# Presentation: 721670

## Using Genomic Data to Improve Dairy Cattle Genetic Evaluations 

## Acknowledgments

**Genotyping and DNA extraction:**
**USDA Bovine Functional Genomics Lab, U. Missouri, U. Alberta, GeneSeek, Genetics & IVF Institute, Genetic Visions, and Illumina**
**Computing:**** **
**AIPL staff (Mel Tooker, Leigh Walton)**
**Funding:**** **
**National Research Initiative grants**
**2006-35205-16888, 2006-35205-16701**
**Agriculture Research Service**
**Holstein and Jersey breed associations**
**Contributors to Cooperative Dairy DNA Repository (CDDR)**

## CDDR Contributors

**National Association of Animal Breeders (NAAB, Columbia, MO)**
**ABS Global ****(DeForest, WI)**
**Accelerated Genetics ****(Baraboo, WI)**
**Alta ****(Balzac, AB, Canada)**
**Genex ****(Shawano, WI)**
**New Generation Genetics ****(Fort Atkinson, WI)**
**Select Sires ****(Plain City, OH)**
**Semex Alliance ****(Guelph, ON, Canada)**
**Taurus-Service ****(Mehoopany, PA)**

## Genetic Markers: Changing GoalsPast and Future

**Determine if major genes exist ****(few)**
**Estimate sparse marker effects**
**Only within family analysis**
**Find causative mutations ****(DGAT1, ABCG2)**
**Estimate dense effects across families**
**Implement routine predictions**
**Increase REL**** with more genotypes**
**Decrease cost**** with a selected SNP subset**

## Old Genetic Terms

**Predicted transmitting ability and parent average**
**PTA**** required progeny or own records**
**PA**** included only parent data**
**Genomics blurs the distinction**
**Reliability**
**REL of PA**** could not exceed 50% because of Mendelian sampling**
**Genomics can predict the other 50%**
**REL**** limit at birth theoretically 99%**

## New Genetic Terms

**Genomic relationships and inbreeding**
**Actual genes in common (****G****) vs. expected genes in common (****A****)**
**Wright’s correlation matrix or Henderson’s numerator relationship (covariance) matrix**
**Average relationship to population**
**Expected future inbreeding (****EFI****) from ****A**
**Genomic future inbreeding (****GFI****) from ****G**
**Daughter merit vs. son merit (****X**** vs. ****Y****)**

## Genomic Studies at Beltsville

**174 markers, 1068 bulls, 8 sires**
**Illinois, Israel, and AIPL**
**1991-1999**
**367 markers, 1415 bulls, 10 sires**
**GEML, AIPL, Illinois, and Israel**
**1995-2004**
**38,416 markers, 19,105 animals**
**BFGL, AIPL, Missouri, Canada, and Illumina**
**Oct 2007- Dec 2008**

## Slide 8

## SNP Edits & Counts

**SNP available**** ****(Illumina SNP50 BeadChip)**** ****	****58,336**
**	****Insufficient average number of beads****	****1,389**** **
**Unscorable SNP****	****4,360**** **
**Monomorphic in Holsteins ****	****5,734**** **
**Minor allele frequency (MAF) of <5% ****	****6,145**** **
**Not in Hardy-Weinberg equilibrium ****	****282**
**Highly correlated ****	****2,010**** **
**Used for genomic prediction ****	****38,416**** **

## Animal Genotype Edits

**Require ****90%**** call rate of SNP / animal**
**Check parent-progeny pair for conflicting homozygotes**
**If many conflicts or if parent not genotyped, check all genotyped animals for possible parent**
**Check maternal grandsire (MGS) for expected relationship**
**Check heterozygous SNP on X ****(only females)**

## Repeatability of Genotypes

**2 laboratories genotyped the same 46 bulls**
**SNP scored the same by both labs**
**About 1% missing genotypes per lab**
**Mean of 37,624 out of 38,416 SNP ****(98% same)**
**Range across animals of 20 to 2,244 SNP missing**
**SNP conflict ****(<0.003%, or 99.997% concordance)**
**Mean of 0.9 SNP error per 38,416**
**Range of 0 to 7 SNP**

## Genotype Data for Elevation             Chromosome 1

- 1000111220020012111011112111101111001121100020122002220111
- 1202101200211122110021112001111001011011010220011002201101
- 1200201101020222121122102010011100011220221222112021120120
- 2010020220200002110001120201122111211102201111000021220200
- 0221012020002211220111012100111211102112110020102100022000
- 2201000201100002202211022112101121110122220012112122200200
- 0200202020122211002222222002212111121002111120011011101120
- 0202220001112011010211121211102022100211201211001111102111
- 2110211122000101101110202200221110102011121111011202102102
- 1211011022122001211011211012022011002220021002110001110021
- 1021101110002220020221212110002220102002222121221121112002
- 0110202001222222112212021211210110012110110200220002001002
- 0001111011001211021212111201010121202210101011111021102112
- 2111111212111210110120011111021111011111220121012121101022
- 202021211222120222002121210121210201100111222121101

