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Integration of Genome-Wide SNP Data and Gene-Expression Profiles Reveals Six Novel Loci and Regulatory Mechanisms for Amino Acids and Acylcarnitines in Whole Blood


Human metabolite levels differ between individuals due to environmental and genetic factors. In the present work, we analyzed whole blood levels of amino acids and acylcarnitines, reflecting disease relevant metabolic pathways, in a cohort of 2,107 individuals. We then performed a genome wide association analysis to discover genetic variants influencing metabolism. Thereby, we discovered six novel regions in the genome and confirmed ten regions previously found to be associated with metabolites in plasma, serum or urine. Subsequently, we analyzed whether these variants regulate gene-expression in peripheral mononuclear cells and at several loci we identified novel causal relations between SNPs, gene-expression and metabolite levels. These findings help explaining the functional mechanisms by which associated genetic variants regulate metabolism. Finally, several SNPs associated with blood metabolites in our study overlap with previously identified loci for human diseases (e.g. kidney disease), suggesting a shared genetic basis or pathomechanisms involving metabolic alterations. The identified loci are strong candidates for future functional studies directed to understand human metabolism and pathogenesis of related diseases.


Vyšlo v časopise: Integration of Genome-Wide SNP Data and Gene-Expression Profiles Reveals Six Novel Loci and Regulatory Mechanisms for Amino Acids and Acylcarnitines in Whole Blood. PLoS Genet 11(9): e32767. doi:10.1371/journal.pgen.1005510
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1005510

Souhrn

Human metabolite levels differ between individuals due to environmental and genetic factors. In the present work, we analyzed whole blood levels of amino acids and acylcarnitines, reflecting disease relevant metabolic pathways, in a cohort of 2,107 individuals. We then performed a genome wide association analysis to discover genetic variants influencing metabolism. Thereby, we discovered six novel regions in the genome and confirmed ten regions previously found to be associated with metabolites in plasma, serum or urine. Subsequently, we analyzed whether these variants regulate gene-expression in peripheral mononuclear cells and at several loci we identified novel causal relations between SNPs, gene-expression and metabolite levels. These findings help explaining the functional mechanisms by which associated genetic variants regulate metabolism. Finally, several SNPs associated with blood metabolites in our study overlap with previously identified loci for human diseases (e.g. kidney disease), suggesting a shared genetic basis or pathomechanisms involving metabolic alterations. The identified loci are strong candidates for future functional studies directed to understand human metabolism and pathogenesis of related diseases.


Zdroje

1. Lehotay DC, Hall P, Lepage J, Eichhorst JC, Etter ML et al. (2011) LC-MS/MS progress in newborn screening. Clinical biochemistry 44 (1): 21–31. doi: 10.1016/j.clinbiochem.2010.08.007 20709048

2. Newgard CB (2012) Interplay between lipids and branched-chain amino acids in development of insulin resistance. Cell metabolism 15 (5): 606–614. doi: 10.1016/j.cmet.2012.01.024 22560213

3. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD et al. (2009) A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell metabolism 9 (4): 311–326. doi: 10.1016/j.cmet.2009.02.002 19356713

4. Adams SH, Hoppel CL, Lok KH, Zhao L, Wong SW et al. (2009) Plasma acylcarnitine profiles suggest incomplete long-chain fatty acid beta-oxidation and altered tricarboxylic acid cycle activity in type 2 diabetic African-American women. The Journal of nutrition 139 (6): 1073–1081. doi: 10.3945/jn.108.103754 19369366

5. Mihalik SJ, Goodpaster BH, Kelley DE, Chace DH, Vockley J et al. (2010) Increased levels of plasma acylcarnitines in obesity and type 2 diabetes and identification of a marker of glucolipotoxicity. Obesity (Silver Spring, Md.) 18 (9): 1695–1700.

