# Presentation: 516273

## Genetic Evaluation for Small Ruminants

## 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**

## 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**

## 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**

## 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**

## What is an evaluation?

**Phenotype is measurable **
**Pounds of milk produced**
**Stature**
**An evaluation is an estimate of Genotype**

**Phenotype = Genotype + Environment**

## 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**

## 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)**

## Trait and pedigree data collection 

**FARM**

**COMPONENT TEST LAB**

**DRPC**

| Center | Data Sent to AIPL |
| --- | --- |
| DRMS – NC | Daily |
| DHI-Provo – UT | Daily |
| Agri-Tech – CA | 2x/week |
| AgSource – WI | Weekly |
| Langston - OK | Monthly |

**AIPL**

**ADGA**

**INTERNET**

**Milk data collected monthly **

**DHIA**

**Pedigree recorded **

**Type scored annually **

## Goat test days by processing centerJune/July, 2005

| Center | Herds | Does | Percent of Does |
| --- | --- | --- | --- |
| DRMS | 150 | 2461 | 33.7 |
| DHI-Provo | 162 | 2558 | 35.0 |
| Agri-Tech | 20 | 940 | 12.9 |
| AgSource | 30 | 855 | 11.7 |
| Langston | 59 | 489 | 6.7 |
| Total | 421 | 7303 |  |

## Examples of non-genetic factors

**Age**
**Lactation**
**Season**
**Litter size **
**Milking frequency**
**Herd**

## 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**

## 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**

## Type Model: y = h + pe + a + e

**y**** = adjusted type record**
**h**** = herd appraisal date**
**pe**** = permanent environment**
**Non-genetic effect common to all of a doe’s lactations **
**a**** = animal genetic effect (breeding value)**
**e**** = unexplained residual**

| Multi-trait evaluation allows scores from one trait to affect the evaluation of another trait through the genetic correlation among the traits. |
| --- |

## Correlations between type traits 

|  | Final Score | Strength | Dairyness | 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 |

## 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(PTA****Milk****) + 1.15(PTA****Fat****) + 2.55(PTA****Protein****)**

## 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**

## 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**

## 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 **

## 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**

## 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**

## Expressing evaluations

**Estimated Breeding value (****EBV****) **
**Animal’s own genetic value**
**Predicted Transmitting ability (****PTA****)  **
**1⁄2 EBV**
**Expected contribution to progeny**

## 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**

## 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**

## 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**

## 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**

## 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**

## 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**

## 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**

## 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**

## 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**

## 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**

## 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**

## Evaluations in other countries

**Australia: LambPlan**
**http://www.mla.com.au/lambplan**
**Canada: Goats**
**http://www.aps.uoguelph.ca/~gking/Ag_2350/ goat.htm**
**http://www.goats.ca**
**Israel: Dairy Sheep and Goats**
**http://www.sheep-goats.org.il/about.htm**

## 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**

## 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**