Steps to implement animal breeding for improved nutritional quality of bovine milk

(1)

Steps to Implement Animal Breeding for Improved

Nutritional Quality of Bovine Milk

N. Gengler

1,2

_{and H. Soyeurt}

1

1 _{Gembloux Agricultural University, Animal Science Unit, Belgium}

2 _{National Fund for Scientific Research, Belgium}

(2)

Context



_{Changing breeding goals}

_{over last forty years}



_{From yields only}



_{Over type (morphologie)}



_{Towards functional traits (e.g., fertility, longevity)}



_{Limited interest in milk composition except}



_{Always: fat and protein content}



_{Mostly: somatic cell count (udder health)}



_{Also: urea and lactoses (management)}

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Steps to Implement Animal Breeding for Improved Nutritional Quality of Bovine Milk

N. Gengler1,2_{and H. Soyeurt}1 1 _{Gembloux Agricultural University, Animal Science Unit, Belgium}2 _{National Fund for Scientific Research, Belgium}

Milk Quality Traits



_{Milk fat composition}

_{as example}



_{Important variability (3% to 7%) in milk}



_{Composed mostly of fatty acids (FA)}



_{3 classes:}



Saturated (SAT): 70%, Unsaturated (UNSAT): 30%



Monounsaturated (MONO): 25%



Polyunsaturated (POLY): 5%



However far from optimal (human health)



SAT: 30%



_{MONO: 60%}



_{POLY: 10%}

(4)

Genetic variability exists

for FA

Previous, next speaker

But implementing Animal Breeding

(5)

Steps to Implement Animal Breeding for Improved Nutritional Quality of Bovine Milk

However Implementing

Animal Breeding  Different Steps

1. Making data available

2. Adapting models

3. Implementing routine computation of breeding values

4. Updating breeding goals and creating and using

adapted selection indices

5. Continuing this ongoing development process towards

most advances methods as genomic selection

(6)

Making Data Available - I



_{Animal breeding needs phenotypes}



_{Until recently}

_{difficult to obtain FA}

composition

easily



_{Based on gas chromatography}



_{Expensive, not in routine}



_{Recent advances based on use of}

mid-infrared (MIR) spectrometry data



Calibration to predict FA

(7)

Steps to Implement Animal Breeding for Improved Nutritional Quality of Bovine Milk

Making Data Available - II



_{What is MIR spectral data ?}

Milk sampling

(e.g., milk recording)

MIR spectrometer

(8)



MIR absorption

correlated to vibration

of specific chemical

bonds



MIR spectral data

‘represents’ global

milk composition

(Sivakesava and Irudayaraj, 2002)

1700 – 1500 cm

-1

_{: N-H}

1200 – 900 cm

-1

_{: C-O}

3000-2800 cm

-1

_{: C-H}

1450-1200 cm

-1

_{: COOH}

(9)

Steps to Implement Animal Breeding for Improved Nutritional Quality of Bovine Milk

Predicted milk components

- Traditional (e.g., fat, protein)

- New (e.g., FA)

Making Data Available - IV



_{Using MIR spectral data}

Milk sampling

(e.g., milk recording)

MIR spectrometer

(10)

Making Data Available - V



_{Routine milk recording}



_{Currently certain traits available}



Major FA (e.g., SAT, MONO, Omega-9)

limitation: minor FA



_Lactoferin



_Minerals



Others under development



_{Storing MIR spectral data now}

(11)

Dosage des AG

SD= Standard-deviation; SEC= Standard error of calibration; R²c= Coefficient of determination of calibration; SEcv= Standard

error of cross-validation; R²cv= Coefficient of determination of cross-validation; RPDcv= SD/SECV

Fatty acids (g/dl)

