USDA Genetic Evaluation Program for Dairy Goats

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USDA Genetic Evaluation Program for Dairy Goats
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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
COMPONENT TEST LAB
FARM
DHIA
Center Data Sent to AIPL
DRMS NC Daily
DHI-Provo UT Weekly
Agri-Tech CA 2/week
AgSource WI Weekly
Langston OK 2/month
DRPC
ADGA
INTERNET
AIPL
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Does on Test at Last Test in 2005By Processing
Center
Center Herds Does Percent of Does
DRMS 157 4,541 37.0
DHI-Provo 158 3,785 30.8
AgSource 37 2,246 18.3
Agri-Tech 20 1,099 8.9
Langston 55 618 5.0
Total 427 12,289
Source DHI Report K-6, 2006 Table 6 Available
http//aipl.arsusda.gov/publish/dhi/current/drpcx.
html
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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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Alpine Milk ProductionLactation Curve
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Alpine Fat PercentageLactation Curve
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Alpine Protein PercentageLactation Curve
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Alpine and Nubian Milk Production Second
Lactation
Alpine
Nubian
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Nubian Fat and Protein PercentageSecond Lactation
Fat
Protein
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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 does
  lactations
• a animal genetic effect (breeding value)
• e unexplained residual

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Indexes

• 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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Type Trait Genetic Correlations
Final Score Strength Dairyness Fore Udder Attachment
Final Score 1.00 .30 -.15 .66
Strength 1.00 -.51 .15
Dairyness 1.00 -.16
F. Udder Att. 1.00
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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 21, weights 2 production 1 type
• PTI 12, 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)
• Animals own genetic value
• Predicted transmitting ability (PTA)
• ½ EBV
• Expected contribution to progeny

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Heritability

• 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 (aipl.arsusda.gov)

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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
Available http//aipl.arsusda.gov/eval/summary/go
ats.cfm?trnd_tblAIm
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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

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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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Future

• 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

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Conclusions

• 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

• http//aipl.arsusda.gov/query/public/tdb.shtmlGoa
  tsTBL
• 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