A tailored approach to fusion transcript identification increases diagnosis of rare inherited disease

Autoři: Gavin R. Oliver aff001;  Xiaojia Tang aff001;  Laura E. Schultz-Rogers aff001;  Noemi Vidal-Folch aff003;  W. Garrett Jenkinson aff001;  Tanya L. Schwab aff004;  Krutika Gaonkar aff001;  Margot A. Cousin aff001;  Asha Nair aff001;  Shubham Basu aff001;  Pritha Chanana aff001;  Devin Oglesbee aff003;  Eric W. Klee aff001
Působiště autorů: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America aff001;  Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America aff002;  Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, United States of America aff003;  Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota, United States of America aff004;  Department of Medical Genetics, Mayo Clinic, Rochester, Minnesota, United States of America aff005;  Department of Clinical Genomics, Mayo Clinic, Rochester, Minnesota, United States of America aff006
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223337



RNA sequencing has been proposed as a means of increasing diagnostic rates in studies of undiagnosed rare inherited disease. Recent studies have reported diagnostic improvements in the range of 7.5–35% by profiling splicing, gene expression quantification and allele specific expression. To-date however, no study has systematically assessed the presence of gene-fusion transcripts in cases of germline disease. Fusion transcripts are routinely identified in cancer studies and are increasingly recognized as having diagnostic, prognostic or therapeutic relevance. Isolated reports exist of fusion transcripts being detected in cases of developmental and neurological phenotypes, and thus, systematic application of fusion detection to germline conditions may further increase diagnostic rates. However, current fusion detection methods are unsuited to the investigation of germline disease due to performance biases arising from their development using tumor, cell-line or in-silico data.


We describe a tailored approach to fusion candidate identification and prioritization in a cohort of 47 undiagnosed, suspected inherited disease patients. We modify an existing fusion transcript detection algorithm by eliminating its cell line-derived filtering steps, and instead, prioritize candidates using a custom workflow that integrates genomic and transcriptomic sequence alignment, biological and technical annotations, customized categorization logic, and phenotypic prioritization.


We demonstrate that our approach to fusion transcript identification and prioritization detects genuine fusion events excluded by standard analyses and efficiently removes phenotypically unimportant candidates and false positive events, resulting in a reduced candidate list enriched for events with potential phenotypic relevance. We describe the successful genetic resolution of two previously undiagnosed disease cases through the detection of pathogenic fusion transcripts. Furthermore, we report the experimental validation of five additional cases of fusion transcripts with potential phenotypic relevance.


The approach we describe can be implemented to enable the detection of phenotypically relevant fusion transcripts in studies of rare inherited disease. Fusion transcript detection has the potential to increase diagnostic rates in rare inherited disease and should be included in RNA-based analytical pipelines aimed at genetic diagnosis.

Klíčová slova:

Blood – Cell fusion – Diagnostic medicine – Genetics of disease – Multiple alignment calculation – Polymerase chain reaction – RNA sequencing – Sequence alignment


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