Novel traits: Opportunities and pitfalls in commercial application - Sonja Dominik

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)