g.r. wiggans animal improvement programs laboratory agricultural research service, usda beltsville,...

G.R. WiggansAnimal Improvement Programs LaboratoryAgricultural Research Service, USDA Beltsville, MD

[email protected]. WiggansAlta Genetics meeting May 2010 (1)

Genomic Evaluations

G.R. WiggansAlta Genetics meeting May 2010 (2)

How the system works

Studs and breeds nominate animals through AIPL web site

Hair, blood, semen, or extracted DNA sent to 1 of 4 Labs

Genotypes sent to AIPL monthly

Monthly evaluation updates released on the first Tuesday of most months

Official evaluations updated only at tri-annual traditional runs (except for C & P bulls)


Pedigree & Nomination

Studs may submit pedigree and nominate in batch files

Pedigree for CAN, AUS, GBR automatically collected from web sites

Nomination expected by the time sample arrives at lab

Sample ID reported at nomination must match ID on sample at lab


Conflict processing

Parent-progeny conflicts detected

Sex and breed checked

Conflicts reported to lab and requester for resolution

Pedigree changes automatically update genotype usability

Foreign pedigree updates not automatic


Changes in April

Deviations of predictor cows adjusted to be like bulls with similar reliability to improve their contribution to accuracy

Genotypes of dams of genotyped animals imputed to add predictor animals

Sum of genomic relationships of each animal with the predictor animals used to improve estimation of Reliability


Imputation

Determine an animal’s genotype from genotypes of its parents and progeny

Genotype separated into sire and dam contributions. Identifies the allele on each member of a chromosome pair


O-Style Haplotypeschromosome 15


Imputation (cont.)

Inheritance of haplotypes tracked

Accuracy of imputation improves with number of progeny

Crossovers during meiosis contribute to uncertainty


Imputation Plans

Add separate genomic indicator code (probably 3) to cow format 105 to identify imputed cows. Already are identified in XML files.

3K genotypes will be imputed to 50K, chip type code to be added to XML

No authorization to release evaluations on imputed bulls. 15 HO bulls have 5+ genotyped progeny whose dams are genotyped.


Genotyped Holstein by run

Run Date

Old* Young**

TotalMale Female Male Femal

e

0904 7600 2711 9690 1943 21944

0906 7883 3049 11459 2974 25365

0908 8512 3728 12137 3670 28047

0910 8568 3965 13288 4797 30618

1001 8974 4348 14061 6031 33414

1002 9378 5086 15328 7620 37412

1004 9770 7415 16007 8630 41822

1005 9958 7940 16594 9772 44264* Animals with traditional evaluation** Animals with no traditional evaluation


Cow Adjustment

Evaluations of elite cows biased upward

Cutoff studies showed little benefit from including cows as predictors

Reducing heritability would reduce the problem but industry is reluctant to do so

Adjustment of cow evaluations implemented


SD of Cow Deviation from PA

0

500

1000

1500

2000

2500

0.4 0.6 1.0 2.5

Daughter Equivalent (progeny)

Std

. D

ev o

f D

ere

g M

.S.

(Milk)

CowBull


Mean of Cow Deviation from PA

-400

-200

0

200

400

600

800

1000

2000 2001 2002 2003 2004 2005 2006 2007

Birth year

Milk (

lbs.)

Cow

BullCow SD Adj


Cow Adjustment Procedure Deregressed Mendelian Sampling (MS) =

(PTA-PA) / f(REL)

Adj. MS = .84*MS - 784

Adj. PTA = f(REL)*(Adj. MS+ PAn) + (1- (REL)*PAn)

f(REL) = fraction of PTA from own records and progeny


Effect of Adjustment on Holstein

Bias Regression Gain REL

No Yes Diff No Yes Diff No Yes Diff

Milk (lb) -75.3

-27.9

47.4

.93 .90 -.03

29.5 32.5

3.0

Fat (lb) -5.7 -2.9 2.8 .98 .97 -.01

34.0 37.1

3.1

Protein (lb)

-0.2 0.8 1.0 .90 .97 .07 25.0 27.1

2.1

Fat (%) 0.0 0.0 0.0 .97 .99 .02 49.8 52.4

2.6

Protein (%)

0.0 0.0 0.0 .87 .88 .01 38.8 41.5

2.7


Effect of Adjustment on Jersey

Bias Regression Gain REL

No Yes Diff No Yes

Diff

No Yes Diff

Milk (lb) -44.0

81.5

125.5

.99 .99 .00 10.8

19.6

8.8

Fat (lb) -7.3 7.9 15.2 .78 .84 .06 9.4 18.2

8.8

Protein (lb)