**Notes:**
0 = homozygous for first allele ; 1 = heterozygous ; 2 = homozygous for second allele 
This shows 864 alleles. (out of over 2000)  If we were to show all of the base pairs for the first chromosome, I would have to show you 1 slide, per second for over 3 days.
Vast portions of the chromosome are homozygous, and not interesting,,  ie all animals have the same DNA sequence.  Looking at a foundation bull such as elevation shows many alleles are heterozygous.
Pedigree analysis by Holstein Association 956 bulls in the April 2008 sire guide red book had 15.2% genes in common with Elevation.  Seven generations back, showed on average Elevation appearing 3.5 times in these bulls pedigrees.  8 generations 2 times.  Nine generations showed Elevation an average of once per bull.   
So what does this really mean?

## Genotype Data from Inbred BullChromosome 24 of Megastar

- 1021222101021021011102110112112211211002202000222020002020220
- 0000220020222202202000020020222222000020222200000220200002002
- 2002000000222200022220000000000020222022002000222020222220002
- 2022222222200002002202022202000200022000000002202220000002200
- 2020002222002020020020202220222222220222020002022022022220202
- 2202020202200022002220220022200000220200002002002000200222220
- 0022220202002220022202000020200000022222020200002002002222000
- 2022022220022000222202200222202020002202202222002220022000200
- 2202000002200220222000022000022000222202002222000220020020202
- 2020002220002220022202202200000220220020020020220002000222202
- 2002220020220200222202220000020220002020020202000220022000002
- 2022200202220200022002000200022002002000200220222220022022000
- 2000020002000020220020220200200002220000222002000200222000022
- 0220020022002202202020202020200022202000220200202202220220000
- 2020200002020200022222200222200020022022220000020220020200202
- 022022020200002000200220220002200

**Notes:**
Look at the genotype of an Inbred Bull.  This is chromosome 24 from Megastar, a double grandson of Aerostar, who has Elevation as a paternal grandsire, and is also on his maternal side.   Vast portions are homozygous;  due to Elevation.

## Close Inbreeding (F=14.7%): Double Grandson of Aerostar

**Aerostar**

**Aerostar**

**Megastar**

**Chromosome 24**

**Notes:**
More conventional way of looking at this.  So now, moving on to the actual experiment:

## Differences in G and A G = genomic and A = traditional relationships

**Detected clones, identical twins, and duplicate samples**
**Detected incorrect DNA samples**
**Detected incorrect pedigrees **
**Identified correct source of DNA by genomic relationships with other animals**

**Notes:**
One of the first things I noticed, by using an incorrect proc corr model, was finding bulls that had a corr of 1.  These 3 bulls were ET clones, also found a set of twins.
When samples are loaded on to the BEAD chip, sometimes things get reversed, or transposed, and we can readily pick this up.  (George)
The wide-spread use of marker research recently led to a pedigree correction: the maternal grandsire of the new and popular Delta bull Dudam Surprise is not Eastland Festival,
but the well-known longevity specialist Delta Luxemburg. Luxemburg is especially known as the sire of the Danish top bull Exces.
 Luxemburg being the real maternal grandsire of Surprise explains with greater clarity the good fitness qualities of the Dustin son Surprise (186 NVI). 
Bulls ... have been selected using markers and their parent-average index has a higher reliability than that of “normal” young sires.      HI Online Newsletter 2008-06-04
Mis-identification of samples, saying a sample is one bull, or a full sib to a bull, when in fact it is actually a son of another bull, can be detected.

## 3 Formulas to Compute G

**Sum products of genotypes (g) adjusted for allele frequency (p)**
**G1****jk**** = ****∑ ****(g****ij****-p****i****) (g****ik****-p****i****) ****/**** [2 ****∑ ****p****i****(1-p****i****)]**
**Or individually weighted by p**
**G2****jk**** = ****∑ ****(g****ij****-p****i****) (g****ik****-p****i****) / 2p****i****(1-p****i****)**
**Or scaled by intercept (b****0****) and regression (b****1****) on A, using p = 0.5**
**G3****jk**** = ****[****∑ (****g****ij**** - 0.5) (g****ik**** - 0.5) – b****0****] / b****1**

**Notes:**
We have 3 formulas to compute the genomic matrix.
G1, developed by Paul VanRaden, Sums of the products of the genotypes,  and divides by sum of allele frequency.   (Markers give more information if they are at .5, rather than the 81% that at 0)
G2, by Leutenegger, the method used by Human genomics, uses an individual frequency for each allele. 
G3, sets the freq to .5, and when that is used, the scale is not the same as 1 and 2, so we need to make it similar to the A matrix by using regression.

