# Presentation: 442976

## Curt Van Tassell, PhD
Bovine Functional Genomics Laboratory & Animal Improvement Programs Laboratory
Beltsville, MD

- Curt Van Tassell, PhD
- Bovine Functional Genomics Laboratory & Animal Improvement Programs Laboratory
- Beltsville, MD

## Background
Genetic Evaluations
Quantitative Genetics
Genomics
Integrating Genetics and Genomics
Case Study: DGAT1
Tangent: Animal Identification
Crystal Ball
Conclusions

- Background
- Genetic Evaluations
- Quantitative Genetics
- Genomics
- Integrating Genetics and Genomics
- Case Study: DGAT1
- Tangent: Animal Identification
- Crystal Ball
- Conclusions

## Bovine Functional Genomics Laboratory (BFGL)
Structural and functional genomics of cattle
Emphasis on health and productivity
Bioinformatics (storage and use of genomic data)
Animal Improvement Programs Laboratory (AIPL)
“Traditional” genetic improvement of dairy cattle
Increasing emphasis on animal health and reproduction

- Bovine Functional Genomics Laboratory (BFGL)
- Structural and functional genomics of cattle
- Emphasis on health and productivity
- Bioinformatics (storage and use of genomic data)
- Animal Improvement Programs Laboratory (AIPL)
- “Traditional” genetic improvement of dairy cattle
- Increasing emphasis on animal health and reproduction

## Estimate genetic merit for animals in a population

Select superior animals as parents of future generations

- Estimate genetic merit for animals in a population
- Select superior animals as parents of future generations

## Genetic Evaluation System

- Traditional selection has been very effective for many economically important traits
- Example: Milk yield
- Moderately heritable
- ~30 million animals evaluated 4x/yr
- Uses ~70 million lactation records
- Includes ~300 million test-day records
- Genetic improvement is near theoretical expectation

## Dairy Cattle Genetics Success

## Dairy Cattle Genetics Industry Cooperation

## Traditional dairy cattle breeding has assumed that an infinite number of genes each with very small effect control most traits of interest
Logical to expect some “major” genes with large effect; these genes are usually called quantitative trait loci (QTL)
The QTL locations are unknown!
Genetic markers can provide information about QTL

- Traditional dairy cattle breeding has assumed that an infinite number of genes each with very small effect control most traits of interest
- Logical to expect some “major” genes with large effect; these genes are usually called quantitative trait loci (QTL)
- The QTL locations are unknown!
- Genetic markers can provide information about QTL

## Allow inheritance of a region of the genome to be followed across generations
Single nucleotide polymorphisms (SNiP) are the markers of the future!
Need lots!
3 million in the genome
10,000 initial goal

- Allow inheritance of a region of the genome to be followed across generations
- Single nucleotide polymorphisms (SNiP) are the markers of the future!
- Need lots!
- 3 million in the genome
- 10,000 initial goal

## Identify genetic markers or polymorphisms in genes that are associated with changes in genetic merit 

Use marker assisted selection (MAS) or gene assisted selection (GAS) to make selection decisions before phenotypes are available

Adjust genetic merit for markers or genes in the genetic evaluation system

- Identify genetic markers or polymorphisms in genes that are associated with changes in genetic merit
- Use marker assisted selection (MAS) or gene assisted selection (GAS) to make selection decisions before phenotypes are available
- Adjust genetic merit for markers or genes in the genetic evaluation system

## QTL Identification

**QTL Identification**

**Genetic**
**Merit**

**DNA**

**Data**

## Compare Genetic

- Merit

**QTL Identification and ****Marker Assisted Selection**

## Gene Assisted Selection

## Largest benefits are for traits that:
have low heritability, i.e., traits where genetics contribute a small fraction of observed variation (e.g., disease resistance and fertility)
are difficult or expensive to measure (e.g., parasite resistance ) 
cannot be measured selection decision needs to be made (e.g., milk yield and carcass characteristics)
Evolution in traditional selection program by improving estimation of genetic merit 

