Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites

Abstract

Genetic factors modifying the blood metabolome have been investigated through genome-wide association studies (GWAS) of common genetic variants and through exome sequencing. We conducted a whole-genome sequencing study of common, low-frequency and rare variants to associate genetic variations with blood metabolite levels using comprehensive metabolite profiling in 1,960 adults. We focused the analysis on 644 metabolites with consistent levels across three longitudinal data collections. Genetic sequence variations at 101 loci were associated with the levels of 246 (38%) metabolites (P ≤ 1.9 × 10−11). We identified 113 (10.7%) among 1,054 unrelated individuals in the cohort who carried heterozygous rare variants likely influencing the function of 17 genes. Thirteen of the 17 genes are associated with inborn errors of metabolism or other pediatric genetic conditions. This study extends the map of loci influencing the metabolome and highlights the importance of heterozygous rare variants in determining abnormal blood metabolic phenotypes in adults.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Heritability of 644 blood metabolites.
Figure 2: Manhattan plot of associations of metabolite levels and genetic loci.
Figure 3: Relationship between effect size and minor allele frequency.
Figure 4: Mapping of rare variants in individual outliers with extreme blood metabolite levels.
Figure 5: Structure visualization of ACADS.
Figure 6: Fatty acid metabolism and beta-oxidation.

Accession codes

Accessions

Protein Data Bank

References

  1. 1

    Yousri, N.A. et al. Long term conservation of human metabolic phenotypes and link to heritability. Metabolomics 10, 1005–1017 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. 2

    Suhre, K. & Gieger, C. Genetic variation in metabolic phenotypes: study designs and applications. Nat. Rev. Genet. 13, 759–769 (2012).

    Article  CAS  Google Scholar 

  3. 3

    Kastenmüller, G., Raffler, J., Gieger, C. & Suhre, K. Genetics of human metabolism: an update. Hum. Mol. Genet. 24, R93–R101 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 4

    Suhre, K. et al. A genome-wide association study of metabolic traits in human urine. Nat. Genet. 43, 565–569 (2011).

    Article  CAS  Google Scholar 

  5. 5

    Shin, S.Y. et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 46, 543–550 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 6

    Draisma, H.H. et al. Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels. Nat. Commun. 6, 7208 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8

    Guo, L. et al. Plasma metabolomic profiles enhance precision medicine for volunteers of normal health. Proc. Natl. Acad. Sci. USA 112, E4901–E4910 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9

    Yu, B. et al. Association of rare loss-of-function alleles in HAL, serum histidine: levels and incident coronary heart disease. Circ Cardiovasc Genet 8, 351–355 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Yazdani, A., Yazdani, A., Liu, X. & Boerwinkle, E. Identification of rare variants in metabolites of the carnitine pathway by whole genome sequencing analysis. Genet. Epidemiol. 40, 486–491 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  11. 11

    Rhee, E.P. et al. An exome array study of the plasma metabolome. Nat. Commun. 7, 12360 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Moayyeri, A., Hammond, C.J., Hart, D.J. & Spector, T.D. The UK Adult Twin Registry (TwinsUK Resource). Twin Res. Hum. Genet. 16, 144–149 (2013).

    Article  Google Scholar 

  13. 13

    Moayyeri, A., Hammond, C.J., Valdes, A.M. & Spector, T.D. Cohort profile: TwinsUK and healthy ageing twin study. Int. J. Epidemiol. 42, 76–85 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  14. 14

    Telenti, A. et al. Deep sequencing of 10,000 human genomes. Proc. Natl. Acad. Sci. USA 113, 11901–11906 (2016).

    Article  CAS  Google Scholar 

  15. 15

    Xu, C. et al. Estimating genome-wide significance for whole-genome sequencing studies. Genet. Epidemiol. 38, 281–290 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16

    Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    CAS  Article  Google Scholar 

