Council on Dairy Cattle BreedingApril 27, 2010

Interpretation of genomic breeding values from a unified, one-step national evaluationResearch project in conjunction with University of

Georgia

Dr. Tom LawlorHolstein USA

SNPs are evenly spaced across the genome

How do these markers help us select better animals.

By looking at marker frequency we can determine breeds, families and relationships.

Linkage equilibrium

Identify the breed.

Identify who an animal is more or less related to.

Marker is associated with a gene.Linkage disequilibrium

• Association caused by selection (higher frequency).– Markers that are more common today than in the 1960’s

will be associated with higher milk yield.

A is higher in modern Holsteins

a is higher in “old genetic” Holsteins

• Association is also caused by actual linkage.

aA High Low

Markers are used to estimate genomic

relationships

We expect full-sibs to have 50% of their genes in common

Britany

Goldwyn

Brandy

BrookeGoldwyn Brooke

Genomics tells us that the actual range amongst full-sibs is between 35 and 65%

Britany

Goldwyn

Brandy

BrookeGoldwyn Brooke

Observed relationships between bulls and their maternal grandsire

• Theoretical: Expect 25% relationship

unrelated population

• Pedigrees: Expect a higher value due to accumulated inbreeding

• Genomic information: Could be higher than pedigree value with high selection.

Grandsire

Sire

Son

Bull – MGS Relationships

USDA-AIPL information, courtesy of GW

Gives us a different view of our population

• Unrelated population: 25% relationship

• Traditional Pedigree: observed 33%

due to accumulated inbreeding

• Genomic information: observed 38%

increased homozygosity over time

Grandsire

Sire

Son

If grandparents 1 & 2 are related, we have increased homozygosity on the paternal side of the pedigree

Animal 8 will transmit more like its paternal side of the pedigree

The more related side receives more weight and the less related side of the pedigree receives less weight

Resemblance among relatives

Genetic covariance:

Animals with more genes in common will have genetic evaluations that are more similar.

Genomic vs. Traditional• If I have an untested animal, how do you think it will

change?– Within a family, potential change is not always 50% up & 50%

down, it will depend on how the relationships have changed.

• Why do 99% reliability bull’s genomic evaluation differ from his traditional evaluation?– It’s suppose to, we’re now using a different set of relationships.– We have distortions in DNA segregation over time.

It’s not always ½, ¼ , 1/8, etc. --- It can be .56, .29, .14, etc.– Everyone’s evaluation changes a little -- parents, mates,

offspring, etc.

Which method of calculating genomic predictions is better?

What might be the better answer today, could change in 6 months as ….

• Number of SNPs change• Number of genotyped animals change• New information comes along --- haplotypes.• Our knowledge of number of genes and how they’re

distributed changes.• Our capability improves …

– New statistical methods

– Better computational procedures.

Current procedure using genomic information Multiple steps

• Calculate traditional PTAs.• Use traditional PTAs along with genomic information from

only genotyped* animals with phenotypes to estimate how each marker (SNP effect) influences a trait.

• Use these SNP effects to calculate a Direct Genomic Prediction for all genotyped animals.

• Blend the Direct Genomic Prediction with Traditional PTAs to get our final Genomic PTA.

* Includes imputed animals

Our research projectOne-step, one BIG analysis

• We completely replace the Traditional Evaluation with a Genomic Evaluation.

• A computational breakthrough, allows us to calculate new genomic relationships for all animals.

• In doing this, we find that some genomic relationships may contain some error in them.

• So, it’s beneficial to combine the genomic and pedigree relationships together.

