# Presentation: 671731

## Dairy Cattle Breeding in the United States

## U.S. dairy statistics (2004)

**9.0 million cows**
**67,000 herds**
**135 cows/herd**
**19,000 lb (8600 kg)/cow**
**~93% Holsteins, ~5% Jerseys**
**~75% bred AI**
**46% milk recorded through Dairy Herd Improvement (DHI)**

## U.S. dairy population and yield

## DHI statistics (2004)

**4.1 million cows**
**97% fat recorded**
**93% protein recorded**
**93% SCC recorded**
**25,000 herds**
**164 cows/herd**
**21,250 lb (9640 kg)/cow**
**3.69% fat**
**3.09% (true) protein **

## U.S. progeny-test bulls (2000)

**Major and marketing-only AI organizations plus breeder-proven**
**Breeds**
**Ayrshire****	****	****	****    10 bulls**
**Brown Swiss****	****	****    53 bulls**
**Guernsey****	****	****	****    15 bulls**
**Holstein****	****	****	****1436 bulls**
**Jersey****	****	****	****  116 bulls**
**Milking Shorthorn****	****      1 bull**

## National Dairy Genetic Evaluation Program

**AIPL**

**CDCB**

**NAAB**

**PDCA**

**DHI**

**Universities**

**AIPL**** ****	****Animal Improvement Programs Lab., USDA**
**CDCB****	****Council on Dairy Cattle Breeding**
**DHI****	****Dairy Herd Improvement (milk recording organizations)**
**NAAB****	****National Association of Animal Breeders (AI)**
**PDCA****	****Purebred Dairy Cattle Association (breed registries)**

## AIPL mission

**Conduct research to discover, test, and implement improved genetic evaluation techniques for economically important traits of dairy cattle and goats**
**Genetically improve efficiency of dairy animals for yield and fitness**

## AIPL research objectives

**Maintain a ****national database**** with animal identification, production, fitness, reproduction, and health traits to support research on dairy genetics and management**
**Provide data to others researchers submitting proposals compatible with industry needs**

## AIPL research objectives (cont.)

**Increase ****accuracy of genetic evaluations**** for traits through improved methodology and through inclusion and appropriate weighting of deviant data**
**Develop ****bioinformatic tools**** to automate data processing in support of ****quantitative trait locus**** ****detection****, marker testing, and mapping methods**

## AIPL research objectives (cont.)

**Improve genetic rankings for ****overall economic merit**** by evaluating appropriate traits and by determining economic values of those traits in the index**
**Improved profit functions are derived from reviewing incomes and expenses associated with each trait available for selection**

## AIPL research objectives (cont.)

**Characterize ****dairy industry practices**** in milk recording, breed registry, and artificial-insemination to ****document status and changes**** in data collection and use and in observed and genetic trends in the population**

## Traits evaluated

**Yield (milk, fat, protein volume; component percentages)**
**Type/conformation**
**Productive life/longevity**
**Somatic cell score/mastitis resistance**
**Fertility**
**Daughter pregnancy rate (cow)**
**Estimated relative conception rate (bull)**
**Dystocia and stillbirth (service sire, daughter)**

## Evaluation methods

**Animal model ****(linear)**
**Yield (milk, fat, protein)**
**Type**
**	****(Ayrshire, Brown Swiss, Guernsey, Jersey)**
**Productive life**
**SCS**
**Daughter pregnancy rate**
**Sire – maternal grandsire model ****(threshold)**
**Service sire calving ease**
**Daughter calving ease**
**Service sire stillbirth**
**Daughter stillbirth**