6. Brauer HA, Libby TE, Mitchell BL, Li L, Chen C et al. (2011) Cruciferous vegetable supplementation in a controlled diet study alters the serum peptidome in a GSTM1-genotype dependent manner. Nutrition journal 10: 11. doi: 10.1186/1475-2891-10-11 21272319

7. Shah SH, Hauser ER, Bain JR, Muehlbauer MJ, Haynes C et al. (2009) High heritability of metabolomic profiles in families burdened with premature cardiovascular disease. Molecular systems biology 5: 258. doi: 10.1038/msb.2009.11 19357637

8. Yu B, Zheng Y, Alexander D, Morrison AC, Coresh J et al. (2014) Genetic determinants influencing human serum metabolome among African Americans. PLoS Genet 10 (3): e1004212. doi: 10.1371/journal.pgen.1004212 24625756

9. Xie W, Wood AR, Lyssenko V, Weedon MN, Knowles JW et al. (2013) Genetic variants associated with glycine metabolism and their role in insulin sensitivity and type 2 diabetes. Diabetes 62 (6): 2141–2150. doi: 10.2337/db12-0876 23378610

10. Tukiainen T, Kettunen J, Kangas AJ, Lyytikäinen L, Soininen P et al. (2012) Detailed metabolic and genetic characterization reveals new associations for 30 known lipid loci. Human molecular genetics 21 (6): 1444–1455. doi: 10.1093/hmg/ddr581 22156771

11. Tanaka T, Shen J, Abecasis GR, Kisialiou A, Ordovas JM et al. (2009) Genome-wide association study of plasma polyunsaturated fatty acids in the InCHIANTI Study. PLoS genetics 5 (1): e1000338. doi: 10.1371/journal.pgen.1000338 19148276

12. Suhre K, Wallaschofski H, Raffler J, Friedrich N, Haring R et al. (2011) A genome-wide association study of metabolic traits in human urine. Nature genetics 43 (6): 565–569. doi: 10.1038/ng.837 21572414

13. Suhre K, Shin SY, Petersen AK, Mohney RP, Meredith D et al. (2011) Human metabolic individuality in biomedical and pharmaceutical research. Nature 477 (7362): 54–60. doi: 10.1038/nature10354 21886157

14. Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R et al. (2014) An atlas of genetic influences on human blood metabolites. Nat Genet 46 (6): 543–550. doi: 10.1038/ng.2982 24816252

15. Rhee EP, Ho JE, Chen MH, Shen D, Cheng S et al. (2013) A genome-wide association study of the human metabolome in a community-based cohort. Cell Metab 18 (1): 130–143. doi: 10.1016/j.cmet.2013.06.013 23823483

16. Nicholson G, Rantalainen M, Li JV, Maher AD, Malmodin D et al. (2011) A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection. PLoS genetics 7 (9): e1002270. doi: 10.1371/journal.pgen.1002270 21931564

17. Luykx JJ, Bakker SC, Lentjes E, Neeleman M, Strengman E et al. (2014) Genome-wide association study of monoamine metabolite levels in human cerebrospinal fluid. Molecular psychiatry 19 (2): 228–234. doi: 10.1038/mp.2012.183 23319000

18. Kettunen J, Tukiainen T, Sarin A, Ortega-Alonso A, Tikkanen E et al. (2012) Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nature genetics 44 (3): 269–276. doi: 10.1038/ng.1073 22286219

19. Inouye M, Ripatti S, Kettunen J, Lyytikäinen L, Oksala N et al. (2012) Novel Loci for metabolic networks and multi-tissue expression studies reveal genes for atherosclerosis. PLoS genetics 8 (8): e1002907. doi: 10.1371/journal.pgen.1002907 22916037

20. Illig T, Gieger C, Zhai G, Römisch-Margl W, Wang-Sattler R et al. (2010) A genome-wide perspective of genetic variation in human metabolism. Nature genetics 42 (2): 137–141. doi: 10.1038/ng.507 20037589

21. Hong M, Karlsson R, Magnusson Patrik K E, Lewis MR, Isaacs W et al. (2013) A genome-wide assessment of variability in human serum metabolism. Human mutation 34 (3): 515–524. doi: 10.1002/humu.22267 23281178

22. Hicks AA, Pramstaller PP, Johansson A, Vitart V, Rudan I et al. (2009) Genetic determinants of circulating sphingolipid concentrations in European populations. PLoS genetics 5 (10): e1000672. doi: 10.1371/journal.pgen.1000672 19798445

23. Dharuri H, Henneman P, Demirkan A, van Klinken Jan Bert, Mook-Kanamori DO et al. (2013) Automated workflow-based exploitation of pathway databases provides new insights into genetic associations of metabolite profiles. BMC Genomics 14: 865. doi: 10.1186/1471-2164-14-865 24320595