1.7 4.3 2.6 .86 .90 .04 4.1 12.7

8.6

Fat (%) 0.0 0.0 0.0 .90 .95 .05 29.9

37.6

7.7

Protein (%)

0.0 0.0 0.0 .87 .93 .06 24.8

34.2

9.4


Increased reliability of genomic predictions

Genomic evaluations of the top cows, top young bulls, and top heifers decreased

Among bulls, foreign bulls with a high proportion of genotyped daughters had largest changes

Adjusted PTA reported in XML traditional fields

Cow Adjustment Summary


Reliability for young HO Bulls

0

500

1000

1500

2000

2500

3000

3500

4000

52 5354 55 5657 58 5960 61626364 6566 67 6869 70 7172 73 7475 76 7778 79

Milk REL

Nu

mb

er

of

Bu

lls

N = 15,226


Reliabilities for HO born ≥ 2005No Traditional

EvaluationWith Traditional

Evaluation

Trait Male Female Male Female

N 15226 7536 752 3191

Milk (lb) 73.9 73.7 85.8 77.9

Protein (lb) 73.9 73.7 85.8 77.8

PL (months) 64.0 63.6 70.1 67.0

SCS 69.7 69.5 78.1 73.0

DPR (%) 61.6 61.2 66.5 64.6

PTAT 70.4 70.1 78.3 74.5

Sire CE 64.9 61.7 80.8 63.5

Daughter CE 60.2 59.0 69.5 61.8

Sire SB 59.8 58.7 66.2 59.6

Daughter SB 58.3 57.6 64.9 59.6

Net Merit ($) 68.6 68.3 77.8 72.0


Accommodating chip diversity Impute to higher density

Calculate effects for all high density SNP

Mechanism for accounting for loss in accuracy due to imputation error needed Percent missing genotypes

Only observed genotypes stored in database

Evaluations labeled as to source of genotype


Illumina 3K chip

SNP chosen 3072, evenly spaced Some Y specific SNP 90 SNP for breed determination

Expect to impute genotypes for 43,382 SNP with high accuracy

Expect breeds to use 3K chip to replace microsatellites for parentage verification

Breeds allowed to genotype bulls for parentage only


Proposed stud use of 3K chip Accuracy adequate for first stage

screening

HD genotyping reserved for bulls acquired. Confirm ID Second stage selection

Genotyping of more candidates

Genotype remaining CDDR predictor bulls to meet or exceed EuroGenomics reliabilities


HD chip

Proposed 860K SNP include current 43,382 so can replace 50K chip in current evaluations

3,000+ genotypes at HD may be adequate to support imputation of HD from current 50K SNP

Expected gain in Rel < 2

May allow HO genotypes to contribute to accuracy of JE & BS genomic evaluations


HD chip (Cont.)

Could share cost of HD genotyping with Europe to get more animals to improve accuracy of imputation

Trend is toward higher densities

Continued genotyping at 50K may be shortsighted

May allow reduction in polygenic effect giving increased accuracy


Will data recording survive?

Progeny test no longer required to market bulls

In 2013, new entrants may have no data collection expense

Loss in accuracy of SNP effect estimates occurs over time

How much data is needed?


What replaces the PT program? G bulls will have thousands of

daughters in their early traditional evaluations

Milk recording is justified for management information

Type data may come from breeder herds because they use G bulls

Data on new traits will have to be paid for


Data into National Evaluations Progeny test herds could become data

supply herds

Data acquisition could be supported by a fee based on animals receiving a genomic evaluation

Plan must be perceived as fair by all industry players

Quality certification model could apply


Questions

How can accuracy of evaluations from EuroGenomics be exceeded?

Should young bull purchases be based on 3K genotypes?

How will continued flow of data into genetic evaluations be assured?


Questions from Bob


Will there be a code on GPTA’s to distinguish between genotyped and imputed animals? Genomic indicator code of 3 planned

for imputed cows in format 105 in August

Already designated in XML files


From which EU countries are cow proofs used in genomics? (All, health, type?)

Cow evaluations for milk, fat, and protein are collected from:

NLD DEU FRA GBR ITA DNK


What caused the DPR changes from Dec – Feb – April? Can it happen again?

