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Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information
Open accessAbstract
Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these "unknown metabolites" is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype-metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.
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“In both targeted and untargeted methods, even for confidently identified compounds, the biological processes that generated them are often poorly understood122–125.”
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Cites
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · cited 2×
“For each enzyme catalyzing one or more reactions in our pathway model, we retrieved functional annotations from two independent sources: (i) GO-Terms from the Gene Ontology [48] and (ii) enzyme pathway annotations from the KEGG PATHWAY database [47].”
MethodKEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077 · cited 2×
“For each enzyme catalyzing one or more reactions in our pathway model, we retrieved functional annotations from two independent sources: (i) GO-Terms from the Gene Ontology [48] and (ii) enzyme pathway annotations from the KEGG PATHWAY database [47].”
MethodPLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089 · cited 1×
“06) [41] with age”
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
Draper, N. R., & Smith, H. (2005) · Technometrics · doi:10.1198/tech.2005.s303 · Cited by 17,993
mice: Multivariate Imputation by Chained Equations in R
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870
(2010) · University of Twente Research Information · Cited by 6,037
Nist chemistry webbook, nist standard reference database number 69
Linstorm, P. (1998) · Medical Entomology and Zoology · Cited by 4,606
Szklarczyk, D., Franceschini, A., Kuhn, M., Simonovic, M., Roth, A. C., Mínguez, P., Doerks, T., Stark, M., Muller, J., Bork, P., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq973 · Cited by 3,469 · cited 1×
“This annotation idea parallels classical concepts from functional genomics, where, for instance, co-expression between RNA transcripts is used to predict the function of poorly characterized genes [23,24].”
BackgroundIntegrating common and rare genetic variation in diverse human populations
The International HapMap 3 Consortium (2010) · Nature · doi:10.1038/nature09298 · Cited by 3,026
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Items 101–198 of 198
Integrative radiation systems biology
Unger, K. (2014) · Radiation Oncology · doi:10.1186/1748-717x-9-21 · Cited by 22
Benedetti, E., Gerstner, N., Pučić‐Baković, M., Keser, T., Reiding, K. R., Ruhaak, L. R., Štambuk, T., Selman, M. H. J., Rudan, I., Polašek, O., et al. (2020) · Metabolites · doi:10.3390/metabo10070271 · Cited by 21
Self-citationIntegrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19
Büyüközkan, M., Alvarez-Mulett, S. L., Racanelli, A. C., Schmidt, F., Batra, R., Hoffman, K. L., Sarwath, H., Engelke, R., Gómez-Escobar, L. G., Simmons, W., et al. (2022) · iScience · doi:10.1016/j.isci.2022.104612 · Cited by 21
“GGMs are correlation-based network models that we have previously demonstrated to accurately reconstruct biological pathways from blood-based omics data (Krumsiek et al., 2011, 2012; Benedetti et al., 2017).”
Self-citationTu, J., Wen, J., Luo, Q., Li, X., Wang, D. Y., & Ye, J. (2024) · Respiratory Research · doi:10.1186/s12931-024-02720-6 · Cited by 20
“Genotyping information of both the KORA dataset and the Twins UK dataset has been described in previous studies [23–25].”
MethodDuan, L., Ma, A., Meng, X., Shen, G., & Qi, X. (2020) · Journal of Chromatography A · doi:10.1016/j.chroma.2020.460999 · Cited by 17
Association of Physical Activity With Bioactive Lipids and Cardiovascular Events
Hoshi, R. A., Liu, Y., Luttmann‐Gibson, H., Tiwari, S., Giulianini, F., Andres, A. M., Watrous, J. D., Cook, N. R., Costenbader, K. H., Okereke, O. I., et al. (2022) · Circulation Research · doi:10.1161/circresaha.122.320952 · Cited by 18
Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy
Rueedi, R., Mallol, R., Raffler, J., Lamparter, D., Friedrich, N., Vollenweider, P., Waeber, G., Kastenmüller, G., Kutalik, Z., & Bergmann, S. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005839 · Cited by 17
Self-citationSurlis, C., Earley, B., McGee, M., Keogh, K. A., Cormican, P., Blackshields, G., Tiernan, K., Dunn, A., Morrison, S. J., Henríquez, A. A., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-33627-0 · Cited by 17
da Silva, I. D. C. G., Levatti, E. V. D. C., Pedroso, A. P., Marchioni, D. M. L., Carioca, A. A. F., & Colleoni, G. W. B. (2020) · Scientific Reports · doi:10.1038/s41598-020-75862-4 · Cited by 17
MetDisease—connecting metabolites to diseases via literature
Duren, W. L., Weymouth, T. E., Hull, T., Omenn, G. S., Athey, B. D., Burant, C., & Karnovsky, A. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu179 · Cited by 17
König, E., Rainer, J., Hernandes, V. V., Paglia, G., Fabiola Del Greco, M., Bottigliengo, D., Yin, X., Chan, L. S., Teumer, A., Pramstaller, P. P., et al. (2022) · Metabolites · doi:10.3390/metabo12070604 · Cited by 15
“8) with a significant variant in ETFDH, which has been associated with various carnitines previously [18,19], it shows colocalization not only with increased ETFDH expression (PP = 0.”
