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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 Unit, Belgium2 National Fund for Scientific Research, Belgium
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
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
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
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%
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
Genetic variability existsfor FA
Previous, next speaker
But implementing Animal Breedingmore complexe process
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
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
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
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
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
Making Data Available - II What is MIR spectral data ?
Milk sampling
(e.g., milk recording)MIR spectrometer
Spectral data
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Adapting Models - V
Consequence: large research needs !!!
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
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
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
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
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
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
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
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
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
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?
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
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
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
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
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
Updating Breeding Goalsand Selection Indices - IV
Also still large research needs !!!
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
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
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
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?
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
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?
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
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
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
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