ArticleNature communications2019
Improving the diagnostic yield of exome- sequencing by predicting gene-phenotype associations using large-scale gene expression analysis.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 4 of them syntheses that pooled it.
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Who cites it
79 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data.Genome biology · 2023Pooled it
- A systematic review and functional bioinformatics analysis of genes associated with Crohn's disease identify more than 120 related genes.BMC genomics · 2022Pooled it
- Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology.Nature genetics · 2021Pooled it
- The Master of Puppets: Pleiotropy of PDGFRB and its Relationship to Multiple Diseases.Journal of molecular neuroscience : MN · 2020Pooled it
- PhenoSS: phenotype semantic similarity-based approach for rare disease prediction and patient clustering.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- PhenoSS: Phenotype semantic similarity-based approach for rare disease prediction and patient clustering.medRxiv : the preprint server for health sciences · 2026Article
- Deep learning-based semantic matching of cis-regulatory DNA sequences facilitates the prediction of gene function.Nature plants · 2026Article
- A multidisciplinary RNA-guided approach to complement genomic analysis of unsolved patients with an inborn error of immunity.Frontiers in immunology · 2026Article
- RNA sequencing provides functional insights and diagnostic resolution in previously unsolved rare disease cases.BMC medical genomics · 2025Article
- Genomic biomarkers of liver toxicity risk from UK Biobank data.The pharmacogenomics journal · 2025Article
- An easy-to-use three-dimensional protein-structure-prediction online platform "DPL3D" based on deep learning algorithms.Current research in structural biology · 2025Article
- MOLGENIS VIP: an end-to-end DNA variant interpretation pipeline for research and diagnostics configurable to support rapid implementation of new methods.NAR genomics and bioinformatics · 2025Article
- Towards a standard benchmark for phenotype-driven variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework.BMC bioinformatics · 2025Article
- A clinical knowledge graph-based framework to prioritize candidate genes for facilitating diagnosis of Mendelian diseases and rare genetic conditions.BMC bioinformatics · 2025Article
- Dominant negative variants in ITPR3 impair T cell Ca2+ dynamics causing combined immunodeficiency.The Journal of experimental medicine · 2025Article
- Identification of rare disease genes as drivers of common diseases through tissue-specific gene regulatory networks.Scientific reports · 2024Article
- Tissue-aware interpretation of genetic variants advances the etiology of rare diseases.Molecular systems biology · 2024Article
- Induction of oxidative- and endoplasmic-reticulum-stress dependent apoptosis in pancreatic cancer cell lines by DDOST knockdown.Scientific reports · 2024Article
- Gene regulatory networks in disease and ageing.Nature reviews. Nephrology · 2024Review
- Exploratory genetic analysis in children with autism spectrum disorder and other developmental disorders using whole exome sequencing.Biomolecules & biomedicine · 2024Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
27 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The diagnostic yield of exome and genome sequencing remains low (8-70%), due to incomplete knowledge on the genes that cause disease. To improve this, we use RNA-seq data from 31,499 samples to predict which genes cause specific disease phenotypes, and develop GeneNetwork Assisted Diagnostic Optimization (GADO). We show that this unbiased method, which does not rely upon specific knowledge on individual genes, is effective in both identifying previously unknown disease gene associations, and flagging genes that have previously been incorrectly implicated in disease. GADO can be run on www.genenetwork.nl by supplying HPO-terms and a list of genes that contain candidate variants. Finally, applying GADO to a cohort of 61 patients for whom exome-sequencing analysis had not resulted in a genetic diagnosis, yields likely causative genes for ten cases.
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