BackgroundShen, H., Zhang, Y., Wang, X.-Y., Wang, C.-J., Wang, Y., Ye, J., & Li, M. (2024) · Biomolecules · doi:10.3390/biom14010116 · Cited by 18
“Shin et al.’s study provided GWAS summary statistics of serum metabolites [19], which comprised a total of 7824 samples from the German KORA F4 study and British Twins UK cohort [20].”
BackgroundThe Pharmacogenetic Footprint of ACE Inhibition: A Population-Based Metabolomics Study
Altmaier, E., Menni, C., Heier, M., Meisinger, C., Thorand, B., Quell, J. D., Kobl, M., Römisch‐Margl, W., Valdes, A. M., Mangino, M., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0153163 · Cited by 16
“In a systems approach combining genetic and metabolic information to identify unknown metabolites, the ACE-SNP rs4343 showed several significant associations with metabolite changes [20].”
BackgroundSelf-citationFlitman, R. S., Khalili, B., Kutalik, Z., Rueedi, R., Brümmer, A., & Bergmann, S. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00585 · Cited by 16
Metabolomics Insights Into Pathophysiological Mechanisms of Interstitial Cystitis
Fiehn, O., & Kim, J. (2014) · International Neurourology Journal · doi:10.5213/inj.2014.18.3.106 · Cited by 16
“These unknowns may eventually be identified using additional analytical methods, and thereby, can provide new insights into the phenotype or the disease [40].”
BackgroundQuell, J. D., Römisch‐Margl, W., Colombo, M., Krumsiek, J., Evans, A. M., Mohney, R. P., Salomaa, V. V., Fairé, U. D., Groop, L., Agakov, F. V., et al. (2017) · Journal of Chromatography B · doi:10.1016/j.jchromb.2017.04.002 · Cited by 16
“This approach was previously shown to reconstruct known metabolic pathways [10,23].”
BackgroundMethodInfluentialSelf-citationUntargeted metabotyping to study phenylpropanoid diversity in crop plants
Garibay‐Hernández, A., Kessler, N., Józefowicz, A. M., Türksoy, G. M., Lohwasser, U., & Mock, H. (2021) · Physiologia Plantarum · doi:10.1111/ppl.13458 · Cited by 16
“As already shown for human serum samples (Krumsiek et al., 2012), this approach could support predicting the identity of unknown metabolites, which is a common bottleneck in phytochemistry analyses.”
BackgroundLin, J., Tabassum, R., Ripatti, S., & Pirinen, M. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00431 · Cited by 12
“Second, variants in the region of rs146327691, near the SLCO1A2 gene, have been previously reported for response to serum metabolites (Krumsiek et al., 2012) and, interestingly, also for response to statins (Ho et al., 2006; Carr et al., 2019).”
BackgroundExploring blood metabolites and thyroid disorders: a bidirectional mendelian randomization study
Zhang, X., Zhou, J., Xie, Z., Li, X., Hu, J., He, H., & Li, Z. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1270336 · Cited by 14
Bovo, S., Ribani, A., Fanelli, F., Galimberti, G., Martelli, P. L., Trevisi, P., Bertolini, F., Bolner, M., Casadio, R., Dall’Olio, S., et al. (2025) · Genetics Selection Evolution · doi:10.1186/s12711-025-00960-8 · Cited by 16
“As expected, metabolites of the same family tended to be more inter-connected with each other than with metabolites of other classes, as also observed in previous studies [55, 56, 67].”