## Compare A with 3 formulas for G  Actual Holstein Data

|  | Diagonals1 of G |  |  |
| --- | --- | --- | --- |
| Formula | Mean | SD | Corr. with A |
| A | 1.05 | .023 | 1.00 |
| G1 | 1.00 | .031 | 0.68 |
| G2 | 0.98 | .031 | 0.69 |
| G3 | 1.13 | .035 | 0.63 |

**1****Diagonal = 1 + Inbreeding**

**Notes:**
Real data!  G2 has the best correlation with A, inbreeding was underestimated.

## Summary of G Formulasfor Genomic Inbreeding

**Correlations ranked ****G3**** > ****G1**** > ****G2**** in simulation vs. ****G2**** > ****G1**** > ****G3 ****with real data (opposite)**
**G2**** and ****G1**** biased down, ****G3**** up**
**G1**** and ****G2**** can be adjusted toward ****A**** using b****0**** and b****1****, similar to ****G3**** formula**
**After adjusting, mean ****G1**** = 1.08 and ****G2**** = 1.09 compared to ****G3**** = 1.13 and ****A**** = 1.05 **
**G1**** was unbiased in simulation using true rather than estimated frequencies**

**Notes:**
Correlations ranked one way for simulated, and another for real data. Welcome to genomics 101.  To correct biases, Paul adjusted G1 and G2 using a similar formula to G3, and means looked better.  When know the true frequency, G1 was unbiased.    Until something better comes along, we will be using G1.

## Genomic vs. PedigreeInbreeding

| Bull | Pedigree F |  | Genomic F |
| --- | --- | --- | --- |
| O Man | 4.5 |  | 15.8 |
| Ramos | 2.3 |  | 11.5 |
| Shottle | 5.6 |  | 11.9 |
| Planet | 6.7 |  | 18.8 |
| Earnit | 6.2 |  | 12.8 |
| Nifty | 3.1 |  | 11.7 |

**Correlation = .68**

## Genomic vs. Expected Future Inbreeding

| Bull | EFI |  | GFI |
| --- | --- | --- | --- |
| Blackstar | 7.9 |  | 7.9 |
| Elevation | 7.6 |  | 7.4 |
| Chief | 7.1 |  | 6.8 |
| Emory | 7.0 |  | 6.9 |
| RC Matt | 7.0 |  | 6.7 |
| Juror | 7.0 |  | 6.7 |

## Experimental DesignHolstein, Jersey, and Brown Swiss breeds

|  | HO | JE | BS |
| --- | --- | --- | --- |
| Predictor: |  |  |  |
| Bulls born <1999 | 3,576 | 743 | 225 |
| Cows with data |  | 202 |  |
|  |  |  |  |
| Predicted: |  |  |  |
| Bulls born >1999 | 1,759 | 425 | 118 |

**Data from 2003 used to predict independent data from 2008**

## Genotyped Holsteins (n=6005)As of April 2008

## Genomic Methods

**Direct**** genomic evaluation**
**Evaluate genotyped animals by summing effects of 38,416 genetic markers (SNPs)**
**Combined**** genomic evaluation**
**Include phenotypes of non-genotyped ancestors by selection index**
**Transferred**** genomic evaluation**
**Propagate info from genotyped animals to non-genotyped relatives by selection index**

## Reliability Gain1 by BreedYield traits and NM$ of young bulls

| Trait | HO | JE | BS |
| --- | --- | --- | --- |
| Net merit | 23 | 9 | 3 |
| Milk | 23 | 11 | 0 |
| Fat | 33 | 15 | 5 |
| Protein | 22 | 4 | 1 |
| Fat % | 43 | 41 | 10 |
| Protein % | 34 | 29 | 5 |

**1****Gain above parent average reliability ~35%**

## Reliability Gain by BreedHealth and type traits of young bulls

| Trait | HO | JE | BS |
| --- | --- | --- | --- |
| Productive life | 18 | 12 | 2 |
| Somatic cell score | 21 | 1 | 16 |
| Dtr pregnancy rate | 16 | 5 | - |
| Final score | 18 | 6 | - |
| Udder depth | 35 | 13 | 3 |
| Foot angle | 14 | 10 | - |
| Stature | 26 | 9 | 3 |

