Whole Exome Sequencing in Atrial Fibrillation
Atrial fibrillation is a common and morbid cardiac arrhythmia. Atrial fibrillation is heritable, and numerous genome-wide susceptibility loci have been identified, predominantly in non-coding regions. Over 35 genes also have been implicated in atrial fibrillation pathogenesis mostly through prior smaller scale candidate gene association studies, which generally did not have robust replication to support the associations. Therefore, the role of coding variation in the biology of atrial fibrillation is unclear. We examined whole exome sequencing data from 1,734 individuals with and 9,423 without atrial fibrillation, and did not observe any significant associations between coding variation and the arrhythmia. Furthermore, we did not observe any enrichment for association in previously implicated atrial fibrillation genes. In aggregate, our findings suggest that large effect coding variation is unlikely to be a predominant mechanism of common forms of atrial fibrillation encountered in the community.
Vyšlo v časopise:
Whole Exome Sequencing in Atrial Fibrillation. PLoS Genet 12(9): e32767. doi:10.1371/journal.pgen.1006284
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pgen.1006284
Souhrn
Atrial fibrillation is a common and morbid cardiac arrhythmia. Atrial fibrillation is heritable, and numerous genome-wide susceptibility loci have been identified, predominantly in non-coding regions. Over 35 genes also have been implicated in atrial fibrillation pathogenesis mostly through prior smaller scale candidate gene association studies, which generally did not have robust replication to support the associations. Therefore, the role of coding variation in the biology of atrial fibrillation is unclear. We examined whole exome sequencing data from 1,734 individuals with and 9,423 without atrial fibrillation, and did not observe any significant associations between coding variation and the arrhythmia. Furthermore, we did not observe any enrichment for association in previously implicated atrial fibrillation genes. In aggregate, our findings suggest that large effect coding variation is unlikely to be a predominant mechanism of common forms of atrial fibrillation encountered in the community.
Zdroje
1. Miyasaka Y, Barnes ME, Gersh BJ, Cha SS, Bailey KR, Abhayaratna WP, et al. Secular trends in incidence of atrial fibrillation in Olmsted County, Minnesota, 1980 to 2000, and implications on the projections for future prevalence. Circulation. 2006;114(2):119–25. 16818816.
2. Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby JV, et al. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. JAMA. 2001;285(18):2370–5. 11343485.
3. Kannel WB, Wolf PA, Benjamin EJ, Levy D. Prevalence, incidence, prognosis, and predisposing conditions for atrial fibrillation: population-based estimates. Am J Cardiol. 1998;82(8A):2N–9N. 9809895.
4. Ott A, Breteler MM, de Bruyne MC, van Harskamp F, Grobbee DE, Hofman A. Atrial fibrillation and dementia in a population-based study. The Rotterdam Study. Stroke. 1997;28(2):316–21. 9040682.
5. Wang TJ, Larson MG, Levy D, Vasan RS, Leip EP, Wolf PA, et al. Temporal relations of atrial fibrillation and congestive heart failure and their joint influence on mortality: the Framingham Heart Study. Circulation. 2003;107(23):2920–5. 12771006.
6. Krahn AD, Manfreda J, Tate RB, Mathewson FA, Cuddy TE. The natural history of atrial fibrillation: incidence, risk factors, and prognosis in the Manitoba Follow-Up Study. Am J Med. 1995;98(5):476–84. 7733127.
7. Stewart S, Hart CL, Hole DJ, McMurray JJ. A population-based study of the long-term risks associated with atrial fibrillation: 20-year follow-up of the Renfrew/Paisley study. Am J Med. 2002;113(5):359–64. 12401529.
8. Cappato R, Calkins H, Chen SA, Davies W, Iesaka Y, Kalman J, et al. Updated worldwide survey on the methods, efficacy, and safety of catheter ablation for human atrial fibrillation. CircArrhythmElectrophysiol. 2010;3(1):32–8. CIRCEP.109.859116 [pii];doi: 10.1161/CIRCEP.109.859116 19995881.
9. January CT, Wann LS, Alpert JS, Calkins H, Cleveland JC Jr., Cigarroa JE, et al. 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society. Circulation. 2014. doi: 10.1161/CIR.0000000000000041 24682347.
10. Lubitz SA, Yin X, Fontes JD, Magnani JW, Rienstra M, Pai M, et al. Association between familial atrial fibrillation and risk of new-onset atrial fibrillation. JAMA. 2010;304(20):2263–9. Epub 2010/11/16. jama.2010.1690 [pii] doi: 10.1001/jama.2010.1690 21076174.