**Heritability**
**25 – 40%**
**7 – 54%**
**8.5%**
**12%**
**4%**

**8.6%**
**3.6%**
**3.0%**
**6.5%**

## Genetic trend – Milk

**Phenotypic base = 11,638 kg**

**-3500**

**-3000**

**-2500**

**-2000**

**-1500**

**-1000**

**-500**

**0**

**500**

**1000**

**1960**

**1970**

**1980**

**1990**

**2000**

**Holstein birth year**

**Breeding value (kg)**

**sires**

**cows**

## Genetic trend – Fat

**Phenotypic base = 424 kg**

**sires**

**cows**

## Genetic trend – Protein

**Phenotypic base = 350 kg**

**cows**

## Genetic trend – Productive life (mo)

**Phenotypic base = 24.6 months**

**cows**

## Genetic trend – Somatic cell score

**Phenotypic base = 3.08 (log base 2)**

**cows**

## Genetic trend – Daughter pregnancy rate (%)

**Phenotypic base = 21.53%**

**cows**

## Genetic trend – calving ease

**Phenotypic base = 8.47% DBH**

**Phenotypic base = 7.99% DBH**

## Genetic trend – stillbirth

**Phenotypic base = 8% SBH**

## Genetic-economic indices

| Trait | Relative value (%) |  |  |
| --- | --- | --- | --- |
|  | Net merit | Cheese
merit | Fluid merit |
| Milk (lb) | 0 | -12 | 24 |
| Fat (lb) | 23 | 18 | 23 |
| Protein (lb) | 23 | 28 | 0 |
| Productive life (mo) (PL) | 17 | 13 | 17 |
| Somatic cell score (log2) (SCS) | –9 | –7 | -9 |
| Udder composite (UDC) | 6 | 5 | 6 |
| Feet/legs composite (FLC) | 3 | 3 | 3 |
| Body size composite (BSC) | –4 | –3 | -4 |
| Daughter pregnancy rate (%) (DPR) | 9 | 7 | 8 |
| Calving ability ($) (CA$) | 6 | 4 | 6 |

## Index changes

| Trait | Relative emphasis on traits in index (%) |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  | PD$
(1971) | MFP$
(1976) | CY$
(1984) | NM$
(1994) | NM$
(2000) | NM$
(2003) | NM$
(2006) |
| Milk | 52 | 27 | –2 | 6 | 5 | 0 | 0 |
| Fat | 48 | 46 | 45 | 25 | 21 | 22 | 23 |
| Protein | ... | 27 | 53 | 43 | 36 | 33 | 23 |
| PL | ... | ... | ... | 20 | 14 | 11 | 17 |
| SCS | ... | ... | ... | –6 | –9 | –9 | –9 |
| UDC | ... | ... | ... | ... | 7 | 7 | 6 |
| FLC | ... | ... | ... | ... | 4 | 4 | 3 |
| BDC | ... | ... | ... | ... | –4 | –3 | –4 |
| DPR | ... | ... | ... | ... | ... | 7 | 9 |
| SCE | ... | ... | ... | ... | ... | –2 | ... |
| DCE | ... | ... | ... | ... | ... | –2 | ... |
| CA$ | ... | ... | ... | ... | ... | ... | 6 |

## Persistency

## Introduction

**At the same level of production**** cows with high persistency milk more at the end than the beginning of lactation**
**Best prediction of persistency is calculated as a function of trait-specific standard lactation curves and the linear regression of a cow’s test day deviations on days in milk**

## Best Prediction

**Selection Index**
**Predict missing yields from measured yields**
**Condense daily into lactation yield and persistency**
**Only phenotypic covariances are needed**
**Mean and variance of herd assumed known**
**Reverse prediction**
**Daily yield predicted from lactation yield and persistency**

## PersistencyCole and VanRaden 2006 JDS 89:2722-2728

**Definition**
**305 daily yield deviations (DIM - DIM****o****)**
**Uncorrelated with yield by choosing DIM****o**
**DIM****o ****were ****161****, ****159****, ****166****, and ****155**** for M, F, P, and SCS**
**DIM****0**** have increased over time**
**Standardized estimate**

## Cow with Average Persistency

## Highest Cow Persistency

## Lowest Cow Persistency

## Model

**Repeatability animal model:**
**	****y****ijkl**** = hys****i**** + lac****j**** + a****k**** + pe****k**** + ****β****(do****jk****) + e****ijkl**
**y****ijkl**** ****	****= persistency of milk, fat, protein, or SCS**
**hys****i**** ****	****= fixed effect of herd-year-season of calving I**
**lac****j**** ****	****= fixed effect of lactation j**
**a****k**** ****	****	****= random additive genetic effect of animal k**
**pe****k**** ****	****= random permanent environmental effect of animal k**
**do****jk**** ****	****= days open for lactation j of animal k**
**e****ijkl**** ****	****= random residual error**

## (Co)variance Components

|  | σa2 | σpe2 | σe2 | h2 | rept |
| --- | --- | --- | --- | --- | --- |
| PM | 0.10 | 0.09 | 0.85 | 0.10 | 0.18 |
| PF | 0.07 | 0.08 | 0.79 | 0.07 | 0.15 |
| PP | 0.08 | 0.07 | 0.70 | 0.09 | 0.17 |
| PSCS | 0.02 | 0.03 | 0.64 | 0.03 | 0.07 |

