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<p>Gene-based rare variant analyses often lack statistical power and may overlook transcript-specific effects. Here, we present a transcript-aware aggregation framework. In simulation studies, the framework maintains appropriate false-positive rates and shows competitive power relative to standard single-transcript analyses, approaching the performance of the ideal case of knowing the most informative transcript in advance. We then apply the approach to 129 cardiopulmonary traits in over 240,000 whole-genome-sequenced <i>All of Us</i> participants. By leveraging transcript-specific annotations, we identify 11 novel associations and recover 47 reported associations, including potentially pleiotropic genes linked to plasma lipid traits (<i>PPARG</i>) and body habitus (<i>TCF12</i>). Notably, for <i>TTN</i>, a gene known for its transcript-specific effects in cardiomyopathy, our framework strengthens the association signal and pinpoints the N2B isoform, which shows a stronger association with cardiomyopathy than other transcripts. These findings highlight the value of a transcript-aware framework for improving rare variant association studies.</p>

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Transcript-aware rare genetic variant association analyses of cardiopulmonary traits in participants from the All of Us Research Program

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月24日

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  • Jingwen Zhang,
  • So-hyeon Hong,
  • Xin Wang,
  • Sean J. Jurgens,
  • Ching-Ti Liu,
  • Josée Dupuis,
  • Patrick T. Ellinor,
  • George T. O’Connor,
  • Quanshun Mei,
  • Seung Hoan Choi

摘要

Gene-based rare variant analyses often lack statistical power and may overlook transcript-specific effects. Here, we present a transcript-aware aggregation framework. In simulation studies, the framework maintains appropriate false-positive rates and shows competitive power relative to standard single-transcript analyses, approaching the performance of the ideal case of knowing the most informative transcript in advance. We then apply the approach to 129 cardiopulmonary traits in over 240,000 whole-genome-sequenced All of Us participants. By leveraging transcript-specific annotations, we identify 11 novel associations and recover 47 reported associations, including potentially pleiotropic genes linked to plasma lipid traits (PPARG) and body habitus (TCF12). Notably, for TTN, a gene known for its transcript-specific effects in cardiomyopathy, our framework strengthens the association signal and pinpoints the N2B isoform, which shows a stronger association with cardiomyopathy than other transcripts. These findings highlight the value of a transcript-aware framework for improving rare variant association studies.