11. Gudbjartsson DF, Arnar DO, Helgadottir A, Gretarsdottir S, Holm H, Sigurdsson A, et al. Variants conferring risk of atrial fibrillation on chromosome 4q25. Nature. 2007;448(7151):353–7. 17603472.
12. Benjamin EJ, Rice KM, Arking DE, Pfeufer A, van Noord C, Smith AV, et al. Variants in ZFHX3 are associated with atrial fibrillation in individuals of European ancestry. Nat Genet. 2009;41(8):879–81. Epub 2009/07/15. ng.416 [pii] doi: 10.1038/ng.416 19597492.
13. Gudbjartsson DF, Holm H, Gretarsdottir S, Thorleifsson G, Walters GB, Thorgeirsson G, et al. A sequence variant in ZFHX3 on 16q22 associates with atrial fibrillation and ischemic stroke. Nat Genet. 2009;41(8):876–8. Epub 2009/07/15. ng.417 [pii] doi: 10.1038/ng.417 19597491.
14. Ellinor PT, Lunetta KL, G N.L., Pfeufer A, Alonso A, Chung MK, et al. Common Variants in KCNN3 are Associated with Lone Atrial Fibrillation Nat Genet. 2010;42(4):240–4. Epub 2010/02/23. ng.537 [pii] doi: 10.1038/ng.537 20173747.
15. Ellinor PT, Lunetta KL, Albert CM, Glazer NL, Ritchie MD, Smith AV, et al. Meta-analysis identifies six new susceptibility loci for atrial fibrillation. Nat Genet. 2012;44(6):670–5. Epub 2012/05/01. doi: 10.1038/ng.2261 22544366; PubMed Central PMCID: PMC3366038.
16. Tucker NR, Ellinor PT. Emerging directions in the genetics of atrial fibrillation. Circ Res. 2014;114(9):1469–82. doi: 10.1161/CIRCRESAHA.114.302225 24763465; PubMed Central PMCID: PMCPMC4040146.
17. Sinner MF, Lubitz SA, Pfeufer A, Makino S, Beckmann BM, Lunetta KL, et al. Lack of replication in polymorphisms reported to be associated with atrial fibrillation. Heart rhythm: the official journal of the Heart Rhythm Society. 2011;8(3):403–9. Epub 2010/11/09. doi: 10.1016/j.hrthm.2010.11.003 21056700; PubMed Central PMCID: PMC3068750.
18. Franke A, Balschun T, Sina C, Ellinghaus D, Hasler R, Mayr G, et al. Genome-wide association study for ulcerative colitis identifies risk loci at 7q22 and 22q13 (IL17REL). Nat Genet. 2010;42(4):292–4. doi: 10.1038/ng.553 20228798.
19. Sasaki MM, Skol AD, Hungate EA, Bao R, Huang L, Kahn SA, et al. Whole-exome Sequence Analysis Implicates Rare Il17REL Variants in Familial and Sporadic Inflammatory Bowel Disease. Inflamm Bowel Dis. 2016;22(1):20–7. doi: 10.1097/MIB.0000000000000610 26480299; PubMed Central PMCID: PMCPMC4679526.
20. Lohmueller KE, Sparso T, Li Q, Andersson E, Korneliussen T, Albrechtsen A, et al. Whole-exome sequencing of 2,000 Danish individuals and the role of rare coding variants in type 2 diabetes. Am J Hum Genet. 2013;93(6):1072–86. doi: 10.1016/j.ajhg.2013.11.005 24290377; PubMed Central PMCID: PMC3852935.
21. Weeke P, Muhammad R, Delaney JT, Shaffer C, Mosley JD, Blair M, et al. Whole-exome sequencing in familial atrial fibrillation. Eur Heart J. 2014;35(36):2477–83. doi: 10.1093/eurheartj/ehu156 24727801; PubMed Central PMCID: PMC4169871.
22. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. Am J Epidemiol. 1989;129(4):687–702. Epub 1989/04/01. 2646917.
23. Fried LP, Borhani NO, Enright P, Furberg CD, Gardin JM, Kronmal RA, et al. The Cardiovascular Health Study: design and rationale. Annals of epidemiology. 1991;1(3):263–76. Epub 1991/02/01. 1669507.
24. Feinleib M, Kannel WB, Garrison RJ, McNamara PM, Castelli WP. The Framingham Offspring Study. Design and preliminary data. Prev Med. 1975;4(4):518–25. Epub 1975/12/01. 1208363.
