A cross-platform approach identifies genetic regulators of human metabolism and health.
Diabetes Mellitus, Type 2
/ genetics
Eye Diseases
/ genetics
Gene Frequency
/ genetics
Genetic Loci
Genetic Pleiotropy
Genome, Human
Glucagon-Like Peptide-2 Receptor
/ genetics
Glycine
/ metabolism
Health
Humans
Linear Models
Mendelian Randomization Analysis
Metabolism
/ genetics
Metabolism, Inborn Errors
/ genetics
Metabolome
/ genetics
Mutation, Missense
/ genetics
Phenotype
Polymorphism, Single Nucleotide
/ genetics
Retinal Telangiectasis
/ genetics
Sample Size
Serine
/ metabolism
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
01 2021
01 2021
Historique:
received:
04
02
2020
accepted:
20
11
2020
entrez:
8
1
2021
pubmed:
9
1
2021
medline:
11
2
2021
Statut:
ppublish
Résumé
In cross-platform analyses of 174 metabolites, we identify 499 associations (P < 4.9 × 10
Identifiants
pubmed: 33414548
doi: 10.1038/s41588-020-00751-5
pii: 10.1038/s41588-020-00751-5
pmc: PMC7612925
mid: EMS146224
doi:
Substances chimiques
GLP2R protein, human
0
Glucagon-Like Peptide-2 Receptor
0
Serine
452VLY9402
Glycine
TE7660XO1C
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
54-64Subventions
Organisme : Medical Research Council
ID : MR/L003120/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00006/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/P01836X/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_13030
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_12015/1
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 106263/Z/14/Z
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_13046
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 106262/Z/14/Z
Pays : United Kingdom
Organisme : Medical Research Council
ID : G1000143
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00002/7
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00011/2
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 204623/Z/16/Z
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/S019669/1
Pays : United Kingdom
Organisme : Chief Scientist Office
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_13048
Pays : United Kingdom
Organisme : British Heart Foundation
ID : SP/09/002
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_12012/3
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00014/3
Pays : United Kingdom
Organisme : Department of Health
ID : BTRU-2014-10024
Pays : United Kingdom
Organisme : British Heart Foundation
ID : RG/18/13/33946
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/N003284/1
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 204623
Pays : United Kingdom
Organisme : Cancer Research UK
ID : C864/A14136
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/S003746/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : G0401527
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/L00002/1
Pays : United Kingdom
Organisme : Wellcome Trust
ID : WT083442AIA
Pays : United Kingdom
Organisme : Cancer Research UK
ID : 14136
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00014/5
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/P011705/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UP_A090_1006
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Medical Research Council
ID : G0500300
Pays : United Kingdom
Organisme : British Heart Foundation
ID : RG/13/13/30194
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_12012/5
Pays : United Kingdom
Organisme : British Heart Foundation
ID : CH/12/2/29428
Pays : United Kingdom
Investigateurs
Melanie Bahlo
(M)
Références
Wishart, D. S. Metabolomics for investigating physiological and pathophysiological processes. Physiol. Rev. 99, 1819–1875 (2019).
pubmed: 31434538
doi: 10.1152/physrev.00035.2018
Shin, S.-Y. Y. et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 46, 543–550 (2014).
pubmed: 24816252
pmcid: 4064254
doi: 10.1038/ng.2982
Long, T. et al. Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat. Genet. 49, 568–578 (2017).
pubmed: 28263315
doi: 10.1038/ng.3809
Draisma, H. H. M. et al. Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels. Nat. Commun. 6, 7208 (2015).
pubmed: 26068415
doi: 10.1038/ncomms8208
Kettunen, J. et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat. Commun. 7, 11122 (2016).
pubmed: 27005778
pmcid: 4814583
doi: 10.1038/ncomms11122
Illig, T. et al. A genome-wide perspective of genetic variation in human metabolism. Nat. Genet. 42, 137–141 (2010).
pubmed: 20037589
doi: 10.1038/ng.507
Suhre, K. et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54–60 (2011).
pubmed: 21886157
doi: 10.1038/nature10354
Rhee, E. P. P. et al. A genome-wide association study of the human metabolome in a community-based cohort. Cell Metab. 18, 130–143 (2013).
pubmed: 23823483
pmcid: 3973158
doi: 10.1016/j.cmet.2013.06.013
Gallois, A. et al. A comprehensive study of metabolite genetics reveals strong pleiotropy and heterogeneity across time and context. Nat. Commun. 10, 4788 (2019).