- Largest benefits are for traits that:
- have low heritability, i.e., traits where genetics contribute a small fraction of observed variation (e.g., disease resistance and fertility)
- are difficult or expensive to measure (e.g., parasite resistance )
- cannot be measured selection decision needs to be made (e.g., milk yield and carcass characteristics)
- Evolution in traditional selection program by improving estimation of genetic merit

## DGAT1: diacylglycerol acyltransferase
Enzyme involved in fat sythesis
Identified using
Genetic marker data
Model organism (mouse) gene function information 
Cattle sequence verified candidate gene

- DGAT1: diacylglycerol acyltransferase
- Enzyme involved in fat sythesis
- Identified using
- Genetic marker data
- Model organism (mouse) gene function information
- Cattle sequence verified candidate gene

## Two forms of the gene in cattle
M = high milk (low fat) form of gene
F = high fat (low milk) form gene
BFGL scientists decided to characterize the gene in North American population
Over 3300 animals genotyped for DGAT1 SNP
Approximately 2900 genotypes verified and used in these analyses

- Two forms of the gene in cattle
- M = high milk (low fat) form of gene
- F = high fat (low milk) form gene
- BFGL scientists decided to characterize the gene in North American population
- Over 3300 animals genotyped for DGAT1 SNP
- Approximately 2900 genotypes verified and used in these analyses

**Notes:**
45 Jersey

## DGAT1 – Average Differencesin Daughters of Bulls

| Trait | MM-FF | Trait | MM-FF |
| --- | --- | --- | --- |
| Milk lbs | 361 | Fat% | 0.13 |
| Fat lbs | 16.5 | Protein% | 0.02 |
| Protein lbs | 5.0 | NM$ | $24 |
| SCS | 0.05 | CM$ | $35 |
| PL | 0.07 | FM$ | $4 |
| DPR | 0.21 |  |  |

## DGAT1 Genotypic Frequencies

## Genes will likely account for a fraction of the total genetic variation

Cannot select solely on gene tests!!

- Genes will likely account for a fraction of the total genetic variation
- Cannot select solely on gene tests!!

## Integrating Genomic Data:An Ideal Situation!

- Bull PTA

**Notes:**
<2% overlap – 3SD

## Integrating Genomic Data: The DGAT1 NM$ Situation!

- Bull PTA NM$

**Notes:**
94% overlap – 9%SD

## Integrating Genomic Data: The DGAT1 Fat Situation!

- Bull PTA Fat

**Notes:**
71% overlap – 50%SD

## Combine information
Ideally would incorporate genomic data into genetic evaluation system
Adjust PTA??
Don’t adjust well proven animals (it’s in there!!)
Adjust parent average for flush mates
Progeny have identical parent averages
Adjusting other PTA is non-trivial!

- Combine information
- Ideally would incorporate genomic data into genetic evaluation system
- Adjust PTA??
- Don’t adjust well proven animals (it’s in there!!)
- Adjust parent average for flush mates
- Progeny have identical parent averages
- Adjusting other PTA is non-trivial!

## Integrating Genomic Data: Another view of DGAT1 NM$!

- Bull PTA NM$

**MM**

**FF**

**Notes:**
94% overlap – 9%SD

## Recent German study evaluated impact on adjusting historic parent averages (PA) for DGAT1 and evaluated impact of predictability of future evaluations
Correlations of original PA with eventual PTA for milk were 45%
Correlations of adjusted PA with eventual PTA for milk were 55% (10% gain)
Incorporation of genomic data will result in increased stability of evaluations

- Recent German study evaluated impact on adjusting historic parent averages (PA) for DGAT1 and evaluated impact of predictability of future evaluations
- Correlations of original PA with eventual PTA for milk were 45%
- Correlations of adjusted PA with eventual PTA for milk were 55% (10% gain)
- Incorporation of genomic data will result in increased stability of evaluations

## Slow!
Progeny testing for production traits take 3 to 4 years from insemination
A bull will be at least 5 years old before his first evaluation is available
Expensive!
Progeny testing costs $25,000 per bull
Only 1 in 8 to 10 bulls graduate from progeny test
At least $200,000 invested in each active bull!!

