Literature DB >> 36138163

Identification and in-silico characterization of splice-site variants from a large cardiogenetic national registry.

Kaveh Rayani1, Brianna Davies1, Matthew Cheung1, Drake Comber1, Jason D Roberts2, Rafik Tadros3,4, Martin S Green5, Jeffrey S Healey6, Christopher S Simpson7, Shubhayan Sanatani8, Christian Steinberg9, Ciorsti MacIntyre10, Paul Angaran11, Henry Duff12, Robert Hamilton13, Laura Arbour14, Richard Leather15, Colette Seifer16, Anne Fournier17, Joseph Atallah18, Shane Kimber19, Bhavanesh Makanjee20, Wael Alqarawi5, Julia Cadrin-Tourigny3,4, Jacqueline Joza21, Martin Gardner10, Mario Talajic3,4, Richard D Bagnall22,23, Andrew D Krahn1, Zachary W M Laksman24.   

Abstract

Splice-site variants in cardiac genes may predispose carriers to potentially lethal arrhythmias. To investigate, we screened 1315 probands and first-degree relatives enrolled in the Canadian Hearts in Rhythm Organization (HiRO) registry. 10% (134/1315) of patients in the HiRO registry carry variants within 10 base-pairs of the intron-exon boundary with 78% (104/134) otherwise genotype negative. These 134 probands were carriers of 57 unique variants. For each variant, American College of Medical Genetics and Genomics (ACMG) classification was revisited based on consensus between nine in silico tools. Due in part to the in silico algorithms, seven variants were reclassified from the original report, with the majority (6/7) downgraded. Our analyses predicted 53% (30/57) of variants to be likely/pathogenic. For the 57 variants, an average of 9 tools were able to score variants within splice sites, while 6.5 tools responded for variants outside these sites. With likely/pathogenic classification considered a positive outcome, the ACMG classification was used to calculate sensitivity/specificity of each tool. Among these, Combined Annotation Dependent Depletion (CADD) had good sensitivity (93%) and the highest response rate (131/134, 98%), dbscSNV was also sensitive (97%), and SpliceAI was the most specific (64%) tool. Splice variants remain an important consideration in gene elusive inherited arrhythmia syndromes. Screening for intronic variants, even when restricted to the ±10 positions as performed here may improve genetic testing yield. We compare 9 freely available in silico tools and provide recommendations regarding their predictive capabilities. Moreover, we highlight several novel cardiomyopathy-associated variants which merit further study.
© 2022. The Author(s), under exclusive licence to European Society of Human Genetics.

Entities:  

Year:  2022        PMID: 36138163     DOI: 10.1038/s41431-022-01193-9

Source DB:  PubMed          Journal:  Eur J Hum Genet        ISSN: 1018-4813            Impact factor:   5.351


  34 in total

1.  ExPASy: The proteomics server for in-depth protein knowledge and analysis.

Authors:  Elisabeth Gasteiger; Alexandre Gattiker; Christine Hoogland; Ivan Ivanyi; Ron D Appel; Amos Bairoch
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

2.  The mutational spectrum of single base-pair substitutions in mRNA splice junctions of human genes: causes and consequences.

Authors:  M Krawczak; J Reiss; D N Cooper
Journal:  Hum Genet       Date:  1992 Sep-Oct       Impact factor: 4.132

Review 3.  Missed threads. The impact of pre-mRNA splicing defects on clinical practice.

Authors:  Diana Baralle; Anneke Lucassen; Emanuele Buratti
Journal:  EMBO Rep       Date:  2009-08       Impact factor: 8.807

4.  Statistical features of human exons and their flanking regions.

Authors:  M Q Zhang
Journal:  Hum Mol Genet       Date:  1998-05       Impact factor: 6.150

5.  RNA splice junctions of different classes of eukaryotes: sequence statistics and functional implications in gene expression.

Authors:  M B Shapiro; P Senapathy
Journal:  Nucleic Acids Res       Date:  1987-09-11       Impact factor: 16.971

6.  Whole Genome Sequencing Improves Outcomes of Genetic Testing in Patients With Hypertrophic Cardiomyopathy.

Authors:  Richard D Bagnall; Jodie Ingles; Marcel E Dinger; Mark J Cowley; Samantha Barratt Ross; André E Minoche; Sean Lal; Christian Turner; Alison Colley; Sulekha Rajagopalan; Yemima Berman; Anne Ronan; Diane Fatkin; Christopher Semsarian
Journal:  J Am Coll Cardiol       Date:  2018-07-24       Impact factor: 24.094

7.  Predicting Splicing from Primary Sequence with Deep Learning.

Authors:  Kishore Jaganathan; Sofia Kyriazopoulou Panagiotopoulou; Jeremy F McRae; Siavash Fazel Darbandi; David Knowles; Yang I Li; Jack A Kosmicki; Juan Arbelaez; Wenwu Cui; Grace B Schwartz; Eric D Chow; Efstathios Kanterakis; Hong Gao; Amirali Kia; Serafim Batzoglou; Stephan J Sanders; Kyle Kai-How Farh
Journal:  Cell       Date:  2019-01-17       Impact factor: 41.582

8.  Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples.

Authors:  Roddy Walsh; Kate L Thomson; James S Ware; Birgit H Funke; Jessica Woodley; Karen J McGuire; Francesco Mazzarotto; Edward Blair; Anneke Seller; Jenny C Taylor; Eric V Minikel; Daniel G MacArthur; Martin Farrall; Stuart A Cook; Hugh Watkins
Journal:  Genet Med       Date:  2016-08-17       Impact factor: 8.822

9.  Ensembl 2009.

Authors:  T J P Hubbard; B L Aken; S Ayling; B Ballester; K Beal; E Bragin; S Brent; Y Chen; P Clapham; L Clarke; G Coates; S Fairley; S Fitzgerald; J Fernandez-Banet; L Gordon; S Graf; S Haider; M Hammond; R Holland; K Howe; A Jenkinson; N Johnson; A Kahari; D Keefe; S Keenan; R Kinsella; F Kokocinski; E Kulesha; D Lawson; I Longden; K Megy; P Meidl; B Overduin; A Parker; B Pritchard; D Rios; M Schuster; G Slater; D Smedley; W Spooner; G Spudich; S Trevanion; A Vilella; J Vogel; S White; S Wilder; A Zadissa; E Birney; F Cunningham; V Curwen; R Durbin; X M Fernandez-Suarez; J Herrero; A Kasprzyk; G Proctor; J Smith; S Searle; P Flicek
Journal:  Nucleic Acids Res       Date:  2008-11-25       Impact factor: 16.971

Review 10.  In silico tools for splicing defect prediction: a survey from the viewpoint of end users.

Authors:  Xueqiu Jian; Eric Boerwinkle; Xiaoming Liu
Journal:  Genet Med       Date:  2013-11-21       Impact factor: 8.822

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