Mean

SD

SEC

R

2 C

SEcv

R

2 cv

RPDcv

C4:0

0.13

0.04

0.01

0.94

0.01

0.86

2.69 C6:0

0.09

0.03

0.01

0.94

0.01

0.91

3.41 C8:0

0.05

0.02

0.01

0.90

0.01

0.87

2.80 C10:0

0.12

0.05

0.01

0.92

0.02

0.84

2.49 C12:0

0.15

0.06

0.01

0.94

0.02

0.84

2.48 C14:0

0.49

0.14

0.03

0.96

0.05

0.90

3.14 C14:1

0.01

0.00

0.41

0.00

0.36

1.25 C16:0

1.40

0.41

0.14

0.88

0.17

0.83

2.46 C16:1

0.08

0.04 0,02

0.76

0.03

0.32

1.22 C18:0

0.56

0.25

0.06

0.94

0.10

0.85

2.62 C18:1 trans

0.17

0.10

0.02

0.95

0.04

0.88

2.83 C18:1

1.07

0.37

0.08

0.95

0.12

0.90

3.23 C18:2

0.11

0.03

0.02

0.73

0.02

0.59

1.57 C18:3

0.03

0.01

0.71

0.01

0.53

1.46 CLA

0.04

0.02

0.01

0.80

0.01

0.52

1.44 SAT

3.20

0.85

0.08

0.99

0.14

0.97

6.06 UNSAT

1.61

0.48

0.08

0.97

0.13

0.93

3.75 MONO

1.40

0.43

0.08

0.97

0.12

0.93

3.67 POLY

0.21

0.06

0.03

0.79

0.04

0.67

1.75 FA Short

0.41

0.12

0.03

0.94

0.04

0.92

3.54 FA Medium

2.32

0.63

0.13

0.96

0.19

0.91

3.40 FA Long

2.08

0.70

0.14

0.96

0.18

0.93

3.81

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Adapting Models - I



Data specific modeling needs

:



Longitudinal data

: data at every test-day



Multitrait

: many (up to 8 and more) milk quality traits

that are correlated



Multilactation

: less data, more interest to use all

available lactations, also linked to absence of historical

data



Absence of historic data for new traits

:

need to use historic correlated traits,

(13)

Steps to Implement Animal Breeding for Improved Nutritional Quality of Bovine Milk

Adapting Models - II



Data specific modeling needs

:



Trait definition

: some new spectral traits only

indicators for chemical traits

(low RPDcv)



Trait definition:

meta-traits



Ratio SAT/UNSAT

: linked positively to

nutritional and technological properties



Ratios product / substrate

: Δ9 indices (next talk)



Potentially adapting models for

new fixed effects



E.g., nutritional influence on FA well-known



Heterogeneous variances



Nature of traits

(14)

Adapting Models - III



Consequence: more complex situation compared

to traditional yield test-day models



Advances computing strategies:



Handling of massive missing values

 data augmenting techniques



Handling of highly correlated traits

 data transformation techniques

(15)

Steps to Implement Animal Breeding for Improved Nutritional Quality of Bovine Milk

Adapting Models - IV



Also complex situation to estimate (co)variance

components:



Multitrait:

many correlated milk quality traits,

(co)variances needed



Not even nature of traits:

different prediction equations

different RPDcv, weighting of records



Some spectral traits only indicators for chemical traits:

interest to predict inside the model, needs (co)variance

between “chemical” and “spectral” traits



Correlations between milk quality and old traits but also

other new traits:

e.g., those linked to

animal robustness

(16)

Adapting Models - V

(17)

Steps to Implement Animal Breeding for Improved Nutritional Quality of Bovine Milk

Implementing Routine

Computations - I



Integration of

acquisition of new traits

inside

genetic evaluation system

data flow



Interest to store spectral data on a large scale



Example (known to us):



Southern Belgium (Walloon Region):

70 000 cows



Luxembourg:

30 000 cows

(18)

Implementing Routine

Computations - II



_{Needed (co)variance components}

first results become available



Some daily

heritabilities

(J. Dairy Sci 91:3611-3626)



Milk (kg/day):

