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
Profiling amino acids and acylcarnitines in whole blood spots is a powerful tool in the laboratory diagnosis of several inborn errors of metabolism. Emerging data suggests that altered blood levels of amino acids and acylcarnitines are also associated with common metabolic diseases in adults. Thus, the identification of common genetic determinants for blood metabolites might shed light on pathways contributing to human physiology and common diseases. We applied a targeted mass-spectrometry-based method to analyze whole blood concentrations of 96 amino acids, acylcarnitines and pathway associated metabolite ratios in a Central European cohort of 2,107 adults and performed genome-wide association (GWA) to identify genetic modifiers of metabolite concentrations. We discovered and replicated six novel loci associated with blood levels of total acylcarnitine, arginine (both on chromosome 6; rs12210538, rs17657775), propionylcarnitine (chromosome 10; rs12779637), 2-hydroxyisovalerylcarnitine (chromosome 21; rs1571700), stearoylcarnitine (chromosome 1; rs3811444), and aspartic acid traits (chromosome 8; rs750472). Based on an integrative analysis of expression quantitative trait loci in blood mononuclear cells and correlations between gene expressions and metabolite levels, we provide evidence for putative causative genes: SLC22A16 for total acylcarnitines, ARG1 for arginine, HLCS for 2-hydroxyisovalerylcarnitine, JAM3 for stearoylcarnitine via a trans-effect at chromosome 1, and PPP1R16A for aspartic acid traits. Further, we report replication and provide additional functional evidence for ten loci that have previously been published for metabolites measured in plasma, serum or urine. In conclusion, our integrative analysis of SNP, gene-expression and metabolite data points to novel genetic factors that may be involved in the regulation of human metabolism. At several loci, we provide evidence for metabolite regulation via gene-expression and observed overlaps with GWAS loci for common diseases. These results form a strong rationale for subsequent functional and disease-related studies.
References
[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. pmid: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. pmid: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. pmid: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. pmid: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. doi: 10.1038/oby.2009.510
[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. pmid: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. pmid: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. pmid: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. pmid:23378610
[10]
Tukiainen T, Kettunen J, Kangas AJ, Lyytik？inen 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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid:22286219
[19]
Inouye M, Ripatti S, Kettunen J, Lyytik？inen 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. pmid:22916037
[20]
Illig T, Gieger C, Zhai G, R？misch-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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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.pmid:23639448 doi: 10.1016/j.ymgme.2013.04.008
[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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. doi: 10.1126/science.1218595
[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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid: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. pmid:20354512
[45]
Ng PC, Henikoff S (2001) Predicting deleterious amino acid substitutions. Genome research 11 (5): 863–874. pmid:11337480 doi: 10.1101/gr.176601
[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. pmid: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. pmid: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. doi: 10.4049/jimmunol.1000556
[49]
Albert FW, Kruglyak L (2015) The role of regulatory variation in complex traits and disease. Nature reviews. Genetics. doi: 10.1038/nrg3891
[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. doi: 10.1126/science.1222794
[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. pmid: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. pmid: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. pmid: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. pmid:21559053
[55]
Hirschhorn JN, Daly MJ (2005) Genome-wide association studies for common diseases and complex traits. Nature reviews. Genetics 6 (2): 95–108. pmid:15716906 doi: 10.1038/nrg1521
[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. pmid:12241016 doi: 10.1515/cclm.2002.119
[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. pmid: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. doi: 10.1007/s11306-010-0256-1
[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. pmid: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. pmid:23861667
[61]
Wang J (2002) An estimator for pairwise relatedness using molecular markers. Genetics 160 (3): 1203–1215. pmid: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. pmid:16862161 doi: 10.1038/ng1847
[63]
T？njes 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. pmid: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. pmid:18060068 doi: 10.1371/journal.pone.0001274
[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. pmid:17660554 doi: 10.1534/genetics.107.075614
[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. pmid: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. pmid:15461798 doi: 10.1186/gb-2004-5-10-r80
[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. pmid: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. pmid:16632515 doi: 10.1093/biostatistics/kxj037
[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. pmid: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. pmid: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. pmid:17886233 doi: 10.1002/sim.3034
[75]
Efron B (1981) Nonparametric estimates of standard error: the jackknife, the bootstrap and other methods. Biometrika 68 (3): 589–599. doi: 10.2307/2335441
