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 skin fibroblasts at the Baylor College of Medicine (BCM) Undiagnosed Diseases Network clinical site from 2014 to 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). Skin 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.CONCLUSIONS 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.TRIAL REGISTRATION Not applicable.FUNDING NIH Common Fund, BCM Intellectual and Developmental Disabilities Research Center, Eunice Kennedy Shriver National Institute of Child Health & Human Development.
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, Undiagnosed Diseases Network, Brendan Lee
Usage data is cumulative from November 2023 through November 2024.
Usage | JCI | PMC |
---|---|---|
Text version | 2,245 | 1,025 |
329 | 289 | |
Figure | 462 | 30 |
Table | 141 | 0 |
Supplemental data | 203 | 93 |
Citation downloads | 111 | 0 |
Totals | 3,491 | 1,437 |
Total Views | 4,928 |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.