pubmed: 31636271
pmcid: 6803661
doi: 10.1038/s41467-019-12703-7
Rhee, E. P. et al. An exome array study of the plasma metabolome. Nat. Commun. 7, 12360 (2016).
pubmed: 27453504
pmcid: 4962516
doi: 10.1038/ncomms12360
Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429 (2016).
pubmed: 27863252
pmcid: 5300907
doi: 10.1016/j.cell.2016.10.042
Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).
pubmed: 29875488
pmcid: 6697541
doi: 10.1038/s41586-018-0175-2
Learn, D. B., Fried, V. A. & Thomas, E. L. Taurine and hypotaurine content of human leukocytes. J. Leukoc. Biol. 48, 174–182 (1990).
pubmed: 2370482
doi: 10.1002/jlb.48.2.174
Yet, I. et al. Genetic influences on metabolite levels: a comparison across metabolomic platforms. PLoS ONE 11, e0153672 (2016).
pubmed: 27073872
pmcid: 4830611
doi: 10.1371/journal.pone.0153672
Foley, C. N. et al. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits. Preprint at bioRiv https://doi.org/10.1101/592238 (2019).
Pedersen, C. B. et al. The ACADS gene variation spectrum in 114 patients with short-chain acyl-CoA dehydrogenase (SCAD) deficiency is dominated by missense variations leading to protein misfolding at the cellular level. Hum. Genet. 124, 43–56 (2008).
pubmed: 18523805
doi: 10.1007/s00439-008-0521-9
Lahiri, S. et al. Kinetic characterization of mammalian ceramide synthases: determination of K
pubmed: 17977534
doi: 10.1016/j.febslet.2007.10.018
Horowitz, B. et al. Asparagine synthetase activity of mouse leukemias. Science 160, 533–535 (1968).
pubmed: 5689413
doi: 10.1126/science.160.3827.533
Babu, E. et al. Identification of a novel system L amino acid transporter structurally distinct from heterodimeric amino acid transporters. J. Biol. Chem. 278, 43838–43845 (2003).
pubmed: 12930836
doi: 10.1074/jbc.M305221200
Scott, R. A. et al. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66, 2888–2902 (2017).
pubmed: 28566273
pmcid: 5652602
doi: 10.2337/db16-1253
Wheeler, E. et al. Impact of common genetic determinants of hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: a transethnic genome-wide meta-analysis. PLoS Med. 14, e1002383 (2017).
Prokopenko, I. et al. A central role for GRB10 in regulation of islet function in man. PLoS Genet. 10, e1004235 (2014).
Manning, A. K. et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat. Genet. 44, 659–669 (2012).
pubmed: 22581228
pmcid: 3613127
doi: 10.1038/ng.2274
Almgren, P. et al. Genetic determinants of circulating GIP and GLP-1 concentrations. JCI Insight 2, e93306 (2017).
Fragkos, K. C. & Forbes, A. Citrulline as a marker of intestinal function and absorption in clinical settings: a systematic review and meta-analysis. United European Gastroenterol. J. 6, 181–191 (2018).
Tseng, C. C. & Zhang, X. Y. The cysteine of the cytoplasmic tail of glucose-dependent insulinotropic peptide receptor mediates its chronic desensitization and down-regulation. Mol. Cell. Endocrinol. 139, 179–186 (1998).
pubmed: 9705086
doi: 10.1016/S0303-7207(98)00061-6
Estall, J. L., Koehler, J. A., Yusta, B. & Drucker, D. J. The glucagon-like peptide-2 receptor C terminus modulates β-arrestin-2 association but is dispensable for ligand-induced desensitization, endocytosis, and G-protein-dependent effector activation. J. Biol. Chem. 280, 22124–22134 (2005).
pubmed: 15817468
doi: 10.1074/jbc.M500078200
Scerri, T. S. et al. Genome-wide analyses identify common variants associated with macular telangiectasia type 2. Nat. Genet. 49, 559–567 (2017).
pubmed: 28250457
doi: 10.1038/ng.3799
Gantner, M. L. et al. Serine and lipid metabolism in macular disease and peripheral neuropathy. N. Engl. J. Med. 381, 1422–1433 (2019).
pubmed: 31509666
pmcid: 7685488
doi: 10.1056/NEJMoa1815111
Garrod, A. E. The incidence of alkaptonuria: a study in chemical individuality. Lancet 160, 1616–1620 (1902).
