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L it di l ff t f l h t h Longitudinal effects of muscular hypertrophy allele on milk production traits during the lactation using a novel equivalent model when molecular information is limited

Colinet F.G.1 and Gengler N.1,2 g

1 Animal Science Unit Gembloux Agro-Bio Tech ULg Belgium

Animal Science Unit, Gembloux Agro Bio Tech, ULg, Belgium

2 National Fund for Scientific Research, Belgium

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9th WCGALP – Leipzig, Germany (August 1‐6, 2010)

Context Context

In 1973, Mid and High Belgian breed was divided

• fficially into 2 types

y yp 1st type: Meat Belgian Blue Young bull Cow

2 9th WCGALP – Leipzig, Germany (August 1‐6, 2010)

Young bull Cow

Context Context

Meat Belgian Blue (BBB)

• Double muscling phenotype

Double muscling phenotype

• Muscle Hypertrophy (mh) syndrome

C d b 11 b d l ti i M t ti

• Caused by 11 bp deletion in Myostatin gene

mh allele: deletion + allele: allele without deletion mh allele frequency close to 100 %

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Context Context

2nd type: Dual Purpose Belgian Blue (DP‐BBB)

• Local breed in Belgium

Local breed in Belgium

• Vulnerable status (FAO criteria)

R l t d t th Bl d N d (i F )

• Related to the Bleue du Nord (in France)
• Supported by an INTERREG IVa project

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Context Context

Dual Purpose Belgian Blue (DP‐BBB)

• Average milk yield: 4 000 kg (up to 7 000 kg)

Average milk yield: 4,000 kg (up to 7,000 kg)

• Strong muscling (much less caesareans)

h ll l

• mh allele

less frequent than BBB (allele frequency: 60 %)

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Context Context

DP‐BBB: mh/mh Bull Cow

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Context Context

DP‐BBB: +/+ Bull Cow

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General objective General objective

mh allele could influence milk production But molecular information is limited But molecular information is limited We need a practical method to integrate molecular information integrate molecular information

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Methodology Methodology

Mixed Inheritance Model

• Combining fixed gene effects g and random polygenic

Combining fixed gene effects g and random polygenic u effects

e Zu ZQg Xβ y + + + =

• Usual assumptions concerning distribution of random

effects

e Zu ZQg Xβ y + + + =

effects

⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ G u u ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ = ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ = ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ R e e Var and E

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Methodology Methodology

Equivalent Mixed Inheritance Model

• Fixed gene effects and random polygenic effects

Fixed gene effects and random polygenic effects replaced by a combined genetic effect u*

u Qg u* e * Zu Xβ y + = + + = where

• Modification of assumptions

⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ G * u Qg * u ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ = ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ = ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ R G e u Qg e u Var and E

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Methodology Methodology

Associated Mixed Model Equations

• Following Quaas (J Dairy Sci 1988 71 1338‐1345)

Following Quaas (J. Dairy Sci. 1988, 71, 1338 1345)

• Same strategy to integrate genetic groups

J i t ti ti f β * d

• Joint estimation of β, u* and g

⎤ ⎡ ⎤ ⎡ ⎤ ⎡

− −

Ry X' β Z R X' X R X'

1 1

ˆ ⎥ ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎢ ⎣ ⎡ = ⎥ ⎥ ⎥ ⎤ ⎢ ⎢ ⎢ ⎡ ⎥ ⎥ ⎥ ⎤ ⎢ ⎢ ⎢ ⎡ − +

− − − −

Ry ' Z Ry X' * û β Q G Q' G Q' Q G G Z R ' Z X R ' Z Z R X' X R X'

1 1 1 1 1 1 1 1

ˆ ⎥ ⎦ ⎢ ⎣ ⎥ ⎦ ⎢ ⎣ ⎥ ⎦ ⎢ ⎣ −

− −

g Q G Q' G Q'

1 1 11 9th WCGALP – Leipzig, Germany (August 1‐6, 2010)

Methodology Methodology

Associated Mixed Model Equations

⎤ ⎡ ⎤ ⎡ ⎤ ⎡

− −

Ry X' β Z R X' X R X'

1 1

ˆ ⎥ ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎢ ⎣ ⎡ = ⎥ ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎢ ⎣ ⎡ ⎥ ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎢ ⎣ ⎡ − +

− − − − − −

Ry ' Z Ry X g * û β Q G Q' G Q' Q G G Z R ' Z X R ' Z Z R X X R X

1 1 1 1 1 1

ˆ

• Solving of whole system is equivalent of solving

⎥ ⎦ ⎢ ⎣ ⎥ ⎦ ⎢ ⎣ ⎥ ⎦ ⎢ ⎣ − g Q G Q G Q

iteratively two systems of equations

1st, solving for the third row

* û G Q' g Q G Q'

1 1 − −

= ˆ

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Methodology Methodology

Associated Mixed Model Equations

⎤ ⎡ ⎤ ⎡ ⎤ ⎡

− −

Ry X' β Z R X' X R X'

1 1

ˆ ⎥ ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎢ ⎣ ⎡ = ⎥ ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎢ ⎣ ⎡ ⎥ ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎢ ⎣ ⎡ − +

