ArticleBMC genomics2017
Leveraging network analytics to infer patient syndrome and identify causal genes in rare disease cases.
Article in BMC genomics, 2017. 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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Who cites it
7 citing papers in PubMed.
- 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
- A Hypergraph powered approach to Phenotype-driven Gene Prioritization and Rare Disease Prediction.Scientific reports · 2025Article
- Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases.Briefings in bioinformatics · 2022Article
- Interpretable Clinical Genomics with a Likelihood Ratio Paradigm.American journal of human genetics · 2020Article
- Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype-weighted knowledge in the CAGI SickKids5 clinical genomes challenge.Human mutation · 2020Article
- Phenotype-driven gene prioritization for rare diseases using graph convolution on heterogeneous networks.BMC medical genomics · 2018Article
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Authors and funding
5 authors.
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Abstract
backgroundNext-generation sequencing is widely used to identify disease-causing variants in patients with rare genetic disorders. Identifying those variants from whole-genome or exome data can be both scientifically challenging and time consuming. A significant amount of time is spent on variant annotation, and interpretation. Fully or partly automated solutions are therefore needed to streamline and scale this process.
resultsWe describe Phenotype Driven Ranking (PDR), an algorithm integrated into Ingenuity Variant Analysis, that uses observed patient phenotypes to prioritize diseases and genes in order to expedite causal-variant discovery. Our method is based on a network of phenotype-disease-gene relationships derived from the QIAGEN Knowledge Base, which allows for efficient computational association of phenotypes to implicated diseases, and also enables scoring and ranking.
conclusionsWe have demonstrated the utility and performance of PDR by applying it to a number of clinical rare-disease cases, where the true causal gene was known beforehand. It is also shown that PDR compares favorably to a representative alternative tool.
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