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Oct 23, 2022 348 likes •592 views

Targeted combination of estimated breeding values for lower accuracy mid-infrared biomarkers increases their usefulness in genetic evaluation of dairy cattle N. Gengler, GplusE Consortium Speaker: Nicolas Gengler Targeted combination of

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Targeted combination of estimated breeding values for lower accuracy mid-infrared biomarkers increases their usefulness in genetic evaluation

  • f dairy cattle
  • N. Gengler, GplusE Consortium

Speaker: Nicolas Gengler

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Targeted combination of estimated breeding values for lower accuracy mid-infrared biomarkers increases their usefulness in genetic evaluation of dairy cattle

  • N. Gengler1 & GplusE Consortium2

1 ULg-GxABT , Belgium (nicolas.gengler@ulg.ac.be) 2 http://www.gpluse.eu (mark.crowe@ucd.ie)

ICAR 2017 Meeting Edinburgh 2

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Major Challenge: Relevant Data

  • Without data
  • No breeding or management possible!
  • But data has also to be relevant
  • As close as possible to the processes we follow
  • Here enters relatively new concept of biomarkers defined

as:

  • “… objectively measured and evaluated … indicator of normal

biological processes, pathogenic processes, or … responses to an … intervention” (National Institutes of Health)

ICAR 2017 Meeting Edinburgh 3

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Usefulness of Milk Composition!

ICAR 2017 Meeting Edinburgh 4

Hamann & Krömker 1997. Livest. Prod. Sci. 48: 201-208.

Factors of influence determining cow health

HERD INDIVIDUAL COW MILKING

Housing Genetic Parlour type Bedding Yield Equipment Feeding Lactation stage Routine Manure disposal Lactation number Records Hygiene Milkability Hygiene

COW STATUS MONITORING Clinical changes Subclinical changes

Body weight Blood Feed intake Lymph Behaviour Urine Milk yield Milk composition

Cow health Udder health

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Major Milk Components (except SCC)

Milk samples (milk payment, milk recording) Raw data = MIR spectra Calibration equations Quantification: fat protein urea lactose MIR analysis

FOSS

ICAR 2017 Meeting Edinburgh 5

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Novel Traits

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Novel Calibration equations Milk samples (milk payment, milk recording) Raw data = MIR spectra MIR analysis

FOSS

Quantification: novel traits protein urea lactose

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Blood Based Biomarkers as Reference

ICAR 2017 Meeting Edinburgh 7

Hamann & Krömker 1997. Livest. Prod. Sci. 48: 201-208.

Factors of influence determining cow health

HERD INDIVIDUAL COW MILKING

Housing Genetic Parlour type Bedding Yield Equipment Feeding Lactation stage Routine Manure disposal Lactation number Records Hygiene Milkability Hygiene

COW STATUS MONITORING Clinical changes Subclinical changes

Body weight Blood Feed intake Lymph Behaviour Urine Milk yield Milk composition

Cow health Udder health

Reference 

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Blood Based  Milk MIR Predicted

ICAR 2017 Meeting Edinburgh 8

Hamann & Krömker 1997. Livest. Prod. Sci. 48: 201-208.

Factors of influence determining cow health

HERD INDIVIDUAL COW MILKING

Housing Genetic Parlour type Bedding Yield Equipment Feeding Lactation stage Routine Manure disposal Lactation number Records Hygiene Milkability Hygiene

COW STATUS MONITORING Clinical changes Subclinical changes

Body weight Blood Feed intake Lymph Behaviour Urine Milk yield Milk composition

Cow health Udder health

Reference 

MIR

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  • Blood based biomarkers: IGF1, glucose, urea, cholesterol,

fructosamine, BOHB and NEFA

  • But milk MIR based predictions required to facilitate

easier access to relevant data:

  • On a very large scale
  • At reasonable costs
  • One of the objectives of the GplusE project

ICAR 2017 Meeting Edinburgh 9

Blood Based  Milk MIR Predicted

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Developing Required Calibrations

