# Presentation: 180034

## 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**

## 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**

## 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**

## 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 longevity**
**Optimal number of kids born**
**Improved conformation score (overall and linear)**
**Increased profitability**

## Examples of Non-genetic Factors

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

## Data Flow

**FARM**

**COMPONENT TEST LAB**

**DRPC**

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

**AIPL**

**ADGA**

**INTERNET**

**Milk Data collected monthly **

**DHIA**

## Does on Test at Last Test in 2005By 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://](http://aipl.arsusda.gov/publish/dhi/current/drpcx.html)__[aipl.arsusda.gov/publish/dhi/current/drpcx.html](http://aipl.arsusda.gov/publish/dhi/current/drpcx.html)_

## 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   **

## 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**

## Alpine Milk ProductionLactation Curve

## Alpine Fat PercentageLactation Curve

## Alpine Protein PercentageLactation Curve

## Alpine and Nubian Milk Production Second Lactation

**Alpine and Nubian Milk Production ****Second Lactation**

**Alpine**

**Nubian**

## Nubian Fat and Protein PercentageSecond Lactation

**Fat**

**Protein**

## 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**

## 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**

## 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

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

## Type Traits

**Describe physical characteristics of animal**
**Final Score (overall assessment)**
**Scored  50-99**
**Linear traits (13 defined traits)**
**Scored 1-50**

## 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. **

## 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 |

## 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**** **

## 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**

## 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**

## Methods of Expressing Evaluations

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

## Heritability

**Portion of total variation due to genetics**
**Milk, Fat, Protein: 25%**
**Range for Type: 19% (r. udder arch) — 52% (stature)**

## 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)**

## 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**

## Trend in Breeding Value for Milk

**Trend in Breeding Value for Milk**

**Available: ****http://aipl.arsusda.gov/eval/summary/goats.cfm?trnd_tbl=AIm**

## 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**

## Factors Affecting Value of Data

**Completeness of ID and parentage reporting**
**Years herd on test**
**Size of herd**
**Frequency of testing and component determination**

## 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**

## 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**

## 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**

## 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**

## 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.)

**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**

## 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**

## 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**

## 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

**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**

## 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**

## AIPL web services

_**[http://aipl.arsusda.gov/query/public/tdb.shtml#GoatsTBL](http://aipl.arsusda.gov/query/public/tdb.shtml#GoatsTBL)**_
**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**