Council on Dairy Cattle Breeding April 27, 2010 Interpretation of genomic breeding values from a unified, one-step national evaluation Research project in conjunction with University of Georgia Dr. Tom Lawlor Holstein 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. a A High Low

Markers are used to estimate genomic relationships

We expect full-sibs to have 50% of their genes in common Britany Goldwyn Brandy Brooke Goldwyn Brooke

Genomics tells us that the actual range amongst full-sibs is between 35 and 65% Britany Goldwyn Brandy Brooke Goldwyn 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 project One-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 G -1 A – relationships calculated traditional pedigree information G – relationships calculated from genomic information Traditional

Little G vs. Big G Holstein Final score:9,100,106 cows plus ancestors Genotyped animals: Males with phenotypes 9,770 Males without phenotypes16,002 Females with phenotypes 7,300 Females 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 Matrix

Genomic relationships for direct ancestors, descendents and other relatives.

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 –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.

TraitBLUPSingle-step MTSingle Trait MT Stature3454 Strength Body Depth Dairy Form2038 Rump Angle2545 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 Score Predict 2009 PTAT from 2004 data True PTAT Combined Genomic & Traditional All Genomic GPTAT 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?