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Symptom-driven idiopathic disease gene identification
- Source :
- Genetics in medicine : official journal of the American College of Medical Genetics
- Publication Year :
- 2015
- Publisher :
- Elsevier BV, 2015.
-
Abstract
- Rare genetic variants are the major cause of Mendelian disorders, yet only half of described genetic diseases have been causally linked to a gene. In addition, the total number of rare genetic diseases is projected to be far greater than that of those already described. Whole-genome sequencing of patients with subsequent genetic and functional analysis is a powerful way to describe these gene anomalies. However, this approach results in tens to hundreds of candidate disease-causative genes, and the identification of additional individuals suffering from the same disorder can be difficult because of rarity and phenotypic heterogeneity. We describe a genetic network–based method to rank candidate genes identified in family-based sequencing studies, termed phenotype informed network (PIN) ranking. Furthermore, we present a case study as an extension of the PIN ranking method in which disease symptoms drive the network ranking and identification of the disease-causative gene. We demonstrate, through simulation, that our method is capable of identifying the correct disease-causative gene in a majority of cases. PIN-rank is available at https://genomics.scripps.edu/pinrank/ . We have developed a method to prioritize candidate disease-causative genes based on symptoms that would be useful for both the prioritization of candidates and the identification of additional subjects. Genet Med 17 11, 859–865.
- Subjects :
- Candidate gene
disease symptoms
Genomics
Disease
Biology
Article
Ranking (information retrieval)
genetic diagnosis
genetic disease
Databases, Genetic
Humans
Computer Simulation
Gene
Genetic Association Studies
Genetics (clinical)
Genetics
Genome, Human
Genetic heterogeneity
Genetic Diseases, Inborn
Computational Biology
Phenotype
idiopathic disease
Identification (biology)
disease phenotypes
Genome-Wide Association Study
Subjects
Details
- ISSN :
- 10983600
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- Genetics in Medicine
- Accession number :
- edsair.doi.dedup.....a4f80f0129daa59cdf0bcade8141c3a9