The influence of rare variants in circulating metabolic biomarkers

Autoři: Fernando Riveros-Mckay aff001;  Clare Oliver-Williams aff002;  Savita Karthikeyan aff002;  Klaudia Walter aff001;  Kousik Kundu aff001;  Willem H. Ouwehand aff001;  David Roberts aff006;  Emanuele Di Angelantonio aff001;  Nicole Soranzo aff001;  John Danesh aff001;  Eleanor Wheeler aff001;  Eleftheria Zeggini aff001;  Adam S. Butterworth aff001;  Inês Barroso aff001
Působiště autorů: Wellcome Sanger Institute, Cambridge, United Kingdom aff001;  MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom aff002;  Homerton College, Cambridge, United Kingdom aff003;  Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom aff004;  NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom aff005;  The National Institute for Health Research Blood and Transplant Research Unit (NIHR BTRU) in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom aff006;  NHS Blood and Transplant—Oxford Centre, Level 2, John Radcliffe Hospital, Oxford, United Kingdom aff007;  Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom aff008;  British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom aff009;  National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, United Kingdom aff010;  Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom aff011;  MRC Epidemiology Unit, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom aff012;  Institute of Translational Genomics, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany aff013
Vyšlo v časopise: The influence of rare variants in circulating metabolic biomarkers. PLoS Genet 16(3): e32767. doi:10.1371/journal.pgen.1008605
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008605


Circulating metabolite levels are biomarkers for cardiovascular disease (CVD). Here we studied, association of rare variants and 226 serum lipoproteins, lipids and amino acids in 7,142 (discovery plus follow-up) healthy participants. We leveraged the information from multiple metabolite measurements on the same participants to improve discovery in rare variant association analyses for gene-based and gene-set tests by incorporating correlated metabolites as covariates in the validation stage. Gene-based analysis corrected for the effective number of tests performed, confirmed established associations at APOB, APOC3, PAH, HAL and PCSK (p<1.32x10-7) and identified novel gene-trait associations at a lower stringency threshold with ACSL1, MYCN, FBXO36 and B4GALNT3 (p<2.5x10-6). Regulation of the pyruvate dehydrogenase (PDH) complex was associated for the first time, in gene-set analyses also corrected for effective number of tests, with IDL and LDL parameters, as well as circulating cholesterol (pMETASKAT<2.41x10-6). In conclusion, using an approach that leverages metabolite measurements obtained in the same participants, we identified novel loci and pathways involved in the regulation of these important metabolic biomarkers. As large-scale biobanks continue to amass sequencing and phenotypic information, analytical approaches such as ours will be useful to fully exploit the copious amounts of biological data generated in these efforts.

Klíčová slova:

Biomarkers – Drug metabolism – Genome-wide association studies – Cholesterol – Lipid metabolism – Lipids – Lipoproteins – Metaanalysis


1. Athersuch TJ, Keun HC (2015) Metabolic profiling in human exposome studies. Mutagenesis 30: 755–762. doi: 10.1093/mutage/gev060 26290610

2. Lydic TA, Goo YH (2018) Lipidomics unveils the complexity of the lipidome in metabolic diseases. Clin Transl Med 7: 4. doi: 10.1186/s40169-018-0182-9 29374337

3. Arsenault BJ, Boekholdt SM, Kastelein JJ (2011) Lipid parameters for measuring risk of cardiovascular disease. Nat Rev Cardiol 8: 197–206. doi: 10.1038/nrcardio.2010.223 21283149

4. Nordestgaard BG, Varbo A (2014) Triglycerides and cardiovascular disease. Lancet 384: 626–635. doi: 10.1016/S0140-6736(14)61177-6 25131982

5. Varbo A, Benn M, Nordestgaard BG (2014) Remnant cholesterol as a cause of ischemic heart disease: evidence, definition, measurement, atherogenicity, high risk patients, and present and future treatment. Pharmacol Ther 141: 358–367. doi: 10.1016/j.pharmthera.2013.11.008 24287311

