No genetic footprints of the fat mass and obesity associated (FTO) gene in human plasma 1H CPMG NMR metabolic profiles

K Kjeldahl, M.A. Rasmussen, Ann Louise Hasselbalch, Kirsten Ohm Kyvik, Lene Christiansen, S Rezzi, S Kochhar, Thorkild I. A. Sørensen, R Bro

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

In this paper it was investigated if any genotypic footprints from the fat mass and obesity associated (FTO) SNP could be found in 600 MHz 1H CPMG NMR profiles of around 1,000 human plasma samples from healthy Danish twins. The problem was addressed with a combination of univariate and multivariate methods. The NMR data was substantially compressed using principal component analysis or multivariate curve resolution-alternating least squares with focus on chemically meaningful feature selection reflecting the nature of chemical signals in an NMR spectrum. The possible existence of an FTO signature in the plasma samples was investigated at the subject level using supervised multivariate classification in the form of extended canonical variate analysis, classification tree modeling and Lasso (L1) regularized linear logistic regression model (GLMNET). Univariate hypothesis testing of peak intensities was used to explore the genotypic effect on the plasma at the population level. The multivariate classification approaches indicated poor discriminative power of the metabolic profiles whereas univariate hypothesis testing provided seven spectral regions with p < 0.05. Applying false discovery rate control, no reliable markers could be identified, which was confirmed by test set validation. We conclude that it is very unlikely that an FTO-correlated signal can be identified in these 1H CPMG NMR plasma metabolic profiles and speculate that high-throughput un-targeted genotype-metabolic correlations will in many cases be a difficult path to follow.
OriginalsprogEngelsk
TidsskriftMetabolomics
Vol/bind10
Udgave nummer1
Sider (fra-til)132-140
ISSN1573-3882
DOI
StatusUdgivet - feb. 2014

Fingeraftryk

Plasma (human)
Genes
Fats
Nuclear magnetic resonance
Plasmas
Logistic Models
Testing
Least-Squares Analysis
Principal component analysis
Single Nucleotide Polymorphism
Logistics
Feature extraction
Linear Models
Throughput
Proton Magnetic Resonance Spectroscopy
Population

Citer dette

Kjeldahl, K ; Rasmussen, M.A. ; Hasselbalch, Ann Louise ; Kyvik, Kirsten Ohm ; Christiansen, Lene ; Rezzi, S ; Kochhar, S ; I. A. Sørensen, Thorkild ; Bro, R. / No genetic footprints of the fat mass and obesity associated (FTO) gene in human plasma 1H CPMG NMR metabolic profiles. I: Metabolomics. 2014 ; Bind 10, Nr. 1. s. 132-140.
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title = "No genetic footprints of the fat mass and obesity associated (FTO) gene in human plasma 1H CPMG NMR metabolic profiles",
abstract = "In this paper it was investigated if any genotypic footprints from the fat mass and obesity associated (FTO) SNP could be found in 600 MHz 1H CPMG NMR profiles of around 1,000 human plasma samples from healthy Danish twins. The problem was addressed with a combination of univariate and multivariate methods. The NMR data was substantially compressed using principal component analysis or multivariate curve resolution-alternating least squares with focus on chemically meaningful feature selection reflecting the nature of chemical signals in an NMR spectrum. The possible existence of an FTO signature in the plasma samples was investigated at the subject level using supervised multivariate classification in the form of extended canonical variate analysis, classification tree modeling and Lasso (L1) regularized linear logistic regression model (GLMNET). Univariate hypothesis testing of peak intensities was used to explore the genotypic effect on the plasma at the population level. The multivariate classification approaches indicated poor discriminative power of the metabolic profiles whereas univariate hypothesis testing provided seven spectral regions with p < 0.05. Applying false discovery rate control, no reliable markers could be identified, which was confirmed by test set validation. We conclude that it is very unlikely that an FTO-correlated signal can be identified in these 1H CPMG NMR plasma metabolic profiles and speculate that high-throughput un-targeted genotype-metabolic correlations will in many cases be a difficult path to follow.",
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author = "K Kjeldahl and M.A. Rasmussen and Hasselbalch, {Ann Louise} and Kyvik, {Kirsten Ohm} and Lene Christiansen and S Rezzi and S Kochhar and {I. A. S{\o}rensen}, Thorkild and R Bro",
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Kjeldahl, K, Rasmussen, MA, Hasselbalch, AL, Kyvik, KO, Christiansen, L, Rezzi, S, Kochhar, S, I. A. Sørensen, T & Bro, R 2014, 'No genetic footprints of the fat mass and obesity associated (FTO) gene in human plasma 1H CPMG NMR metabolic profiles', Metabolomics, bind 10, nr. 1, s. 132-140. https://doi.org/10.1007/s11306-013-0560-7