doi: 10.1016/S0140-6736(01)41972-6
Rath, A. et al. Representation of rare diseases in health information systems: the orphanet approach to serve a wide range of end users. Hum. Mutat. 33, 803–808 (2012).
pubmed: 22422702
doi: 10.1002/humu.22078
Stübiger, G. et al. Targeted profiling of atherogenic phospholipids in human plasma and lipoproteins of hyperlipidemic patients using MALDI-QIT-TOF-MS/MS. Atherosclerosis 224, 177–186 (2012).
pubmed: 22795978
doi: 10.1016/j.atherosclerosis.2012.06.010
van der Graaf, A., Kastelein, J. J. P. & Wiegman, A. Heterozygous familial hypercholesterolaemia in childhood: cardiovascular risk prevention. J. Inherit. Metab. Dis. 32, 699 (2009).
Lindsay, T. et al. Descriptive epidemiology of physical activity energy expenditure in UK adults (the Fenland study). Int. J. Behav. Nutr. Phys. Act. 16, 126 (2019).
pubmed: 31818302
pmcid: 6902569
doi: 10.1186/s12966-019-0882-6
Day, N. et al. EPIC-Norfolk: study design and characteristics of the cohort. European Prospective Investigation of Cancer. Br. J. Cancer 80, 95–103 (1999).
pubmed: 10466767
Moore, C. et al. The INTERVAL trial to determine whether intervals between blood donations can be safely and acceptably decreased to optimise blood supply: study protocol for a randomised controlled trial. Trials 15, 363 (2014).
Soininen, P. et al. High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism. Analyst 134, 1781–1785 (2009).
pubmed: 19684899
doi: 10.1039/b910205a
Wittemans, L. B. L. et al. Assessing the causal association of glycine with risk of cardio-metabolic diseases. Nat. Commun. 10, 1060 (2019).
Lotta, L. A. et al. Genetic predisposition to an impaired metabolism of the branched-chain amino acids and risk of type 2 diabetes: a Mendelian randomisation analysis. PLoS Med. 13, e1002179 (2016).
Di Angelantonio, E. et al. Efficiency and safety of varying the frequency of whole blood donation (INTERVAL): a randomised trial of 45,000 donors. Lancet 390, 2360–2371 (2017).
pubmed: 28941948
pmcid: 5714430
doi: 10.1016/S0140-6736(17)31928-1
Li, J. & Ji, L. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 95, 221–227 (2005).
doi: 10.1038/sj.hdy.6800717
pubmed: 16077740
Ward, L. D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012).
Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).
pubmed: 26854917
pmcid: 4767558
doi: 10.1038/ng.3506
Stacey, D. et al. ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci. Nucleic Acids Res. 47, e3 (2019).
Wishart, D. S. et al. HMDB 4.0: the Human Metabolome Database for 2018. Nucleic Acids Res. 46, D608–D617 (2018).
pubmed: 29140435
doi: 10.1093/nar/gkx1089
Bateman, A. et al. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45, D158–D169 (2017).
doi: 10.1093/nar/gkw1099
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).
pubmed: 27899662
doi: 10.1093/nar/gkw1092
Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).
pubmed: 30297969
pmcid: 6287706
doi: 10.1038/s41588-018-0241-6
Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).
Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665 (2013).
pubmed: 24114802
pmcid: 4377079
doi: 10.1002/gepi.21758
Burgess, S. & Thompson, S. G. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am. J. Epidemiol. 181, 251–260 (2015).
pubmed: 25632051
pmcid: 4325677
doi: 10.1093/aje/kwu283
Harrow, J. et al. GENCODE: the reference human genome annotation for the ENCODE project. Genome Res. 22, 1760–1774 (2012).
pubmed: 22955987
pmcid: 3431492
doi: 10.1101/gr.135350.111
Lee, J. J. Y., Wasserman, W. W., Hoffmann, G. F., Van Karnebeek, C. D. M. & Blau, N. Knowledge base and mini-expert platform for the diagnosis of inborn errors of metabolism. Genet. Med. 20, 151–158 (2018).
pubmed: 28726811
doi: 10.1038/gim.2017.108
Köhler, S. et al. The human phenotype ontology in 2017. Nucleic Acids Res. 45, D865–D876 (2017).
pubmed: 27899602
doi: 10.1093/nar/gkw1039
Wu, P. et al. Mapping ICD-10 and ICD-10-CM codes to phecodes: workflow development and initial evaluation. JMIR Med. Inform. 7, e14325 (2019).
Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).
pubmed: 24830394
pmcid: 4022491
doi: 10.1371/journal.pgen.1004383