Prediction of energy status of dairy cows using milk MIR spectra

Transcription

1 Prediction of energy status of dairy cows using milk MIR spectra C. Grelet 1, A. Vanlierde 1, M. Salavati, M 2. Hostens 3, L. Foldager 4, F. Dehareng 1 & GplusE Consortium 5 1 Walloon Agricultural Research Center (CRA-W), Belgium, 2 Royal Veterinary College (RVC), United Kingdom; 3 Ghent University, Belgium; 4 Aarhus University, Denmark; 5

2 Global objective of the project Optimise production efficiency, fertility, health and environmental footprint of dairy cows: Final steps: First step: Develop models predicting phenotypes of interest based on easily-measured and large scale biomarkers in milk: -Milk glycan -Milk metabolites -Milk MIR spectra Genomic study : Genome-Wide Association Study (GWAS) to relate genotype to phenotypes of interest 15 cows Breeding Management study: new management strategies at herd and cow level to improve phenotypes of interest 6 cows Advisory tools

3 Objective Develop models predicting phenotypes of interest: MIR Energy status: Fertility: -IGF-I level and LH inhibition (Butler, ) -Uterine health (Hammon, 6) Health: -Inflammation (Esposito 213) -Immune response (Hammon, 6) -Digestive issues: ketosis, DA (Collard, ) -Locomotive issues (Collard, )

4 Why MIR? C-O, OH CH Lactose C-O C=O, N-H, C-N C=O CH Fat Fat Fat + protein Protein Fat, organic acids Position of the peaks Qualitative analysis Intensity of the peaks Quantitative analysis

5 Why MIR? Already available Fast Cheap MIR Milk control Bentley Milk composition Fat Proteins Urea Lactose... Models MIR spectra of each cow

6 Objective Develop models predicting phenotypes of interest: MIR Energy status: Feed related phenotypes Blood components Global metabolic status using the clusters metodology

7 Objective Develop models predicting phenotypes of interest: MIR Energy status: Feed related phenotypes Blood components Global metabolic status using the clusters metodology

8 Materials & Methods

9 Experiments 6 experimental farms Common sampling protocol 235 cows Holsteins Calving to DIM # Cows PP MP MY Feed diet roughage AFBI (UK) Grass AU (Denmark) Corn/grass CRAI (Italy) Triticale CRA-W (Belgium) Corn/grass/sugarbeet FBN (Germany) Corn/grass UCD (Ireland) Corn/grass TOTAL

10 Phenotypes of interest Feed related phenotypes Energy balance = NEIntake NEMilk - NEMaintenance (NRC, 1) RFI = DMI PDMI (with PDMI= (.372 * FCM *BW.75 )*(1 - e (-.192*(WeekOfLactation )) ) (NRC, 1) DMI Observed Measured daily Daily DMI measurment in 3 farms (132 cows)

11 Phenotypes of interest Blood plasma metabolites and hormones Glucose energy IGF-I liver status NEFA mobilization BHB (sub)clinical ketosis DIM14 & 35 Sampling in the 6 farms (235 cows) Analyzed at UCD & Aarhus university

12 Phenotypes of interest Blood metabolites and hormones are usefull when used togheter Clusters: Combination of the blood metabolites and hormones into 3 groups of cow status K-means nearest neighbour clustering K=3

13 Phenotypes of interest Data Metabolic clusters Boxplots of metabolites and hormones following clusters 5. 3 : healthy cows : intermediate cows. 2: imbalanced cows k3clusterobs Glucose log1bohb log1nefa 1 2 IGF 3

14 MIR analysis of milk Analyzed locally or at CRA-W Twice per week AM & PM (weighted average) Foss and Delta

15 MIR standardisation Different instruments Standardisation step needed

16 MIR standardisation Different instruments Standardisation step needed

17 MIR standardisation Harmonize the spectral format Creation of common models

18 MIR standardisation Models can be used on all instruments in further steps of the project

19 MIR standardisation Models can be used on all instruments in further steps of the project

20 Results

21 MIR prediction of feed related phenotypes Y CV Predicted 1 Energy balance (Mcal/d) PLS 132 cows 169 data in the model Fit 1:1 AFBI AU UCD Samples/Scores Plot of X latent variables CV 1 subgroups R 2 = Latent Variables RMSEC = RMSECV = Y Measured 1

22 MIR prediction of feed related phenotypes PLS, LOOCV Predictor traits n #LV R²cv RMSECV Energy balance (Mcal/d) MIR Energy balance (Mcal/d) MIR, DIM, FPCM RFI (kg/d) MIR RFI (kg/d) MIR, DIM, FPCM DMI (kg/d) MIR DMI (kg/d) MIR, DIM, FPCM

23 MIR prediction of blood metabolites and hormones Predicted blood BHB BHB blood PLS 235 cows 219 samples Log transformation Random removing of low values LOOCV 8 LV Threshold of 1.2 mmol/l R² = 789 Observed high Observed low Predicted high 22 8 Predicted low Sens/Spec 63% 96% 9% Reference blood BHB

24 MIR prediction of blood metabolites and hormones PLS, LOOCV Phenotype n #LV R²cv RMSECV Blood NEFA (micro-ekv/l) Blood Glucose (mmol/l) Blood BHB (mmol/l) Blood IGF-I (mg/l)

25 MIR prediction of energy status clusters MIR, MY, Parity PLS-DA, 6LV Predicted Healthy Predicted Intermediate Predicted Imbalanced Healthy Intermediate Imbalanced Sens Spec % 84% % 84% % 86% Glob acc. 7%

26 MIR prediction of energy status clusters MIR, MY, Parity PLS-DA, 6LV Predicted Healthy Predicted Intermediate Predicted Imbalanced Healthy Intermediate Imbalanced Sens Spec % 84% % 84% % 86% Glob acc. 7%

27 MIR prediction of energy status clusters MIR, MY, Parity PLS-DA, 6LV Predicted Healthy Predicted Intermediate Predicted Imbalanced Healthy Intermediate Imbalanced Sens Spec % 84% % 84% % 86% Only 4.4% of extreme misclassification Glob acc. 7%

28 MIR prediction of energy status clusters MIR, MY, Parity PLS-DA, 6LV Healthy-or-Intermediate Imbalanced Predicted Healthy or Intermediate Predicted Imbalanced Sens Spec Glob acc. 7% 86% 82%

29 Conclusions MIR models on energy status: Direct EB, RFI or DMI Blood BHB and IGF-I Global metabolic status using the clusters metodology Variable accuracy following traits To be used in genomic studies Herd or individual cows management tools

30 Thank you for your attention! Acknowledgments and Disclaimer This project has received funding from the European Union s Seventh Framework Programme for research, technological development and demonstration under grant agreement n The views expressed in this publication are the sole responsibility of the author(s) and do not necessarily reflect the views of the European Commission. ICAR 217 Meeting Edinburgh 31