Genetic Evaluation for Small Ruminants
description
Transcript of Genetic Evaluation for Small Ruminants
2004
2004
2005
George R. WiggansAnimal Improvement Programs LaboratoryAgricultural Research Service, USDA, Beltsville, [email protected]
Genetic Evaluation for Small Ruminants
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Why small ruminants?
Important contributors to the world supply of meat, milk, and fiber
Can utilize pasture not suitable for cattle
More suitable for small scale operations
People enjoy associating with them
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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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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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What is an evaluation?
Phenotype is measurable Pounds of milk produced Stature
An evaluation is an estimate of Genotype
Phenotype = Genotype + Environment
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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 average daily gain
Increased weaning weight
Optimal birth weight
Optimal litter size
Improved conformation score (overall and linear)
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Trait and pedigree data collection
FARM
COMPONENT TEST LAB
DRPC
CenterData Sent to AIPL
DRMS – NC DailyDHI-Provo – UT
Daily
Agri-Tech – CA
2x/week
AgSource – WI WeeklyLangston - OK Monthly
AIPL
ADGA
INTERNET
Milk data collected monthly
DHIA
Pedigree recorded
Type scored annually
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Examples of non-genetic factors
Age
Lactation
Season
Litter size
Milking frequency
Herd
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Evaluation model
An equation that indicates what factors contribute to an observation
Separates the genetic component from other factors
Solutions 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-seasonEnvironmental effects common to lactations in the same season, within a herd
hs = herd-sireEffects common to daughters of the same sire, within a herd
pe = permanent environmentNon-genetic effect common to all of a doe’s lactations
a = animal genetic effect (breeding value)
e = unexplained residual
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Type Model: y = h + pe + a + e
y = adjusted type record
h = herd appraisal date
pe = permanent environmentNon-genetic effect common to all of a doe’s lactations
a = animal genetic effect (breeding value)
e = unexplained residualMulti-trait evaluation allows scores from one trait to affect the evaluation of another trait through the genetic correlation among the traits.
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Correlations between type traits
Final Scor
eStreng
thDairyne
ss
Fore Udder
Attachment
Final Score 1.00 .30 –.15 .66
Strength .30 1.00 –.51 .15
Dairyness –.15 –.51 1.00 –.16
F. Udder Att.
.66 .15 –.16 1.00
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Evaluations indexes
An index combines evaluations for a group of traits based on their contribution to a selection goal
Example: Milk-Fat-Protein Dollars
MFP$ = 0.01(PTAMilk) + 1.15(PTAFat) + 2.55(PTAProtein)
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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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Factors affecting value of data
Completeness of ID and parentage reporting
Years herd has collected data
Size of herd
Frequency of testing and component determination
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Factors affecting evaluation accuracy
Number of daughters
Number of lactation records
Completeness of pedigree data
Numbers of females kidding in same herd-year-seasons
Numbers of males with daughter records in same herd-year-seasons
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How accurate are evaluations?
Reliability measures the amount of information contributing to an evaluation
Increases at a decreasing rate as daughters are added
Also affected by: Number of contemporaries Reliability of parents’ evaluations Heritability of the trait
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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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Expressing evaluations
Estimated Breeding value (EBV)
Animal’s own genetic value
Predicted Transmitting ability (PTA)
½ EBVExpected contribution to progeny
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Factors in genetic improvement
Heritability is the portion of total variation due to genetics
Milk: 25%Type: 19% (r. udder arch) — 52%
(stature)
Rate of genetic improvement is determined by:
Generation interval Selection intensity Heritability
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Increasing genetic improvement
Use artificial insemination (AI) to use better males in more herds
Identify promising young males for progeny testing (PT)
Use in a representative group of breedings and observe the actual success of progeny
Focus on larger herds to improve accuracy
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Dairy cattle improvement program
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 males for most breedings
Replace males quickly
Bank semen of young males
Use frozen semen from superior proven males as sires of next generation of young males
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Central vs. on-farm testing
Availability of: Central Test Stations Effective genetic evaluation system
Traits analyzed support selection goals
Active participation of many breeders in the centralized data repository
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Centralized performance test
Determine genetic differences of individuals from different herds
Does NOT compare herds or breeders
Optimal environment Allows for ADG and feed conversion
testing Ultrasound testing of final meat
products Marketing venue
Typically only males evaluated
Phenotype compared
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On-farm testing
Comparisons Within herd Across herd through evaluations
Data collection for many traits
Low cost
Whole herd test Records and genetic evaluation of
all animals
Genotype compared
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Available evaluations
AIPL Dairy goat Milk, fat, and protein yields 14 conformation traits http://aipl.arsusda.gov
Boer Goat Improvement Network http://www.abga.org
National Sheep Improvement Program
http://www.nsip.org
Ram testing stations
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Pennsylvania meat goat and ram performance tests
Livestock Evaluation Center (LEC) in Centre County
Purebred males born Sept — Feb
Starts in April 84 days for rams 70 days for goats
ADG and US testing
Results combined in an index
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AIPL dairy goat evaluations
Yield evaluations in July
Type evaluations in December
Evaluations provided to ADGA, DRPC, and publicly via the internet
Web services at:
http://aipl.arsusda.gov/query/public/ tdb.shtml#GoatsTBL
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AIPL web services
Queries provide display of: Pedigree information Yield records Herd test characteristics Genetic evaluations
• Does and bucks• Yield and type
Access information using: ID number Animal name Herd code
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Evaluations in other countries
Australia: LambPlanhttp://www.mla.com.au/lambplan
Canada: Goatshttp://www.aps.uoguelph.ca/~gking/
Ag_2350/ goat.htmhttp://www.goats.ca
Israel: Dairy Sheep and Goatshttp://www.sheep-goats.org.il/about.htm
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Sequencing the genome
Single Nucleotide Polymorphisms (SNP) enable identification of the source for
segments of chromosomes
Parentage verification DNA sequences must match those of a parent Known sequences can suggest unknown
parent ID
EBV calculated for chromosome segments
Sum the value of segments to approximate evaluation
Accuracy approaches progeny test
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Wrap up Genetic principles apply across species
Selection is the method for genetic improvement
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