  17. 17

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

  18. 18

    Demirkan, A. et al. Insight in genome-wide association of metabolite quantitative traits by exome sequence analyses. PLoS Genet. 11, e1004835 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP–trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Krumsiek, J. et al. Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLoS Genet. 8, e1003005 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Wu, M.C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Goodman, S.I., Binard, R.J., Woontner, M.R. & Frerman, F.E. Glutaric acidemia type II: gene structure and mutations of the electron transfer flavoprotein:ubiquinone oxidoreductase (ETF:QO) gene. Mol. Genet. Metab. 77, 86–90 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Ionita-Laza, I., McCallum, K., Xu, B. & Buxbaum, J.D. A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat. Genet. 48, 214–220 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Kuokkanen, M. et al. Mutations in the translated region of the lactase gene (LCT) underlie congenital lactase deficiency. Am. J. Hum. Genet. 78, 339–344 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Enattah, N.S. et al. Identification of a variant associated with adult-type hypolactasia. Nat. Genet. 30, 233–237 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    McGill, J.B. et al. Circulating 1,5-anhydroglucitol levels in adult patients with diabetes reflect longitudinal changes of glycemia: a U.S. trial of the GlycoMark assay. Diabetes Care 27, 1859–1865 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Koga, M., Murai, J., Saito, H., Mukai, M. & Kasayama, S. Habitual intake of dairy products influences serum 1,5-anhydroglucitol levels independently of plasma glucose. Diabetes Res. Clin. Pract. 90, 122–125 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Yamanouchi, T. et al. Common reabsorption system of 1,5-anhydro-D-glucitol, fructose, and mannose in rat renal tubule. Biochim. Biophys. Acta 1291, 89–95 (1996).

    Article  PubMed  PubMed Central  Google Scholar 

  30. 30

    Grempler, R. et al. Functional characterisation of human SGLT-5 as a novel kidney-specific sodium-dependent sugar transporter. FEBS Lett. 586, 248–253 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Dworacka, M. et al. 1,5-anhydro-D-glucitol: a novel marker of glucose excursions. Int. J. Clin. Pract. Suppl. 129, 40–44 (2002).

    Google Scholar 

  32. 32

    Her, C. et al. Human hydroxysteroid sulfotransferase SULT2B1: two enzymes encoded by a single chromosome 19 gene. Genomics 53, 284–295 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 33

    Gregersen, N. et al. Identification of four new mutations in the short-chain acyl-CoA dehydrogenase (SCAD) gene in two patients: one of the variant alleles, 511C→T, is present at an unexpectedly high frequency in the general population, as was the case for 625G→A, together conferring susceptibility to ethylmalonic aciduria. Hum. Mol. Genet. 7, 619–627 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Goldstein, D.S. et al. Sources and physiological significance of plasma dopamine sulfate. J. Clin. Endocrinol. Metab. 84, 2523–2531 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Suhre, K. et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54–60 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Gieger, C. et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet. 4, e1000282 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Cirulli, E.T. & Goldstein, D.B. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat. Rev. Genet. 11, 415–425 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 38

    Menni, C. et al. Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int. J. Epidemiol. 42, 1111–1119 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  39. 39

    Schwenger, B., Schober, S. & Simon, D. DUMPS cattle carry a point mutation in the uridine monophosphate synthase gene. Genomics 16, 241–244 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Imaeda, M. et al. Hereditary orotic aciduria heterozygotes accompanied with neurological symptoms. Tohoku J. Exp. Med. 185, 67–70 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    Corydon, M.J. et al. Role of common gene variations in the molecular pathogenesis of short-chain acyl-CoA dehydrogenase deficiency. Pediatr. Res. 49, 18–23 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Béhin, A. et al. Multiple acyl-CoA dehydrogenase deficiency (MADD) as a cause of late-onset treatable metabolic disease. Rev. Neurol. (Paris) 172, 231–241 (2016).

    Article  Google Scholar 

  43. 43

    Visscher, P.M., Benyamin, B. & White, I. The use of linear mixed models to estimate variance components from data on twin pairs by maximum likelihood. Twin Res. 7, 670–674 (2004).

    Article  Google Scholar 

  44. 44

    Scheike, T.H., Holst, K.K. & Hjelmborg, J.B. Estimating heritability for cause specific mortality based on twin studies. Lifetime Data Anal. 20, 210–233 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  45. 45

    Raczy, C. et al. Isaac: ultra-fast whole-genome secondary analysis on Illumina sequencing platforms. Bioinformatics 29, 2041–2043 (2013).

    Article  CAS  Google Scholar 

  46. 46

    Alexander, D.H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

    Moltke, I. & Albrechtsen, A. RelateAdmix: a software tool for estimating relatedness between admixed individuals. Bioinformatics 30, 1027–1028 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 50

    Yu, J. et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38, 203–208 (2006).

