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

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.