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Splicing QTL analysis focusing on coding sequences reveals mechanisms for disease susceptibility loci

Author

Listed:
  • Kensuke Yamaguchi

    (Tokyo Medical and Dental University
    Tokyo Medical and Dental University
    The University of Tokyo
    RIKEN Center for Integrative Medical Sciences)

  • Kazuyoshi Ishigaki

    (RIKEN Center for Integrative Medical Sciences)

  • Akari Suzuki

    (RIKEN Center for Integrative Medical Sciences)

  • Yumi Tsuchida

    (The University of Tokyo)

  • Haruka Tsuchiya

    (The University of Tokyo)

  • Shuji Sumitomo

    (The University of Tokyo)

  • Yasuo Nagafuchi

    (The University of Tokyo
    The University of Tokyo)

  • Fuyuki Miya

    (Keio University School of Medicine)

  • Tatsuhiko Tsunoda

    (The University of Tokyo
    The University of Tokyo
    RIKEN Center for Integrative Medical Sciences)

  • Hirofumi Shoda

    (The University of Tokyo)

  • Keishi Fujio

    (The University of Tokyo)

  • Kazuhiko Yamamoto

    (RIKEN Center for Integrative Medical Sciences)

  • Yuta Kochi

    (Tokyo Medical and Dental University
    RIKEN Center for Integrative Medical Sciences)

Abstract

Splicing quantitative trait loci (sQTLs) are one of the major causal mechanisms in genome-wide association study (GWAS) loci, but their role in disease pathogenesis is poorly understood. One reason is the complexity of alternative splicing events producing many unknown isoforms. Here, we propose two approaches, namely integration and selection, for this complexity by focusing on protein-structure of isoforms. First, we integrate isoforms with the same coding sequence (CDS) and identify 369-601 integrated-isoform ratio QTLs (i2-rQTLs), which altered protein-structure, in six immune subsets. Second, we select CDS incomplete isoforms annotated in GENCODE and identify 175-337 isoform-ratio QTL (i-rQTL). By comprehensive long-read capture RNA-sequencing among these incomplete isoforms, we reveal 29 full-length isoforms with unannotated CDSs associated with GWAS traits. Furthermore, we show that disease-causal sQTL genes can be identified by evaluating their trans-eQTL effects. Our approaches highlight the understudied role of protein-altering sQTLs and are broadly applicable to other tissues and diseases.

Suggested Citation

  • Kensuke Yamaguchi & Kazuyoshi Ishigaki & Akari Suzuki & Yumi Tsuchida & Haruka Tsuchiya & Shuji Sumitomo & Yasuo Nagafuchi & Fuyuki Miya & Tatsuhiko Tsunoda & Hirofumi Shoda & Keishi Fujio & Kazuhiko , 2022. "Splicing QTL analysis focusing on coding sequences reveals mechanisms for disease susceptibility loci," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32358-1
    DOI: 10.1038/s41467-022-32358-1
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    References listed on IDEAS

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    1. Ruiyan Hou & Chung-Chau Hon & Yuanhua Huang, 2023. "CamoTSS: analysis of alternative transcription start sites for cellular phenotypes and regulatory patterns from 5' scRNA-seq data," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Jun Inamo & Akari Suzuki & Mahoko Takahashi Ueda & Kensuke Yamaguchi & Hiroshi Nishida & Katsuya Suzuki & Yuko Kaneko & Tsutomu Takeuchi & Hiroaki Hatano & Kazuyoshi Ishigaki & Yasushi Ishihama & Kazu, 2024. "Long-read sequencing for 29 immune cell subsets reveals disease-linked isoforms," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    3. Xena Marie Mapel & Naveen Kumar Kadri & Alexander S. Leonard & Qiongyu He & Audald Lloret-Villas & Meenu Bhati & Maya Hiltpold & Hubert Pausch, 2024. "Molecular quantitative trait loci in reproductive tissues impact male fertility in cattle," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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