novel traits: opportunities and pitfalls in commercial application - sonja dominik
TRANSCRIPT
Sonja Dominik
CSIRO AGRICULTURE FLAGSHIP
Novel traits
Opportunities & Pitfalls in commercial application
Sonja Dominik
CSIRO AGRICULTURE FLAGSHIP
Novel traits
Opportunities for Pitfalls in commercial application
NOVEL TRAITS
Behaviour Environment HealthFeed efficiency
Efficiency and sustainability of primary production
OPPORTUNITIES
1. Meeting the needs of sustainable and efficient production
2. Getting to know your trait (QG perspective)
ANIMAL BEHAVIOUR
UNDERLYING TRAIT BIOLOGY
SUSTAINABLE AND EFFICIENT PRODUCTION
CLASSICAL TRAITS
REVISITEDSELECTION INDEX
SELECTION INDEX
Selection criteria
• What we can measure • What informs the breeding objective
Breeding objective traits
• What we want to change• What makes the profit• Needs to be quantifiable
Breeding objective traits???Example Methane emissions
• What are we aiming at?• Reduction in gross methane emissions• Reduction in methane emitted per kg feed eaten (methane yield)• Over the life time of the sheep
• How do we measure it?• Respiration chamber (RC) vs Portable accumulation chambers (PAC)• Do they reflect life time methane emission?
• Dependent on commercial implementation structure• Traditional selection• Genomic selection
Selection criteria???Example Methane emissions
What can we measure? When? For how much? How informative?• PAC or RC
• Protocol
• Methane production, other gases, blood parameters
• Behaviour traits – time spent eating/ruminating
Key message
Focussing on the application as breeding objective trait and / or selection criterion might assist in establishing what
information is important to capture
Genetic Correlations
Breeding objective traits x
Breeding objective traits
Phenotypic Correlations
Breeding objective traits x
selection criteria
At the heart of the selection index....
Consequences of selection
• Milk production affects mastitis incident (Heringstad et al. 2003)
• Selection for NFI does not affect weight traits (Archer et al. 2001)
• Selection for methane emission • Effect on rumen physiology? • Effect on feeding behaviour?• No effect on production in dairy (Kandel et al. 2014)
Key message
Focussing on the relevant relationships with other traits might help establishing what
information is important to capture
I have a all this wonderful technology – we can make major genetic improvements in difficult to measure
traits
Just give me the EBVs and I do the rest!
Selection indexEBVs / GEBVs
… and b=P-1Ga
Including calculating the relative economic importance of 6 traits, 4 of which can only be measured on carcasses, and one in consumer trials, consider 200+ genetic and phenotypic correlations, some quite highly antagonistic, and select the right animals among many thousands in upwards of 10 dimensional space…
I have a all this wonderful technology – we can make major genetic improvements in difficult to measure traits
Just give me the EBVs and I do the rest!
Selection index rocks!
including Hazel (1943), developer ofselection index theory
Mathematics is the language
with which God has written
the book of the universe,
and Chapter 1e42 is about
“Selection Index”
… and b=P-1Ga(selection index)
OPPORTUNITIES
1. Meeting the needs of sustainable and efficient production
2. Getting to know your trait (QG perspective)
Repeatability of methane emission in sheep
• Experiment with 96 Merino sheep
• 12 treatments• Young, pregnant, dry• PAC (animal house and pasture) • RC (animal house) with three feeding regimes
• Repeatabilities for methane emission• Adjust LWT ~ 0.35• Adjust Feed intake ~ 0.2
Repeatability (r)
r = VG + Vbetween
VP
Vbetween
due to permanent environmental differences between individuals
(The variances of the trait are equal and they are genetically the same trait)
Vwithin = (1-r)*VP
Vwithin
within-animal variance arising from temporary or localised
circumstances
VP(n) = VG+ Vbetween+1/n *Vwithin
Increasing the number of measurements• reduces within-animal variance • reduces the phenotypic variance• increases the accuracy
Improving accuracy of phenotypic measurements!
Falconer & Mackay, 1989
20
40
6
0
80
100
r=0.75
r=0.50
r=0.25
r=0.10
1 2 3 4 5 6 7 8 9 10Number of measurements (n)
Vp(n)
Vp
(in%)
Repeatability of methane emission in sheep
Treatment RepeatabilityT1 & T2 0.25T1 & T3 0.26T1 & T4 0.28T2 & T4 0.20T2 & T3 0.32T3 & T4 0.40
• Methane adjusted for live weight• Pasture PAC measures• T1&T2 – young• T3 & T4 Pregnant
Methane emission in sheep
Increasing age
T1 T1+2 T1+2+3 T1+2+3+40
0.0050.01
0.0150.02
0.0250.03
0.0350.04
0.045
VpVbetweenVwithin
Number of measures
Phen
otyp
ic v
aria
nce
THE MORAL OF THE METHANE STORY
• Methane production could be a different trait at different ageswith different measurement protocols
• Still not clear on the breeding objective trait or suitable selection criteria
• Need to be smart about potential effects on other traits
• Economic value??? Why would breeders want to improve methane emissions?
OPPORTUNITIES without PITFALLS
1. Meeting the needs of sustainable and efficient production
2. Getting to know your trait
Harness creativity by focussing on the
commercial application and established implementation
framework
AGRICULTURE FLAGSHIP
Thank you
“Take control of your destiny” in “Buddhism for sheep”
AcknowledgementsHutton Oddy (NSW DPI)Andrew Swan (AGBU)