Suhre, K., Dadhania, D. M., Lee, J. R., Muthukumar, T., Chen, Q., Gross, S. S., & Suthanthiran, M. (2021) · Metabolites · doi:10.3390/metabo11080533 · Cited by 14
Self-citationOtto, L., Budde, K., Kastenmüller, G., Kaul, A., Völker, U., Völzke, H., Adamski, J., Kühn, J., Krumsiek, J., Artati, A., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-58430-8 · Cited by 13
Self-citationAn Integrated Molecular Atlas of Alzheimer’s DiseasePreprint
Wörheide, M. A., Krumsiek, J., Nataf, S., Nho, K. T., Greenwood, A. K., Wiley, J. C., Willruth, L.-L., Wu, T., Bellur, O., Marella, B., et al. (2021) · medRxiv · doi:10.1101/2021.09.14.21263565 · Cited by 12
“These networks provide a rich multi-omics context, integrating multiple layers of information, including eQTLs, pQTLs, mQTLs, gene co-expression and protein coNentities References Nodes AD-related meta-phenotypes 10 AD-related phenotypes 59 (31 unique) Genes (protein-coding) 20,353 with differential gene expression da…”
BackgroundSelf-citationda Silva, I., Vieira, R. D. C., Stella, C., Loturco, E. G., Carvalho, A. L., Véo, C. A. R., Scapulatempo‐Neto, C., Silva, S. M., D’Amora, P., Salzgeber, M. B., et al. (2018) · Oncotarget · doi:10.18632/oncotarget.25839 · Cited by 12
Shared genetic influences on resting‐state functional networks of the brain
Guimaraes, J., Sprooten, E., Beckmann, C. F., Franke, B., & Bralten, J. (2022) · Human Brain Mapping · doi:10.1002/hbm.25712 · Cited by 9
“, 2017), metabolic (Hübel et al., 2019; Krumsiek et al., 2012; Rask-Andersen et al., 2019; Shin et al., 2014), and drug response traits (Cha et al.”
BackgroundDiet-induced and mono-genetic obesity alter volatile organic compound signature in mice
Kistler, M., Muntean, A., Szymczak, W., Rink, N., Fuchs, H., Gailus‐Durner, V., Wurst, W., Hoeschen, C., Klingenspor, M., de Angelis, M. H., et al. (2016) · Journal of Breath Research · doi:10.1088/1752-7155/10/1/016009 · Cited by 12
Li, X., Zhou, H., Xiao, N., Wu, X., Shan, Y., Chen, L., Wang, C., Wang, Z., Huang, J., Li, A., et al. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.06.018 · Cited by 10
Kuehne, A., Mayr, U., Sévin, D. C., Claassen, M., & Zamboni, N. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005577 · Cited by 9
Hong, K. U., Walls, K. M., & Hein, D. W. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1091976 · Cited by 9
“rs1495743 Non-coding NR 6 × 10 ↓ Serum metabolite levels (1methylxanthine) Krumsiek et al. (2012) 8:18415371”
Rodriguez, J. E., Williams, D. R., Rast, P., & Mulder, J. (2020) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/zw7pf · Cited by 8
The HuMet Repository: Watching human metabolism at work
Weinisch, P., Raffler, J., Römisch‐Margl, W., Arnold, M., Mohney, R. P., Rist, M. J., Prehn, C. P., Skurk, T., Hauner, H., Daniel, H., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114416 · Cited by 8
“14-16 The networks reconstructed based on HuMet data are, thus, adequate to provide metabolic context for the global exploration of challenge-induced temporal changes.”
InfluentialSelf-citationAssessing the genetic association between vitamin B6 metabolism and genetic generalized epilepsy
Stevelink, R., Pangilinan, F., Jansen, F. E., Braun, K. P. J., Molloy, A. M., Brody, L. C., & Koeleman, B. P. C. (2019) · Molecular Genetics and Metabolism Reports · doi:10.1016/j.ymgmr.2019.100518 · Cited by 8
“The same SNPs in this locus are associated with levels of several other metabolites in blood (dimethylamine [25], unknown X-12092 [26,27], caprolactam [28], asymmetric dimethylarginine [29]) and urine (trimethylamine [25,30]) but a role for PYROXD2 in vitamin B6 metabolism has not been previously established.”