    CAS  Article  Google Scholar 

  51. 51

    Widmer, C. et al. Further improvements to linear mixed models for genome-wide association studies. Sci. Rep. 4, 6874 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  52. 52

    Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012).

    Article  CAS  Google Scholar 

  53. 53

    Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 54

    Yates, A. et al. Ensembl 2016. Nucleic Acids Res. 44, D710–D716 (2016).

    CAS  Google Scholar 

  55. 55

    Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. 56

    Govindasamy, L. et al. Structural and mutational characterization of L-carnitine binding to human carnitine acetyltransferase. J. Struct. Biol. 146, 416–424 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. 57

    Heinrich, D., Diederichsen, U. & Rudolph, M.G. Lys314 is a nucleophile in non-classical reactions of orotidine-5′-monophosphate decarboxylase. Chemistry 15, 6619–6625 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. 58

    Lee, K.A. et al. Crystal structure of human cholesterol sulfotransferase (SULT2B1b) in the presence of pregnenolone and 3′-phosphoadenosine 5′-phosphate. Rationale for specificity differences between prototypical SULT2A1 and the SULT2BG1 isoforms. J. Biol. Chem. 278, 44593–44599 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 59

    Kiefer, F., Arnold, K., Kunzli, M., Bordoli, L. & Schwede, T. The SWISS-MODEL Repository and associated resources. Nucleic Acids Res. 37, D387–D392 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 60

    Kopp, J. & Schwede, T. The SWISS-MODEL Repository of annotated three-dimensional protein structure homology models. Nucleic Acids Res. 32, D230–D234 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. 61

    Pieper, U. et al. MODBASE, a database of annotated comparative protein structure models, and associated resources. Nucleic Acids Res. 32, D217–D222 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The sequencing and metabolome study was funded by Human Longevity, Inc. TwinsUK was funded by the Wellcome Trust, European Community's Seventh Framework Programme (FP7/2007-2013 277849, 201413 and 259749). The study also receives support from the National Institute for Health Research (NIHR) Clinical Research Facility at Guy's and St Thomas' NHS Foundation Trust and the NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London. T.D.S. is an NIHR senior Investigator.

Author information

Affiliations

Authors

Contributions

J.C.V. conceived the study. A.T. led the analyses. T.L. and A.T. designed the study. T.L. performed genome analyses. M.H. performed structural analysis. H.-C.Y. performed Mendelian and pathway analyses. W.H.B. led the sequencing process. C.M., J.Z., K.S., M.M. and T.D.S. are responsible for the Twin Cohort study. A.M.E., L.A.D.M. and L.G. contributed metabolome expertise and data. H.M., B.A.P. and C.T.C. contributed clinical support. E.F.K., S.B., Y.T., N.J.S. and C.G. supervised research.

Corresponding authors

Correspondence to J Craig Venter or Amalio Telenti.

Ethics declarations

Competing interests

The following authors are current employees or stockholders of Human Longevity, Inc.: J.C.V., A.T., T.L., M.H., H.-C.Y., W.H.B., E.F.K., S.B., Y.T., B.A.P. and N.J.S. The following authors are current employees or stockholders of Metabolome, Inc.: A.M.E., L.A.D.M. and L.G.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–16 (PDF 7521 kb)

Supplementary Table 1

Metabolite h2 and outliers. (XLSX 166 kb)

Supplementary Table 2

GWAS significant independent variants. (XLSX 100 kb)

Supplementary Table 3

GWAS summary statistics. (XLSX 19696 kb)

Supplementary Table 4

GWAS comparison. (XLSX 104 kb)

Supplementary Table 5

SKAT results. (XLSX 155 kb)

Supplementary Table 6

Additional coding rare variants. (XLSX 32 kb)

Supplementary Table 7

Promoter rare variants in outliers. (XLSX 28 kb)

Supplementary Table 8

Rare variants from publications. (XLSX 30 kb)

Supplementary Table 9

Rare variants in genes from publications. (XLSX 30 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Long, T., Hicks, M., Yu, HC. et al. Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat Genet 49, 568–578 (2017). https://doi.org/10.1038/ng.3809

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing