Genome-Wide Association Study of Metabolic Traits Reveals Novel Gene-Metabolite-Disease Links


Metabolic traits are molecular phenotypes that can drive clinical phenotypes and may predict disease progression. Here, we report results from a metabolome- and genome-wide association study on 1H-NMR urine metabolic profiles. The study was conducted within an untargeted approach, employing a novel method for compound identification. From our discovery cohort of 835 Caucasian individuals who participated in the CoLaus study, we identified 139 suggestively significant (P<5×10−8) and independent associations between single nucleotide polymorphisms (SNP) and metabolome features. Fifty-six of these associations replicated in the TasteSensomics cohort, comprising 601 individuals from São Paulo of vastly diverse ethnic background. They correspond to eleven gene-metabolite associations, six of which had been previously identified in the urine metabolome and three in the serum metabolome. Our key novel findings are the associations of two SNPs with NMR spectral signatures pointing to fucose (rs492602, P = 6.9×10−44) and lysine (rs8101881, P = 1.2×10−33), respectively. Fine-mapping of the first locus pinpointed the FUT2 gene, which encodes a fucosyltransferase enzyme and has previously been associated with Crohn's disease. This implicates fucose as a potential prognostic disease marker, for which there is already published evidence from a mouse model. The second SNP lies within the SLC7A9 gene, rare mutations of which have been linked to severe kidney damage. The replication of previous associations and our new discoveries demonstrate the potential of untargeted metabolomics GWAS to robustly identify molecular disease markers.


Vyšlo v časopise: Genome-Wide Association Study of Metabolic Traits Reveals Novel Gene-Metabolite-Disease Links. PLoS Genet 10(2): e32767. doi:10.1371/journal.pgen.1004132
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004132

Souhrn

Metabolic traits are molecular phenotypes that can drive clinical phenotypes and may predict disease progression. Here, we report results from a metabolome- and genome-wide association study on 1H-NMR urine metabolic profiles. The study was conducted within an untargeted approach, employing a novel method for compound identification. From our discovery cohort of 835 Caucasian individuals who participated in the CoLaus study, we identified 139 suggestively significant (P<5×10−8) and independent associations between single nucleotide polymorphisms (SNP) and metabolome features. Fifty-six of these associations replicated in the TasteSensomics cohort, comprising 601 individuals from São Paulo of vastly diverse ethnic background. They correspond to eleven gene-metabolite associations, six of which had been previously identified in the urine metabolome and three in the serum metabolome. Our key novel findings are the associations of two SNPs with NMR spectral signatures pointing to fucose (rs492602, P = 6.9×10−44) and lysine (rs8101881, P = 1.2×10−33), respectively. Fine-mapping of the first locus pinpointed the FUT2 gene, which encodes a fucosyltransferase enzyme and has previously been associated with Crohn's disease. This implicates fucose as a potential prognostic disease marker, for which there is already published evidence from a mouse model. The second SNP lies within the SLC7A9 gene, rare mutations of which have been linked to severe kidney damage. The replication of previous associations and our new discoveries demonstrate the potential of untargeted metabolomics GWAS to robustly identify molecular disease markers.


Zdroje

1. LaFramboiseT (2009) Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances. Nucleic Acids Res 37: 4181–4193.

2. EhretGB, MunroePB, RiceKM, BochudM, JohnsonAD, et al. (2011) Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478: 103–109.

3. BarrettJC, ClaytonDG, ConcannonP, AkolkarB, CooperJD, et al. (2009) Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat Genet 41: 703–707.

4. SpeliotesEK, WillerCJ, BerndtSI, MondaKL, ThorleifssonG, et al. (2010) Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42: 937–948.

5. PatsopoulosNA, EspositoF, ReischlJ, LehrS, BauerD, et al. (2011) Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci. Ann Neurol 70: 897–912.

6. EllinghausD, EllinghausE, NairRP, StuartPE, EskoT, et al. (2012) Combined analysis of genome-wide association studies for Crohn disease and psoriasis identifies seven shared susceptibility loci. Am J Hum Genet 90: 636–647.

7. MontgomerySB, SammethM, Gutierrez-ArcelusM, LachRP, IngleC, et al. (2010) Transcriptome genetics using second generation sequencing in a Caucasian population. Nature 464: 773–777.

8. StrangerBE, NicaAC, ForrestMS, DimasA, BirdCP, et al. (2007) Population genomics of human gene expression. Nat Genet 39: 1217–1224.

9. GiegerC, GeistlingerL, AltmaierE, Hrabe de AngelisM, KronenbergF, et al. (2008) Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet 4: e1000282.

10. IlligT, GiegerC, ZhaiG, Romisch-MarglW, Wang-SattlerR, et al. (2010) A genome-wide perspective of genetic variation in human metabolism. Nat Genet 42: 137–141.

11. SuhreK, ShinSY, PetersenAK, MohneyRP, MeredithD, et al. (2011) Human metabolic individuality in biomedical and pharmaceutical research. Nature 477: 54–60.

12. SuhreK, WallaschofskiH, RafflerJ, FriedrichN, HaringR, et al. (2011) A genome-wide association study of metabolic traits in human urine. Nat Genet 43: 565–569.

13. MontoliuI, GenickU, LeddaM, CollinoS, MartinFP, et al. (2012) Current status on genome-metabolome-wide associations: an opportunity in nutrition research. Genes Nutr 8 (1) 19–27.

14. HomuthG, TeumerA, VolkerU, NauckM (2012) A description of large-scale metabolomics studies: increasing value by combining metabolomics with genome-wide SNP genotyping and transcriptional profiling. J Endocrinol 215: 17–28.

15. ManolioTA, CollinsFS, CoxNJ, GoldsteinDB, HindorffLA, et al. (2009) Finding the missing heritability of complex diseases. Nature 461: 747–753.

16. NicaAC, MontgomerySB, DimasAS, StrangerBE, BeazleyC, et al. (2010) Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet 6: e1000895.

17. SuhreK, GiegerC (2012) Genetic variation in metabolic phenotypes: study designs and applications. Nat Rev Genet 13: 759–769.

18. KettunenJ, TukiainenT, SarinAP, Ortega-AlonsoA, TikkanenE, et al. (2012) Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet 44: 269–276.

19. Wang-SattlerR, YuZ, HerderC, MessiasAC, FloegelA, et al. (2012) Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol 8: 615.

20. DumasME, WilderSP, BihoreauMT, BartonRH, FearnsideJF, et al. (2007) Direct quantitative trait locus mapping of mammalian metabolic phenotypes in diabetic and normoglycemic rat models. Nat Genet 39: 666–672.

21. RobinetteSL, HolmesE, NicholsonJK, DumasME (2012) Genetic determinants of metabolism in health and disease: from biochemical genetics to genome-wide associations. Genome Med 4: 30.

22. NicholsonG, RantalainenM, LiJV, MaherAD, MalmodinD, et al. (2011) A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection. PLoS Genet 7: e1002270.

23. GenickUK, KutalikZ, LeddaM, DestitoMC, SouzaMM, et al. (2011) Sensitivity of genome-wide-association signals to phenotyping strategy: the PROP-TAS2R38 taste association as a benchmark. PLoS One 6: e27745.

24. LeddaM, KutalikZ, Souza DestitoMC, SouzaMM, CirilloCA, et al. (2014) GWAS of human bitter taste perception identifies new loci and reveals additional complexity of bitter taste genetics. Hum Mol Genet 23: 259–267.

25. KutalikZ, BenyaminB, BergmannS, MooserV, WaeberG, et al. (2011) Genome-wide association study identifies two loci strongly affecting transferrin glycosylation. Hum Mol Genet 20: 3710–3717.

26. EhretGB, LamparterD, HoggartCJ, WhittakerJC, BeckmannJS, et al. (2012) A multi-SNP locus-association method reveals a substantial fraction of the missing heritability. Am J Hum Genet 91: 863–871.

27. McGovernDP, JonesMR, TaylorKD, MarcianteK, YanX, et al. (2010) Fucosyltransferase 2 (FUT2) non-secretor status is associated with Crohn's disease. Hum Mol Genet 19: 3468–3476.

28. RauschP, RehmanA, KunzelS, HaslerR, OttSJ, et al. (2011) Colonic mucosa-associated microbiota is influenced by an interaction of Crohn disease and FUT2 (Secretor) genotype. Proc Natl Acad Sci U S A 108: 19030–19035.

29. Ferrer-AdmetllaA, SikoraM, LaayouniH, EsteveA, RoubinetF, et al. (2009) A natural history of FUT2 polymorphism in humans. Mol Biol Evol 26: 1993–2003.

30. PachecoAR, CurtisMM, RitchieJM, MuneraD, WaldorMK, et al. (2012) Fucose sensing regulates bacterial intestinal colonization. Nature 492: 113–117.

31. CoyneMJ, ReinapB, LeeMM, ComstockLE (2005) Human symbionts use a host-like pathway for surface fucosylation. Science 307: 1778–1781.

32. HooperLV, GordonJI (2001) Commensal host-bacterial relationships in the gut. Science 292: 1115–1118.

33. MorganXC, TickleTL, SokolH, GeversD, DevaneyKL, et al. (2012) Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biology 13: R79.

