An atlas of genetic influences on human blood metabolites
Open accessAbstract
Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism thus far, comprising 7,824 adult individuals from 2 European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metabolism and pharmacological targets. We further developed a database and web-based resources for data mining and results visualization. Our findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease.
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The divergent effects of CPS1 probably reflect its independent roles in creatine metabolism and immune signaling.
“probably reflecting its independent roles in creatine metabolism39 and immune signaling.40”
From Cystatin C is glucocorticoid responsive, directs recruitment of Trem2+ macrophages, and predicts failure of cancer immunotherapy (2023) · cites this paper as [39]
Citation 39 is attached to the creatine-metabolism part of the sentence; immune signaling has a separate citation. The Disease and pharmacological relevance section describes CPS1’s role in producing carbamoyl phosphate and its association with glycine levels, but the passages found for “CPS1 creatine metabolism” and “creatine” do not address a role in creatine metabolism or explain the stated divergent effects. (LLM, conf=0.72)
Experimental — accuracy not yet measuredSeveral systems genetics studies of metabolites in human plasma or serum have been reported.
“Several systems genetics studies of metabolites in human plasma or serum have been reported (Gieger et al., 2008; Kettunen et al., 2012; Shin et al., 2014; Suhre et al., 2011; Yu et al., 2013).”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Shin et al., 2014
The most recent large-scale mQTL study investigated 529 plasma or serum metabolites from 7,824 adult Europeans using MS and found 299 SNP-metabolite associations at 145 independent loci genome-wide significant after correction.
“The most recent large scale mQTL study investigated 529 metabolites of plasma or serum samples from 7,824 adult Europeans using MS technology. A total of 299 SNP-metabolite associations (i.e. mQTLs) at 145 independent loci were genome-wide significant after correction for the number of SNP-metabolite association tests (Shin et al., 2014).”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Shin et al., 2014
Nascent efforts to capture transcriptomic, proteomic and metabolomic phenotypes are not yet at equivalent scale to genetic data.
“There are nascent efforts to capture transcriptomic, proteomic and metabolomic phenotypes, although these are not yet at equivalent scale to the genetic data79,80.”
From A brief history of human disease genetics (2020) · cites this paper as [80]
Connections between genetic variation and circulating proteomic and metabolomic data provide additional mechanistic links between cellular events and whole-body physiology.
“Connections between genetic variation and circulating proteomic and metabolomic data provide additional mechanistic links between cellular events and whole-body physiology79,80.”
From A brief history of human disease genetics (2020) · cites this paper as [80]
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- Authors
- The Multiple Tissue Human Expression Resource (MuTHER) Consortium, Shin, S.-Y., Fauman, E. B., Petersen, A.-K., Krumsiek, J., Santos, R., Huang, J., Arnold, M., Erte, I., Forgetta, V., Yang, T.-P., Walter, K., Menni, C., Chen, L., Vasquez, L., Valdes, A. M., Hyde, C. L., Wang, V., Ziemek, D., Roberts, P., Xi, L., Grundberg, E., Waldenberger, M., Richards, J. B., Mohney, R. P., Milburn, M. V., John, S. L., Trimmer, J., Theis, F. J., Overington, J. P., Suhre, K., Brosnan, M. J., Gieger, C., Kastenmüller, G., Spector, T. D., & Soranzo, N.
- Affiliations
- Wellcome Sanger Institute (GB)
- Pfizer (United States) (US)
- Helmholtz Munich (DE)
- European Bioinformatics Institute (GB)
- Lady Davis Institute for Medical Research (CA)
- King's College London (GB)
- University of Cambridge (GB)
- University of Nottingham (GB)
- McGill University and Génome Québec Innovation Centre (CA)
- Metabolon (United States) (US)
- Corresponding author
- Kastenmüller, Gabi
- Spector, Tim D
- Soranzo, Nicole
- ORCID
- Shin, So-Youn
- Fauman, Eric B
- Krumsiek, Jan
- Santos, Rita
- Huang, Jie
- Arnold, Matthias
- Forgetta, Vincenzo
- Yang, Tsun-Po
- Walter, Klaudia
- Menni, Cristina
- Chen, Lu
- Vasquez, Louella
- Valdes, Ana M
- Hyde, Craig L
- Roberts, Phoebe
- Grundberg, Elin
- Richards, J Brent
- Mohney, Robert P
- Theis, Fabian J
- Overington, John P
- Suhre, Karsten
- Gieger, Christian
- Kastenmüller, Gabi
- Spector, Tim D
- Soranzo, Nicole
- Venue
- Nature Genetics
- Published
- 2014-05-11
- Volume
- 46
- Issue
- 6
- Pages
- 543-550
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC4064254
- OpenAlex
- W1989568128
- Cited by
- 1,515
- References
- 50
- Citations by year
- 2026: 71 · 2025: 169 · 2024: 284 · 2023: 157 · 2022: 128 · 2021: 122 · 2020: 119 · 2019: 107 · 2018: 91 · 2017: 88 · 2016: 92 · 2015: 70 · 2014: 15
- FWCI
- 57.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Site cant be reached (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- Funders
- Pfizer
- Qatar Foundation
- Wellcome Trust (FP7/2007–2013, WT 098051, WT091310, 098051, 091310)
- National Institute for Health and Care Research (FP7/2007–2013)
- King's College London
- European Commission (EU-FP7, 282510, 2007–2013, 257082, FP7 2007-2013, FP7/2007, 305280)
- Deutsche Forschungsgemeinschaft (SPP1395, FP7/2007–2013)
- Russian Foundation for Basic Research
- Bundesministerium für Bildung und Forschung (01GS0823)
- Bayerische Akademie der Wissenschaften
- National Institutes of Health (FP7/2007-2013)
- Weill Cornell Medical College
- Weill Cornell Medicine - Qatar
- Canadian Institutes of Health Research
- Leibniz-Gemeinschaft
- Helmholtz Zentrum München
- Münchner Zentrum für Gesundheitswissenschaften
- Leibniz-Rechenzentrum
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- blood metabolites, genetic variants, genome-wide association study, metabolite profiling, human metabolism, heritability, gene expression, complex diseases, inborn errors of metabolism, therapeutic target, drug development, metabolomics databases
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Blood, Blood Chemical Analysis, Europe, Female, Genotype, Germany, United Kingdom, Humans, Male, Metabolism, Inborn Errors, Middle Aged, Genetic Variation, Cohort Studies, Computational Biology, Internet, Gene Expression Profiling, Genome-Wide Association Study, Metabolomics, Young Adult, Genetic Loci, Data Mining
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