Usda genetic evaluation program for dairy goats
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USDA Genetic Evaluation Program for Dairy Goats - PowerPoint PPT Presentation

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USDA Genetic Evaluation Program for Dairy Goats . Why Genetic Evaluations?. A valuable tool for genetic selection Allows for comparison of animals in different environments Can include all of the information available for each animal Greatest impact on progress is from selection for males.

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Why genetic evaluations l.jpg
Why Genetic Evaluations?

  • A valuable tool for genetic selection

  • Allows for comparison of animals in different environments

  • Can include all of the information available for each animal

  • Greatest impact on progress is from selection for males

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Why Genetic Selection?

  • Genetic selection can improve fitness, utility, and profitability

  • Females must be bred to provide replacements and initiate milk production

  • Mate selection is an opportunity to make genetic change

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Selection is a Continuous Process

  • Decisions

    • Which females to breed

    • Which males to use

    • Which specific matings to make

    • Which progeny to raise

    • Which females to keep and breed

  • Goals

    • Improve production and efficiency

    • Avoiding inbreeding

    • Correct faults

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Genetic Improvement Program

Phenotype = Genotype + Environment

  • Genetic improvement programs only change genotype

  • Rate of genetic improvement determined by:

    • Generation interval

    • Selection intensity

    • Heritability

  • Heritability is the portion of total variation due to genetics

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Steps in Genetic Evaluation

  • Define a breeding goal

  • Measure traits related to the goal

  • Record pedigree to allow detection of relationships across generations

  • Identify non-genetic factors that affect records and could bias evaluations

    • Make adjustments

    • Include in the model

  • Define an evaluation model

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Examples of Breeding Goals

  • Increased milk, fat, or protein yield

  • Increased longevity

  • Optimal number of kids born

  • Improved conformation score (overall and linear)

  • Increased profitability

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Examples of Non-genetic Factors

  • Age

  • Lactation

  • Season

  • Litter size

  • Milking frequency

  • Herd

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Data Flow

Milk Data collected monthly








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Does on Test at Last Test in 2005By Processing Center

Source: DHI Report K-6, 2006 Table 6 Available:

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Data Validation

  • Incoming data is checked against database for verification

    • Birth date is checked against kidding date

    • Sire and dam are checked against breeding records and ADGA

  • Cross-references are assigned when identification changes

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Data Validation (Cont.)

  • Cross-references are determined based on control number

  • Abnormal yields are detected and reported to DRPC

  • Test dates and testing characteristics are compared with herd data

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Lactation 1

Lactation 2

Lactation 3

Alpine Milk ProductionLactation Curve

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Lactation 1

Lactation 2

Lactation 3

Alpine Fat PercentageLactation Curve

Alpine protein percentage lactation curve l.jpg

Lactation 1

Lactation 2

Lactation 3

Alpine Protein PercentageLactation Curve

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Alpine and Nubian Milk Production Second Lactation



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Nubian Fat and Protein PercentageSecond Lactation



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Evaluation Calculation

  • Goal

    • Predict productivity of progeny

  • Method

    • Separate genetic component from other factors influencing evaluated traits

  • All relationships are considered

    • Bucks receive evaluations from the records on their female relatives

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Evaluation model

  • An equation that indicates what factors contribute to an observation

  • Separates the genetic component from other factors

  • Solutions used to predict the genetic potential of progeny

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Yield Model: y = hys + hs + pe + a + e

y = yield of milk, fat, or protein during a lactation

hys = herd-year-season

Environmental effects common to lactations in the same season, within a herd

hs = herd-sire

Effects common to daughters of the same sire, within a herd

pe = permanent environment

Non-genetic effect common to all of a doe’s lactations

a = animal genetic effect (breeding value)

e = unexplained residual

Indexes l.jpg

  • An index combines evaluations for a group of traits based on their contribution to a selection goal

  • Milk-Fat-Protein Dollars

    • Combines yield evaluations into a single number

      MFP$ = 0.01(PTAMilk) + 1.15(PTAFat) + 2.55(PTAProtein)

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Type Traits

  • Describe physical characteristics of animal

  • Final Score (overall assessment)

    • Scored  50-99

  • Linear traits (13 defined traits)

    • Scored 1-50

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Type Evaluation Model

MODEL: y = h + a + p + e

y = Adjusted type record

h = Herd appraisal date

a = Animal genetic effect (breeding value)

p = Permanent environment

- Effect common to all a doe's lactations that is not genetic

e = Unexplained residual

Multi-trait - Scores of one trait affect evaluations of other traits.

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Combining type and production

Production-Type index (PTI)

  • Combines yield and type evaluations into a single value

  • There are 2 versions:

    • PTI 2:1, weights 2 production : 1 type

    • PTI 1:2, weights 2 type : 1 production

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How Accurate are Evaluations?

