Literature DB >> 27569544

A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease.

Damian Smedley1, Max Schubach2, Julius O B Jacobsen3, Sebastian Köhler2, Tomasz Zemojtel4, Malte Spielmann5, Marten Jäger6, Harry Hochheiser7, Nicole L Washington8, Julie A McMurry9, Melissa A Haendel9, Christopher J Mungall8, Suzanna E Lewis8, Tudor Groza10, Giorgio Valentini11, Peter N Robinson12.   

Abstract

The interpretation of non-coding variants still constitutes a major challenge in the application of whole-genome sequencing in Mendelian disease, especially for single-nucleotide and other small non-coding variants. Here we present Genomiser, an analysis framework that is able not only to score the relevance of variation in the non-coding genome, but also to associate regulatory variants to specific Mendelian diseases. Genomiser scores variants through either existing methods such as CADD or a bespoke machine learning method and combines these with allele frequency, regulatory sequences, chromosomal topological domains, and phenotypic relevance to discover variants associated to specific Mendelian disorders. Overall, Genomiser is able to identify causal regulatory variants as the top candidate in 77% of simulated whole genomes, allowing effective detection and discovery of regulatory variants in Mendelian disease.
Copyright © 2016 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

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Year:  2016        PMID: 27569544      PMCID: PMC5011059          DOI: 10.1016/j.ajhg.2016.07.005

Source DB:  PubMed          Journal:  Am J Hum Genet        ISSN: 0002-9297            Impact factor:   11.025


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