6. Wyler von Ballmoos MC, Haring B, Sacks FM (2015) The risk of cardiovascular events with increased apolipoprotein CIII: A systematic review and meta-analysis. J Clin Lipidol 9: 498–510. doi: 10.1016/j.jacl.2015.05.002 26228667

7. Geng P, Ding Y, Qiu L, Lu Y (2015) Serum mannose-binding lectin is a strong biomarker of diabetic retinopathy in chinese patients with diabetes. Diabetes Care 38: 868–875. doi: 10.2337/dc14-1873 25758771

8. Trpkovic A, Resanovic I, Stanimirovic J, Radak D, Mousa SA, et al. (2015) Oxidized low-density lipoprotein as a biomarker of cardiovascular diseases. Crit Rev Clin Lab Sci 52: 70–85. doi: 10.3109/10408363.2014.992063 25537066

9. Hobbs HH, Brown MS, Goldstein JL (1992) Molecular genetics of the LDL receptor gene in familial hypercholesterolemia. Hum Mutat 1: 445–466. doi: 10.1002/humu.1380010602 1301956

10. Shichiri M, Tanaka A, Hirata Y (2003) Intravenous gene therapy for familial hypercholesterolemia using ligand-facilitated transfer of a liposome:LDL receptor gene complex. Gene Ther 10: 827–831. doi: 10.1038/ 12704424

11. Soria LF, Ludwig EH, Clarke HR, Vega GL, Grundy SM, et al. (1989) Association between a specific apolipoprotein B mutation and familial defective apolipoprotein B-100. Proc Natl Acad Sci U S A 86: 587–591. doi: 10.1073/pnas.86.2.587 2563166

12. Gebhard C, Huard G, Kritikou EA, Tardif JC (2013) Apolipoprotein B antisense inhibition—update on mipomersen. Curr Pharm Des 19: 3132–3142. doi: 10.2174/13816128113199990312 23317401

13. Abifadel M, Varret M, Rabes JP, Allard D, Ouguerram K, et al. (2003) Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat Genet 34: 154–156. doi: 10.1038/ng1161 12730697

14. Duff CJ, Hooper NM (2011) PCSK9: an emerging target for treatment of hypercholesterolemia. Expert Opin Ther Targets 15: 157–168. doi: 10.1517/14728222.2011.547480 21204732

15. Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, et al. (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466: 707–713. doi: 10.1038/nature09270 20686565

16. Asselbergs FW, Guo Y, van Iperen EP, Sivapalaratnam S, Tragante V, et al. (2012) Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci. Am J Hum Genet 91: 823–838. doi: 10.1016/j.ajhg.2012.08.032 23063622

17. 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: 1274–1283. doi: 10.1038/ng.2797 24097068

18. Albrechtsen A, Grarup N, Li Y, Sparso T, Tian G, et al. (2013) Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes. Diabetologia 56: 298–310. doi: 10.1007/s00125-012-2756-1 23160641

19. Peloso GM, Auer PL, Bis JC, Voorman A, Morrison AC, et al. (2014) Association of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks. Am J Hum Genet 94: 223–232. doi: 10.1016/j.ajhg.2014.01.009 24507774

20. Surakka I, Horikoshi M, Magi R, Sarin AP, Mahajan A, et al. (2015) The impact of low-frequency and rare variants on lipid levels. Nat Genet 47: 589–597. doi: 10.1038/ng.3300 25961943

21. Tang CS, Zhang H, Cheung CY, Xu M, Ho JC, et al. (2015) Exome-wide association analysis reveals novel coding sequence variants associated with lipid traits in Chinese. Nat Commun 6: 10206. doi: 10.1038/ncomms10206 26690388

22. Liu DJ, Peloso GM, Yu H, Butterworth AS, Wang X, et al. (2017) Exome-wide association study of plasma lipids in >300,000 individuals. Nat Genet 49: 1758–1766. doi: 10.1038/ng.3977 29083408

23. Natarajan P, Peloso GM, Zekavat SM, Montasser M, Ganna A, et al. (2018) Deep-coverage whole genome sequences and blood lipids among 16,324 individuals. Nat Commun 9.

