Literature DB >> 30735170

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation.

Edward G Jones1, Andrew P Landstrom2.   

Abstract

Advancements in the cost and speed of next generation genetic sequencing have generated an explosion of clinical whole exome and whole genome testing. While this has led to increased identification of likely pathogenic mutations associated with genetic syndromes, it has also dramatically increased the number of incidentally found genetic variants of unknown significance (VUS). Determining the clinical significance of these variants is a major challenge for both scientists and clinicians. An approach to assist in determining the likelihood of pathogenicity is signal-to-noise analysis at the protein sequence level. This protocol describes a method for amino acid-level signal-to-noise analysis that leverages variant frequency at each amino acid position of the protein with known protein topology to identify areas of the primary sequence with elevated likelihood of pathologic variation (relative to population "background" variation). This method can identify amino acid residue location "hotspots" of high pathologic signal, which can be used to refine the diagnostic weight of VUSs such as those identified by next generation genetic testing.

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Year:  2019        PMID: 30735170      PMCID: PMC6698361          DOI: 10.3791/58907

Source DB:  PubMed          Journal:  J Vis Exp        ISSN: 1940-087X            Impact factor:   1.355


  29 in total

1.  Interpreting Incidentally Identified Variants in Genes Associated With Catecholaminergic Polymorphic Ventricular Tachycardia in a Large Cohort of Clinical Whole-Exome Genetic Test Referrals.

Authors:  Andrew P Landstrom; Andrew L Dailey-Schwartz; Jill A Rosenfeld; Yaping Yang; Margaret J McLean; Christina Y Miyake; Santiago O Valdes; Yuxin Fan; Hugh D Allen; Daniel J Penny; Jeffrey J Kim
Journal:  Circ Arrhythm Electrophysiol       Date:  2017-04

2.  Identification of deleterious synonymous variants in human genomes.

Authors:  Orion J Buske; AshokKumar Manickaraj; Seema Mital; Peter N Ray; Michael Brudno
Journal:  Bioinformatics       Date:  2014-12-08       Impact factor: 6.937

3.  dbDSM: a manually curated database for deleterious synonymous mutations.

Authors:  Pengbo Wen; Peng Xiao; Junfeng Xia
Journal:  Bioinformatics       Date:  2016-02-15       Impact factor: 6.937

4.  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

Review 5.  Artificial Intelligence in Precision Cardiovascular Medicine.

Authors:  Chayakrit Krittanawong; HongJu Zhang; Zhen Wang; Mehmet Aydar; Takeshi Kitai
Journal:  J Am Coll Cardiol       Date:  2017-05-30       Impact factor: 24.094

6.  Genetic testing for long-QT syndrome: distinguishing pathogenic mutations from benign variants.

Authors:  Suraj Kapa; David J Tester; Benjamin A Salisbury; Carole Harris-Kerr; Manish S Pungliya; Marielle Alders; Arthur A M Wilde; Michael J Ackerman
Journal:  Circulation       Date:  2009-10-19       Impact factor: 29.690

Review 7.  Tools and resources for identifying protein families, domains and motifs.

Authors:  Nicola J Mulder; Rolf Apweiler
Journal:  Genome Biol       Date:  2001-12-19       Impact factor: 13.583

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.  Predicting the clinical impact of human mutation with deep neural networks.

Authors:  Laksshman Sundaram; Hong Gao; Samskruthi Reddy Padigepati; Jeremy F McRae; Yanjun Li; Jack A Kosmicki; Nondas Fritzilas; Jörg Hakenberg; Anindita Dutta; John Shon; Jinbo Xu; Serafim Batzoglou; Xiaolin Li; Kyle Kai-How Farh
Journal:  Nat Genet       Date:  2018-07-23       Impact factor: 38.330

10.  Using high-resolution variant frequencies to empower clinical genome interpretation.

Authors:  Nicola Whiffin; Eric Minikel; Roddy Walsh; Anne H O'Donnell-Luria; Konrad Karczewski; Alexander Y Ing; Paul J R Barton; Birgit Funke; Stuart A Cook; Daniel MacArthur; James S Ware
Journal:  Genet Med       Date:  2017-05-18       Impact factor: 8.822

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  3 in total

1.  Meta-analysis of cardiomyopathy-associated variants in troponin genes identifies loci and intragenic hot spots that are associated with worse clinical outcomes.

Authors:  Hanna J Tadros; Chelsea S Life; Gustavo Garcia; Elisa Pirozzi; Edward G Jones; Susmita Datta; Michelle S Parvatiyar; P Bryant Chase; Hugh D Allen; Jeffrey J Kim; Jose R Pinto; Andrew P Landstrom
Journal:  J Mol Cell Cardiol       Date:  2020-04-09       Impact factor: 5.000

2.  GENESIS: Gene-Specific Machine Learning Models for Variants of Uncertain Significance Found in Catecholaminergic Polymorphic Ventricular Tachycardia and Long QT Syndrome-Associated Genes.

Authors:  Rachel L Draelos; Jordan E Ezekian; Farica Zhuang; Mary E Moya-Mendez; Zhushan Zhang; Michael B Rosamilia; Perathu K R Manivannan; Ricardo Henao; Andrew P Landstrom
Journal:  Circ Arrhythm Electrophysiol       Date:  2022-03-31

3.  Protein Subdomain Enrichment of NUP155 Variants Identify a Novel Predicted Pathogenic Hotspot.

Authors:  Riley J Leonard; Claudia C Preston; Melanie E Gucwa; Yohannes Afeworki; Arielle S Selya; Randolph S Faustino
Journal:  Front Cardiovasc Med       Date:  2020-02-07
  3 in total

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