- Slow!
- Progeny testing for production traits take 3 to 4 years from insemination
- A bull will be **at least 5 years old **before his first evaluation is available
- Expensive!
- Progeny testing costs $25,000 per bull
- Only 1 in 8 to 10 bulls graduate from progeny test
- At least $200,000 invested in each active bull!!

## Faster
Use of gene and marker tests allow preliminary selection decisions beyond parent average before performance and progeny test data are available
Cheaper
Improved selection decisions should result in higher graduation rates or enhanced genetic improvement

- Faster
- Use of gene and marker tests allow preliminary selection decisions beyond parent average before performance and progeny test data are available
- Cheaper
- Improved selection decisions should result in higher graduation rates or enhanced genetic improvement

**Notes:**
Lower semen cost or higher genetic improvement

## Increase number of genetic markers
Continue QTL discovery for MAS/GAS
Better characterize the genome
Compare genome to well characterized human and mouse genome

- Increase number of genetic markers
- Continue QTL discovery for MAS/GAS
- Better characterize the genome
- Compare genome to well characterized human and mouse genome

## Bovine Genome Sequence

## Inbred Hereford is primary animal being sequenced
Genome size is similar to humans 
Sequencing about half completed
First assembly released yesterday!!
2.3 of 2.8 billion base pairs
84% coverage

- Inbred Hereford is primary animal being sequenced
- Genome size is similar to humans
- Sequencing about half completed
- First assembly released yesterday!!
- 2.3 of 2.8 billion base pairs
- 84% coverage

- L1 Dominette 01449

## Six breeds selected for low level sequencing
Holstein and Jersey cows represent dairy breeds
Useful for SNP marker development
Expect 3 million SNPs in the genome
Preliminary goal is to characterize 10,000

- Six breeds selected for low level sequencing
- Holstein and Jersey cows represent dairy breeds
- Useful for SNP marker development
- Expect 3 million SNPs in the genome
- Preliminary goal is to characterize 10,000

- Wa-Del RC Blckstr Martha-ET

-  Mason Berretta Jenetta

**Notes:**
Thanks to Fred and Dale Rice of Ricecrest Holstein and Nico de Boer of Hilltop Jersey Farm

## Genomic Tools for Parentage Verification

- Low-cost high-throughput SNP marker tests would facilitate parentage verification and traceability
- $10 to $20 per sample seems to be a common break point
- Progeny test herds would likely be early adopters
- Support from studs?
- Results in increased stability on first proofs?
- Nearly impossible to make mistake on parentage
- Punished on second crop proofs?
- With widespread implementation
- Increase effective heritability
- Decrease evaluation variability
- Enhanced genetic improvement

## Large number of validated genetic tests available
Large amounts of marker and gene data publicly available
Genomic data incorporated into genetic evaluation
Management decisions facilitated by genomics data

- Large number of validated genetic tests available
- Large amounts of marker and gene data publicly available
- Genomic data incorporated into genetic evaluation
- Management decisions facilitated by genomics data

## How big is the effect?
Traits of interest, economic index (NM$, TPI, PTI)
How many genetic standard deviation units?
Has this been validated by a sufficiently large independent study?
What correlated response is expected & observed?
What are allele frequencies?
What is the value of this test?
not simple to answer

- How big is the effect?
- Traits of interest, economic index (NM$, TPI, PTI)
- How many genetic standard deviation units?
- Has this been validated by a sufficiently large independent study?
- What correlated response is expected & observed?
- What are allele frequencies?
- What is the value of this test?
- not simple to answer

## Genomics is enhancing genetic improvement
DGAT1 has large impacts on milk, fat, protein, SCS
Genetic tests need to be weighted appropriately for optimal selection decisions
Genomic tools will be extremely powerful for parentage verification and traceability
Could impact genetic evaluations

- Genomics is enhancing genetic improvement
- DGAT1 has large impacts on milk, fat, protein, SCS
- Genetic tests need to be weighted appropriately for optimal selection decisions
- Genomic tools will be extremely powerful for parentage verification and traceability
- Could impact genetic evaluations