BackgroundSerum albumin levels and risk of atrial fibrillation: a Mendelian randomization study
Chen, B., Wang, C., & Li, W. (2024) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2024.1385223 · Cited by 7
Bovo, S., Bolner, M., Schiavo, G., Galimberti, G., Bertolini, F., Dall’Olio, S., Ribani, A., Zambonelli, P., Gallo, M., & Fontanesi, L. (2024) · animal · doi:10.1016/j.animal.2024.101393 · Cited by 7
Thareja, G., Evans, A. M., Wood, S. D., Stephan, N., Zaghlool, S. B., Halama, A., Kastenmüller, G., Belkadi, A., Albagha, O., & Suhre, K. (2022) · Metabolites · doi:10.3390/metabo12060496 · Cited by 8
“We identified three additional variants at the UGT2B15 locus and one variant near COMT (Table 2 and Figure 1).”
BackgroundInfluentialSelf-citationMechanistic insights from combining genomics with metabolomics
Drenos, F. (2017) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000000393 · Cited by 7
“Gestaltomics”: Systems Biology Schemes for the Study of Neuropsychiatric Diseases
Gutiérrez-Nájera, N., Reséndis-Antonio, O., & Nicolini, H. (2017) · Frontiers in Physiology · doi:10.3389/fphys.2017.00286 · Cited by 8
“Genome-wide association (GWA) studies with metabolic measurements have shown that genetic variation in metabolic enzymes and transporters lead to concentration changes of their respective metabolites (Suhre et al., 2011; Krumsiek et al., 2012).”
BackgroundCausal role of metabolites in non-small cell lung cancer: Mendelian randomization (MR) study
Li, Q., Wei, Z., Zhang, Y., & Zheng, C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300904 · Cited by 6
“It is a widely applicable method built upon Genome-Wide Association Studies (GWAS) summary data [24].”
MethodDu, Y., Lu, C., Bi, L., Wang, C., Zhao, M., Ding, Y., & Fan, W. (2024) · Skin Research and Technology · doi:10.1111/srt.13732 · Cited by 6
RetractedA genome-wide association study of neonatal metabolites
He, Q., Liu, H., Lu, L., Zhang, Q., Wang, Q., Wang, B., Wu, X., Guan, L., Mao, J., Xue, Y., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100668 · Cited by 5
Robust Moiety Model Selection Using Mass Spectrometry Measured Isotopologues
Jin, H., & Moseley, H. (2020) · Metabolites · doi:10.3390/metabo10030118 · Cited by 6
N‐lactoyl amino acids are potential biomarkers for insulin resistance and diabetic complications
Naja, K., Elashi, A. A., Anwardeen, N. R., Razzaq, A., Hedaya, L., Almuraikhy, S., Diboun, I., Suhre, K., Albagha, O., & Elrayess, M. A. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.16633 · Cited by 5
“27,28”
Self-citationGholami, M., Zoughi, M., Hasanzad, M., Larijani, B. A., & Amoli, M. M. (2022) · Journal of Medical Virology · doi:10.1002/jmv.28355 · Cited by 4
Krumsiek, J., Stückler, F., Suhre, K., Gieger, C., Spector, T. D., Soranzo, N., Kastenmüller, G., & Theis, F. J. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/048512 · Cited by 5
Self-citationCrosstalk between Host Genome and Metabolome among People with HIV in South Africa
Liu, C., Wang, Z., Hui, Q., Chiang, Y., Chen, J., Brijkumar, J., Edwards, J. A., Ordóñez, C. E., Dudgeon, M. R., Sunpath, H., et al. (2022) · Metabolites · doi:10.3390/metabo12070624 · Cited by 4
“2012 Jan Krumsiek [16] serum 517 1768, Germany European”
BackgroundIntegration of epigenetic, transcriptomic and proteomic data
Zenere, A. (2023) · Linköping studies in science and technology. Dissertations · doi:10.3384/9789180750691 · Cited by 5
Orct, T., Kovačić, J., Peremin, I., Kljaković‐Gašpić, Z., Pašalić, D., Sekovanić, A., Dorotić, A., Lovaković, B. T., Jurič, A., Pizent, A., et al. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1710613 · Cited by 3
Sun, T., Chen, X., Yan, H., & Liu, J. X. (2024) · Medicine · doi:10.1097/md.0000000000037085 · Cited by 3
“We downloaded the most comprehensive pooled association statistics from the Human Metabolic Genetics Study, [25,26] which were publicly accessible in the article published by Shin [25] (website: https://pubmed.ncbi.nlm.nih. gov/24816252/).”