− − − − − −

Ry ' Z Ry X g * û β Q G Q' G Q' Q G G Z R ' Z X R ' Z Z R X X R X

1 1 1 1 1 1

ˆ

• Solving of whole system is equivalent of solving

⎥ ⎦ ⎢ ⎣ ⎥ ⎦ ⎢ ⎣ ⎥ ⎦ ⎢ ⎣ − g Q G Q G Q

iteratively two systems of equations

2nd, solving the system

⎥ ⎦ ⎤ ⎢ ⎣ ⎡ + = ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎣ ⎡ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ +

− − − − − − − −

g Q G y R ' Z y R X' * û β G Z R ' Z X R ' Z Z R X' X R X'

1 1 1 1 1 1 1 1

ˆ ˆ

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⎦ ⎣ ⎥ ⎦ ⎢ ⎣ ⎦ ⎣ g y

Methodology Methodology

Associated Mixed Model Equations

• Solving iteratively until relative differences in

Solving iteratively until relative differences in estimation of g < 10‐5

Advantages

• Could allow solving when only limited number of

genotyped animals

• Gene effect could be estimated from limited known

genotypes

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g yp

9th WCGALP – Leipzig, Germany (August 1‐6, 2010)

Material Material

Data used for official genetic evaluations in the Walloon Region of Belgium g g

• Pedigree: 1,606,024 animals
• Data: 11 117 505 Test day records
• Data: 11,117,505 Test‐day records
• 689,057 cows with production records

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Material Material

Molecular information

• mh genotypes available

mh genotypes available

108 DP‐BBB bulls 1 891 DP BBB cows with production records 1,891 DP‐BBB cows with production records

• Offspring of genotyped animals
• i h

d i d 11,768 cows with production records

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Statistical Model Statistical Model

Random regression test‐day model

• Official Walloon Model used for routine run

Official Walloon Model used for routine run

• Multi‐trait multi‐lactation model

3 t it 3 l t ti 3 traits x 3 lactations

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Results

Phenotypic means of daily milk production in 3rd lactation

Results

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Phenotypic means of daily milk production in 3 lactation

305‐days milk production

(modified Best Prediction (Gillon et al., 2010))

+ / + 5471 kg

20 25

)

+ / + 5471 kg mh / + 4922 kg mh / mh 4434 kg

15

Milk (kg +/+ mh/+

5 10

mh/mh Days in milk

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Results Results

Allelic substitution effect of the mh allele on 305‐days production yields (kg) y p y ( g)

Milk Fat Protein 1st lactation ‐ 155 8 ‐ 8 73 ‐ 5 27 1 lactation 155.8 8.73 5.27 2nd lactation ‐ 142.0 ‐ 8.40 ‐ 5.43 3rd lactation ‐ 178.2 ‐ 9.67 ‐ 6.23

305‐days production yields (kg, modified Best Prediction, Gillon et al., 2010)

Means ‐ 3 lact. ‐ 158.7 ‐ 8.93 ‐ 5.64 Milk Fat Protein Means ‐ 3 lact. 4420 157 145

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Results Results

Allelic substitution effect of the mh allele on 305‐days production yields (kg) y p y ( g)

• Buske et al. (2010) J. Anim. Breed. Genet. 127: 272‐279

Based on a Bayesian approach using additional prior Based on a Bayesian approach using additional prior information on the distribution of external EBV Additive effect Additive effect ‐ 120.3 kg Milk ‐ 5.5 kg Fat ‐ 4.0 kg Protein

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Results Results

Allelic substitution effect of the mh allele on 305‐days production yields (kg) y p y ( g)

• Buske et al. (2010) Animal (accepted)

By regression on observed or estimated gene content By regression on observed or estimated gene content Estimated gene content using method of Gengler et al. (2007 Animal 1:21‐28) (2007, Animal 1:21‐28) Additive effect 76 1 kg Milk 3 6 kg Fat 2 8 kg Protein ‐ 76.1 kg Milk ‐ 3.6 kg Fat ‐ 2.8 kg Protein

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Results Results

When applied on a random regression model

Possibility to model the gene effect Possibility to model the gene effect Within the first 3 lactations F Milk F t d P t i i ld For Milk, Fat and Protein yields

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Results Results

Estimated substitution effect of the mh allele on

Days in milk

daily milk yields (kg)

0 2 30 60 90 120 150 180 210 240 270 300

The effects increase

‐0.4 ‐0.2

• n effect

eld (kg)

The effects increase throughout a lactation

‐0.8 ‐0.6

substitutio aily milk yie

‐1.2 ‐1

Allelic

• n da

1st lactation 2nd lactation d l

‐1.4

3rd lactation

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Conclusion Conclusion

Equivalent mixed inheritance model

• We can use incomplete genotyped population

We can use incomplete genotyped population

• When applied on a random regression model,

possibility to model the gene effect within lactations possibility to model the gene effect within lactations

Estimation of mh allele substitution effects

• Negative effect on milk production as expected

g p p

• Effect variable during the first 3 lactations
• Presence of mh allele reduces persistency of lactations

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• Presence of mh allele reduces persistency of lactations

Thank you for your attention Thank you for your attention

Corresponding author’s e‐mail: Frederic.Colinet@ulg.ac.be

Acknowledgments for collaboration Acknowledgments for collaboration

• Walloon Breeding Association (AWE asbl)

Acknowledgments for financial support

• Walloon Regional Ministry of Agriculture

9th WCGALP – Leipzig, Germany (August 1‐6, 2010)

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