Ellen, Esther

roslin.ed.ac.uk

Ellen, Esther

Reducing mortality due to cannibalism in

layers:

Social genetic effects

Esther Ellen, Katrijn Peeters, J. Visscher, Piter Bijma

Animal breeding and Genomics Centre, Wageningen University, The Netherlands

2

Animal Breeding &

Genomics Centre


Cannibalism

Picture provided by Bill Muir

Substantial mortality due to cannibalism

Currently beak-trimming is used as a “solution”

Genetic solution is desired

Classical breeding has not solved the problem

Animal Breeding &

Genomics Centre


1. Theory: The Genetic Model

Phenotype = Breeding Value (A) + Environment

Heritability = Var(A)/Var(P)

Genetic progress: ≤ 1 genetic std.

This has worked very well

for many traits

But cannibalism

introduces new issues

Animal Breeding &

Genomics Centre


1. Theory: The Genetic Model

The Phenotype

The Total Breeding Value

Parameters of interest:

Direct genetic variance:

Direct-social covariance

Social genetic variance:

Total heritable variance:

Animal Breeding &

Genomics Centre


2. How important are those social effects

Purebreds:

Survival data from ISA (Hendrix Genetics)

2 purebred White Leghorn layer lines: W1 and WB

~13,000 hens

4 birds/cage

Randomly composed cages

Intact beaks

Mean survival ~55%

Trait: survival days

Models:

y = Xb + Za + e

y =Xb + Z D a D +Z S a S + e

Survival days = number of days from start of laying till either death

or end of study (max = 447 days)

Animal Breeding &

Genomics Centre


2. How important are those social effects

Parameter Line W1 Line WB

Phen std (days) 113 142

Gen std (days) 30 44

heritability 0.07 0.10

Ellen et al., 2008

Animal Breeding &

Genomics Centre


2. How important are those social effects

Parameter Line W1 Line WB

Phen std (days) 113 142

Gen std (days) 30 44

heritability 0.07 0.10

Gen std (days) 50 55

Var(A T )/Var(P) 0.19 0.15

Direct-social gen corr. +0.18 -0.31

Ellen et al., 2008

- Social effects contributed ~50-70% of heritable variation in survival time

- Heritable variation is greater than classical model predicts

- Line WB: classical selection will fail because of negative genetic correlation

Animal Breeding &

Genomics Centre


2. How important are those social effects

Crossbreds

15,012 Individuals

7,668 W1 x WB 7,344 WB x W1

♂ x ♀

Mean survival ~50-60%

♂ x ♀

Bivariate direct-social animal model:

Animal Breeding &

Genomics Centre


2. How important are those social effects

Total Var(A T )/Var(P)





Social effects clearly larger than in purebreds

Strong negative direct-social genetic correlation

Poor social correlation between reciprocal crosses

Indications of sex-linked social effects

Katrijn Peeters

Animal Breeding &

Genomics Centre


Sex-linked social effects

Literature:

This dataset: paternal WB chrom.

Higher sire-based h 2

for feather pecking

(Rodenburg et al., 2003)

Ellen et al.,

2008

Z-chromosome linked

associative effects

for feather scores

(Biscarini et al., 2010)

Animal Breeding &

Genomics Centre


2. How important are those social effects

Social QTL

QTL-mapping for feather damage (Biscarini et al. 2010)

Material

9 lines

660 individuals

~1000 SNPs

Across-line association study

Phenotypes

Results

11 DGE SNPs

81 IGE SNPs

• Related to serotonin & immunology

• 6 SNPs on the sex-chromosome

snp

Animal Breeding &

Genomics Centre


What do those results mean for potential improvement

Social effects contribute meaningful genetic variation

Genetic standard deviations full vs. classical:

• Purebreds: 52 days vs. 37 days

• Crossbreds: 62 days vs. 31 days

There are clear opportunities to improve survival time

Classical selection may fail

WBxW1 cross: h 2 -realized = -0.06

• Classical selection will increase mortality

Breeding programs may need to be adjusted

Estimate social breeding values

Cage composition

Animal Breeding &

Genomics Centre


3. How to utilize social genetic effects

in layer breeding

3.1 Genetic parameter estimation

3.2 Breeding value estimation

3.3 Breeding without knowledge of genetic parameters

Animal Breeding &

Genomics Centre


How to utilize social genetic effects in layer breeding

3.1 Estimating genetic parameters in layers

Mixed model is straightforward in principle

• Z S links an individual’s social breeding value (a S ) to the phenotypes of

its cage mates

You need to know who interacts with who

• Cages → OK

• Floor systems → problematic

• Individual ID’s with pedigree needed

Many groups are needed for power

Beware of confounding / overestimation

• Include a random group effect (Vg) to avoid overestimation

Animal Breeding &

Genomics Centre


3.1 Estimating genetic parameters: The Optimum Design

Optimum design for 5 fam:

- Combine 2 families per group

- Combine “all” families with each other

- This gives much lower SE than random groups

- Particularly when groups are larger

Animal Breeding &

Genomics Centre


3.2 Breeding value estimation and selection


BLUP: You need to know the genetic parameters


BLUP → EBV D , EBV S


Optimum index: EBV = EBV D + (n-1) EBV S

Factors determining accuracy of EBVs

Animal Breeding &

Genomics Centre


3.2 Breeding value estimation: Factors determining accuracy

Relatedness among cage members is the key driver of accuracy

This is better than this

Accuracy

Put one family in a cage

Trade-off: with 1 family/cage, genetic parameters cannot be estimated

Animal Breeding &

Genomics Centre


3.3 Breeding without knowledge of genetic parameters

Often individual ID’s are not recorded in layers

E.g. recurrent test kept in family groups

Genetic parameters cannot be estimated

How to capture social effects in such cases

Solution: Use family cages

Direct and social effects are fully confounded

The ordinary EBV picks up the Total Breeding Value

• EBV = EBV D + (n-1)EBV S

• Var(A) = Var(A T )

Animal Breeding &

Genomics Centre


3.3 Breeding without knowledge of genetic parameters

Selection based on sibs kept in family groups is robust

Select candidate with best sibs

Sib Selection

sibs

sibs

Animal Breeding &

Genomics Centre


3.3 Breeding without knowledge of genetic parameters

One generation of selection of sib selection for mortality

Problems in next generations:

- Lack of a true control line

- Very large batch effects → lack of repeatability (age of mother)

Animal Breeding &

Genomics Centre


4. Future challenges

Breeding for systems with large groups (e.g. floor systems)

Problem: Who interacts with who

Solutions

• Breeding in larger cages

• Sensor technology to record interaction or location

Phenotypic info becomes available rather late

Genomic selection

Crossbreds seem very different from purebreds

RT-data from family groups

• Dam ID-recording

Genomic selection

Repeatability of the trait

Animal welfare issues

Animal Breeding &

Genomics Centre


Reducing mortality due to cannibalism in

layers:

Social genetic effects

Esther Ellen, Katrijn Peeters, Piter Bijma

Animal breeding and Genomics Centre, Wageningen University, The Netherlands

2

Animal Breeding &

Genomics Centre


Results – Cross validation: “response to selection”

W1

WB

2STEP LAM 2STEP LAM

Mean 354 ± 2 326 ± 2

Predicted best 377 ± 3 377 ± 3 359 ± 2 357 ± 3

Predicted worst 327 ± 2 327 ± 3 292 ± 6 290 ± 6

Difference 50 50 67 67

Both methods yield the same “response to selection”

Animal Breeding &

Genomics Centre

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