Abstract
BackgroundRNA-seq emerges as a valuable method for clinical genetics. The transcriptome is “dynamic” and tissue-specific, but typically the probed tissues to analyze (TA) are different from the tissue of interest (TI) based on pathophysiology.ResultsWe developed Phenotype-Tissue Expression and Exploration (PTEE), a tool to facilitate the decision about the most suitable TA for RNA-seq. We integrated phenotype-annotated genes, used 54 tissues from GTEx to perform correlation analyses and identify expressed genes and transcripts between TAs and TIs. We identified skeletal muscle as the most appropriate TA to inquire for cardiac arrhythmia genes and skin as a good proxy to study neurodevelopmental disorders. We also explored RNA-seq limitations and show that on-off switching of gene expression during ontogenesis or circadian rhythm can cause blind spots for RNA-seq-based analyses.ConclusionsPTEE aids the identification of tissues suitable for RNA-seq for a given pathology to increase the success rate of diagnosis and gene discovery. PTEE is freely available at https://bioinf.eva.mpg.de/PTEE/
Highlights
RNA-seq emerges as a valuable method for clinical genetics
We show that, based on expression levels, the highest correlation occurs, as expected, for skeletal muscle in comparison to all other accessible to analyze (TA) (r = 0.48 compared to other accessible TAs with r ≤ 0.45 Fig. 1A)
Beside the correlation analysis based on gene expression levels, it is important to know how many of the target genes are expressed by the tissue of interest (TI) and TA to identify blind spots of the analysis
Summary
RNA-seq emerges as a valuable method for clinical genetics. The transcriptome is “dynamic” and tissue-specific, but typically the probed tissues to analyze (TA) are different from the tissue of interest (TI) based on pathophysiology. Exome sequencing (ES) is a well-established method for diagnosing Mendelian disorders and improving precision medicine. Genome sequencing (GS) of patients offered a promising alternative, GS led to only a marginal increase in the yield compared to ES, with additional 10–15% of patients being diagnosed [4, 7,8,9]. In the endeavor of finding the diagnosis for the patient’s phenotype, clinicians are left with the decision of which tissue is most suitable to inquire, since biopsies to acquire the tissue of interest (TI) based on inferred pathophysiology are fairly rare. MAJIQ-CAT, a web-based tool has been designed to inform the tissue choice according to splicing pattern similarities between a clinically accessible tissue for analysis (TA) and a TI [7]. While the tool is very useful when the gene of interest is known, incorporation of human phenotype ontology [17] could prove additional utility for candidate gene identification and diagnosis [7]
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