## Correlations Among Persistency Traits

|  | PM | PF | PP | PSCS |
| --- | --- | --- | --- | --- |
| PM |  | 0.83 | 0.87 | -0.48 |
| PF | 0.72 |  | 0.82 | -0.41 |
| PP | 0.91 | 0.72 |  | -0.58 |
| PSCS | -0.19 | -0.11 | -0.14 |  |

**1****Genetic correlations above diagonal, residual correlations below diagonal.**

## Genetic Correlations Among Persistency and Yield

|  | M | F | P | SCS |
| --- | --- | --- | --- | --- |
| PM | 0.05 | 0.10 | 0.03 | -0.04 |
| PF | 0.12 | 0.12 | 0.00 | 0.00 |
| PP | -0.02 | 0.08 | -0.09 | -0.11 |
| PSCS | -0.23 | -0.28 | -0.20 | 0.41 |

## Factors Affecting Persistency

**Parity:**** ****1st lactation cows tend to have flatter lactation curves than later lactation cows **
**Nutrition:****  underfeeding energy will reduce peak yield, leading to higher persistency**
**Stress:**** low persistency in cows under handling or heat stress **
**Diseases?**
**Breed differences?**

## Summary

**Heritabilities and repeatabilities are low to moderate**
**Routine genetic evaluations for persistency are feasible**
**The shape of the lactation curve may be altered without affecting production**

## Diseases and PersistencyAppuhamy, Cassell, and Cole 2006

**Other measures may improve disease resistance through indirect selection, e.g. productive life (PL), body condition scores, and persistency****	**
**Studies of the effect of diseases on milk yield is abundant in literature **
**Investigations of relationships between diseases and other traits are lacking (Muir et al., 2004)**

## Objectives

**Investigate the effect of common health disorders on persistency**
**Estimate phenotypic correlations among diseases and persistency**
**Measure breed effects (Holstein and Jersey) on these relationships**

## Materials and Methods

**	****Daily milk yield records of ****Holstein**** and ****Jersey**** cows at the Virginia Tech Dairy Complex from 07/18/2004 to 06/07/2006**

|  | Holstein | Jersey |
| --- | --- | --- |
| First lactation (L1) | 41 | 10 |
| Second lactation (L2) | 34 | 08 |
| Third and later (L3+) | 40 | 15 |
| Total Lactations | 115 | 33 |
| Total cows | 93 | 33 |

## Definition of Disease Variables

- Mastitis (MAST) 	: All causes of udder infections
- 		MAST1 	: in first 100 days (stage1)
-    		MAST2  	: after 100th DIM (stage2)
- Post Partum  Metabolic Diseases (METAB): Milk fever and/or ketosis
- Other diseases:  LAME, DA, MET, PNEU, DIARR

## Statistical Analysis

- where:
- Yijklm 	= Lactation persistency of cow m
- Li       	= Effect of ith lactation (i = 1, 2, & 3)
- YSj  	= Effect of jth calving year-season ( j=1, 2, 3, 4, 5 & 6)
- Dk      	= Effect of kth status of the disease ( k =1 or 0)
- Ol  		= Effect of lth status of other diseases (l=1 or 0)
- eijklm 	= residual effect
- (Other diseases includes all diseases beside the disease of interest.)

**P****ijklm**** = L****i**** + Y****j**** +D****k ****+ O****l** + **e****ijklm**

## Disease incidence rates in Holstein (H) & Jersey (J) cows

##  Diseases and Breed on Persistency

- ** Significant (p<0.05)

|  Factor |  Levels |  LS Mean |  Correlation |
| --- | --- | --- | --- |
|  MAST1** | 0 | -0.18 | -0.24 |
|  | 1 | -0.76 |  |
|  MAST2 | 0 | -0.3 | -0.09 |
|  | 1 | -0.55 |  |
| METAB  | 0 | -0.35 | -0.08 |
|  | 1 | 0.37 |  |
| BREED**  | H | -0.11 |   |
|  | J | -0.74 |   |

## Conclusions

**Mastitis in early lactation has a significant, negative effect on persistency**
**Mastitis in late lactation and post partum metabolic diseases have non-significant, but negative, effects on persistency**
**Persistency differs significantly between Holstein and Jersey cows**

## All-Breeds Evaluation

## Goals

**Evaluate crossbred animals without biasing purebred evaluations**
**Accurately estimate breed differences**
**Compute national evaluations and examine changes**
**PTA of purebreds and crossbreds**
**Changes in reliability**
**Display results without confusion**

