Whole-genome discovery of pathogenic snRNA variants and efficient extended-exome screening

Pathogenic variants in small nuclear RNA (snRNA) genes have recently emerged as a major cause of Mendelian disorders, particularly neurodevelopmental disorders, yet they remain difficult to detect in routine diagnostics because conventional whole-exome sequencing (WES) does not capture snRNA loci. Here, we reanalyzed whole-genome sequencing (WGS) data from 1,578 unsolved probands and identified pathogenic variants in multiple snRNA genes, including RNU4-2 , RNU2-2 , RNU5B-1 , and RNU4ATAC , accounting for 1.2% (19 patients) of previously unsolved cases. We then developed an snRNA-extended WES approach by incorporating capture probes targeting 50 snRNA genes into a standard exome design. Benchmarking demonstrated robust, uniform coverage across all targeted snRNA loci without increasing sequencing depth. Applying this approach to patient samples reliably detected disease-causing snRNA variants previously identified by WGS. Our results establish snRNA-extended WES as a cost-effective and scalable strategy to improve diagnostic yield and bridge the gap between recent gene discoveries and clinical genomic practice.

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Publication Details

Journal
iScience
Published
2026-07-16
DOI
https://doi.org/10.1016/j.isci.2026.116814
Primary Topic
Genomics and Rare Diseases
Type
article
Field-Weighted Citation Impact
0.00

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article

Whole-genome discovery of pathogenic snRNA variants and efficient extended-exome screening

Mayumi Matsufuji, Sok-Kun Tae, Takashi Enokizono, Yuka Nakano et al.
iScience
Genomics and Rare Diseases
article

Whole-genome discovery of pathogenic snRNA variants and efficient extended-exome screening

Mayumi Matsufuji, Sok-Kun Tae, Takashi Enokizono, Yuka Nakano, Hiroshi Suzumura, Hiroshi Yoshihashi, Daisuke Nakato, Hitomi Yagi, Fuyuki Miya, Toshiki Takenouchi, Emi Qian, Natsuki Nakamura, Kenichi Kashimada, Akane Kondo, Toshihide Kurihara, Mie Inaba, Shinji Kosugi, Rika Kosaki, Nobuhiko Okamoto, Seiji Mizuno, Mamiko Yamada, Eri Ogawa, Yukiko Kuroda, Takeshi Yoshida, Hisato Suzuki, Meow-Keong Thong, Kenjiro Kosaki, Mazlan Rifhan
article en

Abstract

Pathogenic variants in small nuclear RNA (snRNA) genes have recently emerged as a major cause of Mendelian disorders, particularly neurodevelopmental disorders, yet they remain difficult to detect in routine diagnostics because conventional whole-exome sequencing (WES) does not capture snRNA loci. Here, we reanalyzed whole-genome sequencing (WGS) data from 1,578 unsolved probands and identified pathogenic variants in multiple snRNA genes, including RNU4-2 , RNU2-2 , RNU5B-1 , and RNU4ATAC , accounting for 1.2% (19 patients) of previously unsolved cases. We then developed an snRNA-extended WES approach by incorporating capture probes targeting 50 snRNA genes into a standard exome design. Benchmarking demonstrated robust, uniform coverage across all targeted snRNA loci without increasing sequencing depth. Applying this approach to patient samples reliably detected disease-causing snRNA variants previously identified by WGS. Our results establish snRNA-extended WES as a cost-effective and scalable strategy to improve diagnostic yield and bridge the gap between recent gene discoveries and clinical genomic practice.

iScienceVol. 29(8)
University Malaya Medical Centre (MY), University of Tsukuba (JP), Okayama University (JP), Keio University (JP), Kyoto University (JP), University of Malaya (MY), Osaka Women's and Children's Hospital (JP), Kanagawa Children's Medical Center (JP), Kagoshima City Hospital (JP), National Center For Child Health and Development (JP), The Cancer Institute Hospital (JP), Tachikawa Hospital (JP), Tokyo Metropolitan Children's Medical Center (JP), Shikoku Medical Center for Children and Adults (JP), Aichi Developmental Disability Center (JP), Dokkyo Medical University (JP), Universiti Tunku Abdul Rahman (MY)
Japan Agency for Medical Research and Development, Japan Society for the Promotion of Science, Japan Science and Technology Agency
Zero hunger
Openalex Percentile: Top 9%
Genomics and Rare Diseases
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