Transcriptome-directed analysis for Mendelian disease diagnosis overcomes limitations of conventional genomic testing

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Abstract

BACKGROUND. Transcriptome sequencing (RNA-seq) improves diagnostic rates in individuals with suspected Mendelian conditions to varying degrees, primarily by directing the prioritization of candidate DNA variants identified on exome or genome sequencing (ES/GS). Here we implemented an RNA-seq guided method to diagnose individuals across a wide range of ages and clinical phenotypes. METHODS. One hundred fifteen undiagnosed adult and pediatric patients with diverse phenotypes and 67 family members (182 total individuals) underwent RNA-seq from whole blood and fibroblasts at the Baylor College of Medicine (BCM) Undiagnosed Diseases Network (UDN) clinical site from 2014-2020. We implemented a workflow to detect outliers in gene expression and splicing for cases that remained undiagnosed despite standard genomic and transcriptomic analysis. RESULTS. The transcriptome-directed approach resulted in a diagnostic rate of 12% across the entire cohort, or 17% after excluding cases solved on ES/GS alone. Newly diagnosed conditions included Koolen-de Vries syndrome (KANSL1), Renpenning syndrome (PQBP1), TBCK-associated encephalopathy, NSD2- and CLTC-related intellectual disability, and others, all with negative conventional genomic testing, including ES and chromosomal microarray (CMA). Fibroblasts exhibited higher and more consistent expression of clinically relevant genes than whole blood. In solved cases with RNA-seq from both tissues, the causative defect was missed in blood in half the cases but none from fibroblasts. CONCLUSION. For our cohort of undiagnosed individuals with suspected Mendelian conditions, transcriptome-directed genomic analysis facilitated diagnoses, primarily through the identification of variants missed on ES and CMA.

Authors

David R. Murdock, Hongzheng Dai, Lindsay C. Burrage, Jill A. Rosenfeld, Shamika Ketkar, Michaela F. Müller, Vicente A. Yépez, Julien Gagneur, Pengfei Liu, Shan Chen, Mahim Jain, Gladys Zapata, Carlos A. Bacino, Hsiao-Tuan Chao, Paolo Moretti, William J. Craigen, Neil A. Hanchard, Brendan Lee