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

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 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. Gengler 1,2 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%

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 Genetic variability exists for FA Previous, next speaker But implementing Animal Breeding more complexe process

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 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  Presentation will follow this outline

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 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. Gengler 1,2 and H. Soyeurt 1 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. Gengler 1,2 and H. Soyeurt 1 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-H 1200 – 900 cm -1 : C-O cm -1 : C-H cm -1 : COOH Making Data Available - III

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 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. Gengler 1,2 and H. Soyeurt 1 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

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 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. Gengler 1,2 and H. Soyeurt 1 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 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  Intra-herd variability  feeding practices

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 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. Gengler 1,2 and H. Soyeurt 1 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. Gengler 1,2 and H. Soyeurt 1 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. Gengler 1,2 and H. Soyeurt 1 1 Gembloux Agricultural University, Animal Science Unit, Belgium 2 National Fund for Scientific Research, Belgium 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): cows  Luxembourg: cows  Already generates nearly records a year

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 Implementing Routine Computations - II  Needed (co)variance components first results become available  Some daily heritabilities (J. Dairy Sci 91: )  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. Gengler 1,2 and H. Soyeurt 1 1 Gembloux Agricultural University, Animal Science Unit, Belgium 2 National Fund for Scientific Research, Belgium 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

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 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

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 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?

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 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

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 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

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 Updating Breeding Goals and Selection Indices - IV Also still large research needs !!!

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 Near Future: Genomic Selection - I  Genomic selection≠QTL detection (previous talk)  Based on dense marker maps ( 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. Gengler 1,2 and H. Soyeurt 1 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. Gengler 1,2 and H. Soyeurt 1 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. Gengler 1,2 and H. Soyeurt 1 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. Gengler 1,2 and H. Soyeurt 1 1 Gembloux Agricultural University, Animal Science Unit, Belgium 2 National Fund for Scientific Research, Belgium Thank you for your attention Acknowledgments SPW – DGA-RNE different projects FNRS: F (2) F FRFC