24. Demirkan A, van Duijn Cornelia M, Ugocsai P, Isaacs A, Pramstaller PP et al. (2012) Genome-wide association study identifies novel loci associated with circulating phospho- and sphingolipid concentrations. PLoS genetics 8 (2): e1002490. doi: 10.1371/journal.pgen.1002490 22359512

25. de Sain-van der Velden, Monique G M, Diekman EF, Jans JJ, van der Ham Maria, Prinsen Berthil H C M T et al. (2013) Differences between acylcarnitine profiles in plasma and bloodspots. Molecular genetics and metabolism 110 (1–2): 116–121.23639448

26. Gieger C, Radhakrishnan A, Cvejic A, Tang W, Porcu E et al. (2011) New gene functions in megakaryopoiesis and platelet formation. Nature 480 (7376): 201–208. doi: 10.1038/nature10659 22139419

27. van der Harst Pim, Zhang W, Mateo Leach I, Rendon A, Verweij N et al. (2012) Seventy-five genetic loci influencing the human red blood cell. Nature 492 (7429): 369–375. doi: 10.1038/nature11677 23222517

28. Kamatani Y, Matsuda K, Okada Y, Kubo M, Hosono N et al. (2010) Genome-wide association study of hematological and biochemical traits in a Japanese population. Nature genetics 42 (3): 210–215. doi: 10.1038/ng.531 20139978

29. Danik JS, Paré G, Chasman DI, Zee Robert Y L, Kwiatkowski DJ et al. (2009) Novel loci, including those related to Crohn disease, psoriasis, and inflammation, identified in a genome-wide association study of fibrinogen in 17 686 women: the Women's Genome Health Study. Circulation. Cardiovascular genetics 2 (2): 134–141. doi: 10.1161/CIRCGENETICS.108.825273 20031577

30. Lange LA, Croteau-Chonka DC, Marvelle AF, Qin L, Gaulton KJ et al. (2010) Genome-wide association study of homocysteine levels in Filipinos provides evidence for CPS1 in women and a stronger MTHFR effect in young adults. Hum Mol Genet 19 (10): 2050–2058. doi: 10.1093/hmg/ddq062 20154341

31. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S et al. (2013) Discovery and refinement of loci associated with lipid levels. Nat Genet 45 (11): 1274–1283. doi: 10.1038/ng.2797 24097068

32. Kolz M, Johnson T, Sanna S, Teumer A, Vitart V et al. (2009) Meta-analysis of 28,141 individuals identifies common variants within five new loci that influence uric acid concentrations. PLoS genetics 5 (6): e1000504. doi: 10.1371/journal.pgen.1000504 19503597

33. Chambers JC, Zhang W, Lord GM, van der Harst P, Lawlor DA et al. (2010) Genetic loci influencing kidney function and chronic kidney disease. Nat Genet 42 (5): 373–375. doi: 10.1038/ng.566 20383145

34. Kottgen A, Pattaro C, Boger CA, Fuchsberger C, Olden M et al. (2010) New loci associated with kidney function and chronic kidney disease. Nat Genet 42 (5): 376–384. doi: 10.1038/ng.568 20383146

35. Lee Y, Yoon KA, Joo J, Lee D, Bae K et al. (2013) Prognostic implications of genetic variants in advanced non-small cell lung cancer. a genome-wide association study. Carcinogenesis 34 (2): 307–313. doi: 10.1093/carcin/bgs356 23144319

36. Zhang WC, Shyh-Chang N, Yang H, Rai A, Umashankar S et al. (2012) Glycine decarboxylase activity drives non-small cell lung cancer tumor-initiating cells and tumorigenesis. Cell 148 (1–2): 259–272. doi: 10.1016/j.cell.2011.11.050 22225612

37. Jain M, Nilsson R, Sharma S, Madhusudhan N, Kitami T et al. (2012) Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science (New York, N.Y.) 336 (6084): 1040–1044.

