Imperial College London

DrTimothyEbbels

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Reader in Computational Bioinformatics
 
 
 
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Contact

 

+44 (0)20 7594 3160t.ebbels Website

 
 
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Location

 

131Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{castagne:2017:10.1021/acs.jproteome.7b00344,
author = {castagne, R and Boulange, CL and Karaman, I and campanella and Santos, Ferreira DL and Kaluarachchi, MR and lehne and Moayyeri, A and Lewis, MR and Spagou, K and DOna, AC and Evangelos, V and Tracy, R and Greenland, P and Lindon, JC and ebbels, TMD and elliott and tzoulaki and Chadeau, M},
doi = {10.1021/acs.jproteome.7b00344},
journal = {Journal of Proteome Research},
pages = {3623--3633},
title = {Improving visualisation and interpretation of metabolome-wide association studies (MWAS): an application in a population-based cohort using untargeted 1H NMR metabolic profiling.},
url = {http://dx.doi.org/10.1021/acs.jproteome.7b00344},
volume = {16},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - 1H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum 1H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.
AU - castagne,R
AU - Boulange,CL
AU - Karaman,I
AU - campanella
AU - Santos,Ferreira DL
AU - Kaluarachchi,MR
AU - lehne
AU - Moayyeri,A
AU - Lewis,MR
AU - Spagou,K
AU - DOna,AC
AU - Evangelos,V
AU - Tracy,R
AU - Greenland,P
AU - Lindon,JC
AU - ebbels,TMD
AU - elliott
AU - tzoulaki
AU - Chadeau,M
DO - 10.1021/acs.jproteome.7b00344
EP - 3633
PY - 2017///
SN - 1535-3893
SP - 3623
TI - Improving visualisation and interpretation of metabolome-wide association studies (MWAS): an application in a population-based cohort using untargeted 1H NMR metabolic profiling.
T2 - Journal of Proteome Research
UR - http://dx.doi.org/10.1021/acs.jproteome.7b00344
UR - http://hdl.handle.net/10044/1/50456
VL - 16
ER -