• To prevent selection bias… high genetic merit genotyped animals that go on to get phenotypic data will need to be combined with animals that are culled due to low genomic test. *

Uses the new genomic information and applies it to the whole population

Current Evaluation:

ˆ

ˆ

-1

X'X X'Z X'yβ

Z'X Z'Z + A Z'yu

One-step evaluation:

ˆ

ˆ

-1

X'X X'Z X'yβ

Z'X Z'Z + H Z'yuG-1

A – relationships calculated traditional pedigree information

G – relationships calculated from genomic information

Traditional

Little G vs. Big GHolstein Final score: 9,100,106 cows plus ancestorsGenotyped animals:

Males with phenotypes 9,770Males without phenotypes 16,002

Females with phenotypes 7,300Females without phenotypes 8,732

Little G = 17,000 by 17,000 * * then propagated to millions of non-

genotyped descendants by selection index (code 2)

Big G = 9.1 million by 9.1 million

Genomic relationships

.4 .6

Genomic Relationship Matrix1 0 0.15 0.15 0 0 0 0 0.5 0.3 0 0 0.4 0 0.075 0.238 0.20 1 0.15 0.15 0 0 0 0 0.5 0.3 0 0 0.4 0 0.075 0.238 0.2

0.15 0.15 1 0 0 0 0 0 0.3 0.5 0 0 0.4 0 0 0.2 0.20.15 0.15 0 1 0 0 0 0 0.3 0.5 0 0 0.4 0 0.5 0.45 0.2

0 0 0 0 1 0 0.1 0.1 0 0 0.5 0.2 0 0.35 0.25 0.125 0.1750 0 0 0 0 1 0.1 0.1 0 0 0.5 0.2 0 0.35 0.25 0.125 0.1750 0 0 0 0.1 0.1 1 0 0 0 0.2 0.5 0 0.35 0.1 0.05 0.1750 0 0 0 0.1 0.1 0 1 0 0 0.2 0.5 0 0.35 0.1 0.05 0.175

0.5 0.5 0.3 0.3 0 0 0 0 1 0.6 0 0 0.8 0 0.15 0.475 0.40.3 0.3 0.5 0.5 0 0 0 0 0.6 1 0 0 0.8 0 0.25 0.525 0.4

0 0 0 0 0.5 0.5 0.2 0.2 0 0 1 0.4 0 0.7 0.5 0.25 0.350 0 0 0 0.2 0.2 0.5 0.5 0 0 0.4 1 0 0.7 0.2 0.1 0.35

0.4 0.4 0.4 0.4 0 0 0 0 0.8 0.8 0 0 1.3 0 0.2 0.75 0.650 0 0 0 0.35 0.35 0.35 0.35 0 0 0.7 0.7 0 1.2 0.35 0.175 0.6

0.075 0.075 0 0.5 0.25 0.25 0.1 0.1 0.15 0.25 0.5 0.2 0.2 0.35 1 0.6 0.2750.238 0.238 0.2 0.45 0.125 0.125 0.05 0.05 0.475 0.525 0.25 0.1 0.75 0.175 0.6 1.175 0.463

0.2 0.2 0.2 0.2 0.175 0.175 0.175 0.175 0.4 0.4 0.35 0.35 0.65 0.6 0.275 0.463 1.125

Genomic relationships for direct ancestors, descendents and other relatives.

1 0 0.15 0.15 0 0 0 0 0.5 0.3 0 0 0.4 0 0.075 0.238 0.20 1 0.15 0.15 0 0 0 0 0.5 0.3 0 0 0.4 0 0.075 0.238 0.2

0.15 0.15 1 0 0 0 0 0 0.3 0.5 0 0 0.4 0 0 0.2 0.20.15 0.15 0 1 0 0 0 0 0.3 0.5 0 0 0.4 0 0.5 0.45 0.2

0 0 0 0 1 0 0.1 0.1 0 0 0.5 0.2 0 0.35 0.25 0.125 0.1750 0 0 0 0 1 0.1 0.1 0 0 0.5 0.2 0 0.35 0.25 0.125 0.1750 0 0 0 0.1 0.1 1 0 0 0 0.2 0.5 0 0.35 0.1 0.05 0.1750 0 0 0 0.1 0.1 0 1 0 0 0.2 0.5 0 0.35 0.1 0.05 0.175

