RNA-sequencing has improved the diagnostic yield of individuals with rare diseases. Current analyses predominantly focus on identifying outliers in single genes that can be attributed to cis-acting variants within or near that gene. This approach overlooks causal variants with trans-acting effects on splicing transcriptome-wide, such as variants impacting spliceosome function. We present a transcriptomics-first method to diagnose individuals with rare diseases by examining transcriptome-wide patterns of splicing outliers. Using splicing outlier detection methods - FRASER and FRASER2 - we identified splicing outliers from whole blood for 390 individuals from the Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) and Undiagnosed Diseases Network (UDN) consortia. We examined all samples for excess intron retention events in minor intron containing genes. Minor introns, which make up about 0.5% of all introns in the human genome, are removed by small nuclear RNAs (snRNAs) in the minor spliceosome. This approach identified five cases with excess intron retention events in minor intron containing genes, all of which were found to harbor rare, biallelic variants in the minor spliceosome snRNAs. Four had rare, compound heterozygous variants in RNU4ATAC. These results led to the reclassification of four variants. Additionally, one case had rare, highly conserved, compound heterozygous variants in RNU6ATAC that may disrupt the formation of the catalytic spliceosome, suggesting a novel disease-gene candidate. These results demonstrate that examining RNA-sequencing data for known transcriptome-wide signatures can increase the diagnostic yield of individuals with rare diseases, provide variant-to-functional interpretation of spliceopathies, and potentially uncover novel disease genes.

AODL was a paid consultant for Tome Biosciences, Ono Pharma USA, and Addition Therapeutics. SBM is an advisor to Character Bio, Myome, PhiTech and Tenaya Therapeutics.

This work required computing resources from the Stanford Genetics Bioinformatics Service Center (supported by NIH Instrumentation Grant S10 OD025082). Research reported in this manuscript was partly funded by the National Human Genome Research Institute at the National Institutes of Health, as part of GREGoR consortium, through Grant Nos. U01HG011762 and U01HG011755. This publication was also supported by the Undiagnosed Diseases Network, which was aided by the NIH Common Fund through the Office of the NIH Director, the National Institute of Neurological Disorders and Stroke, and the Office of Strategic Coordination as part of Grant Nos. U01HG010217 and U01HG010218. M.T.A. was further supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-2146755. V. S. G. is supported by the NIH NIAMS K23AR083505, and the BroadIgnite Award. The content of this manuscript does not necessarily represent the official views of the National Institutes of Health or the National Science Foundation and is solely the responsibility of the authors.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethical and research approvals were provided by the Stanford University IRB (protocol 60837), and the National Human Genome Research Institute Institutional Review Board (IRB) (protocol 15-HG-0130). All participants provided informed consent.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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Our analyses and pipeline are fully available at https://github.com/maurermaggie/Transcriptome_Wide_Splicing_Analysis/tree/main. All RNA-seq data for samples enrolled in GREGoR are available in AnVIL through dbGap (phs003047.v1.p1). Most of the RNA-seq data for samples enrolled in the UDN is currently available through dbGap (phs001232.v6.p2). The remaining data will be uploaded to dbGaP as a part of the next UDN data freeze. Data is also available before the next freeze by a request to the author with evidence of dbGaP approval for UDN data.

https://github.com/maurermaggie/Transcriptome_Wide_Splicing_Analysis/tree/main