Potential and Pitfalls for Genomic Selection- Chad Dechow
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Transcript of 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?