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

CORNELL ADGA 2005 (2) G.R. WIGGANS200

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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

genetic evaluation for small ruminants

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