed average.
  •  
  • Across-breed EPDs
    • In general, cannot compare EPDs computed by different breed associations.
      • Each breed conducts separate analysis.
      • Genetic base (zero-point) is different for each breed.
    • Table of across-breed adjustment factors from USDA MARC.
    • Simmental uses some data from other breeds.
  • Across-breed EPDs --- -5.7 -4 3.3 Red Angus 12.4 35.1 26.9 5.1 Salers 17.2 10.5 28.5 3.7 Tarentaise 7.2 21.3 26.1 7.6 Pinzgauer 13.1 -19.9 8.1 5.8 Gelbvieh 10.8 0.7 16 6.5 Maine Anjou 6 50.8 37.7 10.5 Charolais -1 16.2 22.1 5.9 Limousin 13.2 18.1 20.7 6.8 Simmental 24.6 -9.1 34.1 13.1 Brahman 2.2 36 20.1 6.8 South Devon 13.1 39.1 28 7.4 Shorthorn 0 0 0 0 Angus -14.4 -8.8 0.4 3.6 Hereford Milk Yearling Weight Weaning Weight Birth Weight Breed   TO ESTIMATE AB-EPDs     ADJUSTMENT FACTORS TO ADD TO EPDs OF FOURTEEN DIFFERENT BREEDS
  • National Sheep Improvement Program (NSIP)
    • Across-flock EPDs are available for some animals in 6 breeds:
      • Columbia
      • Dorset
      • Hampshire
      • Polypay
      • Suffolk
      • Targhee
    • Cannot compare EPDs between breeds
  • Sheep Maternal traits
    • Number of lambs born per ewe lambing.
    • Milk EPD.
    • Milk + growth EPD =
      • milk EPD + 1/2 (60-day wt EPD).
  • Sheep Growth traits
    • Farm flocks
      • 60-day and 120-day weight
    • Range flocks
      • 120-day and yearling weights
  • Sheep Wool traits.
    • fleece weight (lb).
    • fiber length (in).
    • fiber diameter (microns).
  • Dairy Genetic Evaluation
    • USDA computes across-herd values
    • Some animals are also included in an across-country analysis (Interbull).
    • Predicted value is based on records from all relatives.
    • Values are calculated as deviations from the base.
    • The base for production traits was recently updated to cows born in 1995.
  • Dairy Cattle Genetic Evaluation
    • Production traits
      • PTA = predicted transmitting ability (like EPD)
      • PPA = predicted producing ability (like MPPA); females only (repeatability).
    • Type traits
      • STA = standardized transmitting ability (standard deviation units)
    • REL = reliability (like ACC)
  • Production Traits
    • PTA M (lb milk)
    • PTA F (lb fat)
    • PTA F% (% fat)
    • PTA P (lb protein)
    • PTA P% (% protein)
    • PTA PL (productive life, months)
    • PTA SCS (somatic cell score; lower better)
  • Dairy Linear (type) Traits
    • Stature (height)
    • Strength (frail vs. strong)
    • Body depth
    • Feet & leg score
    • Udder traits
    • Others
  • Dairy Management Traits
    • Milking speed
    • Temperament
    • Non-return rate
  • Dairy Cattle
    • Milk Yield (305-day) _
    • PTA, lb Rel
    • Bull A +1125 .66
    • Bull B +2525 .92
    • Future daughters of B are expected to produce 1400 lb more milk per lactation than daughters of A, on average.
  • Dairy Cattle
    • Protein _
    • PTA, lb PTA, %
    • Bull C +58 - 0.05
    • Bull D +48 +0.04
  • Standard Indexes
    • Net Merit
    • Fluid Merit
    • Cheese Merit
    • TPI = type/production index
    • Udder composite
    • Feet & leg composite