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An atlas of genetic influences on human blood metabolites


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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Figure 1: Ideogram of metabolomic associations.
Figure 2: A network view of genetic and metabolic associations.
Figure 3: Heritability and variance explained.
Figure 4: Epistatic effects and mendelian randomization analyses on eQTL loci.
Figure 5: Medical and pharmacological relevance of metabolomic associations.


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For TwinsUK, we thank the Genotyping Facilities at the Wellcome Trust Sanger Institute and the Center for Inherited Disease Research (CIDR)/US National Institutes of Health (NIH) for SNP genotyping. The KORA Study Group consists of A. Peters (speaker), J. Heinrich, R. Holle, R. Leidl, C. Meisinger, K. Strauch and their coworkers, who are responsible for the design and implementation of the KORA studies. For KORA, we thank P. Lichtner, G. Eckstein, G. Fischer, T. Strom, the Helmholtz Zentrum München genotyping staff and the field staff of the MONICA/KORA Augsburg studies. We also thank G. Fischer (KORA) and G. Surdulescu (TwinsUK) for sample handling and H. Chavez (KORA) and D. Hodgkiss (TwinsUK) for sample shipment. We are grateful to the MuTHER investigators for the transcriptomic data. Finally, we wish to express our appreciation to all study participants of the TwinsUK and KORA studies for donating their blood and time.

Part of this work was funded by Pfizer Worldwide Research and Development. For TwinsUK, the study was funded by the Wellcome Trust; European Community's Seventh Framework Programme (FP7/2007-2013). The study also receives support from the National Institute for Health Research (NIHR) BioResource Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' National Health Service (NHS) Foundation Trust and King's College London. T.D.S. is the holder of a European Research Council (ERC) Advanced Principal Investigator award. SNP genotyping was performed by the Wellcome Trust Sanger Institute and the National Eye Institute via NIH/CIDR. The KORA (Kooperative Gesundheitsforschung in der Region Augsburg) research platform and the MONICA Augsburg studies were initiated and financed by the Helmholtz Zentrum München National Research Center for Environmental Health, which is funded by the German Federal Ministry of Education, Science, Research and Technology and by the state of Bavaria. This study was supported by a grant from the German Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD). The German National Genome Research Network financed part of this work (NGFNPlus 01GS0823). Computing resources have been made available by the Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (HLRB project h1231) and by the DEISA Extreme Computing Initiative (project PHAGEDA). Part of this research was supported within the Munich Center of Health Sciences (MC Health) as part of LMUinnovativ. S.-Y.S. is supported by a Post-Doctoral Research Fellowship from the Oak Foundation. F.J.T. is supported by an ERC starting grant (LatentCauses). J.K. is supported by the German Research Foundation (SPP 1395, InKoMBio) and by a grant from the German Helmholtz Postdoctoral Programme. K.S. is supported by Biomedical Research Program funds at Weill Cornell Medical College in Qatar, a program funded by the Qatar Foundation. C.G. is supported by the European Union's Seventh Framework project MIMOmics (FP7-Health-F5-2012-305280) and by the Russian Foundation for Basic Research (RFBR)-Helmholtz research group program. N.S. is supported by the Wellcome Trust (grants WT098051 and WT091310) and by the European Commission (EUFP7 EPIGENESYS grant 257082 and BLUEPRINT grant HEALTH-F5-2011-282510). J.B.R. and V.F. are supported by the Canadian Institutes of Health Research, Fonds du Recherche du Science Québec and the Québec Consortium for Drug Discovery.

The Pfizer colleagues dedicate this manuscript to the memory of our friend Phoebe Roberts, whose passion for text mining, molecular biology and drug discovery contributed to the identification of causal genes in this research and to our work in general.

