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

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Steps to Implement Animal Breeding for Improved Nutritional Quality of Bovine Milk N. Gengler1,2 and H. Soyeurt1 1 Gembloux Agricultural University, Animal Science ... – PowerPoint PPT presentation

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Title: 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. Gengler1,2 and H. Soyeurt1
  • 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)
  • Recently nutritional quality

3
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 more complexe process
5
However Implementing Animal Breeding ? Different
Steps
  • Making data available
  • Adapting models
  • Implementing routine computation of breeding
    values
  • Updating breeding goals and creating and using
    adapted selection indices
  • Continuing this ongoing development process
    towards most advances methods as genomic
    selection
  • ? Presentation will follow this outline

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
  • Similar to predicting fat and protein content

7
Making Data Available - II
  • What is MIR spectral data ?

MIR spectrometer
Spectral data
8
Making Data Available - III
1700 1500 cm-1 N-H
1200 900 cm-1 C-O
  • MIR absorption correlated to vibration of
    specific chemical bonds
  • MIR spectral data represents global milk
    composition

(Sivakesava and Irudayaraj, 2002)
1450-1200 cm-1 COOH
9
Making Data Available - IV
  • Using MIR spectral data

MIR spectrometer
Predicted milk components - Traditional (e.g.,
fat, protein) - New (e.g., FA)
Spectral data
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
  • Predicting other traits later

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
12
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

13
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 ?9 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

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
  • Numerous other issues

15
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

16
Adapting Models - V
  • Consequence large research needs !!!

17
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
  • Already generates nearly 1 000 000 records a year

18
Implementing Routine Computations - II
  • Needed (co)variance components first results
    become available
  • Some daily heritabilities (J. Dairy Sci
    913611-3626)
  • 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

19
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
    possible selection bias

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

21
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 healthy
    product?

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 traits

23
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
  • Also still large research needs !!!

25
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

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
  • Why?

27
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?

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
  • Combining G and A, potentially on an inverted
    scale

29
Thank you for your attention
Acknowledgments SPW DGA-RNE different
projects FNRS 2.4507.02F (2) F.4552.05 FRFC
2.4623.08
Email gengler.n_at_fsagx.ac.be
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