Abstract
Exome sequencing has enabled molecular diagnoses for rare disease patients but often with initial diagnostic rates of ~25−30%. Here we develop a robust computational pipeline to rank variants for reassessment of unsolved rare disease patients. A comprehensive web-based patient report is generated in which all deleterious variants can be filtered by gene, variant characteristics, OMIM disease and Phenolyzer scores, and all are annotated with an ACMG classification and links to ClinVar. The pipeline ranked 21/34 previously diagnosed variants as top, with 26 in total ranked ≤7th, 3 ranked ≥13th; 5 failed the pipeline filters. Pathogenic/likely pathogenic variants by ACMG criteria were identified for 22/145 unsolved cases, and a previously undefined candidate disease variant for 27/145. This open access pipeline supports the partnership between clinical and research laboratories to improve the diagnosis of unsolved exomes. It provides a flexible framework for iterative developments to further improve diagnosis.
Highlights
Exome sequencing (ES) has enabled molecular diagnoses for thousands of rare disease patients
Based on the performance of our pipeline in the reassessment of previously diagnosed cases, we focused our initial review of putative candidate variants on those ranked[10]
Our primary purpose here is to report on the potential for this computational pipeline to rank variants using a variety of tools to capture both phenotypic input and variant evaluation according to ACMG guidelines and ClinVar entries
Summary
Exome sequencing (ES) has enabled molecular diagnoses for thousands of rare disease patients (reviewed[1]). Such studies generally report an initial diagnostic rate of ~25−30%2–8, generating interest in the development of better computational tools to improve the diagnostic rate. By recruiting additional family members from 74 initially proband-only ES cases they identified a potential contributing variant in 51% (38/74) of cases. They concluded that additional family members combined with enhanced bioinformatics, including relaxed variant filtering, improves the diagnostic yield. Others report successful reassessment of unsolved cases leading to improved diagnostic yields, including through enhanced annotation and computational analyses[8,11,12,13] as well as through implementation of machine-learning algorithms[14]
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