34. FrankeA, McGovernDP, BarrettJC, WangK, Radford-SmithGL, et al. (2010) Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci. Nat Genet 42: 1118–1125.

35. StephensNS, SiffledeenJ, SuX, MurdochTB, FedorakRN, et al. (2012) Urinary NMR metabolomic profiles discriminate inflammatory bowel disease from healthy. J Crohns Colitis

36. LinHM, BarnettMP, RoyNC, JoyceNI, ZhuS, et al. (2010) Metabolomic analysis identifies inflammatory and noninflammatory metabolic effects of genetic modification in a mouse model of Crohn's disease. J Proteome Res 9: 1965–1975.

37. LinHM, EdmundsSI, HelsbyNA, FergusonLR, RowanDD (2009) Nontargeted urinary metabolite profiling of a mouse model of Crohn's disease. J Proteome Res 8: 2045–2057.

38. FeliubadaloL, FontM, PurroyJ, RousaudF, EstivillX, et al. (1999) Non-type I cystinuria caused by mutations in SLC7A9, encoding a subunit (bo,+AT) of rBAT. Nat Genet 23: 52–57.

39. KottgenA, PattaroC, BogerCA, FuchsbergerC, OldenM, et al. (2010) New loci associated with kidney function and chronic kidney disease. Nat Genet 42: 376–384.

40. D. J. Balding MJB, Cannings C, editor (2007) Handbook of statistical genetics. Chichester: John Wiley & Sons.

41. SheehanNA, DidelezV, BurtonPR, TobinMD (2008) Mendelian randomisation and causal inference in observational epidemiology. PLoS Med 5: e177.

42. GlymourMM, TchetgenEJ, RobinsJM (2012) Credible Mendelian randomization studies: approaches for evaluating the instrumental variable assumptions. Am J Epidemiol 175: 332–339.

43. Ala-KorpelaM, KangasAJ, SoininenP (2012) Quantitative high-throughput metabolomics: a new era in epidemiology and genetics. Genome Med 4: 36.

44. WishartDS (2008) Quantitative metabolomics using NMR. Trends in Analytical Chemistry 27: 228–237.

45. GallWE, BeebeK, LawtonKA, AdamKP, MitchellMW, et al. (2010) alpha-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS One 5: e10883.

46. FiehnO, GarveyWT, NewmanJW, LokKH, HoppelCL, et al. (2010) Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS One 5: e15234.

47. KrumsiekJ, SuhreK, EvansAM, MitchellMW, MohneyRP, et al. (2012) Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information. PLoS Genet 8: e1003005.

48. Affymetrix (2006) BRLMM: an improved genotype calling method for the GeneChip© Human Mapping 500 K array set. pp. 1–18.

49. PurcellS, NealeB, Todd-BrownK, ThomasL, FerreiraMA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: 559–575.

50. MarchiniJ, HowieB, MyersS, McVeanG, DonnellyP (2007) A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 39: 906–913.

51. De MeyerT, SinnaeveD, Van GasseB, TsiporkovaE, RietzschelER, et al. (2008) NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm. Anal Chem 80: 3783–3790.

52. AndersonP, MahleD, DoomT, ReoN, DelRasoN, et al. (2010) Dynamic adaptive binning: an improved quantification technique. Metabolomics

53. CollinoS, MontoliuI, MartinFP, SchererM, MariD, et al. (2013) Metabolic signatures of extreme longevity in northern italian centenarians reveal a complex remodeling of lipids, amino acids, and gut microbiota metabolism. PLoS One 8: e56564.

54. ClausSP, ElleroSL, BergerB, KrauseL, BruttinA, et al. (2011) Colonization-induced host-gut microbial metabolic interaction. MBio 2: e00271–00210.

55. StaabJM, O'ConnellTM, GomezSM (2010) Enhancing metabolomic data analysis with Progressive Consensus Alignment of NMR Spectra (PCANS). BMC Bioinformatics 11: 123.

56. KohlSM, KleinMS, HochreinJ, OefnerPJ, SpangR, et al. (2012) State-of-the art data normalization methods improve NMR-based metabolomic analysis. Metabolomics 8: 146–160.

57. GaoX, StarmerJ, MartinER (2008) A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet Epidemiol 32: 361–369.

58. LawlorDA, HarbordRM, SterneJA, TimpsonN, Davey SmithG (2008) Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 27: 1133–1163.

Štítky
Genetika Reprodukčná medicína

Článok vyšiel v časopise

PLOS Genetics


2014 Číslo 2
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Eozinofilní granulomatóza s polyangiitidou
nový kurz
Autori: doc. MUDr. Martina Doubková, Ph.D.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa