Literature DB >> 21700766

A probabilistic disease-gene finder for personal genomes.

Mark Yandell1, Chad Huff, Hao Hu, Marc Singleton, Barry Moore, Jinchuan Xing, Lynn B Jorde, Martin G Reese.   

Abstract

VAAST (the Variant Annotation, Analysis & Search Tool) is a probabilistic search tool for identifying damaged genes and their disease-causing variants in personal genome sequences. VAAST builds on existing amino acid substitution (AAS) and aggregative approaches to variant prioritization, combining elements of both into a single unified likelihood framework that allows users to identify damaged genes and deleterious variants with greater accuracy, and in an easy-to-use fashion. VAAST can score both coding and noncoding variants, evaluating the cumulative impact of both types of variants simultaneously. VAAST can identify rare variants causing rare genetic diseases, and it can also use both rare and common variants to identify genes responsible for common diseases. VAAST thus has a much greater scope of use than any existing methodology. Here we demonstrate its ability to identify damaged genes using small cohorts (n = 3) of unrelated individuals, wherein no two share the same deleterious variants, and for common, multigenic diseases using as few as 150 cases.

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Year:  2011        PMID: 21700766      PMCID: PMC3166837          DOI: 10.1101/gr.123158.111

Source DB:  PubMed          Journal:  Genome Res        ISSN: 1088-9051            Impact factor:   9.043


  31 in total

1.  Prediction of deleterious human alleles.

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Journal:  Hum Mol Genet       Date:  2001-03-15       Impact factor: 6.150

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Journal:  Proc Natl Acad Sci U S A       Date:  1992-11-15       Impact factor: 11.205

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Authors:  S F Altschul; W Gish; W Miller; E W Myers; D J Lipman
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Review 4.  Predicting the effects of amino acid substitutions on protein function.

Authors:  Pauline C Ng; Steven Henikoff
Journal:  Annu Rev Genomics Hum Genet       Date:  2006       Impact factor: 8.929

5.  Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data.

Authors:  Bingshan Li; Suzanne M Leal
Journal:  Am J Hum Genet       Date:  2008-08-07       Impact factor: 11.025

6.  Prediction of complete gene structures in human genomic DNA.

Authors:  C Burge; S Karlin
Journal:  J Mol Biol       Date:  1997-04-25       Impact factor: 5.469

7.  A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST).

Authors:  Stephan Morgenthaler; William G Thilly
Journal:  Mutat Res       Date:  2006-11-13       Impact factor: 2.433

8.  A map of human genome variation from population-scale sequencing.

Authors:  Gonçalo R Abecasis; David Altshuler; Adam Auton; Lisa D Brooks; Richard M Durbin; Richard A Gibbs; Matt E Hurles; Gil A McVean
Journal:  Nature       Date:  2010-10-28       Impact factor: 49.962

9.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.

Authors: 
Journal:  Nature       Date:  2007-06-07       Impact factor: 49.962

10.  Genome-wide analysis of human disease alleles reveals that their locations are correlated in paralogous proteins.

Authors:  Mark Yandell; Barry Moore; Fidel Salas; Chris Mungall; Andrew MacBride; Charles White; Martin G Reese
Journal:  PLoS Comput Biol       Date:  2008-11-07       Impact factor: 4.475

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  120 in total

Review 1.  Bioinformatics for personal genome interpretation.

Authors:  Emidio Capriotti; Nathan L Nehrt; Maricel G Kann; Yana Bromberg
Journal:  Brief Bioinform       Date:  2012-01-13       Impact factor: 11.622

2.  VarSifter: visualizing and analyzing exome-scale sequence variation data on a desktop computer.

Authors:  Jamie K Teer; Eric D Green; James C Mullikin; Leslie G Biesecker
Journal:  Bioinformatics       Date:  2011-12-30       Impact factor: 6.937

3.  Functional genomics: The changes that count.

Authors:  Monya Baker
Journal:  Nature       Date:  2012-02-08       Impact factor: 49.962

4.  Sorting out sequencing data.

Authors:  Monya Baker
Journal:  Nat Methods       Date:  2011-09-29       Impact factor: 28.547

5.  gSearch: a fast and flexible general search tool for whole-genome sequencing.

Authors:  Taemin Song; Kyu-Baek Hwang; Michael Hsing; Kyungjoon Lee; Justin Bohn; Sek Won Kong
Journal:  Bioinformatics       Date:  2012-06-23       Impact factor: 6.937

Review 6.  The impact of genomics on pediatric research and medicine.

Authors:  John J Connolly; Hakon Hakonarson
Journal:  Pediatrics       Date:  2012-05-07       Impact factor: 7.124

7.  eXtasy: variant prioritization by genomic data fusion.

Authors:  Alejandro Sifrim; Dusan Popovic; Leon-Charles Tranchevent; Amin Ardeshirdavani; Ryo Sakai; Peter Konings; Joris R Vermeesch; Jan Aerts; Bart De Moor; Yves Moreau
Journal:  Nat Methods       Date:  2013-09-29       Impact factor: 28.547

8.  Loss of carbonic anhydrase XII function in individuals with elevated sweat chloride concentration and pulmonary airway disease.

Authors:  Melissa Lee; Briana Vecchio-Pagán; Neeraj Sharma; Abdul Waheed; Xiaopeng Li; Karen S Raraigh; Sarah Robbins; Sangwoo T Han; Arianna L Franca; Matthew J Pellicore; Taylor A Evans; Kristin M Arcara; Hien Nguyen; Shan Luan; Deborah Belchis; Jozef Hertecant; Joseph Zabner; William S Sly; Garry R Cutting
Journal:  Hum Mol Genet       Date:  2016-02-23       Impact factor: 6.150

9.  Using BioBin to explore rare variant population stratification.

Authors:  Carrie B Moore; John R Wallace; Alex T Frase; Sarah A Pendergrass; Marylyn D Ritchie
Journal:  Pac Symp Biocomput       Date:  2013

10.  Exome analysis of a family with Wolff-Parkinson-White syndrome identifies a novel disease locus.

Authors:  Neil E Bowles; Chuanchau J Jou; Cammon B Arrington; Brett J Kennedy; Aubree Earl; Norisada Matsunami; Lindsay L Meyers; Susan P Etheridge; Elizabeth V Saarel; Steven B Bleyl; H Joseph Yost; Mark Yandell; Mark F Leppert; Martin Tristani-Firouzi; Peter J Gruber
Journal:  Am J Med Genet A       Date:  2015-08-18       Impact factor: 2.802

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