potential and pitfalls for genomic selection- chad dechow

Potential and Pitfalls for Genomic Selection

Topics

• Review of genomic technology and implementation 4-path model

• Comparisons of early genomic predictions to actual daughter proofs Traits to be careful Who should be using genomics, who not? Spread risk

• Genomics as a herd management tool• Inbreeding• Beyond SNPs

From Phenotype to Genotype:diacylglycerol acyltransferase 1

• Enzyme involved in triglyceride synthesis Chromosome 14 Knockout mice: complete absence of milk production

• Bi-nucleotide substitution: lysine to alanine +300 lbs milk +5 lbs protein +.17% fat -13 lbs fat Fatty acid profiles altered

• Terrific – but…Grisart et al., 2002

Whole Genome Approach

• Single nucleotide polymorphisms 10 – 50 million present in genome Not inherited independently of each other

• Tests Bovine SNP 50

• Cost: $125 Low density

• 9,000 currently (replaces 6K, which replaced 3K)• Used to “impute” 50K• Cost: $45

High density• ~777,000• Early research has not been exciting• Cost: $250

Association of SNP with Fat Yield

Association of SNP with Final Score

Genetic Progress

• How does this speed genetic progress?

IntervalGeneration

ianceGeneticVarntensitySelectionIyreliabilitYearG

**/

Calf

SireSire of Sire

Dam of Sire

DamSire of Dam

Dam of Dam

1.Lower generation interval2.Higher accuracy for females3.Selection Intensity

Implementation

• First official proofs in January of 2009• Quickly adopted

Sires of sons – vast majority• Marketing differs by

bull stud Mixed lineup separate lineups

2008 201105

101520253035404550

Young sire matings

Holstein Jersey

Perc

ent

Comparison of Jan 2009 to Dec 2012 Daughters Deviations

517 bulls0 daughters in 2009 and ≥100 daughters currently

R² = 0.546563821115401

Milk Yield

2009 PTAM

2012

Dau

Yie

ld D

evia

tion

R² = 0.340733769228037

Productive Life

2009 PTAPL

2012

Dau

Dev

iatio

n

Realized Reliabilities

Milk yield Daughter Preg Rate Productive Life0%

10%

20%

30%

40%

50%

60%

70%

80%

HolsteinJerseyBrown Swiss

Top 25 Young Sires and Proven Bulls in 2009

Average 2009 Average 2012 Top 20120

100

200

300

400

500

600

700

800

900

Genomic YSProven

Net Merit Changes

Aug-08

Nov-08

Feb-09

May

-09

Aug-09

Nov-09

Feb-10

May

-10

Aug-10

Nov-10

Feb-11

May

-11

Aug-11

Nov-11

Feb-12

May

-12

Aug-12

0100200300400500600700800900

1000

FreddieCassinoSholtonAtwood

$

Traits to watch

• Productive Life Must wait for cows to die Predictors to help

• Calving related traits

Body Size

Udder Feet & Legs

DPR SCS-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Productive Life Genetic Corre-lations

Previous Current

Who Should Use Genomic Young Sires?

Use• Involved with marketing

Will have hits and misses Goes with the territory

• Not marketing Watching calving traits on

virgin heifers Spreading risk by using a

selection Willing to accept some

misses

Do not use• Not marketing• You want to minimize

calving issues• Willing to miss out on the

best for 3 years Average may not be

different, but top will be lower

Beyond Sire Selection

DNA Level Mating Decisions

• Replacement for visual appraisal mating programs?

• Chromosome level mating http://

aipl.arsusda.gov/CF-queries/Bull_Chromosomal_EBV/bull_chromosomal_ebv.cfm

Use 17 digit ID style (HOUSA000000000000) Cows entered on same page as bulls

Can we Improve Her?

23 gallons/day for a year

Haplotype Projections: Milk

Brown Swiss Holstein Jersey0

10000

20000

30000

40000

50000

60000

70000

80000

90000

Largest DGV Lower Bound Upper Bound

Sele

ction

Lim

it M

ilk (l

bs)

Cole et al., 2011

Haplotype Projections: DPR

Brown Swiss Holstein Jersey0

20

40

60

80

100

120

140

160

Largest DGV Lower Bound Upper Bound

Sele

ction

Lim

it D

PR

Cole et al., 2011

Opportunity 2013

• Only bull studs can genotype males 6 Studs• Contributed $ and DNA

License agreement• Newer chips detect Y chromosome genes• Agreement ends in 2013• If you have a good bull, do you sell him?

Market your own bull? What will it cost?

Genomics as a Herd Management Tool

• Premise: Genomics can play a role for commercial milk producers with excess heifers

• Helpful link http://edis.ifas.ufl.edu/pdffiles/AN/AN27000.pdf

NY-PA Replacement Rates

NY-PA Cull Rates

Maintaining Herd Size

• More replacements than needed Increase cull rate?• Fewer problem cows• Less “mature milk”

Sell heifers?• Lower feed costs• Heifer market sustainable?

Selling Heifers

• Value of testing• Herd improvement by culling the bottom end

70%, 80%, or 90% of calves kept What happens to the value of my remaining

calves if I genomically test first? What is the $ Net Present Value of testing?

**First culling threshold: sick/diseased calves

$Net Merit of Remaining Calves

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%0

50

100

150

200

250

90% kept 80% kept 70% kept

% of calves tested

$ N

et M

erit

$Value = $NM – Test Cost

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%0

20

40

60

80

100

120

140

90% Kept 80% Kept 70% Kept

% Tested

$ N

et M

erit

Net Present Value

• We don’t need to test every calf Top sires will rarely have offspring you want to

cull• Net Present Value compared with parent

average selection

What to Sell

• Lots of heifers = limited marketing potential Save on feed costs

• Beef sires Male sexed semen Gaining traction Helpful with Jerseys

Individualized Cow Management?

• Should we alter management to accommodate genetic potential? High dairy form = high early lactation BCS loss

risk• Calving BCS should be LOW

Lower yield potential• Breed back more quickly?

• Group cows by genetic potential?

Will Genomics Impact Inbreeding Rates?

Close Inbreeding (F=14.7%): Double Grandson of Aerostar

Aerostar

Aerostar

Megastar

Chromosome 24

Megabuck

Digne

VanRaden, 2008

• Likely to accelerate with genomics Shorter generation interval Technology is “pattern recognition”• Unusual genetic make-up = unrecognized pattern

• Line developmentAerostar

AerostarMegastar

Chromosome 24

Megabuck

Digne

Inbreeding

Identical by descent = inbred

If we know the DNA code

• Why are genomic tests 100% accurate? Markers are random & may have nothing to do

with performance themselves Copy number variation Not accounting for dominance/gene interactions “Epigenetic” effects• Alter gene expression independently of DNA code• High milk yield during gestation = lower milk yield

daughter?

The more we learn, the less we know• Intelligent design cannot explain the presence of a

nonfunctional pseudogene … the designer made serious errors, wasting millions of bases of DNA … junk … Evolution, however, can explain them easily … they persist in the genome as evolutionary remnants of the past history (Miller, 1994)

Marker Effects

Thank you and are there any questions?