  • Assembling reference values and standardized spectra
  • Blood measurements collected on lactating Holstein cows
  • At DIM 14 (ranging from 11 to 20) and DIM 35 (ranging from 31 to 38).
  • In total 373 samples from 5 farms
  • Not one “calibration”  process of calibration “model” development
  • Numerous different multivariate methods
  • Different pre-treatment of MIR data
  • Variable selection, etc….
  • Still ongoing  first results
  • R2

CV ranging from 0.21 to 0.51  used in this study

  • Still improving…

ICAR 2017 Meeting Edinburgh 10

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Usefulness of Low-Accuracy Predictors

  • Use for management ???

 not the topic here

  • Use for genetic improvement
  • Usual to predict traits from other “information”

 selection index

  • Hypothesis here: target combination of EBV for those traits increases

their usefulness in genetic evaluations of dairy cattle

ICAR 2017 Meeting Edinburgh 11

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Genetic Evaluations

  • MIR records  predictions
  • 59,303 records (closest to DIM25) from 33,968 cows in Walloon region
  • f Belgium
  • Model
  • Single-trait, multi-lactation (1, 2, 3+)
  • Variance components
  • h2 ranging from 0.15 to 0.30
  • Estimated breeding values (EBV) used when based on at least

20 daughters

  • A total of 171 bulls met these criteria

ICAR 2017 Meeting Edinburgh 12

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Observed Correlations

(i.e. lower bound estimates of genetic correlations)

  • MIR biomarker EBV correlated to official EBV for somatic cell score

(udder health - UDH), fertility (FER) and longevity (LONG)

  • Observed correlations diverse (in absolute values) ranging from

0.00 to 0.31

  • Highest value was found between fertility and fructosamine in 3rd

lactation  individual correlations disappointing

  • Hypothesis: targeted combination will do better

ICAR 2017 Meeting Edinburgh 13

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Observed Correlations

(i.e. lower bound estimates of genetic correlations)

  • MIR biomarker EBV correlated to official EBV for somatic cell score

(udder health - UDH), fertility (FER) and longevity (LONG)

  • Observed correlations diverse (in absolute values) ranging from

0.00 to 0.31

  • Highest value was found between fertility and fructosamine in 3rd

lactation  defining and computing pUDH, pFER, pLONG as best linear predictors from Biomarker EBV

ICAR 2017 Meeting Edinburgh 14

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MIR Biomarker EBV  pUDH

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Udder Health (UDH) Predicted Udder Health (pUDH)

r = 0.62

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MIR Biomarker EBV  pFER

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Fertility (FER) Predicted Fertility (pFER)

r = 0.59

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MIR Biomarker EBV  pLONG

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Longevity (LONG) Predicted Longevity (pLONG)

r = 0.52

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Different Predictors of Longevity

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EBVLONG EBVUDH EBVFER

r = 0.57

EBVpLONG

MIR Biomarker EBV

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Combining Predictors of Longevity

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EBVLONG EBVUDH EBVFER EBVpLONG

MIR Biomarker EBV

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Adding pLONG  MIR Biomarker EBV

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EBVLONG EBVUDH EBVFER EBVpLONG

r = 0.68

MIR Biomarker EBV

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Conclusions

  • Even lower accuracy milk MIR based biomarkers can become

useful in the context of animal breeding

  • Targeted combination of associated EBV increased their

correlation to breeding goal traits and therefore their usefulness for genetic evaluation

  • Demonstrated in the context of longevity

ICAR 2017 Meeting Edinburgh 21

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Potential Improvements in Strategy

  • Alternative phenotype definitions directly targeting desired

phenotypes (e.g. metabolic status)

  • Better calibration models
  • Multi-trait genetic and genomic evaluations

 massive multivariate models  direct use of MIR

  • Optimized selection index procedures to combine individual

information sources

ICAR 2017 Meeting Edinburgh 22

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Acknowledgments and Disclaimer

  • Support of the whole GplusE team, in particuliar
  • Hedi Hammami ULg-GxABT (for the genetic evaluations)
  • Clément Grelet CRA-W (for the calibrations)
  • Support of the Futurospectre Consortium* providing access to MIR data
  • Support of European Milk Recording (EMR) providing access to MIR data

standardization

ICAR 2017 Meeting Edinburgh 23

This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement n° 613689 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.

*Walloon Breeding Association, CRA-W, Milk Committee and ULg-GxABT