  • Reliability measures the amount of information contributing to an evaluation

  • Increases as daughters are added (at decreasing rate)

  • Also affected by:

    • Number of contemporaries

    • Reliability of parents’ evaluations

    • Heritability

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Accuracy of Evaluations

  • Does kidding in same season

    • More records  better estimate of herd-year-season (hys) effect

  • Bucks with daughters having records in same hys

    • More direct comparisons  better ranking of bucks

  • Number of lactation records

  • Number of daughters

  • Completeness of pedigree data

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Methods of Expressing Evaluations

  • Estimated breeding value (EBV)

    • Animal’s own genetic value

  • Predicted transmitting ability (PTA)

    • ½ EBV

    • Expected contribution to progeny

Heritability l.jpg

  • Portion of total variation due to genetics

    • Milk, Fat, Protein: 25%

    • Range for Type: 19% (r. udder arch) — 52% (stature)

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USDA Dairy Goat Evaluations

  • Evaluations for milk, fat, protein, and type

  • Yield evaluations in July

    Type evaluations in November

  • Evaluations provided to ADGA, DRPC, and public via the Internet (

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What Do the Numbers Mean?

  • Evaluations are predictions

    • The true value is unknown

  • The predictions rank animals relative to one another using a defined base

  • The base is the zero- or center-point for evaluations

    • For example: the performance of animals born in a given year

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Trend in Breeding Value for Milk


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Ways to Increase Rate of Improvement

  • Use artificial insemination (AI) to use better males in more herds

  • Identify promising young males for progeny testing (PT)

    • Use on a representative group of does and observe the actual success of progeny

  • Focus on larger herds to improve accuracy

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Factors Affecting Value of Data

  • Completeness of ID and parentage reporting

  • Years herd on test

  • Size of herd

  • Frequency of testing and component determination

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Why Evaluations Go Wrong

  • Important factors ignored

    • Litter size

    • Milking Frequency

    • Preferential treatment

  • Unlucky

    • Current data not representative of future data

    • Traits with low heritability require large numbers to be accurate

  • Recording errors

    • Wrong daughters assigned to a sire

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Dairy Cattle Program for Genetic Improvement

  • Artificial insemination (AI)

    • Allows for many progeny from superior males

    • Allows semen to be used in geographically diverse locations

  • Progeny testing (PT)

    • Use young males to get a representative group of daughters

    • Wait until those daughters are milking

    • Based on the evaluations, return the best males to heavy use

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Dairy Cattle Program for Genetic Improvement (Cont.)

  • Pre-select only promising bulls for PT

  • Select only the best of the PT bulls for widespread use

    • Only about 1 in 10 PT bulls enter active service

  • Remove bulls from active service as better new bulls become available

    • Bulls remain active only a few years

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Alternative to Waiting for PT

  • Use young bucks for most breedings

  • Replace bucks quickly

  • Bank semen of young bucks

  • Use frozen semen from superior proven bucks as sires of next generation of young bucks

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Recent Changes to System

  • Web query for accessing data by animal name

  • Yield data since 1998 extracted from the master file each run

    • Incorporates corrections, deletions, and ID changes

  • Standardized yields back to 1974 available

Recent changes to system cont l.jpg
Recent Changes to System (Cont.)

  • Added Breed codes

    • CC – Sable

    • ND – Nigerian Dwarf

  • ID simplified by removing G and 18 prefixes when not required for uniqueness

  • More complete breeding information stored

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Possible Enhancements

  • Add evaluations for more traits

    • Productive Life

    • Somatic Cell Score

    • Daughter Pregnancy Rate

  • Switch to test day model

    • Provides better accounting for environment

    • Accounts for genetic differences in shape of lactation curve

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  • DNA analysis

    • Parentage verification

    • Genetic evaluation

      • Genomic information may enable reasonably accurate evaluation at birth

  • National Animal Identification System (NAIS)

    • May cause changes in ID

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Genomic Data

  • Single Nucleotide Polymorphisms (SNP)

    • Large number of markers with 2 alleles

    • Tags segments of chromosomes

  • Parentage verification

    • Marker alleles must match those of a parent

    • Often can infer unknown parent ID

  • EBV calculated for chromosome segments

    • Sum the value of segments to approximate evaluation

    • Accuracy may approach progeny test

Conclusions l.jpg

  • Genetic evaluations are available for type and production

  • Traits can be improved through selection

  • Rate of improvement increases with accuracy of evaluations

  • AI enables widespread use of superior bucks and enables PT bucks to be used across herds

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Conclusions (cont.)

  • Genetic evaluations improve selection accuracy

  • Accurate evaluations also require adequate data and an appropriate model

  • Evaluations are based on comparisons

    • Differences for non-genetic reasons must be removed

  • DNA technology is of great interest

    • Still requires reliable evaluations

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AIPL web services

  • Queries provide display of:

    • Pedigree information

    • Yield records

    • Herd test characteristics

    • Genetic evaluations of does & bucks

      • Yield

      • Type

  • Access information using:

    • ID number

    • Animal name

    • Herd code