NEW YORK (GenomeWeb) – A new study suggests that the use of tissue-specific transcriptome sequencing data can enhance diagnoses in individual with rare muscle conditions who have been profiled by exome or genome sequencing.
An international team led by investigators in the US did RNA sequencing on muscle biopsy tissue from dozens of individuals with rare, apparently monogenic dystrophies, generating transcriptome sequences that were analyzed alongside available genetic data from the individuals. Along with muscle transcriptome data from nearly 200 unaffected individuals, the patient transcripts made is possible to pick up splice site mutations that may have been missed using genome or exome sequences alone.
In particular, the investigators diagnosed some 35 percent of cases that lacked clear candidate mutations based on exome or genome sequences alone. In the process, they also uncovered a recurrent splice-gain change to a COL6A1 intron in the process — a de novo mutation that was subsequently linked to around 25 percent of the collagen VI dystrophy cases considered in a follow-up analysis.
"We explored the utility of analyzing patient RNA-seq data to detect aberrant splice events and allele-specific expression and performed variant calling from RNA-seq data to identify pathogenic events or to prioritize genes for closer analysis," Massachusetts General Hospital and Broad Institute researcher Daniel MacArthur, corresponding author on the Science Translational Medicine study, and his co-authors wrote. "The resulting diagnoses were made primarily through the detection of aberrant splice events in patients, with information on gene-level allele imbalance playing a complementary role."
“For selected disorders, RNA-seq has the potential to identify pathogenic variants that are missed by conventional, exon-centric DNA analysis," Amanda Lindy, assistant director of neurogenetics for the genetic testing company GeneDX, said in an email.
"Studies such as this illustrate the utility of RNA-seq as a complementary methodology in the molecular diagnosis of patients with neuromuscular and other specific disorders if the proper tissue types can be obtained for RNA analysis,” added Lindy, who was not involved in the study.
Methods for validating suspicious mutations detected by exome or genome sequencing currently cannot keep pace with the rate at which genetic glitches are unearthed by exome or genome sequencing, the team noted, making it tricky to make reliable diagnoses and discern the functional consequence of potentially problematic alterations. Reasoning that "[a] majority of the most commonly disrupted genes in muscle disease are poorly expressed in blood and fibroblasts," the authors suspected that "RNA-seq from these easily accessible tissues may be underpowered to detect relevant transcriptional aberrations in certain genes."
Instead, the researchers took a crack at using primary muscle biopsy tissue transcriptomes to help diagnose 63 individuals with rare monogenic dystrophies, including 13 cases already linked to mutations with ties to transcription. For comparison, they also included RNA sequencing data for 184 muscle samples assessed for the Genotype-Tissue Expression project in their analyses.
To do this, the team developed a computational strategy for scouring split-mapped reads to pick up patient-specific splice junctions — those found in one or more individuals with a muscle condition but not in the muscle samples from unaffected control individuals. The group also established a specialized remapping scheme to profile variants in large and/or repeat-rich, muscle-related genes such as TTN.
Along with known pathogenic mutations in the previously diagnosed individuals, the researchers found genetic explanations for 17 new cases that had not been solved using DNA sequence data alone. They also narrowed in on still other candidate mutations that could not be verified with the available data.
"[T]his study represents a large systematic application of transcriptome sequencing to rare disease diagnosis and highlights its utility for the detection and interpretation of variants missed by current standard diagnostic approaches," the authors wrote.