Genomics: what we have and what is coming

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

[email protected]. WiggansSelect Sire committee meeting, March 2010 (1)

Genomics: what we have and what is coming

G.R. WiggansSelect Sire committee meeting March 2009 (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

Starting in April monthly updates will be released on the first Tuesday of most months

All official evaluations updated at tri-annual traditional runs


Recent improvements

Studs may submit pedigree and nominate in batch files

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

Polygenic effect set at 10% to include genetic variation not captured by SNP

Net Merit calculated from component traits, not analyzed as a separate trait


Changes planned for 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

Inheritance of haplotypes tracked

Accuracy of imputation improves with number of progeny

Crossovers during meiosis contribute to uncertainty


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 7300 16002 8732 41804* Animals with traditional evaluation** Animals with no traditional evaluation


Cow Problem

Evaluations of elite cows appear biased upward

Cutoff studies show only a small benefit from including cows as predictors

Reducing heritability would reduce the problem but appears unacceptable

Adjustment of cow evaluations investigated


SD of Cow Deviation from PA

0

500

1000

1500

2000

2500

0.4 0.6 1.0 2.5Daughter Equivalent (progeny)S

td.

Dev o

f D

ere

gre

ssed

Valu

e (

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 Parameters

PTA calculated from adjusted deregressed values and used in PA

High reliability bulls (99%) not adjusted

Adjusted values used to calculate % traits

Trait

Std. Deviation Mean

Holstein

Jersey Holstein

Jersey

Milk .84 .72 784 643

Fat .72 .67 27.5 31.4

Protein .77 .67 23.0 24.2


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 will be 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


Bulls First Traditional Eval. Jan., 2010

Genomic

August January

Trait N PTA REL PTA REL Diff PTA

Milk 703 533 76.3 475 83.4 -58

Protein 703 17.6 76.3 17.0 83.6 -0.6

PTAT 471 1.0 70.4 1.0 77.5 0.0

Traditional

August January

Trait N PA REL PTA REL Diff PTA

Milk 703 657 42.0 478 74.9 -179

Protein 703 22.1 42.0 17.0 74.9 -5.1

PTAT 471 1.0 40.9 0.9 65.1 -0.1


Cows First Traditional Eval. Jan., 2010

Genomic

August January

Trait N PTA REL PTA REL Diff PTA

Milk 594 772 74.4 845 75.4 73

Protein 570 25.5 74.4 29.4 75.1 3.9

PTAT 294 1.7 69.4 1.8 71.6 0.1

Traditional

August January

Trait N PA REL PTA REL Diff PTA

Milk 594 774 35.6 943 51.1 167

Protein 570 26.2 35.2 32.9 49.8 6.7

PTAT 294 1.6 36.8 1.7 48.9 0.1


Accommodating chip diversity Impute to highest density

Calculate SNP effects for all HD SNP

Mechanism for accounting for loss in accuracy due to imputation error needed Percent missing may be enough

Only observed genotypes stored in database

Evaluations labeled as to source of genotype


Illumina 3K chip

SNP chosen to Be evenly spaced Include some Y specific SNP Include 90 SNP for breed determination

Expect to impute genotypes for 43,385 SNP with high accuracy

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

Breeds allowed to genotypes bulls for parentage only.


Proposed Stud use of 3K

Genomic evaluation accuracy adequate for first stage screening

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

Lower cost enables genotyping more candidates

Savings could be applied to genotype more predictor bulls to meet EuroGenomics challenge


HD chip

Includes current 43,385 SNP so can replace 50K chip in current evaluations

5,000+ genotypes at HD required 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 ensure enough animals to enable accurate 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?


Assumptions About Future Data

Trait and heritability

Dtrs / Yield SCS DPR

Bull Stat .30 .10 .04

100 RELtrad .90 .76 .60

100 RELpa .40 .35 .32

50 RELtrad .83 .65 .48

50 RELpa .38 .32 .29

RELtrad of foreign bulls multiplied by square of genetic correlation (.9)2


Reliability from Additional Data

Young bull REL for:

Options to add data: Yield SCS DPR

9,000 current bulls 72 68 60

+16,000 from Europe 87 85 79

+7,500 with 50 dtrs 84 81 73

+7,500 with 100 dtrs 85 82 76

7,500 N. American bulls = 1500 / year over next 5 years


What replaces the PT program G bulls will have 1,000s of daughters

in their early Trad 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 require investment


Data into National Evaluations Progeny Test herds could become

Data Supply herds

Data acquisition could be supported by a fee based on bulls genotyped

Plan must be perceived as fair by all industry players

Quality Certification model could apply


Questions

How to match accuracy of evaluations from EuroGenomics

Should young bull purchases be based on 3K genotypes

How will continued flow of data into genetic evaluations be assured


Financial support National Research Initiative grants, USDA NAAB (Columbia, MO)

ABS Global (DeForest, WI) Accelerated Genetics (Baraboo, WI) Alta (Balzac, AB) Genex (Shawano, WI) New Generation Genetics (Fort Atkinson, WI) Select Sires (Plain City, OH) Semex Alliance (Guelph, ON) Taurus-Service (Mehoopany, PA)

Holstein Association USA (Brattleboro, VT) American Jersey Cattle Association

(Reynoldsburg, OH) American Brown Swiss Association (Beloit, WI) Agricultural Research Service, USDA

Documents

Genomics: what we have and what is coming