The traditional DPR PAs for some foreign bulls were incorrect in Feb

May have been due to missing the dam or MGS

We have increased checking for missing pedigree


What is the difference between selection index (US) and blending of proofs (CAN)? Selection Index combines

Genomic Traditional Traditional computed using only genotyped

animals

Theoretically justified

DGV includes all information when both parents genotyped

Used in most countries


What is the difference between Selection Index (US) and Blending of proofs (CAN)? (Cont.) Blending combines

Genomic

Traditional

Weighted by reliability

Simple to explain


What are the criteria for an animal to be included in the reference population for genomics? Traditional Rel > PA Rel


What other factors can change SNP effect estimates, beyond adding new animals to the reference population? New traditional evaluations – tri-

annual runs

Insufficient iterations in previous run

Change in SNP used


Why do genomic evaluations change?

Reference population animals are added

Changes in traditional PTA cause genomic evaluation to change particularly for high reliability bulls

Small changes due to filling in missing SNP genotypes when possible


How much do the SNP effect estimates change

Trait

SNP effect differences from

April to May

MaximumMean Std.

Dev.

Milk (lbs) 0.42 0.38 24.5

Fat (lbs) 0.02 0.01 1.1

Protein (lbs) 0.02 0.01 0.8

Fat (%) 3.9E-5 3.4E-5 9.1E-4

Protein (%) 1.9E-5 1.6E-5 4.1E-4


Are reliability calculations different in the US vs. EU? And, why are reliability values similar with a large discrepancy in the number of predictor bulls? (9,000 vs. 17,000) ~8,000 cows also contribute to accuracy

US Rel is adjusted to reflect gains from cutoff studies


Can sire proofs be imputed? And, is this likely to happen?

Genotypes of bulls can be imputed

Only 15 non-genotyped HO bulls with 5+ genotyped progeny with genotyped dams

May be approved for bulls controlled by participating studs


Will there be an adjustment made to type in August?

No


Is it possible to estimate the variation in the offspring for $NM when two genotyped animals are mated? Yes, sum absolute value of SNP effects

weighed as:

Parents both 0 or 2, weight 0

Parents 0 and 2, weight 1

1 or both parents 1, weight 2

Weights are max difference in progeny genotypes


Making genotyped and non-genotyped cows more comparable High priority research area

Reduce h2

Add herd x dam interaction

Differential adjustment by herd


Will AIPL continue to impute cows during each genomic evaluation?

Yes


Explanation of changes in SHOTTLE evaluation from Jan. to April

January April

Trait Traditional

Genomic

Traditional

Genomic

Milk (lb) 1597 1784 1292 1399

Protein (lb)

45 51 38 40

PL (months)

3.0 6.2 3.6 4.4

Net Merit ($)

529 729 507 551


Why were cows in Advantage herds with no preferential treatment adjusted?

All genotyped cows were adjusted in the same way

The maternal component of PA was adjusted

Investigating if accuracy can be improved by adjusting each herd based on its own average PTA-PA


What changes to the imputation process were made in May?

Maternal grandparents were checked for haplotypes where parents were not available

Current allele frequencies replaced base population frequencies for unknown genotypes


How do you incorporate various chip sets (ex. 3K, 50K, 700K, 850K) into a single genomic evaluation? And, what level of imputing will take place? Lower densities will be imputed to

highest density

If the larger HD chip does not include all the SNP of the smaller one, then combined set must be imputed or some SNP ignored


What will be the gain in accuracy from going from 50K to 850K?

<2 increase in reliability


What are the biggest changes and challenges after March 2013 when anyone can get a genomic evaluation of a bull?

Maintaining support for data collection for genetic evaluations


What are the relative weights given to SNP information for the GPTAs of 1st lactation cows, 1st crop bulls, and 2nd crop bulls? Proportional to daughter equivalents

(DE)

DE = k*Rel/(100 – Rel)

Calculate DE at each stage of evaluation

DEG = DEtotal - DEPA


How is a DGV calculated?

∑ SNP effects + base

Polygenic effect not included


Do SNP estimates change based on family?

No, SNP effect is change in PTA from having an A allele instead of a B allele (substitution effect)


How can 99% Reliability bulls change between runs?

Traditional evaluations change

High reliability evaluations force SNP effects to adjust to equal evaluation

Possible because more SNP effects than predictor animals

g.r. wiggans animal improvement programs laboratory agricultural research service, usda beltsville,...

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