MethodPeilong, L., Zhao, Q., & Shuqing, G. (2024) · Skin Research and Technology · doi:10.1111/srt.70000 · Cited by 3
“14,15”
RetractedStudies on Chicken Hatchability and Its Relation with Egg Yolk Metabolites
Zhang, Y. (2017) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-6218 · Cited by 3
Li, Y., Chen, Z., Huang, Z., Wang, J., Wang, J., Lin, L., Lin, R., Lai, J., Zhang, L., & Qiu, S. (2025) · Hereditas · doi:10.1186/s41065-025-00408-5 · Cited by 2
“…MR, Mendelian randomization; SNPs, single nucleotide polymorphisms ID: met-a), derived from a meta-analysis of up to 7824 individuals of European ancestry across three independent cohorts: two TwinsUK cohorts, and Cooperative Health Research in the Region of Augsburg (KORA) study [15, 21, 22].”
Metabolomic signatures of cognitive function in a type 2 Diabetes-Enriched cohort
Powell, J., Hugenschmidt, C. E., Hsu, F., Flashman, L. A., Xu, J., Bowden, D. W., Freedman, B. I., & Palmer, N. D. (2025) · Scientific Reports · doi:10.1038/s41598-025-99606-4 · Cited by 2
“…and 3MSE and X-11442 was positively associated with RAVLT Delayed Recall and DSC; both of these metabolites are highly correlated with bilirubin 51 Further exploration and identification of partially characterized metabolites could positively influence biomarker discovery towards…”
Wang, C., Ding, Y., Hu, Q., Wang, B., Xie, S., Yang, Z., Zhang, Z., Dai, D., Xiong, A., Sun, R., et al. (2025) · Journal of Cancer · doi:10.7150/jca.112416 · Cited by 2
“The serum levels of EPA and AA were measured using the Metabolon platform, with specific details thoroughly described in previous studies [33, 40, 41] .”
Chen, S., Lin, Z., Shen, X., Li, L., & Pan, W. (2023) · Genetic Epidemiology · doi:10.1002/gepi.22535 · Cited by 2
“Inference of metabolite regulatory networks can offer insights into the relationships among metabolites in biological processes, and thus is of great interest (Bartel et al., 2013; Krumsiek et al., 2012; Shin et al., 2014).”
BackgroundSalzer, L., Novoa‐del‐Toro, E. M., Frainay, C., Kissoyan, K. A. B., Jourdan, F., Dierking, K., & Witting, M. A. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.3c02797 · Cited by 3
Liu, L., Yin, J., Liu, Y., Li, B., Kang, S., & Du, N. (2024) · Medicine · doi:10.1097/md.0000000000040690 · Cited by 2
“[14] The GWAS summary datasets for EMs were obtained from the open GWAS database.”
Bacterial biofilm sample preparation for spatial metabolomics
Rosado-Rosa, J. M., & Sweedler, J. V. (2025) · The Analyst · doi:10.1039/d5an00466g · Cited by 2
Structured hierarchical models for probabilistic inference from perturbation screening data
Dirmeier, S., & Beerenwinkel, N. (2022) · The Annals of Applied Statistics · doi:10.1101/848234 · Cited by 2
Genetic influence on the metabolome
Rhee, E. P. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-812784-1.00006-2 · Cited by 2
Distilling Mechanistic Models From Multi-Omics DataPreprint
Erwin, S., Fletcher, J. R., Sweeney, D. C., Theriot, C. M., & Lanzas, C. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.06.556597 · Cited by 2
Slenter, D. (2024) · Research Publications (Maastricht University) · doi:10.26481/dis.20240624ds · Cited by 2
Non-Enzymatic Formation of N-acetylated Amino Acid Conjugates in Urine
Jacobs, J., van Sittert, C. G. C. E., Mienie, L. J., Dercksen, M., Opperman, M., & Vorster, B. C. (2023) · Applied Sciences · doi:10.3390/app131810002 · Cited by 1
Yuan, Y., Tang, Y., & Sun, Y. (2025) · Cureus · doi:10.7759/cureus.99600 · Cited by 2
Bolner, M., Bovo, S., Schiavo, G., Galimberti, G., Bertolini, F., Dall’Olio, S., Ribani, A., Zambonelli, P., Gallo, M., & Fontanesi, L. (2026) · Italian Journal of Animal Science · doi:10.1080/1828051x.2026.2624165 · Cited by 1
Shen, C., Adams, M., Davyson, E., Iveson, M., Cox, S. R., Harris, S. E., McIntosh, A., & Shen, X.-Y. (2026) · Clinical Epigenetics · doi:10.1186/s13148-026-02125-w
“CYP2C18 is another CYP superfamily member associated with metabolite levels of warfarin and clopidogrel as well as serum metabolites [48, 49, 54].”
