Fine-Mapping and Initial Characterization of QT Interval Loci in African Americans
The QT interval (QT) is heritable and its prolongation is a risk factor for ventricular tachyarrhythmias and sudden death. Most genetic studies of QT have examined European ancestral populations; however, the increased genetic diversity in African Americans provides opportunities to narrow association signals and identify population-specific variants. We therefore evaluated 6,670 SNPs spanning eleven previously identified QT loci in 8,644 African American participants from two Population Architecture using Genomics and Epidemiology (PAGE) studies: the Atherosclerosis Risk in Communities study and Women's Health Initiative Clinical Trial. Of the fifteen known independent QT variants at the eleven previously identified loci, six were significantly associated with QT in African American populations (P≤1.20×10−4): ATP1B1, PLN1, KCNQ1, NDRG4, and two NOS1AP independent signals. We also identified three population-specific signals significantly associated with QT in African Americans (P≤1.37×10−5): one at NOS1AP and two at ATP1B1. Linkage disequilibrium (LD) patterns in African Americans assisted in narrowing the region likely to contain the functional variants for several loci. For example, African American LD patterns showed that 0 SNPs were in LD with NOS1AP signal rs12143842, compared with European LD patterns that indicated 87 SNPs, which spanned 114.2 Kb, were in LD with rs12143842. Finally, bioinformatic-based characterization of the nine African American signals pointed to functional candidates located exclusively within non-coding regions, including predicted binding sites for transcription factors such as TBX5, which has been implicated in cardiac structure and conductance. In this detailed evaluation of QT loci, we identified several African Americans SNPs that better define the association with QT and successfully narrowed intervals surrounding established loci. These results demonstrate that the same loci influence variation in QT across multiple populations, that novel signals exist in African Americans, and that the SNPs identified as strong candidates for functional evaluation implicate gene regulatory dysfunction in QT prolongation.
Vyšlo v časopise:
Fine-Mapping and Initial Characterization of QT Interval Loci in African Americans. PLoS Genet 8(8): e32767. doi:10.1371/journal.pgen.1002870
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pgen.1002870
Souhrn
The QT interval (QT) is heritable and its prolongation is a risk factor for ventricular tachyarrhythmias and sudden death. Most genetic studies of QT have examined European ancestral populations; however, the increased genetic diversity in African Americans provides opportunities to narrow association signals and identify population-specific variants. We therefore evaluated 6,670 SNPs spanning eleven previously identified QT loci in 8,644 African American participants from two Population Architecture using Genomics and Epidemiology (PAGE) studies: the Atherosclerosis Risk in Communities study and Women's Health Initiative Clinical Trial. Of the fifteen known independent QT variants at the eleven previously identified loci, six were significantly associated with QT in African American populations (P≤1.20×10−4): ATP1B1, PLN1, KCNQ1, NDRG4, and two NOS1AP independent signals. We also identified three population-specific signals significantly associated with QT in African Americans (P≤1.37×10−5): one at NOS1AP and two at ATP1B1. Linkage disequilibrium (LD) patterns in African Americans assisted in narrowing the region likely to contain the functional variants for several loci. For example, African American LD patterns showed that 0 SNPs were in LD with NOS1AP signal rs12143842, compared with European LD patterns that indicated 87 SNPs, which spanned 114.2 Kb, were in LD with rs12143842. Finally, bioinformatic-based characterization of the nine African American signals pointed to functional candidates located exclusively within non-coding regions, including predicted binding sites for transcription factors such as TBX5, which has been implicated in cardiac structure and conductance. In this detailed evaluation of QT loci, we identified several African Americans SNPs that better define the association with QT and successfully narrowed intervals surrounding established loci. These results demonstrate that the same loci influence variation in QT across multiple populations, that novel signals exist in African Americans, and that the SNPs identified as strong candidates for functional evaluation implicate gene regulatory dysfunction in QT prolongation.
Zdroje
1. MossAJ (1999) The QT interval and torsade de pointes. Drug Saf 21 Suppl 1: 5–10 discussion 81-17.
2. DekkerJM, CrowRS, HannanPJ, SchoutenEG, FolsomAR (2004) Heart rate-corrected QT interval prolongation predicts risk of coronary heart disease in black and white middle-aged men and women: the ARIC study. J Am Coll Cardiol 43: 565–571.
3. ZhangY, PostWS, Blasco-ColmenaresE, DalalD, TomaselliGF, et al. (2011) Electrocardiographic QT Interval and Mortality: A Meta-analysis. Epidemiology 22: 660–670.
