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The GENDULF Algorithm: Mining Transcriptomics to Uncover Modifier Genes for Monogenic Diseases

48 Pages Posted: 13 Jan 2020 Sneak Peek Status: Under Review

See all articles by Noam Auslander

Noam Auslander

National Institutes of Health - Cancer Data Science Laboratory

Daniel M. Ramos

Johns Hopkins University - Department of Neuroscience

Ivette Zelaya

University of California, Los Angeles (UCLA) - Interdepartmental Program in Bioinformatics

Hiren Karathia

National Institutes of Health - Laboratory of Receptor Biology and Gene Expression

Alejandro A. Schäffer

National Institutes of Health - Cancer Data Science Laboratory

Thomas O. Crawford

Johns Hopkins University - Department of Pediatrics

Charlotte J. Sumner

Johns Hopkins University - Department of Neuroscience

Eytan Ruppin

National Institutes of Health - Cancer Data Science Laboratory

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Abstract

Modifier genes are believed to account for the clinical variability observed in many Mendelian disorders, but their identification remains challenging due to the limited availability of genomics data from large patient cohorts. Here, we present GENDULF (GENetic moDULators identiFication), one of the first methods to predict disease modifiers from healthy and diseased tissue gene expression data. When applied to cystic fibrosis, GENDULF successfully identifies multiple, previously established disease modifiers, including EHFSLC6A14, and CLCA1. It is then utilized in spinal muscular atrophy (SMA) and predicts U2AF1 as a modifier whose under-expression correlates with higher SMN2 pre-mRNA exon 7 retention.  Indeed, knockdown of U2AF1 in SMA patient-derived cells leads to increased full-length SMN2 transcript and SMN protein expression. Taking advantage of the increasing availability of transcriptomic data, GENDULF is a novel tool to predict genetic disease modifiers, providing insights into disease pathogenesis and uncovering novel therapeutic targets.

Keywords: Monogenic disorders, Genetic modifiers, Gene expression analysis, Cystic Fibrosis, Spinal Muscular Atrophy

Suggested Citation

Auslander, Noam and Ramos, Daniel M. and Zelaya, Ivette and Karathia, Hiren and Schäffer, Alejandro A. and Crawford, Thomas O. and Sumner, Charlotte J. and Ruppin, Eytan, The GENDULF Algorithm: Mining Transcriptomics to Uncover Modifier Genes for Monogenic Diseases. CR-MEDICINE-D-19-00045. Available at SSRN: https://ssrn.com/abstract=3517533 or http://dx.doi.org/10.2139/ssrn.3517533
This is a paper under consideration at Cell Press and has not been peer-reviewed.

Noam Auslander

National Institutes of Health - Cancer Data Science Laboratory ( email )

9000 Rockville Pike
Bethesda, MD 20892
United States

Daniel M. Ramos

Johns Hopkins University - Department of Neuroscience

Baltimore, MD 21218
United States

Ivette Zelaya

University of California, Los Angeles (UCLA) - Interdepartmental Program in Bioinformatics ( email )

172 Boyer Hall
611 Charles E. Young Drive
Los Angeles, CA 90095-1570
United States

Hiren Karathia

National Institutes of Health - Laboratory of Receptor Biology and Gene Expression ( email )

Bethesda, MD
United States

Alejandro A. Schäffer

National Institutes of Health - Cancer Data Science Laboratory ( email )

9000 Rockville Pike
Bethesda, MD 20892
United States

Thomas O. Crawford

Johns Hopkins University - Department of Pediatrics

Baltimore, MD
United States

Charlotte J. Sumner

Johns Hopkins University - Department of Neuroscience

Baltimore, MD 21218
United States

Eytan Ruppin (Contact Author)

National Institutes of Health - Cancer Data Science Laboratory ( email )

9000 Rockville Pike
Bethesda, MD 20892
United States

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