A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease Damian Smedley, Max Schubach, Julius O.B.

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A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease Damian Smedley, Max Schubach, Julius O.B.

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A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease Damian Smedley, Max Schubach, Julius O.B. Jacobsen, Sebastian Köhler, Tomasz Zemojtel, Malte Spielmann, Marten Jäger, Harry Hochheiser, Nicole L. Washington, Julie A. McMurry, Melissa A. Haendel, Christopher J. Mungall, Suzanna E. Lewis, Tudor Groza, Giorgio Valentini, Peter N. Robinson The American Journal of Human Genetics Volume 99, Issue 3, Pages 595-606 (September 2016) DOI: 10.1016/j.ajhg.2016.07.005 Copyright © 2016 American Society of Human Genetics Terms and Conditions

Figure 1 Genomic Attributes of Regulatory Mendelian Mutations (A) Centered mean and scaled genomic attributes of Mendelian non-coding mutations as compared with the derived non-deleterious positions. Five highly informative attributes of different attribute groups are shown. The information content of single attributes was computed with a univariate logistic regression model (Table S3). (B) Principal-component analysis plot showing the first two principle components, which make up 32% of the total variability. The American Journal of Human Genetics 2016 99, 595-606DOI: (10.1016/j.ajhg.2016.07.005) Copyright © 2016 American Society of Human Genetics Terms and Conditions

Figure 2 Regulatory Mendelian Mutation-Deleteriousness Score (A) Summary of the algorithm for deriving the ReMM score. (B and C) Performance comparison between ReMM and other state-of-the-art genome-wide deleteriousness score. (B) Receiver operating characteristic curves. (C) Precision-recall curves. The American Journal of Human Genetics 2016 99, 595-606DOI: (10.1016/j.ajhg.2016.07.005) Copyright © 2016 American Society of Human Genetics Terms and Conditions

Figure 3 The Genomiser Analysis Framework Genomiser takes as input a whole-genome variant call format (VCF) file, a list of human phenotype ontology (HPO) terms representing the clinical signs and symptoms observed in the individual being investigated by WGS, and optional user parameters that control the filtering and prioritization steps. See text for details of the prioritization procedure. The American Journal of Human Genetics 2016 99, 595-606DOI: (10.1016/j.ajhg.2016.07.005) Copyright © 2016 American Society of Human Genetics Terms and Conditions

Figure 4 Performance Evaluation of Genomiser The curated Mendelian regulatory mutations were added one at a time to unaffected genomes from the 1000 Genomes Project to generate 10,419 simulated disease genomes (see Material and Methods). As an additional test, the same simulations were performed using the CADD score instead of the ReMM score. The genomes were also run under the same frequency, inheritance, and phenotype conditions through Phen-Gen. Bars show percentage of genomes in which the true variant was prioritized as the top hit when assessing all the genomes or the subcategories involving promoter, UTR, enhancer, RNA gene, microRNA gene (miRNA), and imprinting control region (ICR) variants. The American Journal of Human Genetics 2016 99, 595-606DOI: (10.1016/j.ajhg.2016.07.005) Copyright © 2016 American Society of Human Genetics Terms and Conditions