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

25. Kettunen J, Demirkan A, Wurtz P, Draisma HH, Haller T, et al. (2016) Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun 7: 11122. doi: 10.1038/ncomms11122 27005778

26. Davis JP, Huyghe JR, Locke AE, Jackson AU, Sim X, et al. (2017) Common, low-frequency, and rare genetic variants associated with lipoprotein subclasses and triglyceride measures in Finnish men from the METSIM study. PLoS Genet 13: e1007079. doi: 10.1371/journal.pgen.1007079 29084231

27. Dewey FE, Gusarova V, O'Dushlaine C, Gottesman O, Trejos J, et al. (2016) Inactivating Variants in ANGPTL4 and Risk of Coronary Artery Disease. N Engl J Med 374: 1123–1133. doi: 10.1056/NEJMoa1510926 26933753

28. Dewey FE, Murray MF, Overton JD, Habegger L, Leader JB, et al. (2016) Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study. Science 354.

29. Jagadeesh KA, Wenger AM, Berger MJ, Guturu H, Stenson PD, et al. (2016) M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity. Nat Genet 48: 1581–1586. doi: 10.1038/ng.3703 27776117

30. Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, et al. (2016) The genetic architecture of type 2 diabetes. Nature 536: 41–47. doi: 10.1038/nature18642 27398621

31. Kiezun A, Garimella K, Do R, Stitziel NO, Neale BM, et al. (2012) Exome sequencing and the genetic basis of complex traits. Nat Genet 44: 623–630. doi: 10.1038/ng.2303 22641211

32. Turcot V, Lu Y, Highland HM, Schurmann C, Justice AE, et al. (2018) Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat Genet 50: 26–41. doi: 10.1038/s41588-017-0011-x 29273807

33. Drenos F, Davey Smith G, Ala-Korpela M, Kettunen J, Wurtz P, et al. (2016) Metabolic Characterization of a Rare Genetic Variation Within APOC3 and Its Lipoprotein Lipase-Independent Effects. Circ Cardiovasc Genet 9: 231–239. doi: 10.1161/CIRCGENETICS.115.001302 27114411

34. Rhee EP, Yang Q, Yu B, Liu X, Cheng S, et al. (2016) An exome array study of the plasma metabolome. Nat Commun 7: 12360. doi: 10.1038/ncomms12360 27453504

35. Aschard H, Vilhjalmsson BJ, Joshi AD, Price AL, Kraft P (2015) Adjusting for heritable covariates can bias effect estimates in genome-wide association studies. Am J Hum Genet 96: 329–339. doi: 10.1016/j.ajhg.2014.12.021 25640676

36. Aschard H, Guillemot V, Vilhjalmsson B, Patel CJ, Skurnik D, et al. (2017) Covariate selection for association screening in multiphenotype genetic studies. Nat Genet 49: 1789–1795. doi: 10.1038/ng.3975 29038595

37. Lee S, Wu MC, Lin X (2012) Optimal tests for rare variant effects in sequencing association studies. Biostatistics 13: 762–775. doi: 10.1093/biostatistics/kxs014 22699862

38. Zaykin DV (2011) Optimally weighted Z-test is a powerful method for combining probabilities in meta-analysis. J Evol Biol 24: 1836–1841. doi: 10.1111/j.1420-9101.2011.02297.x 21605215

39. Grevengoed TJ, Martin SA, Katunga L, Cooper DE, Anderson EJ, et al. (2015) Acyl-CoA synthetase 1 deficiency alters cardiolipin species and impairs mitochondrial function. J Lipid Res 56: 1572–1582. doi: 10.1194/jlr.M059717 26136511

40. Yan S, Yang XF, Liu HL, Fu N, Ouyang Y, et al. (2015) Long-chain acyl-CoA synthetase in fatty acid metabolism involved in liver and other diseases: an update. World J Gastroenterol 21: 3492–3498. doi: 10.3748/wjg.v21.i12.3492 25834313