38. Do CB, Tung JY, Dorfman E, Kiefer AK, Drabant EM et al. (2011) Web-based genome-wide association study identifies two novel loci and a substantial genetic component for Parkinson's disease. PLoS genetics 7 (6): e1002141. doi: 10.1371/journal.pgen.1002141 21738487

39. Moffatt MF, Gut IG, Demenais F, Strachan DP, Bouzigon E et al. (2010) A large-scale, consortium-based genomewide association study of asthma. The New England journal of medicine 363 (13): 1211–1221. doi: 10.1056/NEJMoa0906312 20860503

40. Khor S, Miyagawa T, Toyoda H, Yamasaki M, Kawamura Y et al. (2013) Genome-wide association study of HLA-DQB1*06:02 negative essential hypersomnia. PeerJ 1: e66. doi: 10.7717/peerj.66 23646285

41. Ludwig KU, Mangold E, Herms S, Nowak S, Reutter H et al. (2012) Genome-wide meta-analyses of nonsyndromic cleft lip with or without cleft palate identify six new risk loci. Nature genetics 44 (9): 968–971. doi: 10.1038/ng.2360 22863734

42. Rueedi R, Ledda M, Nicholls AW, Salek RM, Marques-Vidal P et al. (2014) Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links. PLoS genetics 10 (2): e1004132. doi: 10.1371/journal.pgen.1004132 24586186

43. Schramm K, Marzi C, Schurmann C, Carstensen M, Reinmaa E et al. (2014) Mapping the genetic architecture of gene regulation in whole blood. PLoS One 9 (4): e93844. doi: 10.1371/journal.pone.0093844 24740359

44. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A et al. (2010) A method and server for predicting damaging missense mutations. Nature methods 7 (4): 248–249. doi: 10.1038/nmeth0410-248 20354512

45. Ng PC, Henikoff S (2001) Predicting deleterious amino acid substitutions. Genome research 11 (5): 863–874. 11337480

46. Sueyoshi T, Moore R, Sugatani J, Matsumura Y, Negishi M (2008) PPP1R16A, the membrane subunit of protein phosphatase 1beta, signals nuclear translocation of the nuclear receptor constitutive active/androstane receptor. Molecular pharmacology 73 (4): 1113–1121. doi: 10.1124/mol.107.042960 18202305

47. Westra H, Peters MJ, Esko T, Yaghootkar H, Schurmann C et al. (2013) Systematic identification of trans eQTLs as putative drivers of known disease associations. Nature genetics 45 (10): 1238–1243. doi: 10.1038/ng.2756 24013639

48. Rabquer BJ, Amin MA, Teegala N, Shaheen MK, Tsou P et al. (2010) Junctional adhesion molecule-C is a soluble mediator of angiogenesis. Journal of immunology (Baltimore, Md.: 1950) 185 (3): 1777–1785.

49. Albert FW, Kruglyak L (2015) The role of regulatory variation in complex traits and disease. Nature reviews. Genetics.

50. Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E et al. (2012) Systematic localization of common disease-associated variation in regulatory DNA. Science (New York, N.Y.) 337 (6099): 1190–1195.

51. Beutner F, Teupser D, Gielen S, Holdt LM, Scholz M et al. Rationale and design of the Leipzig (LIFE) Heart Study. phenotyping and cardiovascular characteristics of patients with coronary artery disease. PLoS One 6 (12): e29070. doi: 10.1371/journal.pone.0029070 22216169

52. Gross A, Tonjes A, Kovacs P, Veeramah KR, Ahnert P et al. (2011) Population-genetic comparison of the Sorbian isolate population in Germany with the German KORA population using genome-wide SNP arrays. BMC Genet 12: 67. doi: 10.1186/1471-2156-12-67 21798003

53. Tonjes A, Koriath M, Schleinitz D, Dietrich K, Bottcher Y et al. (2009) Genetic variation in GPR133 is associated with height. genome wide association study in the self-contained population of Sorbs. Hum Mol Genet 18 (23): 4662–4668. doi: 10.1093/hmg/ddp423 19729412

54. Veeramah KR, Tonjes A, Kovacs P, Gross A, Wegmann D et al. (2011) Genetic variation in the Sorbs of eastern Germany in the context of broader European genetic diversity. Eur J Hum Genet 19 (9): 995–1001. doi: 10.1038/ejhg.2011.65 21559053

55. Hirschhorn JN, Daly MJ (2005) Genome-wide association studies for common diseases and complex traits. Nature reviews. Genetics 6 (2): 95–108. 15716906