4. BazettHC (1920) The time relations of the blood-pressure changes after excision of the adrenal glands, with some observations on blood volume changes. J Physiol 53: 320–339.
5. KramerB, BrillM, BruhnA, KublerW (1986) Relationship between the degree of coronary artery disease and of left ventricular function and the duration of the QT-interval in ECG. Eur Heart J 7: 14–24.
6. HartG (1994) Cellular electrophysiology in cardiac hypertrophy and failure. Cardiovasc Res 28: 933–946.
7. BazettHC (1920) An analysis of the time-relations of electrocardiograms. Heart 7: 353–357.
8. LepeschkinE, SurawiczB (1953) The duration of the Q-U interval and its components in electrograms of normal persons. Am Heart J 46.
9. MangoniAA, KinironsMT, SwiftCG, JacksonSH (2003) Impact of age on QT interval and QT dispersion in healthy subjects: a regression analysis. Age Ageing 32: 326–331.
10. MeinertCL, KnatterudGL, ProutTE, KlimtCR (1970) A study of the effects of hypoglycemic agents on vascular complications in patients with adult-onset diabetes. II. Mortality results. Diabetes 19: Suppl: 789–830.
11. NajeedSA, KhanIA, MolnarJ, SombergJC (2002) Differential effect of glyburide (glibenclamide) and metformin on QT dispersion: a potential adenosine triphosphate sensitive K+ channel effect. Am J Cardiol 90: 1103–1106.
12. RodenDM (2004) Drug-induced prolongation of the QT interval. N Engl J Med 350: 1013–1022.
13. ShahM, AkarFG, TomaselliGF (2005) Molecular basis of arrhythmias. Circulation 112: 2517–2529.
14. BusjahnA, KnoblauchH, FaulhaberHD, BoeckelT, RosenthalM, et al. (1999) QT interval is linked to 2 long-QT syndrome loci in normal subjects. Circulation 99: 3161–3164.
15. HansonB, TunaN, BouchardT, HestonL, EckertE, et al. (1989) Genetic factors in the electrocardiogram and heart rate of twins reared apart and together. Am J Cardiol 63: 606–609.
16. Newton-ChehC, LarsonMG, CoreyDC, BenjaminEJ, HerbertAG, et al. (2005) QT interval is a heritable quantitative trait with evidence of linkage to chromosome 3 in a genome-wide linkage analysis: The Framingham Heart Study. Heart Rhythm 2: 277–284.
17. AkylbekovaEL, CrowRS, JohnsonWD, BuxbaumSG, NjemanzeS, et al. (2009) Clinical correlates and heritability of QT interval duration in blacks: the Jackson Heart Study. Circ Arrhythm Electrophysiol 2: 427–432.
18. ArkingDE, PfeuferA, PostW, KaoWH, Newton-ChehC, et al. (2006) A common genetic variant in the NOS1 regulator NOS1AP modulates cardiac repolarization. Nat Genet 38: 644–651.
19. MarroniF, PfeuferA, AulchenkoYS, FranklinCS, IsaacsA, et al. (2009) A genome-wide association scan of RR and QT interval duration in 3 European genetically isolated populations: the EUROSPAN project. Circ Cardiovasc Genet 2: 322–328.
20. Newton-ChehC, EijgelsheimM, RiceKM, de BakkerPI, YinX, et al. (2009) Common variants at ten loci influence QT interval duration in the QTGEN Study. Nat Genet 41: 399–406.
21. NolteIM, WallaceC, NewhouseSJ, WaggottD, FuJ, et al. (2009) Common genetic variation near the phospholamban gene is associated with cardiac repolarisation: meta-analysis of three genome-wide association studies. PLoS One 4: e6138.
22. PfeuferA, SannaS, ArkingDE, MullerM, GatevaV, et al. (2009) Common variants at ten loci modulate the QT interval duration in the QTSCD Study. Nat Genet 41: 407–414.
23. ChambersJC, ZhaoJ, TerraccianoCM, BezzinaCR, ZhangW, et al. (2010) Genetic variation in SCN10A influences cardiac conduction. Nat Genet 42: 149–152.
24. McCarthyMI, HirschhornJN (2008) Genome-wide association studies: potential next steps on a genetic journey. Hum Mol Genet 17: R156–165.
25. SannaS, LiB, MulasA, SidoreC, KangHM, et al. (2011) Fine mapping of five Loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability. PLoS Genet 7: e1002198.