Zhang, X., Chen, C. F., & Zhu, X. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2026.1827858 · Cited by 1
“These metabolites were retained because they may still contain biologically informative signals (Krumsiek et al., 2012).”
Lelievre, R., Rakesh, M., Hysi, P. G., Little, J., Freeman, E. E., & Roy‐Gagnon, M. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1411931 · Cited by 1
Sookoian, S. C., & Pirola, C. J. (2014) · Springer eBooks · doi:10.1007/978-1-4939-1932-1_20 · Cited by 1
Genetic association tests in family samples for multi-category phenotypes
Wang, S., Meigs, J. B., & Dupuis, J. (2021) · BMC Genomics · doi:10.1186/s12864-021-08107-x · Cited by 1
“Two variants on CYP3A43 were identified to be associated with Ticagrelor levels in individuals with acute coronary syndromes treated with ticagrelor [11] and serum metabolite measurement [12] respectively.”
BackgroundOmics: Potential Role in Early-Phase Drug Development
Grallert, H., Marzi, C., Hauck, S. M., & Gieger, C. (2014) · Springer eBooks · doi:10.1007/978-1-4471-4920-0_8
Self-citationMetabolomics and Cardiovascular Medicine
Qureshi, W., & Herrington, D. M. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-799961-6.00001-9
Applications of Metabolic Phenotyping in Epidemiology
Pinto, R. C., Karaman, İ., Fussell, J. C., Εvangelou, Ε., Kelly, F. J., Elliott, P., & Tzoulaki, I. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812293-8.00017-7
Wang, Z. (2018) · Digital Commons-TMC (Texas Medical Center)
Advanced LC–MS applications for identification and quantification of the metabolome
Jankevics, A., & Breitling, R. (2015) · doi:10.4155/fseb2013.14.55
Identification of biomarkers from metabolic profiling studies
Nicholls, A. (2014) · doi:10.4155/ebo.13.385
DEVELOPMENT OF TOOLS FOR ATOM-LEVEL INTERPRETATION OF STABLE ISOTOPE-RESOLVED METABOLOMICS DATASETS
Jin, H. (2021) · UKnowledge (University of Kentucky) · doi:10.13023/etd.2021.303
Omics: Potential Role in Early Phase Drug Development
Grallert, H., Marzi, C., Hauck, S. M., & Gieger, C. (2019) · Springer eBooks · doi:10.1007/978-3-030-11748-1_12
Self-citationGenome-driven metabolome identification
(2012) · Nature Methods · doi:10.1038/nmeth.2264
The road towards precision therapy for genetic epilepsies
Stevelink, R. (2022) · Utrecht University Repository (Utrecht University) · doi:10.33540/1102
Nogal, A., Louca, P., Valdes, A. M., Padmanabhan, S., & Menni, C. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-822951-4.00007-2
Huang, H., Zhi, W., Li, H., Wang, C., Chen, C., Liu, Y., Qin, J., Cao, S., & Yang, X. (2025) · Urolithiasis · doi:10.1007/s00240-025-01738-9
Boone, E. L., Ghanam, R., Alamri, F. S., & Amona, E. B. (2025) · Research Square · doi:10.21203/rs.3.rs-6565029/v1
Zheng, L., Yin, Y., Wang, X., Wang, B., Cui, R., & Zhang, G. (2025) · Frontiers in Genetics · doi:10.3389/fgene.2025.1631446
Application of Mendelian randomization to explore metabolic pathways in colorectal cancer
Hoang, T., Truong, V. M., & Tran, T. T. A. (2025) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000004020
Application of metabolomics methods as an occupational risk assessment tool (literature review)
Orlova, O. I., Уколов, А. И., & Radilov, A. S. (2025) · Toxicological Review · doi:10.47470/0869-7922-2025-33-5-337-346
Liu, S., Wang, B., & Yang, X. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114469
Tu, J., Wen, J., Luo, Q., Li, X., Wang, D., & Ye, J. (2024) · Figshare · doi:10.6084/m9.figshare.26683378
Additional file 3 of Genetic association tests in family samples for multi-category phenotypes
