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

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

N. Gengler1,2 and H. Soyeurt1

1 Gembloux Agricultural University, Animal Science Unit, Belgium2 National Fund for Scientific Research, Belgium


N. Gengler1,2 and H. Soyeurt1 1 Gembloux Agricultural University, Animal Science Unit, Belgium 2 National Fund for Scientific Research, Belgium

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)

Recently: nutritional quality



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%



Genetic variability existsfor FA

Previous, next speaker

But implementing Animal Breedingmore complexe process



However ImplementingAnimal Breeding Different Steps1. 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

Presentation will follow this outline



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 Similar to predicting fat and protein content



Making Data Available - II What is MIR spectral data ?

Milk sampling

(e.g., milk recording)MIR spectrometer

Spectral data



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-H1200 – 900 cm-1: C-O

3000-2800 cm-1: C-H

1450-1200 cm-1: COOH

Making Data Available - III



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

Spectral data



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 Predicting other traits later

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 R2C SEcv R2cv RPDcvC4:0 0.13 0.04 0.01 0.94 0.01 0.86 2.69C6:0 0.09 0.03 0.01 0.94 0.01 0.91 3.41C8:0 0.05 0.02 0.01 0.90 0.01 0.87 2.80C10:0 0.12 0.05 0.01 0.92 0.02 0.84 2.49C12:0 0.15 0.06 0.01 0.94 0.02 0.84 2.48C14:0 0.49 0.14 0.03 0.96 0.05 0.90 3.14C14:1 0.01 0.00 0.00 0.41 0.00 0.36 1.25C16:0 1.40 0.41 0.14 0.88 0.17 0.83 2.46C16:1 0.08 0.04 0,02 0.76 0.03 0.32 1.22C18:0 0.56 0.25 0.06 0.94 0.10 0.85 2.62C18:1 trans 0.17 0.10 0.02 0.95 0.04 0.88 2.83C18:1 1.07 0.37 0.08 0.95 0.12 0.90 3.23C18:2 0.11 0.03 0.02 0.73 0.02 0.59 1.57C18:3 0.03 0.01 0.01 0.71 0.01 0.53 1.46CLA 0.04 0.02 0.01 0.80 0.01 0.52 1.44SAT 3.20 0.85 0.08 0.99 0.14 0.97 6.06UNSAT 1.61 0.48 0.08 0.97 0.13 0.93 3.75MONO 1.40 0.43 0.08 0.97 0.12 0.93 3.67POLY 0.21 0.06 0.03 0.79 0.04 0.67 1.75FA Short 0.41 0.12 0.03 0.94 0.04 0.92 3.54FA Medium 2.32 0.63 0.13 0.96 0.19 0.91 3.40FA Long 2.08 0.70 0.14 0.96 0.18 0.93 3.81



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,e.g., milk yield, fat and protein contents



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 tonutritional 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 Intra-herd variability feeding practices



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 Numerous other issues



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 as lactoferine



Adapting Models - V

Consequence: large research needs !!!



Implementing RoutineComputations - 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

Already generates nearly 1 000 000 records a year



Implementing RoutineComputations - 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

Same publication also some needed (co)variances



Implementing RoutineComputations - 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

possible selection bias



Implementing RoutineComputations - 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



Updating Breeding Goalsand 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

healthy product?



Updating Breeding Goalsand 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 traits



Updating Breeding Goalsand 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



Updating Breeding Goalsand Selection Indices - IV

Also still large research needs !!!



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 Predicted population young untested sires



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 Why?



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

Question: How?



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

Combining G and A, potentially on an inverted scale



Thank you for your attention

Email: [email protected]

Acknowledgments

SPW – DGA-RNE different projects

FNRS:2.4507.02F (2)F.4552.05FRFC 2.4623.08

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