# Clustering and genetic analysis of body condition score and body weight variations in meat sheep

Tiphaine Mace, Dominique Hazard, Sebastien Douls, Didier Foulquie, Eliel

González García

► To cite this version:

Tiphaine Mace, Dominique Hazard, Sebastien Douls, Didier Foulquie, Eliel González García. Clustering and genetic analysis of body condition score and body weight variations in meat sheep. 2017, 27 p. hal-01607238

HAL Id: hal-01607238

https://hal.science/hal-01607238

Submitted on 5 Jun 2020

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Distributed under a Creative Commons Attribution - ShareAlike 4.0 International License
CLUSTERING AND GENETIC ANALYSIS OF BODY
CONDITION SCORE AND BODY WEIGHT VARIATIONS
IN MEAT SHEEP

MACE Tiphaine ¹,

Hazard D.¹,

Carriere F.², Douls S.², Foulquié D.²

González-García E. ³

¹INRA UMR1388 GENPHYSE, 31326 Castanet-Tolosan, France

²INRA UE321 La Fage, 12250 Roquefort-sur-soulzon, France ;

³ INRA UMR868 SELMET, 34060 Montpellier, France
Plan

➤ Context - Robustness

➤ Cluster analysis

➤ Materials and methods

➤ Results

➤ Genetic analysis

➤ Creation of new traits

➤ Materials and methods

➤ Results

➤ Discussion – Conclusion

➤ Perspective

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# Robustness

* Robust animals to assure the farm sustainability in harsh condition

> Ability to mobilize or restore body reserves

Russel *et al.*, 1971

Blanc *et al.*, 2006
# Robustness

* Robust animals to assure the farm sustainability in harsh condition

<figure><img src="image_3.png" /></figure>

Russel *et al.*, 1971

Blanc *et al.*, 2006
# Robustness – Body Condition Score and Body Weight

* Body Condition Score (BCS) and BW to characterize robustness

* BCS: grid from 1 to 5

* Capacity of mobilization or accretion of body reserves (BR)

* Heritability

* BCS

* From 0.10 to 0.30 for Targhee ewes

* Body Weight (BW)

* From 0.30 to 0.56 for adult sheep

* No study on genetic for BCS and BW variations across productive cycle
# Robustness – Body Condition Score and Body Weight

## Objectives

* To characterize the different patterns of BCS and BW variation during productive cycle and career

* To analyze the genetic variability of BCS and BW changes throughout several productive cycles
Materials and Methods

* La Fage: meat Romane flock (n=250 ewes) reared exclusively outdoors in harsh conditions

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# Materials and Methods

* La Fage: meat Romane flock (n=250 ewes) reared exclusively outdoors in harsh conditions

* BCS and BW measured on 1448 ewes from 78 sires (2002-2015)

* Cycle 1: 1448

* Cycle 2: 1170

* Cycle 3 and more: 665
# Materials and Methods

* La Fage: meat Romane flock (n=250 ewes) reared exclusively outdoors in harsh conditions

* BCS and BW measured on 1448 ewes from 78 sires (2002-2015)

* Cycle 1: 1448

* Cycle 2: 1170

* Cycle 3 and more: 665

* 8 BCS and 7 BW measurements per production cycle

<figure><img src="image_6.png" /></figure>
# Materials and Methods – Cluster analysis

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## Clustering

* To describe profiles

* Smoothing

* PCA

* Clusters construction

* Expectation-Maximization Algorithm

<figure><img src="image_8.png" /></figure>
<figure><img src="image_9.png" /><figcaption>Results for BCS Clusters</figcaption></figure>
Results for BCS Clusters

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# Results for BCS Clusters

<figure><img src="image_12.png" /><figcaption>BCS clusters for cycle 2</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>BCS clusters for cycle 3 and more</figcaption></figure>

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Creation of new traits

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$$Y = \text{BCS}_{\text{Weaning}} - \text{BCS}_{\text{Early Pregnancy}}$$
Creation of new traits

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$$Y = \text{BCS}_{\text{Suckling}} - \text{BCS}_{\text{Lambing}}$$
Creation of new traits

<figure><img src="image_17.png" /></figure>

$$Y = \text{BCS}_{\text{Dry off}} - \text{BCS}_{\text{Weaning}}$$
Creation of new traits

<figure><img src="image_18.png" /></figure>

$$Y = \text{BCS}_{\text{Mating}} - \text{BCS}_{\text{Weaning}}$$

<figure><img src="image_19.png" /></figure>
# Materials and Methods – Genetic Analysis

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* Univariate and multivariate analysis (animal model): Heritabilities and correlations estimation with (ASReml), use of all cycles

$$Y = \mu + \text{animal} + \text{perm} + \text{age} + \text{parity} + \text{litter} + \text{year} + e$$

Y: trait

mu: mean of the trait

animal: additive genetic effect of the ewe

perm: environmental permanent effect of the ewe

age of the ewe, parity, litter, year of measurements as fixed effects

e: residual
# Results

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* Low to medium heritabilities (0.06 ± 0.01 to 0.15 ± 0.01)

* Positive correlations between periods characterizing BR mobilization

<table><thead><tr><td>Variables BCS</td><td>Lambing - Suckling</td></tr></thead><tbody><tr><td>Early pregnancy - Weaning</td><td>0.49 (0.12)</td></tr></tbody></table>
# Results

<figure><img src="image_22.png" /></figure>

* Low to medium heritabilities (0.06 ± 0.01 to 0.15 ± 0.01)

* Positive correlations between periods characterizing BR mobilization

* Positive correlations between periods characterizing BR accretion

<table><thead><tr><td>Variables BCS</td><td>Weaning - Mating</td></tr></thead><tbody><tr><td>Weaning - Dry off</td><td>0.75 (0.10)</td></tr></tbody></table>
# Results

<figure><img src="image_23.png" /></figure>

* Low to medium heritabilities (0.06 ± 0.01 to 0.15 ± 0.01)

* Positive correlations between periods characterizing BR mobilization

* Positive correlations between periods characterizing BR accretion

* Negative correlations between periods characterizing BR accretion and mobilization

<table><thead><tr><td>Variables BCS</td><td>Weaning – Dry off</td></tr></thead><tbody><tr><td>Early pregnancy - Weaning</td><td>-0.71 (0.08)</td></tr><tr><td>Lambing - Suckling</td><td>-0.46 (0.12)</td></tr></tbody></table>
Discussion - Conclusion

➤ BCS: three similar profiles across productive cycles

➤ BCS and BW variations heritable: first demonstration

➤ Increase in weight during dry-off correlated to heavier lambs

➤ Accretion and mobilization of BR linked at the genetic level

o Common genes?
# Discussion - Conclusion

## Perspectives

* Modelisation of the variations profiles (BCS and BW)

* Longitudinal genetic analysis with reaction norm model

* Joint analysis with climatic data (reaction norm model)

* Better phenotyping for BR variations (key metabolites /hormones /ultra sound...)
Thanks for your attention

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Results

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