Literature DB >> 27251404

Semiconductor Whole Exome Sequencing for the Identification of Genetic Variants in Colombian Patients Clinically Diagnosed with Long QT Syndrome.

Mariana Burgos1, Alvaro Arenas2, Rodrigo Cabrera3.   

Abstract

BACKGROUND AND
OBJECTIVE: Inherited long QT syndrome (LQTS) is a cardiac channelopathy characterized by a prolongation of QT interval and the risk of syncope, cardiac arrest, and sudden cardiac death. Genetic diagnosis of LQTS is critical in medical practice as results can guide adequate management of patients and distinguish phenocopies such as catecholaminergic polymorphic ventricular tachycardia (CPVT). However, extensive screening of large genomic regions is required in order to reliably identify genetic causes. Semiconductor whole exome sequencing (WES) is a promising approach for the identification of variants in the coding regions of most human genes.
METHODS: DNA samples from 21 Colombian patients clinically diagnosed with LQTS were enriched for coding regions using multiplex polymerase chain reaction (PCR) and subjected to WES using a semiconductor sequencer.
RESULTS: Semiconductor WES showed mean coverage of 93.6 % for all coding regions relevant to LQTS at >10× depth with high intra- and inter-assay depth heterogeneity. Fifteen variants were detected in 12 patients in genes associated with LQTS. Three variants were identified in three patients in genes associated with CPVT. Co-segregation analysis was performed when possible. All variants were analyzed with two pathogenicity prediction algorithms. The overall prevalence of LQTS and CPVT variants in our cohort was 71.4 %. All LQTS variants previously identified through commercial genetic testing were identified.
CONCLUSION: Standardized WES assays can be easily implemented, often at a lower cost than sequencing panels. Our results show that WES can identify LQTS-causing mutations and permits differential diagnosis of related conditions in a real-world clinical setting. However, high heterogeneity in sequencing depth and low coverage in the most relevant genes is expected to be associated with reduced analytical sensitivity.

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Year:  2016        PMID: 27251404     DOI: 10.1007/s40291-016-0207-2

Source DB:  PubMed          Journal:  Mol Diagn Ther        ISSN: 1177-1062            Impact factor:   4.074


  39 in total

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Authors:  S T Sherry; M H Ward; M Kholodov; J Baker; L Phan; E M Smigielski; K Sirotkin
Journal:  Nucleic Acids Res       Date:  2001-01-01       Impact factor: 16.971

2.  Prevalence of the congenital long-QT syndrome.

Authors:  Peter J Schwartz; Marco Stramba-Badiale; Lia Crotti; Matteo Pedrazzini; Alessandra Besana; Giuliano Bosi; Fulvio Gabbarini; Karine Goulene; Roberto Insolia; Savina Mannarino; Fabio Mosca; Luigi Nespoli; Alessandro Rimini; Enrico Rosati; Patrizia Salice; Carla Spazzolini
Journal:  Circulation       Date:  2009-10-19       Impact factor: 29.690

3.  A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

Authors:  Heng Li
Journal:  Bioinformatics       Date:  2011-09-08       Impact factor: 6.937

4.  Spectrum and prevalence of cardiac ryanodine receptor (RyR2) mutations in a cohort of unrelated patients referred explicitly for long QT syndrome genetic testing.

Authors:  David J Tester; Laura J Kopplin; Melissa L Will; Michael J Ackerman
Journal:  Heart Rhythm       Date:  2005-10       Impact factor: 6.343

5.  KVLQT1 mutations in three families with familial or sporadic long QT syndrome.

Authors:  M W Russell; M Dick; F S Collins; L C Brody
Journal:  Hum Mol Genet       Date:  1996-09       Impact factor: 6.150

6.  Genotype-phenotype correlation in the long-QT syndrome: gene-specific triggers for life-threatening arrhythmias.

Authors:  P J Schwartz; S G Priori; C Spazzolini; A J Moss; G M Vincent; C Napolitano; I Denjoy; P Guicheney; G Breithardt; M T Keating; J A Towbin; A H Beggs; P Brink; A A Wilde; L Toivonen; W Zareba; J L Robinson; K W Timothy; V Corfield; D Wattanasirichaigoon; C Corbett; W Haverkamp; E Schulze-Bahr; M H Lehmann; K Schwartz; P Coumel; R Bloise
Journal:  Circulation       Date:  2001-01-02       Impact factor: 29.690

7.  Non optical semi-conductor next generation sequencing of the main cardiac QT-interval duration genes in pooled DNA samples.

Authors:  Juan Gómez; Julian R Reguero; César Morís; Victoria Alvarez; Eliecer Coto
Journal:  J Cardiovasc Transl Res       Date:  2013-11-05       Impact factor: 4.132

8.  Spectrum and prevalence of mutations from the first 2,500 consecutive unrelated patients referred for the FAMILION long QT syndrome genetic test.

Authors:  Jamie D Kapplinger; David J Tester; Benjamin A Salisbury; Janet L Carr; Carole Harris-Kerr; Guido D Pollevick; Arthur A M Wilde; Michael J Ackerman
Journal:  Heart Rhythm       Date:  2009-06-23       Impact factor: 6.343

9.  A recessive variant of the Romano-Ward long-QT syndrome?

Authors:  S G Priori; P J Schwartz; C Napolitano; L Bianchi; A Dennis; M De Fusco; A M Brown; G Casari
Journal:  Circulation       Date:  1998-06-23       Impact factor: 29.690

10.  ClinVar: public archive of relationships among sequence variation and human phenotype.

Authors:  Melissa J Landrum; Jennifer M Lee; George R Riley; Wonhee Jang; Wendy S Rubinstein; Deanna M Church; Donna R Maglott
Journal:  Nucleic Acids Res       Date:  2013-11-14       Impact factor: 16.971

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  1 in total

1.  Structural Modelling of KCNQ1 and KCNH2 Double Mutant Proteins, Identified in Two Severe Long QT Syndrome Cases, Reveals New Insights into Cardiac Channelopathies.

Authors:  William A Agudelo; Sebastian Ramiro Gil-Quiñones; Alejandra Fonseca; Alvaro Arenas; Laura Castro; Diana Carolina Sierra-Díaz; Manuel A Patarroyo; Paul Laissue; Carlos F Suárez; Rodrigo Cabrera
Journal:  Int J Mol Sci       Date:  2021-11-28       Impact factor: 5.923

  1 in total

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