principal components approach for estimating heritability of mid-infrared spectrum in bovine milk
DESCRIPTION
Joint ADSA-PSA-AMPA-ASAS Meeting July 8-12, 2007, San Antonio, Texas. Principal Components Approach for Estimating Heritability of Mid-Infrared Spectrum in Bovine Milk. H. Soyeurt 1,2,* , S. Tsuruta 3 , I. Misztal 3 & N. Gengler 1,4. - PowerPoint PPT PresentationTRANSCRIPT
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Principal Components Approach for Estimating Heritability of Mid-Infrared
Spectrum in Bovine Milk
H. Soyeurt1,2,*, S. Tsuruta3, I. Misztal3 & N. Gengler1,4
Joint ADSA-PSA-AMPA-ASAS MeetingJuly 8-12, 2007, San Antonio, Texas
1Animal Science Unit, Gembloux Agricultural University, Belgium2 FRIA, Brussels, Belgium
3 University of Georgia, Athens, USA4 FNRS, Brussels, Belgium
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Introduction
MIR Spectrometry used in milk recording Absorptions of IR at frequencies correlated to
the vibrations of specific chemical bonds within a molecule MIR milk spectrum represents the milk composition
Improve the nutritional quality of milk Difficulties:
1,060 spectral data Enough genetic variability ?
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Objectives
Reduce the number of traits Estimate the genetic parameters of
spectral data
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Spectral Data
9,663 milk samples: MilkoScan FT6000 during the Walloon milk recording April 2005 and May 2006 1,937 cows in 26 herds 7 breeds :
• Brown Swiss, Dual Purpose Belgian Blue, Holstein Friesian, Jersey, Montbeliarde, Normande and non Holstein Meuse-Rhine-Yssel type Red and White
Limited data base: cows in first lactation: 2,850 test-day spectral records 750 cows
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Trait Reduction
PCA pre-treatment: V = UDU’
• V = phenotypic (co)variances matrix for the 1,060 spectral initial traits
• U = matrix of eigenvectors • D = diagonal matrix of eigenvalues
48 principal components described 99.02% of phenotypic spectral variability
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Trait Reduction
48 new traits were estimated:
UR = the reduced matrix containing the chosen eigenvectors
In = identity matrix of dimension n equal to the number of records
y = the vector including the 1,060 original traits yU = the vector including the 48 new traits
y'UIy RnU
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Estimation of (Co)Variances
Model: Fixed effects:
• Herd * test date• Class of 15 days in milk
Random effects:• Permanent environment effect• Animal genetic effect• Residual effect
Multiple diagonalization and REML Back transformation
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Estimation of (Co)Variances
Heritability values ranged between 0.80 and 49.66%.
PCRelative
eigenvalues
Heritability Permanent
Environment Residual
Estimate SE Estimate SE Estimate SE
1 45.95 21.13 1.82 10.51 1.71 68.36 1.50
2 18.92 18.89 1.99 7.45 1.15 73.66 1.58
3 14.17 49.66 3.00 3.11 3.35 47.24 1.06
4 3.59 2.96 0.39 2.51 0.42 94.53 1.95
5 3.34 15.54 3.54 16.65 2.35 67.81 1.47
6 1.73 2.19 0.25 1.58 0.31 96.23 1.94
7 0.93 35.54 2.99 1.58 1.76 62.88 1.32
8 0.82 31.37 3.47 1.11 2.48 67.52 1.40
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0
0.5
1
1.5
2
2.5
926 1157 1388 1620 1851 2083 2314 2546 2777 3008 3240 3471 3703 3934 4165 4397 4628 4860
Wave number (cm-1)
Tra
nsm
itta
nce
___
0
10
20
30
40
50
60
70
Her
itab
ilit
y (i
n %
)
__
spectrum
heritability
Heritability of MIR Spectrum
Heritability ranged between 0.48 and 57.20 %
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Heritability of MIR Spectrum
0
0.5
1
1.5
2
2.5
926 1157 1388 1620 1851 2083 2314 2546 2777 3008 3240 3471 3703 3934 4165 4397 4628 4860
Wave number (cm-1)
Tra
nsm
itta
nce
___
0
10
20
30
40
50
60
70
Her
itab
ilit
y (i
n %
)
__
spectrum
heritability
2 MIR regions with no potential genetic interest :
1,620 and 1,670 cm-1
3,074 and 3,656 cm-1
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0
0.5
1
1.5
2
2.5
926 1157 1388 1620 1851 2083 2314 2546 2777 3008 3240 3471 3703 3934 4165 4397 4628 4860
Wave number (cm-1)
Tra
nsm
itta
nce
___
0
10
20
30
40
50
60
70
Her
itab
ilit
y (i
n %
)
__
spectrum
heritability
Fingerprint region (related to C-O and C-C stretching modes)
926 and 1,616 cm-1
Heritability of MIR Spectrum
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0
0.5
1
1.5
2
2.5
926 1157 1388 1620 1851 2083 2314 2546 2777 3008 3240 3471 3703 3934 4165 4397 4628 4860
Wave number (cm-1)
Tra
nsm
itta
nce
___
0
10
20
30
40
50
60
70
Her
itab
ilit
y (i
n %
)
__
spectrum
heritability
Lipids region :
2,800 and 3,000 cm-1
Average heritability : 36.09 %
Heritability for %FAT :43%
Heritability of MIR Spectrum
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0
0.5
1
1.5
2
2.5
926 1157 1388 1620 1851 2083 2314 2546 2777 3008 3240 3471 3703 3934 4165 4397 4628 4860
Wave number (cm-1)
Tra
nsm
itta
nce
___
0
10
20
30
40
50
60
70
Her
itab
ilit
y (i
n %
)
__
spectrum
heritability
Lactose region :
1,100 cm-1
Heritability at 1,099 cm-1 = 53.13%
Heritability for %lactose = 47.80%
Heritability of MIR Spectrum
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0
0.5
1
1.5
2
2.5
926 1157 1388 1620 1851 2083 2314 2546 2777 3008 3240 3471 3703 3934 4165 4397 4628 4860
Wave number (cm-1)
Tra
nsm
itta
nce
___
0
10
20
30
40
50
60
70
Her
itab
ilit
y (i
n %
)
__
spectrum
heritability
Lactate region :
1,515 and 1,593 cm-1
Average heritability = 24.86%
Heritability of MIR Spectrum
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Conclusion
PCA pre-treatment is interesting All wave numbers are not necessary for genetic
improvement Genetic variability of MIR milk spectrum exists
Enough genetic variability to improve the nutritional quality of milk by animal selection.
Perspectives: Increase the spectral data Improve the model Study in details the link between milk components
and spectral data
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DRINK BETTER
MILK
Thank you for your attention
Study supported of FRIA through grant scholarship and
FNRS through grants F.4552.05 and 2.4507.02
Presenting author’s e-mail : [email protected]