41. Zirath H, Frenzel A, Oliynyk G, Segerstrom L, Westermark UK, et al. (2013) MYC inhibition induces metabolic changes leading to accumulation of lipid droplets in tumor cells. Proc Natl Acad Sci U S A 110: 10258–10263. doi: 10.1073/pnas.1222404110 23733953

42. Li LO, Ellis JM, Paich HA, Wang S, Gong N, et al. (2009) Liver-specific loss of long chain acyl-CoA synthetase-1 decreases triacylglycerol synthesis and beta-oxidation and alters phospholipid fatty acid composition. J Biol Chem 284: 27816–27826. doi: 10.1074/jbc.M109.022467 19648649

43. Scott RA, Scott LJ, Magi R, Marullo L, Gaulton KJ, et al. (2017) An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans. Diabetes 66: 2888–2902. doi: 10.2337/db16-1253 28566273

44. Yang L, Yang Y, Si D, Shi K, Liu D, et al. (2017) High expression of long chain acyl-coenzyme A synthetase 1 in peripheral blood may be a molecular marker for assessing the risk of acute myocardial infarction. Exp Ther Med 14: 4065–4072. doi: 10.3892/etm.2017.5091 29104625

45. Brodeur GM, Seeger RC, Schwab M, Varmus HE, Bishop JM (1984) Amplification of N-myc in untreated human neuroblastomas correlates with advanced disease stage. Science 224: 1121–1124. doi: 10.1126/science.6719137 6719137

46. Emanuel BS, Balaban G, Boyd JP, Grossman A, Negishi M, et al. (1985) N-myc amplification in multiple homogeneously staining regions in two human neuroblastomas. Proc Natl Acad Sci U S A 82: 3736–3740. doi: 10.1073/pnas.82.11.3736 2582423

47. Sato T, Gotoh M, Kiyohara K, Kameyama A, Kubota T, et al. (2003) Molecular cloning and characterization of a novel human beta 1,4-N-acetylgalactosaminyltransferase, beta 4GalNAc-T3, responsible for the synthesis of N,N'-diacetyllactosediamine, galNAc beta 1-4GlcNAc. J Biol Chem 278: 47534–47544. doi: 10.1074/jbc.M308857200 12966086

48. Morgan H, Beck T, Blake A, Gates H, Adams N, et al. (2010) EuroPhenome: a repository for high-throughput mouse phenotyping data. Nucleic Acids Res 38: D577–585. doi: 10.1093/nar/gkp1007 19933761

49. Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, et al. (2015) New genetic loci link adipose and insulin biology to body fat distribution. Nature 518: 187–196. doi: 10.1038/nature14132 25673412

50. Wood AR, Esko T, Yang J, Vedantam S, Pers TH, et al. (2014) Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet 46: 1173–1186. doi: 10.1038/ng.3097 25282103

51. Jin J, Cardozo T, Lovering RC, Elledge SJ, Pagano M, et al. (2004) Systematic analysis and nomenclature of mammalian F-box proteins. Genes Dev 18: 2573–2580. doi: 10.1101/gad.1255304 15520277

52. Zhang S, Hulver MW, McMillan RP, Cline MA, Gilbert ER (2014) The pivotal role of pyruvate dehydrogenase kinases in metabolic flexibility. Nutr Metab (Lond) 11: 10.

53. Holmes MV, Millwood IY, Kartsonaki C, Hill MR, Bennett DA, et al. (2018) Lipids, Lipoproteins, and Metabolites and Risk of Myocardial Infarction and Stroke. J Am Coll Cardiol 71: 620–632. doi: 10.1016/j.jacc.2017.12.006 29420958

54. Dron JS, Wang J, Low-Kam C, Khetarpal SA, Robinson JF, et al. (2017) Polygenic determinants in extremes of high-density lipoprotein cholesterol. J Lipid Res 58: 2162–2170. doi: 10.1194/jlr.M079822 28870971