## Methods

**All-breed animal model**
**Purebreds and crossbreds together**
**Unknown parents grouped by breed**
**Variance adjustments by breed**
**Age adjust to 36 months, not mature**
**1988 software, good convergence**
**Within-breed-of-sire model examined but not used**

## Unknown Parent Groups

**Groups formed based on**
**Birth year (flexible) **
**Breed (must have >10,000 cows)**
**Path (dams of cows, sires of cows, parents of bulls)**
**Origin (domestic vs other countries)**
**Paths have >1000 in last 15 years **
**Groups each have >500 animals**

## Data

**Numbers of cows of all breeds**
**22.6 million for milk and fat **
**16.1 million for protein**
**22.5 million for productive life**
**19.9 million for daughter pregnancy rate**
**10.5 million for somatic cell score**
**Type evaluated in separate breed files **
**Calving ease joint HO, BS, and HO x BS**
**Goats in all-breed model since 1988**

## Crossbred Cowswith 1st parity records

| Year | F1    (%) | F1 cows | Back-cross | Het   > 0 | XX cows |
| --- | --- | --- | --- | --- | --- |
| 2005 | 1.3 | 8647 | 2495 | 12621 | 4465 |
| 2004 | 1.2 | 7863 | 1983 | 11191 | 3947 |
| 2003 | .9 | 6248 | 1492 | 9051 | 3111 |
| 2002 | .7 | 4689 | 1467 | 7338 | 2564 |
| 2001 | .5 | 3491 | 1330 | 5878 | 2081 |

## Reliability

**Crossbred cows**
**Will have PTA, most did not before**
**Accurate PTA from both parents**
**Purebred animals**
**Information from crossbred relatives**
**More contemporaries**

## All- vs Within-Breed EvaluationsCorrelations of PTA Milk

| Breed | 99% REL bulls | Recent bulls | Recent cows |
| --- | --- | --- | --- |
| Holstein | >.999 | .994 | .989 |
| Jersey |   .997 | .988 | .972 |
| Brown Swiss |   .990 | .960 | .942 |
| Guernsey |   .991 | .988 | .969 |
| Ayrshire |   .990 | .963 | .943 |
| Milking Shorthorn |   .997 | .986 | .947 |

## Display of PTAs

**Genetic base**
**Convert all-breed base back to within-breed-of-sire bases**
**Each animal gets just one PTA**
**PTA****brd**** = (****PTA****all**** – ****mean****brd****) ****SD****brd****/****SD****all**** **
**Heterosis and inbreeding**
**Both effects removed in the animal model**
**Heterosis added to crossbred animal PTA**
**Expected Future Inbreeding (EFI) and merit differ with mate breed **

## Schedule

**Interbull test run Feb. 1, 2006**
**Trend validation**
**Convert all-breed PTA back to within-breed bases**
**Scientific publication (JDS)**
**Implementation**
**Expected May 2007**

## Conclusions

**All breed model accounts for:**
**General heterosis**
**Unknown parent groups by breed**
**Heterogeneous variance by breed**
**PTA converted back to within breed bases, crossbreds to breed of sire**
**PTA changes more in breeds with fewer animals**

## Genomics

## SNP Project Outcomes

**Genome-wide selection**
**Parentage verification & traceability panels**
**Enhanced QTL mapping & gene discovery**

## Linkage disequilibrium (LD)

**Non-random association of alleles at two or more loci, not necessarily on the same chromosome**
**Not the same as linkage, which describes the association of two or more loci on a chromosome with limited recombination between them**

## Concept of a HapMap

- The population isyoung enough that large segments of the genome are not disrupted by recombination (LD)

**Concept of a HapMap**

- Many
- Generations

## Genome Selection

**Animals are genotyped at birth**
**Genomic EBV calculated for many traits**
**Even those not typically recorded (e.g. semen quality)**
**Accuracy is predicted to be similar to progeny test evaluation**

## Advantages of Genome Selection

**Generation intervals can be reduced**
**Costs of progeny testing can be decreased**
**More accurate selection among full sibs**
**Decreased risk in selection program**

## Low-cost parentage verification

**SNP tests may make parentage validation cheap enough for widespread adoption**
**Develop a database and software to check parentage and suggest alternatives for invalid IDs**
**Determine rate of parentage errors in a sample of herds**

## In Conclusion

## Ongoing Work

**New traits**
**Stillbirth (HOL)**
**Milking speed (BSW)**
**Rear legs/rear view (BSW, GUE)**
**Bull fertility (transferred from DRMS)**
**Improved online tools**
**Fully buzzword-compliant**
**Web services for data delivery**
**Choice of scales**

## Senior research staff