0.27 

Fat (%):

0.37 

Protein (%):

0.45 

FA:



SAT (g/100 g milk):

0.42 

MONO (g/100 g milk):

0.14

(19)

Steps to Implement Animal Breeding for Improved Nutritional Quality of Bovine Milk

Implementing Routine

Computations - III



_Currently

_{few component evaluations}



Most genetic evaluations for yields

(few exceptions as France)



_{Milk quality inside evaluation for milk components}



E.g., fat, protein



Those traits also needed



As

historical correlated data

to avoid as much as

(20)

Implementing Routine

Computations - IV



_{Expressing genetic results, various possibilities}

_:



Daily base, lactation base



Individual traits: e.g., SAT, UNSAT, MONO



Meta traits: e.g., ratios



_{Estimate breeding values}

_{for all animals}



However results for other effects huge potential for

management advice:



Not subject of this talk

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Steps to Implement Animal Breeding for Improved Nutritional Quality of Bovine Milk

Updating Breeding Goals

and Selection Indices - I



_Determine

_{“economic” weights}

_{, not easy task}



Economic:

 better milk price



Some dairy companies start to move on this



Health related:

 social value of more healthy milk

 economic value of more healthy milk,

reduction of health costs



Other elements, as reputation of milk as

(22)

Updating Breeding Goals

and Selection Indices - II



_{Breeding for improved nutritional quality of bovine}

milk 

not at the expenses of other traits



Therefore:



Need to

know correlations to traditional traits



E.g., yields, type and functional traits



Also,

correlations to other new traits



In particular to robustness traits



However other

specific issues

to nutritional quality

(23)

Steps to Implement Animal Breeding for Improved Nutritional Quality of Bovine Milk

Updating Breeding Goals

and Selection Indices - III



_{Specific issues of nutritional quality traits}



Large number of traits

:



Which traits to choose and how to choose?



Potential difference between breeding goal traits and

index traits

:



Breeding goal traits: “chemical traits”



Index traits:

“spectral traits”



Doubts that one index fits all situation:



Differentiated index per market as former cheese merit (CM$)

and fluid merit (FM$) in USA

(24)

Updating Breeding Goals

and Selection Indices - IV

(25)

Steps to Implement Animal Breeding for Improved Nutritional Quality of Bovine Milk

Near Future:

Genomic Selection - I



Genomic selection≠QTL detection

(previous talk)



Based on

dense marker maps

(50 000+ SNP)



Linking phenotypic variability to genomic variability



New idea



However under development in

nearly all countries



Current implementations

mostly



Training population

 older reliable sires

(26)

Near Future:

Genomic Selection - II



Milk quality traits

on first hand interesting

for

genomic selection (prediction)



However



Current implementation needs

reliable breeding values

from many animals (sires) for training,

but genetic evaluations not able to provide this



Genomic selection multitrait setting not yet clear



Nevertheless interesting idea

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Steps to Implement Animal Breeding for Improved Nutritional Quality of Bovine Milk

Near Future:

Genomic Selection - III



Genomic information natural way to

avoid some

current shortcomings

:



Few ancestors recorded

, risk of selection bias

 sires (maternal grand sires) could be genotyped



Only recent data

,

low reliabilities

even for older sires

 larger interest to improve using genomic information



Therefore nutritional quality traits



Ideal candidates for genomic selection

(28)

Near Future:

Genomic Selection - IV



How?



Next generation genomic prediction: single step



Recent advances, idea

equivalent model



Genomic relationship matrix G reflecting genomic

variability

replaces (or augments) pedigree based

relationship matrix A



Many details under development

, progress on



Computing G, inverting G

(29)

Steps to Implement Animal Breeding for Improved Nutritional Quality of Bovine Milk

Thank you for your attention

Email:

gengler.n@fsagx.ac.be

Acknowledgments

SPW – DGA-RNE different projects

FNRS:

2.4507.02F (2)

F.4552.05