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Published on: December 9, 2016
Transcriptome-wide outlier approach identifies individuals with minor spliceopathies
Maggie T Arriaga1, Rodrigo Mendez2, Rachel A Ungar1,3
1Dept. of Genetics, Stanford Univ., Stanford, CA.
This study introduces a transcriptomics-first approach to diagnose rare diseases by analyzing RNA sequencing data for splicing outliers. The method successfully identified novel genetic variants impacting the minor spliceosome, increasing diagnostic yield for rare genetic disorders.
Area of Science:
- Genomics and Transcriptomics
- Rare Disease Diagnostics
- Molecular Biology
Background:
- RNA sequencing enhances rare disease diagnosis, but current methods often miss trans-acting variants affecting splicing transcriptome-wide.
- Existing analyses primarily focus on cis-acting variants, overlooking spliceosome function disruptions.
- Rare diseases require novel diagnostic strategies to identify causative genetic variants.
Purpose of the Study:
- To develop and apply a transcriptomics-first method for diagnosing rare diseases by detecting transcriptome-wide splicing outliers.
- To investigate the role of minor introns and the minor spliceosome in rare disease pathogenesis.
- To identify novel gene-disease associations through comprehensive splicing analysis.
Main Methods:
- Utilized FRASER and FRASER2 splicing outlier detection methods on whole blood RNA sequencing data from 390 individuals (GREGoR and UDN consortia).
- Focused on identifying excess intron retention outliers specifically in minor intron-containing genes (MIGs).
- Analyzed identified variants for their impact on minor spliceosome small nuclear RNAs (snRNAs).
Main Results:
- Identified five individuals with excess intron retention outliers in MIGs, all harboring rare, biallelic variants in minor spliceosome snRNAs.
- Discovered compound heterozygous variants in RNU4ATAC in four individuals, leading to reclassification of four variants.
- Found rare, conserved compound heterozygous variants in RNU6ATAC in one individual, suggesting a novel disease candidate.
Conclusions:
- Analyzing RNA sequencing data for transcriptome-wide splicing signatures significantly increases rare disease diagnostic yield.
- This approach provides crucial variant-to-function interpretation for spliceopathies.
- The study successfully uncovered novel gene-disease associations by examining splicing patterns.
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