t. a. cooper and g.r. wiggans animal improvement programs laboratory agricultural research service,...

T. A. Cooper and G.R. WiggansAnimal Improvement Programs LaboratoryAgricultural Research Service, USDA Beltsville, MD

[email protected]

Council on Dairy Cattle Breeding meeting April 2010 (1)

Cow Adjustmentand

Genomic Database Update


Cow Adjustment


Gains in Reliability as of 2008


DGV vs Traditional PTA (Bulls)

DGV – based on allele effects of all genotyped animalsTraditional PTA – no genomics

-500

0

500

1000

1500

2000

-155 276 588 794 962 1104 1252 1403 1578 1867

PA Milk (pounds)

Milk

(p

ou

nd

s)

Bull DGV Bull Traditional PTA


DGV vs Traditional PTA (Cows)

DGV – based on allele effects of all genotyped animalsTraditional PTA – no genomics

-1000

-500

0

500

1000

1500

2000

2500

-715 284 576 793 960 1104 1253 1414 1592 1936

PA Milk (pounds)

Mil

k (p

ou

nd

s)

Cow DGV Cow Traditional PTA

-1000

-500

0

500

1000

1500

2000

2500

-715 284 576 793 960 1104 1253 1414 1592 1936

PA Milk (pounds)

Mil

k (p

ou

nd

s)

Cow DGV Cow Traditional PTA Adjusted Traditional PTA


high PTA values were causing the genomic predictions to suffer in accuracy

added information from genotyped cows was not increasing reliability

Why reduce cow bias through adjustment?


Which animals were adjusted?

All genotyped and imputed cows

All genotyped animals including bulls, were affected by the adjustment made to the maternal portion of the parent average

Brown Swiss adjustments were not implemented due to low numbers of genotyped cows


How was the adjustment made?

0

500

1000

1500

2000

2500

0.4 0.6 1.0 2.5Daughter Equivalent (progeny)

Std

. D

ev o

f D

ere

g M

.S.

(Milk)

CowBull

-400

-200

0

200

400

600

800

1000

2000 2001 2002 2003 2004 2005 2006 2007

Birth year

Milk (

lbs.)

Cow

Bull

Cow SD Adj

Variance Adjustment

Mean Adjustment


How was the adjustment made?

Deregressed Mendelian Sampling (MS) =

(PTA-PA) / f(REL)

Adj. MS = .84*MS - 784

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

f(REL) = weight in PTA from own records and progeny


Effects of Cow Adjustment (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


Effects of Cow Adjustment (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


Example (Cow Milk PTA 1934 381)

0

500

1000

1500

2000

2500

Progeny 1 Jan PA April PA Progeny2

Mil

k (

po

un

ds

)


The future

Investigate solutions to the problem of not being able to compare genotyped and non-genotyped cows

− Reduce heritability− Add dam-herd interaction− Varying heritability by herd− Other

With 3K chip, adjustments may need to vary by sub-population


Genomic Database


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

10041005Imputed

97709958

73007940

(1955)

1600216594

87329772

4180444264

* Animals with traditional evaluation** Animals with no traditional evaluation


Genotyped Jersey by run

Run Date

Old* Young**


e

1002 1977 479 1172 197 3825

10041005Imputed

20722079

637702

(150)

12501368

202231

41614320



Genotyped Brown Swiss by run

Run Date

Old* Young**


e

1002 1168 54 179 15 1416

10041005Imputed

11851188

98114(63)

188199

3134

15021535



Genotype Processing

2,000 New Genotypes a month

Four labs

− GeneSeek

− Genetic Visions

− DNA LandMarks

− GIVF


All animals should be nominated by the time sample reaches lab

Goal: Parents and Grandparents on every genotyped animal

Minimum: Valid ID

Genotype Processing: Nomination


Genotypes must pass 62 edits to be added to the database

Most common reasons a genotype fails

− Low call rate

− Parent / Progeny conflict

− Possible split embryo / twin

− Wrong gender

− Breed Check

− Switched samples

Genotype Processing: Edits


Continuous Updates

Pedigree changes update genotype usability daily

Harmonization with breed association important to maintain usability

Blanking a genotyped dam will make the genotype unusable

No automatic receipt of foreign pedigree updates


Low call rate

80% on X chromosome

90% on autosomal chromosomes (43,382)

Labs generally do not send genotypes with low call rates


Parent Progeny Conflict

Sire / Dam conflict

> 200 SNP conflicts

Sire / dam proposed if genotyped

Sire conflict - young animals from mixed flush

Sire conflicts represent $50,000 genotype cost


Split embryo / Identical twin / Clone <1000 SNP differences considered

identical

98% similar, accounts for genotyping errors

Stored in clone table if valid

Animals registered as ETS or ETN (automatic)

Otherwise verification must come from requester


Wrong Gender

> 50 heterozygous SNP on X (not male)

< 50 heterozygous SNP on X (not female)

Homozygous X female

Common ancestor (source of X)


Source of X

Jeff

Erin

Elegant

BW Marshall

Tanya

Sam

12144058

BellwoodMara

PatronMary

MarkSue

Patron

14860763


Breed Check

622 SNP

Nearly monomorphic in 1 breed and have fewer than 30% of animals homozygous for that allele in another breed

An error when a higher number of conflicts for the reported breed than for another breed

> 10 SNP conflicts reported to requester, but remains usable

Higher conflicts for foreign animals


Switched Samples

Sample pair with reversed parents

Switched at the lab

Switched at the farm


Conflicts to Requester

Genotype conflicts reported to the requester immediately

Genotype remains stored in AIPL database as an unusable genotype until conflict is resolved

2-3 days to fix conflicts before cutoff

Once a month all conflicts remaining in the system are sent to requesters


Issues Reported to Lab for QC

SNP that have call rate <90%

SNP that have high parent-progeny conflicts

SNP that deviate from HW equilibrium

Labs have the opportunity to re-cluster genotypes


Closing Thoughts

Currently 477 animals with failed or conflicted genotypes

Big increase in volume when 3K becomes available

Edits will remain with different thresholds for 3K data

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