Author information

Authors and Affiliations




Study organization: M.J.B., K.S., C.G., G.K. and N.S. Manuscript preparation: S.-Y.S., J.K., K.S., G.K. and N.S. Data collection: R.P.M. and M.V.M. Analysis of associations: S.-Y.S., A.-K.P., J.H., G.K. and N.S. Locus bioinformatics annotation: E.B.F., D.Z., R.S., V.F., L.C., L.J.V., K.W., V.W., P.R., L.X., J.B.R., J.P.O. and G.K. GGM network: J.K., F.J.T. and G.K. Supplementary websites and online resources: M.A., G.K. and N.S. Provision of materials, data and analysis tools: T.D.S., K.S., G.K., E.B.F., M.W., C.G., J.T., I.E., A.M.V., C.L.H., T.-P.Y., C.M., S.L.J. and E.G.

Corresponding authors

Correspondence to Gabi Kastenmüller, Tim D Spector or Nicole Soranzo.

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Competing interests

M.V.M. and R.P.M. are employees of Metabolon, Inc. E.B.F., C.L.H., V.W., D.Z., P.R., L.X., S.L.J., J.T. and M.J.B. are full-time employees and shareholders of Pfizer.

Additional information

A full list of members and affiliations appears in the Supplementary Note.

Integrated supplementary information

Supplementary Figure 1 Study design.

Supplementary Figure 2 Manhattan plots.

Association results for raw metabolite concentrations are shown for genome-wide SNPs. Top, TwinsUK; bottom, KORA. Only SNPs with P < 1 ×10–6 are displayed. The green line indicates the genome-wide cutoff of P < 1.03 ×10–10. Loci with P value < 1 ×10–30 are indicated with a red symbol.

Supplementary Figure 3 Comparison of imputation based on HapMap 2 and 1000 Genomes Project data.

Correlation between (a) minor allele frequency (MAF) in meta-analysis, (b) association P value in meta-analysis (on a –log10 scale) and (c) average variance explained for the most significant SNPs selected from imputation based on either the HapMap 2 (x axis) or 1000 Genomes Project (y axis) panels. High correlations between the HapMap 2 and 1000 Genomes Project data sets support the view that metabolic associations are driven by common variants that are well tagged by HapMap 2 imputation. (d,e) One exception is the CYP3A4-CYP3A5-CYP3A7 locus where the 1000 Genomes Project scan reveals an additional variant (rs10278040) with greater association and variance explained for androsterone sulfate than for the corresponding HapMap 2 variant (rs148982377). (HapMap 2: rs148982377, MAF = 0.038, P = 7.65 ×10–244, R2 = 15.6%; 1000 Genomes Project: rs10278040, MAF = 0.042, P = 8.82 ×10–113, = 10.3%.)

Supplementary Figure 4 Interaction between NAT8 and PYROXD2 variants.

(a) Box plots of PYROXD2 and NAT8 transcript levels in fat, skin and LCLs as a function of the genotype conformation between the two variants rs10469966 (NAT8) and rs4488133 (PYROXD2). (b) Summary of association and interaction effects at the two loci, summarizing association statistics and variances explained under the single-SNP, additive and interaction models. An ANOVA F test was used to test the significance of the interactive model over the additive model. The association test with X-12093 reported here is based on a combined TwinsUK and KORA data set; all other analyses were carried out using unrelated TwinsUK singletons. See also Figure 4.

Supplementary Figure 5 Cardiovascular disease and hypertension metabolic subnetwork.

Network data were annotated with expert knowledge to illustrate correlations between molecular relationships and knowledge on blood pressure regulation, blood coagulation and known molecular risk factors for cardiovascular disease and hypertension. Black nodes and edges represent a subnetwork from the metabolite network in Figure 2. This subnetwork contains metabolites (circular nodes) and genes (diamond-shaped nodes) of the fibrinogen cleavage (left) and kininogen/kinin (right) systems and their interconnections as derived from our data. Gray nodes and edges, annotations of biochemical function based on expert knowledge27; colored nodes and edges, reported associations based on genome-wide studies for blood pressure regulation (orange), blood coagulation (blue) and cholesterol levels (purple; information in Supplementary Table 6).

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Supplementary Note and Supplementary Figures 1–5 (PDF 4947 kb)

Supplementary Tables 1–14

Supplementary Tables 1–14 (XLSX 12504 kb)

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Shin, SY., Fauman, E., Petersen, AK. et al. An atlas of genetic influences on human blood metabolites. Nat Genet 46, 543–550 (2014).

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