The GENDULF Algorithm: Mining Transcriptomics to Uncover Modifier Genes for Monogenic Diseases
| Autor: | Thomas O. Crawford, Charlotte J. Sumner, Hiren Karathia, Ivette Zelaya, Eytan Ruppin, Alejandro A. Schäffer, Daniel M. Ramos, Noam Auslander |
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| Rok vydání: | 2020 |
| Předmět: |
Medicine (General)
Genetic Linkage Method Disease digenic inheritance cystic fibrosis Transcriptome Exon 0302 clinical medicine Gene expression Methods Data Mining Molecular Biology of Disease Biology (General) spinal muscular atrophy 0303 health sciences Gene knockdown Applied Mathematics SMA Computational Theory and Mathematics General Agricultural and Biological Sciences Algorithms Information Systems QH301-705.5 Genomics Computational biology Biology General Biochemistry Genetics and Molecular Biology 03 medical and health sciences R5-920 medicine Humans Gene Genetic Association Studies Loss function 030304 developmental biology modifier gene Genes Modifier General Immunology and Microbiology Mechanism (biology) Computational Biology Reproducibility of Results Spinal muscular atrophy medicine.disease HEK293 Cells gene expression Genetics Gene Therapy & Genetic Disease 030217 neurology & neurosurgery |
| Zdroj: | Molecular Systems Biology, Vol 16, Iss 12, Pp n/a-n/a (2020) Molecular Systems Biology |
| ISSN: | 1556-5068 |
| DOI: | 10.2139/ssrn.3517533 |
| Popis: | 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 facilitate prediction of disease modifiers using healthy and diseased tissue gene expression data. GENDULF is designed for monogenic diseases in which the mechanism is loss of function leading to reduced expression of the mutated gene. When applied to cystic fibrosis, GENDULF successfully identifies multiple, previously established disease modifiers, including EHF, SLC6A14, and CLCA1. It is then utilized in spinal muscular atrophy (SMA) and predicts U2AF1 as a modifier whose low 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 addition to existing strategies for prediction of genetic disease modifiers, providing insights into disease pathogenesis and uncovering novel therapeutic targets. GENDULF predicts modifiers of loss‐of‐function monogenetic diseases using healthy and disease gene expression data. Application to cystic fibrosis (CF) and spinal muscular atrophy (SMA) identifies established CF modifiers and a new putative modifier of SMA, U2AF1. |
| Databáze: | OpenAIRE |
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