RT Journal Article
SR Electronic
T1 A workflow for UPLC-MS non-targeted metabolomic profiling in large human population-based studies
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 002782
DO 10.1101/002782
A1 Andrea Ganna
A1 Tove Fall
A1 Woojoo Lee
A1 Corey D. Broeckling
A1 Jitender Kumar
A1 Sara Hägg
A1 Patrik K. E. Magnusson
A1 Jessica Prenni
A1 Lars Lind
A1 Yudi Pawitan
A1 Erik Ingelsson
YR 2014
UL http://biorxiv.org/content/early/2014/02/17/002782.abstract
AB Metabolomic profiling is an emerging technique in life sciences. Human studies using these techniques have been performed in a small number of individuals or have been targeted at a restricted number of metabolites. In this article, we propose a data analysis workflow to perform non-targeted metabolomic profiling in large human population-based studies using ultra performance liquid chromatography-mass spectrometry (UPLC-MS). We describe challenges and propose solutions for quality control, statistical analysis and annotation of metabolic features. Using the data analysis workflow, we detected more than 8,000 metabolic features in serum samples from 2,489 fasting individuals. As an illustrative example, we performed a non-targeted metabolome-wide association analysis of high-sensitive C-reactive protein (hsCRP) and detected 407 metabolic features corresponding to 90 unique metabolites that could be replicated in an external population. Our results reveal unexpected biological associations, such as metabolites identified as monoacylphosphorylcholines (LysoPC) being negatively associated with hsCRP. R code and fragmentation spectra for all metabolites are made publically available. In conclusion, the results presented here illustrate the viability and potential of non-targeted metabolomic profiling in large population-based studies.