No genetic footprints of the fat mass and obesity associated (FTO) gene in human plasma 1H CPMG NMR metabolic profiles. / Kjeldahl, K; Rasmussen, M.A.; Hasselbalch, Ann Louise; Kyvik, Kirsten Ohm; Christiansen, Lene; Rezzi, S; Kochhar, S; I. A. Sørensen, Thorkild; Bro, R.

I: Metabolomics, Bind 10, Nr. 1, 02.2014, s. 132-140.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - No genetic footprints of the fat mass and obesity associated (FTO) gene in human plasma 1H CPMG NMR metabolic profiles

AU - Kjeldahl, K

AU - Rasmussen, M.A.

AU - Hasselbalch, Ann Louise

AU - Kyvik, Kirsten Ohm

AU - Christiansen, Lene

AU - Rezzi, S

AU - Kochhar, S

AU - I. A. Sørensen, Thorkild

AU - Bro, R

PY - 2014/2

Y1 - 2014/2

N2 - In this paper it was investigated if any genotypic footprints from the fat mass and obesity associated (FTO) SNP could be found in 600 MHz 1H CPMG NMR profiles of around 1,000 human plasma samples from healthy Danish twins. The problem was addressed with a combination of univariate and multivariate methods. The NMR data was substantially compressed using principal component analysis or multivariate curve resolution-alternating least squares with focus on chemically meaningful feature selection reflecting the nature of chemical signals in an NMR spectrum. The possible existence of an FTO signature in the plasma samples was investigated at the subject level using supervised multivariate classification in the form of extended canonical variate analysis, classification tree modeling and Lasso (L1) regularized linear logistic regression model (GLMNET). Univariate hypothesis testing of peak intensities was used to explore the genotypic effect on the plasma at the population level. The multivariate classification approaches indicated poor discriminative power of the metabolic profiles whereas univariate hypothesis testing provided seven spectral regions with p < 0.05. Applying false discovery rate control, no reliable markers could be identified, which was confirmed by test set validation. We conclude that it is very unlikely that an FTO-correlated signal can be identified in these 1H CPMG NMR plasma metabolic profiles and speculate that high-throughput un-targeted genotype-metabolic correlations will in many cases be a difficult path to follow.

AB - In this paper it was investigated if any genotypic footprints from the fat mass and obesity associated (FTO) SNP could be found in 600 MHz 1H CPMG NMR profiles of around 1,000 human plasma samples from healthy Danish twins. The problem was addressed with a combination of univariate and multivariate methods. The NMR data was substantially compressed using principal component analysis or multivariate curve resolution-alternating least squares with focus on chemically meaningful feature selection reflecting the nature of chemical signals in an NMR spectrum. The possible existence of an FTO signature in the plasma samples was investigated at the subject level using supervised multivariate classification in the form of extended canonical variate analysis, classification tree modeling and Lasso (L1) regularized linear logistic regression model (GLMNET). Univariate hypothesis testing of peak intensities was used to explore the genotypic effect on the plasma at the population level. The multivariate classification approaches indicated poor discriminative power of the metabolic profiles whereas univariate hypothesis testing provided seven spectral regions with p < 0.05. Applying false discovery rate control, no reliable markers could be identified, which was confirmed by test set validation. We conclude that it is very unlikely that an FTO-correlated signal can be identified in these 1H CPMG NMR plasma metabolic profiles and speculate that high-throughput un-targeted genotype-metabolic correlations will in many cases be a difficult path to follow.

KW - FTO

KW - NMR

KW - CPMG

KW - Data compression

KW - ECVA

KW - MCR-ALS

U2 - 10.1007/s11306-013-0560-7

DO - 10.1007/s11306-013-0560-7

M3 - Journal article

VL - 10

SP - 132

EP - 140

JO - Metabolomics

JF - Metabolomics

SN - 1573-3882

IS - 1

ER -