25. Kannel WB, Dawber TR, Kagan A, Revotskie N, Stokes J 3rd. Factors of risk in the development of coronary heart disease—six year follow-up experience. The Framingham Study. Ann Intern Med. 1961;55:33–50. Epub 1961/07/01. 13751193.
26. Fu W, O'Connor TD, Jun G, Kang HMAbecasis G, Leal SM, et al. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature. 2013;493(7431):216–20. doi: 10.1038/nature11690 23201682; PubMed Central PMCID: PMC3676746.
27. Psaty BM, O'Donnell CJ, Gudnason V, Lunetta KL, Folsom AR, Rotter JI, et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ Cardiovasc Genet. 2009;2(1):73–80. doi: 10.1161/CIRCGENETICS.108.829747 20031568; PubMed Central PMCID: PMC2875693.
28. Reid JG, Carroll A, Veeraraghavan N, Dahdouli M, Sundquist A, English A, et al. Launching genomics into the cloud: deployment of Mercury, a next generation sequence analysis pipeline. BMC bioinformatics. 2014;15:30. Epub 2014/01/31. doi: 10.1186/1471-2105-15-30 24475911; PubMed Central PMCID: PMC3922167.
29. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60. doi: 10.1093/bioinformatics/btp324 19451168; PubMed Central PMCID: PMC2705234.
30. Challis D, Yu J, Evani US, Jackson AR, Paithankar S, Coarfa C, et al. An integrative variant analysis suite for whole exome next-generation sequencing data. BMC Bioinformatics. 2012;13:8. doi: 10.1186/1471-2105-13-8 22239737; PubMed Central PMCID: PMC3292476.
31. Reiner AP, Beleza S, Franceschini N, Auer PL, Robinson JG, Kooperberg C, et al. Genome-wide association and population genetic analysis of C-reactive protein in African American and Hispanic American women. Am J Hum Genet. 2012;91(3):502–12. doi: 10.1016/j.ajhg.2012.07.023 22939635; PubMed Central PMCID: PMC3511984.
32. Tennessen JA, Bigham AW, O'Connor TD, Fu W, Kenny EE, Gravel S, et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science. 2012;337(6090):64–9. doi: 10.1126/science.1219240 22604720.
33. Lange LA, Hu Y, Zhang H, Xue C, Schmidt EM, Tang ZZ, et al. Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol. Am J Hum Genet. 2014;94(2):233–45. doi: 10.1016/j.ajhg.2014.01.010 24507775; PubMed Central PMCID: PMC3928660.
34. Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test. American journal of human genetics. 2011;89(1):82–93. Epub 2011/07/09. doi: 10.1016/j.ajhg.2011.05.029 21737059; PubMed Central PMCID: PMC3135811.
35. Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Current protocols in human genetics / editorial board, Jonathan L Haines [et al]. 2013;Chapter 7:Unit7 20. doi: 10.1002/0471142905.hg0720s76 23315928; PubMed Central PMCID: PMC4480630.
36. Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4(7):1073–81. Epub 2009/06/30. nprot.2009.86 [pii] doi: 10.1038/nprot.2009.86 19561590.
37. Segre AV, Consortium D, investigators M, Groop L, Mootha VK, Daly MJ, et al. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 2010;6(8). Epub 2010/08/18. doi: 10.1371/journal.pgen.1001058 20714348; PubMed Central PMCID: PMC2920848.
38. Raychaudhuri S, Plenge RM, Rossin EJ, Ng AC, International Schizophrenia C, Purcell SM, et al. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 2009;5(6):e1000534. doi: 10.1371/journal.pgen.1000534 19557189; PubMed Central PMCID: PMCPMC2694358.
39. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50. Epub 2005/10/04. 0506580102 [pii] doi: 10.1073/pnas.0506580102 16199517; PubMed Central PMCID: PMC1239896.
Štítky
Genetika Reprodukčná medicínaČlánok vyšiel v časopise
PLOS Genetics
2016 Číslo 9
- Je „freeze-all“ pro všechny? Odborníci na fertilitu diskutovali na virtuálním summitu
- Gynekologové a odborníci na reprodukční medicínu se sejdou na prvním virtuálním summitu
Najčítanejšie v tomto čísle
- Reactive Oxygen Species: From Harmful Molecules to Fine-Tuning Regulators of Stem Cell Niche Maintenance
- The Clock Is Ticking: Countdown to Metastases
- Whole Exome Sequencing in Atrial Fibrillation