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

Abstract

BackgroundRNA 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.MethodsWe 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.ResultsWe 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.ConclusionsThe 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.

Computational Detection of Known Pathogenic Gene Fusions in a Normal Tissue Database and Implications for Genetic Disease Research

“…Thus, detection algorithms primarily trained with oncogenic fusions may be biased by these and not optimized to account for different expression levels and patterns of read support. Such difficulties were demonstrated in our study where TopHat Fusion (Kim and Salzberg, 2011) using default parameters succeeded in detecting only one of eight fusion events detected and laboratory-validated in our rare disease cohort (Oliver et al, 2019b). To address this, we implemented a series of filtering and classification steps to detect fusions potentially linked to rare genetic constitutive disease.…”

“…Discussion of fusion transcripts detected in normal tissues centered on apparently benign events resulting from co-transcription of neighboring genes or more controversially from trans-splicing (Akiva et al, 2006;Peng et al, 2015;Babiceanu et al, 2016;Yuan et al, 2017;He et al, 2018). Reports of fusions in the context of inherited disease existed only in isolated case studies and were not systematically reported on until 2019 (Oliver et al, 2019b). The formulation of computational fusion detection software reflected the field's focus on oncology-related fusion events and algorithms were primarily trained using incompletely characterized tumors or cancer cell-lines (Kumar et al, 2016).…”

“…Our GTEx fusion database was queried for exon to exon fusions involving the gene pairs comprising eleven fusion candidates reported in our prior study (Oliver et al, 2019b; Table 1 rows FIGURE 1 | Dot plots illustrating the number of observations of selected exon-exon fusion transcripts in the GTEx RNA-Seq data by tissue type. Fusion analysis was performed using RNA-Seq data from 8187 samples passing QC, representing 549 individuals and 52 tissue types, extracted from GTEx (version 6p).…”

“…Fusion transcript identification was performed using STAR-Fusion (Haas et al, 2017) with default settings following STAR (v2.5.2b) two-pass alignment (Dobin et al, 2013). Similar to our previously described methods, preliminary fusion calls were used to maximize sensitivity by avoiding default filters encoded in the callers (Oliver et al, 2019b). Fusion-supporting junction and spanning reads identified by STAR Fusion were combined into a single supporting read count for each event.…”

“…Fusion-supporting junction and spanning reads identified by STAR Fusion were combined into a single supporting read count for each event. Fusions (A)-(F) are fusion candidates originating from a cohort analysis of rare disease patients previously published by the authors (Oliver et al, 2019b). Five fusions experimentally validated in the authors' cohort analysis were not observed in the GTEx database and are not displayed in the figure.…”