55. Teslovich TM, Kim DS, Yin X, Stancakova A, Jackson AU, et al. (2018) Identification of seven novel loci associated with amino acid levels using single-variant and gene-based tests in 8545 Finnish men from the METSIM study. Hum Mol Genet 27: 1664–1674. doi: 10.1093/hmg/ddy067 29481666

56. Moore C, Sambrook J, Walker M, Tolkien Z, Kaptoge S, et al. (2014) The INTERVAL trial to determine whether intervals between blood donations can be safely and acceptably decreased to optimise blood supply: study protocol for a randomised controlled trial. Trials 15: 363. doi: 10.1186/1745-6215-15-363 25230735

57. Astle WJ, Elding H, Jiang T, Allen D, Ruklisa D, et al. (2016) The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease. Cell 167: 1415–1429 e1419. doi: 10.1016/j.cell.2016.10.042 27863252

58. Singh T, Kurki MI, Curtis D, Purcell SM, Crooks L, et al. (2016) Rare loss-of-function variants in SETD1A are associated with schizophrenia and developmental disorders. Nat Neurosci 19: 571–577. doi: 10.1038/nn.4267 26974950

59. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25: 1754–1760. doi: 10.1093/bioinformatics/btp324 19451168

60. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, et al. (2010) The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20: 1297–1303. doi: 10.1101/gr.107524.110 20644199

61. Jun G, Flickinger M, Hetrick KN, Romm JM, Doheny KF, et al. (2012) Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data. Am J Hum Genet 91: 839–848. doi: 10.1016/j.ajhg.2012.09.004 23103226

62. Consortium UK, Walter K, Min JL, Huang J, Crooks L, et al. (2015) The UK10K project identifies rare variants in health and disease. Nature 526: 82–90. doi: 10.1038/nature14962 26367797

63. Soininen P, Kangas AJ, Wurtz P, Tukiainen T, Tynkkynen T, et al. (2009) High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism. Analyst 134: 1781–1785. doi: 10.1039/b910205a 19684899

64. McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, et al. (2016) The Ensembl Variant Effect Predictor. Genome Biol 17: 122. doi: 10.1186/s13059-016-0974-4 27268795

65. R Core Team (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

66. Wang K, Li M, Hakonarson H (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38: e164. doi: 10.1093/nar/gkq603 20601685

67. Lee S, Teslovich TM, Boehnke M, Lin X (2013) General framework for meta-analysis of rare variants in sequencing association studies. Am J Hum Genet 93: 42–53. doi: 10.1016/j.ajhg.2013.05.010 23768515

68. Pinero J, Bravo A, Queralt-Rosinach N, Gutierrez-Sacristan A, Deu-Pons J, et al. (2017) DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res 45: D833–D839. doi: 10.1093/nar/gkw943 27924018

69. Pinero J, Queralt-Rosinach N, Bravo A, Deu-Pons J, Bauer-Mehren A, et al. (2015) DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database (Oxford) 2015: bav028.

70. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45: D353–D361. doi: 10.1093/nar/gkw1092 27899662

71. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28: 27–30. doi: 10.1093/nar/28.1.27 10592173

72. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M (2016) KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 44: D457–462. doi: 10.1093/nar/gkv1070 26476454

73. Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, et al. (2018) The Reactome Pathway Knowledgebase. Nucleic Acids Res 46: D649–D655. doi: 10.1093/nar/gkx1132 29145629

74. Milacic M, Haw R, Rothfels K, Wu G, Croft D, et al. (2012) Annotating cancer variants and anti-cancer therapeutics in reactome. Cancers (Basel) 4: 1180–1211.

75. MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, et al. (2017) The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res 45: D896–D901. doi: 10.1093/nar/gkw1133 27899670

76. Dewey M (2020) metap: meta-analysis of significance values. R package version 1.3.

77. Gopinathrao G (2007) Image for “Regulation of pyruvate dehydrogenase (PDH) complex”. Reactome, release 65, doi: 10.3180/REACT_12528.1

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2020 Číslo 3

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