Abstract
IntroductionPhenotype-driven rare disease gene prioritization relies on high quality curated resources containing disease, gene and phenotype annotations. However, the effectiveness of gene prioritization tools is constrained by the incomplete coverage of rare disease, phenotype and gene annotations in such curated resources.MethodsWe extracted rare disease correlation pairs involving diseases, phenotypes and genes from MEDLINE abstracts and used the information propagation algorithm GCAS to build an association network. We built a tool called PRIORI-T for rare disease gene prioritization that uses this network for phenotype-driven rare disease gene prioritization. The quality of disease-gene associations in PRIORI-T was compared with resources such as DisGeNET and Open Targets in the context of rare diseases. The gene prioritization performance of PRIORI-T was evaluated using phenotype descriptions of 230 real-world rare disease clinical cases collated from recent publications, as well as compared to other gene prioritization tools such as HANRD and Orphamizer.ResultsPRIORI-T contains qualitatively better associations than DisGeNET and Open Targets. Furthermore, the causal genes were captured within Top-50 for more than 40% of the real-world clinical cases and within Top-300 for more than 72% of the cases when PRIORI-T was used for gene prioritization. It outperformed other gene prioritization tools such as HANRD and Orphamizer that primarily rely on curated resources.ConclusionsPRIORI-T exhibited improved gene prioritization performance without requiring high quality curated data. Thus, it holds great promise in phenotype-driven gene prioritization for rare disease studies.
Highlights
Phenotype-driven rare disease gene prioritization relies on high quality curated resources containing disease, gene and phenotype annotations
The causal genes were captured within Top-50 for more than 40% of the realworld clinical cases and within Top-300 for more than 72% of the cases when PRIORI-T was used for gene prioritization
It outperformed other gene prioritization tools such as HANRD and Orphamizer that primarily rely on curated resources
Summary
Phenotype-driven rare disease gene prioritization relies on high quality curated resources containing disease, gene and phenotype annotations. Computational deep phenotyping is considered an important aid in the analysis of genomic data for personalized genomic medicine Tools such as Phenomizer [6], Orphamizer (a version of Phenomizer that uses Orphanet) [6] and HANRD [7] use a list of phenotype terms as input to find potential candidate diseases and their corresponding causal genes. These tools use associations from curated resources such as Orphanet, HPO [8] and OMIM [9], amongst others. The potential of using text-mining of disease-phenotype associations from clinical case reports as a means of improving the performance of phenotype-driven differential-diagnosis systems for rare diseases has been reported [13]
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