Details

Autor(en) / Beteiligte

• Auslander, Noam
• Ramos, Daniel M
• Zelaya, Ivette
• Karathia, Hiren
• Crawford, Thomas O.
• Schäffer, Alejandro A
• Sumner, Charlotte J
• Ruppin, Eytan

Titel

The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases

Ist Teil von

• Molecular systems biology, 2020-12, Vol.16 (12), p.e9701-n/a

Ort / Verlag

Germany: EMBO Press

Erscheinungsjahr

2020

Quelle

Wiley Online Library - AutoHoldings Journals

Beschreibungen/Notizen

• 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. SYNOPSIS 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. GENDULF is a novel algorithm that identifies genetic modifiers for monogenetic diseases from healthy and disease gene expression data, by detecting patterns of co‐expression that are uniquely observed in healthy tissues. GENDULF may be used to provide a list of candidates for large‐scale analysis or may be incorporated with other approaches or a knowledge‐based step to yield a small list of candidates for small‐scale experimental evaluation. Different applications are demonstrated for CF, where the performance is estimated against previously established modifiers, and for SMA where it is used to uncover a new modifier, U2AF1. 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.