Journal
JOURNAL OF PROTEOME RESEARCH
Volume 9, Issue 9, Pages 4620-4627Publisher
AMER CHEMICAL SOC
DOI: 10.1021/pr1003449
Keywords
metabonomics; metabolome wide association; significance level; multiple testing
Categories
Funding
- National Heart, Lung, and Blood Institute [RO1 HL50490, RO1 HL084228]
- Ministry of Education, Science, Sports, and Culture, Tokyo, Japan [090357003]
- Biotechnology and Biological Sciences Research Council [P09870_DFHM]
- National agency in the Peoples Republic of China
- National agency in the U.K.
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [R01HL084228, R01HL050490] Funding Source: NIH RePORTER
- BBSRC [BB/E020372/1] Funding Source: UKRI
- MRC [G0801056] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/E020372/1] Funding Source: researchfish
- Medical Research Council [G0801056B, G0801056] Funding Source: researchfish
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High throughput metabolic profiling via the metabolome-wide association study (MWAS) is a powerful new approach to identify biomarkers of disease risk, but there are methodological challenges: high dimensionality, high level of collinearity, the existence of peak overlap within metabolic spectral data, multiple testing, and selection of a suitable significance threshold. We define the metabolome-wide significance level (MWSL) as the threshold required to control the family wise error rate through a permutation approach. We used H-1 NMR spectroscopic profiles of 24 h urinary collections from the INTERMAP study. Our results show that the MWSL primarily depends on sample size and spectral resolution. The MWSL estimates can be used to guide selection of discriminatory biomarkers in MWA studies. In a simulation study, we compare statistical performance of the MWSL approach to two variants of orthogonal partial least-squares (OPLS) method with respect to statistical power, false positive rate and correspondence of ranking of the most significant spectral variables. Our results show that the MWSL approach as estimated by the univariate t test is not outperformed by OPLS and offers a fast and simple method to detect disease-related discriminatory features in human NMR urinary metabolic profiles.
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