ArticleMolecular systems biology2020
The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases.
Article in Molecular systems biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Distinct regulatory elements of SLC6A14 expression contribute to modification of cystic fibrosis phenotypes.Human genetics · 2026Article
- Single-cell RNA-sequencing co-expression analysis with CFTR and CF modifier genes in lung tissue.Scientific reports · 2025Article
- In Search of Spinal Muscular Atrophy Disease Modifiers.International journal of molecular sciences · 2024Review
- Spinal muscular atrophy.Nature reviews. Disease primers · 2022Review
- A high-throughput genome-wide RNAi screen identifies modifiers of survival motor neuron protein.Cell reports · 2021Article
- The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases.Molecular systems biology · 2020Article
- RNA sequencing analysis reveals increased expression of interferon signaling genes and dysregulation of bone metabolism affecting pathways in the whole blood of patients with osteogenesis imperfecta.BMC medical genomics · 2020Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
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 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.
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Registered trials
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