Wang, S., Meigs, J. B., & Dupuis, J. (2021) · Figshare · doi:10.6084/m9.figshare.17126095
Tu, J., Wen, J., Luo, Q., Li, X., Wang, D., & Ye, J. (2024) · Figshare · doi:10.6084/m9.figshare.26683381
Long-Term Mortality After Tuberculosis Among People With Disabilities in Korea
Min, J., Kim, S. Y., Park, J. E., Kim, Y.-Y., Yang, H. S., Park, B. H., Jeon, S. W., Kim, Y. J., Yang, B., Yeob, K. E., et al. (2025) · Journal of Korean Medical Science · doi:10.3346/jkms.2026.41.e33
Use of Mendelian Randomization to Unveil Metabolic Markers in Helicobacter pylori Infection
Hoang, T., & Kim, J. (2025) · Journal of Korean Medical Science · doi:10.3346/jkms.2026.41.e13
Zhang, H., Xie, J., He, C., Hu, H., Zhai, C., Qian, G., & Mao, M. (2026) · Clinical and Applied Thrombosis/Hemostasis · doi:10.1177/10760296261420227
The KD Atlas: A Multi-Omics Network Resource for Kidney Disease ResearchPreprint
Nsangou, Y. A. N., Haug, S., Wörheide, M. A., Bellur, O., Römisch‐Margl, W., Dönitz, J., Köttgen, A., Arnold, M., & Kastenmüller, G. (2026) · medRxiv · doi:10.64898/2026.02.19.26346652
Self-citationShared genetic factors between lung function and asthma by age at onsetPreprint
Li, Y., Cornejo-Sanchez, D. M., Dong, R., Naderi, E., Gao, W., Leal, S. M., & DeWan, A. T. (2026) · medRxiv · doi:10.64898/2026.02.20.26346655
BMI-Dependent Correlations of Sex Hormone Genes with Simple Endometrial Hyperplasia
Churnosov, V., Churnosova, M., Reshetnikov, E., Aristova, I., Tsoy, K., Николаевна, С. И., Polonikov, A., Solodilova, M. A., Churnosov, M. I., & Ponomarenko, I. (2026) · Life · doi:10.3390/life16060937
“Two more SNPs were associated with the metabolite X-21410 and androsterone sulfate, which were in LD with rs148982377 [T>C] ZNF789 —rs12535424 (r 2 = 0.67/D’ = 1.00) [21] and rs13243267 (r 2 = 0.67/D’ = 1.00) respectively [105].”
InfluentialXu, X., Gu, Y., Yang, X., Zhou, J., Song, C., Lv, H., Jiang, Y., Hu, L., Meng, Q., Yin, X., et al. (2026) · Vita · doi:10.15302/vita.2026.07.0057
Metadata
- Authors
- Krumsiek, J., Suhre, K., Evans, A. M., Mitchell, M. W., Mohney, R. P., Milburn, M. V., Wägele, B., Römisch-Margl, W., Illig, T., Adamski, J., Gieger, C., Theis, F. J., & Kastenmüller, G.
- Affiliations
- Helmholtz Munich (DE)
- Metabolon (United States) (US)
- Corresponding author
- Kastenmüller, Gabi
- ORCID
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-10-18
- Volume
- 8
- Issue
- 10
- Pages
- e1003005
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC3475673
- OpenAlex
- W2080426060
- Cited by
- 197
- References
- 63
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 19 · 2023: 8 · 2022: 15 · 2021: 16 · 2020: 16 · 2019: 9 · 2018: 16 · 2017: 18 · 2016: 14 · 2015: 16 · 2014: 17 · 2013: 6 · 2012: 3
- FWCI
- 5.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Site cant be reached (submittedVersion, cc-by)
- Funders
- Qatar Foundation
- Bundesministerium für Bildung und Forschung (0315494A, 03IS2061B)
- Studienstiftung des Deutschen Volkes
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- genome-wide association study, metabolic networks, SLC22A2, catechol-O-methyltransferase, CYP3A5, CYP2C18, liver detoxification, hypertension, insulin resistance, blood metabolites, systems biology
- MeSH terms
- Humans, Reproducibility of Results, Models, Statistical, Signal Transduction, Computational Biology, Polymorphism, Single Nucleotide, Genomics, Genome-Wide Association Study, Metabolomics, Metabolome, Data Mining
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