0.5 0.5 0.3 0.3 0 0 0 0 1 0.6 0 0 0.8 0 0.15 0.475 0.40.3 0.3 0.5 0.5 0 0 0 0 0.6 1 0 0 0.8 0 0.25 0.525 0.4

0 0 0 0 0.5 0.5 0.2 0.2 0 0 1 0.4 0 0.7 0.5 0.25 0.350 0 0 0 0.2 0.2 0.5 0.5 0 0 0.4 1 0 0.7 0.2 0.1 0.35

0.4 0.4 0.4 0.4 0 0 0 0 0.8 0.8 0 0 1.3 0 0.2 0.75 0.650 0 0 0 0.35 0.35 0.35 0.35 0 0 0.7 0.7 0 1.2 0.35 0.175 0.6

0.075 0.075 0 0.5 0.25 0.25 0.1 0.1 0.15 0.25 0.5 0.2 0.2 0.35 1 0.6 0.2750.238 0.238 0.2 0.45 0.125 0.125 0.05 0.05 0.475 0.525 0.25 0.1 0.75 0.175 0.6 1.175 0.463

0.2 0.2 0.2 0.2 0.175 0.175 0.175 0.175 0.4 0.4 0.35 0.35 0.65 0.6 0.275 0.463 1.125

ParentsGenotyped AnimalsProgenyGrandprogeny

Implications

• Genomic evaluations for everyone!

• Actual genotypes are very important, but, if missing we calculate a genomic relationship for an animal utilizing the genomic information from those relatives who are genotyped.

• We believe there are benefits of simultaneously solving for all animals at one time.

With Genomic information, it’s the Animal Model on

“steroids”

• We can detect distortions in gene segregation.

• We can identify which animals share More or Less genes than expected.

• We can include this “additional” or “refined” information into our genetic evaluations.

Genomic Selection

The results are in and it works !

Really ?

Final Score

• All required scores since 1955.– 10,466,066 records

– 9,100,106 cows plus ancestors

• 6,508 bulls genotyped with classified daughters.

• Validation – Cut off the last 4 years of data and check predictability. Are genomic predictions better?yes, Genomic Predictions allow you to do a much

better job selecting young animals.

Extended study to Linear Trait

• Can we compute them in a reasonable amount of time? yes, about twice as long.

• Do Multiple-Trait Genomic predictions give you more accuracy than Single-Trait Genomic Predictions?yes, but primary benefit is due to adding genomic

information.

Trait BLUP Single-step

MT SingleTrait

MT

Stature 34 54 54

Strength 29 41 45

Body Depth 30 45 47

Dairy Form 20 38 38

Rump Angle 25 45 45

Correlations of 2004 predictions with 2009 Daughter Deviations

What about inflation of young genomic bulls?

• Add a polygenic effect.

Meaning genomic information doesn’t explain all of the genetic variance. Part of it is explained by traditional pedigree information.

• Another reason to place some emphasis on traditional pedigree information is that the genomic relationships may be too high or low for some animals.

Vary the emphasis that you put on the genomic information

= 0, use pedigree relationship completely***

= 1, use genomic relationships completely

Experience with Final ScorePredict 2009 PTAT from 2004 data

True

PTAT

Combined Genomic & Traditional

All Genomic

GPTAT

4.00 4.35 4.82

2.00 2.17 2.41

Combined relationships is giving us less inflation.

Conclusions

• Genomic information ≈ better relationships.

• Genomic evaluations can now be run with “small” modification to current BLUP system.

• Including a polygenic effect gives us more accurate predictions.

Lingering question how BIG can we go?

• Dense G – limit of 100,000 genotypes.

• Since, most off-diagonal elements of G-1 are close to 0, we may be able to set them to 0. Making G much easier to invert and little decline in accuracy– Making new limit > 1 million genotypes

• Current research topic– Don’t create G and then invert, calculate G-1 directly.– We’re working on developing method to calculate G-1

directly.

Thank you.

Any questions?