26. RutenbergJB, FischerA, JiaH, GesslerM, ZhongTP, et al. (2006) Developmental patterning of the cardiac atrioventricular canal by Notch and Hairy-related transcription factors. Development 133: 4381–4390.
27. LiQY, Newbury-EcobRA, TerrettJA, WilsonDI, CurtisAR, et al. (1997) Holt-Oram syndrome is caused by mutations in TBX5, a member of the Brachyury (T) gene family. Nat Genet 15: 21–29.
28. SmithJG, MagnaniJW, PalmerC, MengYA, SolimanEZ, et al. (2011) Genome-wide association studies of the PR interval in African Americans. PLoS Genet 7: e1001304.
29. ThackaberryEA, GabaldonDM, WalkerMK, SmithSM (2002) Aryl hydrocarbon receptor null mice develop cardiac hypertrophy and increased hypoxia-inducible factor-1alpha in the absence of cardiac hypoxia. Cardiovasc Toxicol 2: 263–274.
30. KangYJ (2006) Cardiac hypertrophy: a risk factor for QT-prolongation and cardiac sudden death. Toxicol Pathol 34: 58–66.
31. PfeuferA, van NoordC, MarcianteKD, ArkingDE, LarsonMG, et al. (2010) Genome-wide association study of PR interval. Nat Genet 42: 153–159.
32. SotoodehniaN, IsaacsA, de BakkerPI, DorrM, Newton-ChehC, et al. (2010) Common variants in 22 loci are associated with QRS duration and cardiac ventricular conduction. Nat Genet 42: 1068–1076.
33. Tristani-FirouziM, JensenJL, DonaldsonMR, SansoneV, MeolaG, et al. (2002) Functional and clinical characterization of KCNJ2 mutations associated with LQT7 (Andersen syndrome). J Clin Invest 110: 381–388.
34. ManolioTA, CollinsFS, CoxNJ, GoldsteinDB, HindorffLA, et al. (2009) Finding the missing heritability of complex diseases. Nature 461: 747–753.
35. MatiseTC, AmbiteJL, BuyskeS, CarlsonCS, ColeSA, et al. (2011) The Next PAGE in understanding complex traits: design for the analysis of Population Architecture Using Genetics and Epidemiology (PAGE) Study. Am J Epidemiol 174: 849–859.
36. LiuEY, BuyskeS, AragakiAK, PetersU, BoerwinkleE, et al. (2011) Genotype Imputation of Metabochip SNPs Using a Study Specific Reference Panel of ∼4,000 Haplotypes in African Americans from the Women's Health Initiative. Genetic Epidemiology 36: 107–117.
37. GiannoulatouE, YauC, ColellaS, RagoussisJ, HolmesCC (2008) GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population. Bioinformatics 24: 2209–2214.
38. BuyskeS, WuY, CartyCL, AssimesTL, DumitrescuL, et al. (2012) Evaluation of the Metabochip Genotyping Array in African Americans and Implications for Fine Mapping of GWAS-Identified Loci: The PAGE Study. PLoS One In press.
39. PurcellS, NealeB, Todd-BrownK, ThomasL, FerreiraMA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. American journal of human genetics 81: 559–575.
40. PattersonN, PriceAL, ReichD (2006) Population structure and eigenanalysis. PLoS genetics 2: e190.
41. PriceAL, PattersonNJ, PlengeRM, WeinblattME, ShadickNA, et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nature genetics 38: 904–909.
42. BerglundG, ElmstahlS, JanzonL, LarssonSA (1993) The Malmo Diet and Cancer Study. Design and feasibility. Journal of internal medicine 233: 45–51.
43. WillerCJ, LiY, AbecasisGR (2010) METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26: 2190–2191.
44. LinDY (2005) An efficient Monte Carlo approach to assessing statistical significance in genomic studies. Bioinformatics 21: 781–787.
45. RautaharjuPM, ZhangZM (2002) Linearly scaled, rate-invariant normal limits for QT interval: eight decades of incorrect application of power functions. J Cardiovasc Electrophysiol 13: 1211–1218.
46. LevyS, HannenhalliS (2002) Identification of transcription factor binding sites in the human genome sequence. Mamm Genome 13: 510–514.
47. SethupathyP, GiangH, PlotkinJB, HannenhalliS (2008) Genome-wide analysis of natural selection on human cis-elements. PLoS One 3: e3137.
Štítky
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