## Reliability Gains for Proven Bulls

**Proven bulls included in test had:**
**>10 daughters in August 2003**
**>10% increase in reliability by 2008**
**Numbers of bulls in test ranged from 104 to 735 across traits**
**Predicted the change in evaluation**
**Significant increase in R****2**** (P < .001) for ****26**** of ****27**** traits**

## Value of Genotyping More Bulls

| Bulls |  | R2 for Net Merit |  |  |
| --- | --- | --- | --- | --- |
| Predictor | Predicted | PA | Genomic | Gain |
| 1151 | 251 | 8 | 12 | 4 |
| 2130 | 261 | 8 | 17 | 9 |
| 2609 | 510 | 8 | 21 | 13 |
| 3576 | 1759 | 11 | 28 | 17 |

## Value of Genotyping More SNP9,604 (10K), 19,208 (20K), and 38,416 (40K) SNP

|  | REL of PA | Genomic REL |  |  |
| --- | --- | --- | --- | --- |
| Trait |  | 10K | 20K | 40K |
| Net Merit $ | 30 | 48 | 50 | 53 |
| Milk yield | 35 | 53 | 56 | 58 |
| Fat yield | 35 | 64 | 66 | 68 |
| Protein yield | 35 | 54 | 56 | 57 |
| Productive Life | 27 | 38 | 41 | 45 |
| SCS (mastitis) | 30 | 45 | 47 | 51 |
| Dtr Preg Rate | 25 | 37 | 39 | 41 |

## Simulated ResultsWorld Holstein Population

**15,197**** older and ****5,987 ****younger bulls in Interbull file**
**40,000 SNPs and 10,000 QTLs **
**Provided timing, memory test**
**Reliability vs parent average REL**
**REL = corr****2**** (EBV, true BV) **
**80% vs 34%**** expected for young bulls**
**72% vs 30%**** observed in simulation**

## Marker Effects for Milk

## Marker Effects for Net Merit

## Major Gene on Chromosome 18Net Merit, Productive Life, Calving Ease, Stature, Strength, Rump Width

## Net Merit by ChromosomePlanet - high Net Merit bull

## X, Y, Pseudo-autosomal SNPs

**487 SNPs**

**35 SNPs**

**0 SNPs**

**35 SNPs**

## SNPs on X Chromosome

**Each animal has two evaluations:**
**Expected genetic merit of daughters**
**Expected genetic merit of sons**
**Difference is sum of effects on X**
**SD = .1 ****σ****G****, smaller than expected**
**Correlation with sire’s daughter vs. son PTA difference was significant (P<.0001), regression close to 1.0 **

## Linear and Nonlinear Predictions

**Linear model**
**Infinitesimal alleles model: all SNP have normally distributed effects**
**Nonlinear models**
**Model ****A****: all SNP have effects, but with a heavy-tailed prior distribution**
**Model ****B****: some SNP have no effects,  the rest are normally distributed**
**Model**** ****A****B****: some SNP have no effect, the rest have a heavy-tailed prior**

## Regressions for marker allele effects 

## R2 of Linear and Nonlinear Genomic Predictions

|  | Model |  |  |  |
| --- | --- | --- | --- | --- |
| Trait | Linear | A | B | AB |
| Net merit | 28.2 | 28.4 | 27.6 | 27.6 |
| Milk | 47.2 | 48.5 | 46.7 | 47.3 |
| Fat | 41.8 | 44.2 | 41.5 | 43.6 |
| Protein | 47.5 | 47.0 | 46.8 | 46.6 |
| Fat % | 55.3 | 63.3 | 57.5 | 63.9 |
| Longevity | 25.6 | 27.4 | 25.4 | 26.4 |
| Mastitis | 37.3 | 38.3 | 37.3 | 37.6 |

## Genetic Progress

**Assume 60% REL for net merit**
**Sires mostly ****2**** instead of ****6**** years old**
**Dams of sons mostly heifers with 60% REL instead of cows with phenotype and genotype (66% REL)**
**Progress could increase by ****>50%**
**0.37 vs. 0.23 genetic SD per year**
**Reduce generation interval more than accuracy**

## Low Density SNP Chip

**Choose ****384**** marker subset**
**SNP that best predict net merit**
**Parentage markers to be shared**
**Use for initial screening of cows**
**40%**** benefit of full set for ****10%**** cost**
**Could get larger benefits using haplotyping (Habier et al., 2008)**

## Conclusions

**100X**** more markers allows MAS across rather than within families**
**10X**** more bulls allows estimation of much smaller QTL effects (HO)**
**Reliability ****increases**** by tracing actual genes inherited instead of expected average from parents**