56. Ceglarek U, Muller P, Stach B, Buhrdel P, Thiery J et al. (2002) Validation of the phenylalanine/tyrosine ratio determined by tandem mass spectrometry. sensitive newborn screening for phenylketonuria. Clin Chem Lab Med 40 (7): 693–697. 12241016

57. Ceglarek U, Leichtle A, Brugel M, Kortz L, Brauer R et al. (2009) Challenges and developments in tandem mass spectrometry based clinical metabolomics. Mol Cell Endocrinol 301 (1–2): 266–271. doi: 10.1016/j.mce.2008.10.013 19007853

58. Brauer R, Leichtle AB, Fiedler GM, Thiery J, Ceglarek U (2011) Preanalytical standardization of amino acid and acylcarnitine metabolite profiling in human blood using tandem mass spectrometry. Metabolomics 7 (3): 344–352.

59. Fischer JE, Rosen HM, Ebeid AM, James JH, Keane JM et al. (1976) The effect of normalization of plasma amino acids on hepatic encephalopathy in man. Surgery 80 (1): 77–91. 818729

60. Holdt LM, Hoffmann S, Sass K, Langenberger D, Scholz M et al. (2013) Alu elements in ANRIL non-coding RNA at chromosome 9p21 modulate atherogenic cell functions through trans-regulation of gene networks. PLoS Genet 9 (7): e1003588. doi: 10.1371/journal.pgen.1003588 23861667

61. Wang J (2002) An estimator for pairwise relatedness using molecular markers. Genetics 160 (3): 1203–1215. 11901134

62. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38 (8): 904–909. 16862161

63. Tönjes A, Scholz M, Breitfeld J, Marzi C, Grallert H et al. (2014) Genome Wide Meta-analysis Highlights the Role of Genetic Variation in RARRES2 in the Regulation of Circulating Serum Chemerin. PLoS genetics 10 (12): e1004854. doi: 10.1371/journal.pgen.1004854 25521368

64. Amin N, van Duijn Cornelia M, Aulchenko YS (2007) A genomic background based method for association analysis in related individuals. PLoS One 2 (12): e1274. 18060068

65. Aulchenko YS, Koning de D, Haley C (2007) Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis. Genetics 177 (1): 577–585. 17660554

66. Holdt LM, Beutner F, Scholz M, Gielen S, Gabel G et al. (2010) ANRIL expression is associated with atherosclerosis risk at chromosome 9p21. Arterioscler Thromb Vasc Biol 30 (3): 620–627. doi: 10.1161/ATVBAHA.109.196832 20056914

67. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M et al. (2004) Bioconductor. open software development for computational biology and bioinformatics. Genome Biol 5 (10): R80. 15461798

68. Schmid R, Baum P, Ittrich C, Fundel-Clemens K, Huber W et al. (2010) Comparison of normalization methods for Illumina BeadChip HumanHT-12 v3. BMC Genomics 11: 349. doi: 10.1186/1471-2164-11-349 20525181

69. Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8 (1): 118–127. 16632515

70. Fehrmann RS, Jansen RC, Veldink JH, Westra HJ, Arends D et al. (2011) Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. PLoS Genet 7 (8): e1002197. doi: 10.1371/journal.pgen.1002197 21829388

71. Shabalin AA (2012) Matrix eQTL. ultra fast eQTL analysis via large matrix operations. Bioinformatics 28 (10): 1353–1358. doi: 10.1093/bioinformatics/bts163 22492648

72. Kirsten H, Al-Hasani H, Holdt LM, Gross A, Beutner F et al. (2014) Dissecting the Genetics of the Human Transcriptome identifies novel trait-related trans-eQTLs and corroborates the regulatory relevance of non-protein coding loci (submitted).

73. Nelson CR, Startz R (1988) The distribution of the instrumental variables estimator and its t-ratio when the instrument is a poor one. NBER TECHNICAL WORKING PAPER SERIES (#69).

74. Lawlor DA, Harbord RM, Sterne Jonathan A C, Timpson N, Davey Smith G (2008) Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Statistics in medicine 27 (8): 1133–1163. 17886233

75. Efron B (1981) Nonparametric estimates of standard error: the jackknife, the bootstrap and other methods. Biometrika 68 (3): 589–599.

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