Transcript-aware rare genetic variant association analyses of cardiopulmonary traits in participants from the All of Us Research Program.
J, Z., SH, H., X, W., SJ, J., CT, L., J, D., PT, E., GT, O., Q, M., & SH, C. (2026). Transcript-aware rare genetic variant association analyses of cardiopulmonary traits in participants from the All of Us Research Program.. Nature communications. https://doi.org/10.1038/s41467-026-75569-6
J Z, SH H, X W, SJ J, CT L, J D, et al. Transcript-aware rare genetic variant association analyses of cardiopulmonary traits in participants from the All of Us Research Program.. Nature communications. 2026; doi: 10.1038/s41467-026-75569-6
J Z, SH H, X W, et al. Transcript-aware rare genetic variant association analyses of cardiopulmonary traits in participants from the All of Us Research Program.[J]. Nature communications. 2026. DOI: 10.1038/s41467-026-75569-6.
@article{j2026,
author = {Zhang J and Hong SH and Wang X and Jurgens SJ and Liu CT and Dupuis J and Ellinor PT and O'Connor GT and Mei Q and Choi SH},
title = {Transcript-aware rare genetic variant association analyses of cardiopulmonary traits in participants from the All of Us Research Program.},
journal = {Nature communications},
year = {2026},
doi = {10.1038/s41467-026-75569-6},
note = {PMID: 42642362},
}
TY - JOUR AU - Zhang J AU - Hong SH AU - Wang X AU - Jurgens SJ AU - Liu CT AU - Dupuis J AU - Ellinor PT AU - O'Connor GT AU - Mei Q AU - Choi SH TI - Transcript-aware rare genetic variant association analyses of cardiopulmonary traits in participants from the All of Us Research Program. T2 - Nature communications PY - 2026 DO - 10.1038/s41467-026-75569-6 AN - PMID:42642362 ER -
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.