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

• Published in: American journal of human genetics 2016-09, Vol.99 (3)
• Main Authors: Smedley, Damian, Schubach, Max, Jacobsen, Julius OB, Köhler, Sebastian, Zemojtel, Tomasz, Spielmann, Malte, Jäger, Marten, Hochheiser, Harry, Washington, Nicole L, McMurry, Julie A, Haendel, Melissa A, Mungall, Christopher J, Lewis, Suzanna E, Groza, Tudor, Valentini, Giorgio, Robinson, Peter N
• Format: Article
• Language: English
• ISSN: 0002-9297; 1537-6605
• DOI: 10.1016/j.ajhg.2016.07.005

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.