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

Damian Smedley; Max Schubach; Julius O O.B. Jacobsen; Sebastian Köhler; Tomasz Zemojtel; Malte Spielmann; Marten Jäger; Harry Hochheiser; Nicole L L. Washington; Julie A A. McMurry; Melissa A A. Haendel; Christopher J J. Mungall; Suzanna E E. Lewis; Tudor Groza; Giorgio Valentini; Peter N N. Robinson

doi:10.1016/j.ajhg.2016.07.005

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

Damian Smedley, Max Schubach, Julius O O.B. Jacobsen, Sebastian Köhler, Tomasz Zemojtel, Malte Spielmann, Marten Jäger, Harry Hochheiser, Nicole L L. Washington, Julie A A. McMurry, Melissa A A. Haendel, Christopher J J. Mungall, Suzanna E E. Lewis, Tudor Groza, Giorgio Valentini, Peter N N. Robinson

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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.

Original language	English (US)
Pages (from-to)	595-606
Number of pages	12
Journal	American Journal of Human Genetics
Volume	99
Issue number	3
DOIs	https://doi.org/10.1016/j.ajhg.2016.07.005
State	Published - Sep 1 2016

ASJC Scopus subject areas

Genetics
Genetics(clinical)

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Smedley, D., Schubach, M., Jacobsen, JO. O. B., Köhler, S., Zemojtel, T., Spielmann, M., Jäger, M., Hochheiser, H., Washington, NL. L., McMurry, JA. A., Haendel, MA. A., Mungall, CJ. J., Lewis, SE. E., Groza, T., Valentini, G., & Robinson, PN. N. (2016). A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease. American Journal of Human Genetics, 99(3), 595-606. https://doi.org/10.1016/j.ajhg.2016.07.005

Smedley, D, Schubach, M, Jacobsen, JOOB, Köhler, S, Zemojtel, T, Spielmann, M, Jäger, M, Hochheiser, H, Washington, NLL, McMurry, JAA, Haendel, MAA, Mungall, CJJ, Lewis, SEE, Groza, T, Valentini, G & Robinson, PNN 2016, 'A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease', American Journal of Human Genetics, vol. 99, no. 3, pp. 595-606. https://doi.org/10.1016/j.ajhg.2016.07.005

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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.",

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

Abstract

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