A framework for detecting noncoding rare variant associations of large-scale whole-genome sequencing studies

Li, Z.; Li, X.; Zhou, H.; Gaynor, S. M.; Selvaraj, M. S.; Arapoglou, T.; Quick, C.; Liu, Y.; Chen, H.; Sun, R.; Dey, R.; Arnett, D. K.; Bielak, L. F.; Bis, J. C.; Blackwell, T.; Blangero, J.; Boerwinkle, E.; Bowden, D. W.; Brody, J. A.; Cade, B. E.; Conomos, M.; Correa, A.; Cupples, L. A.; Curran, J. E.; de Vries, P. S.; Duggirala, R.; Freedman, B. I.; Goring, H. H. H.; Guo, X.; Kalyani, R. R.; Kooperberg, C.; Kral, B. G.; Lange, L. A.; Manichaikul, A. W.; Martin, L. W.; Mitchell, B. D.; Montasser, M.; Morrison, A.; Naseri, T.; O'Connell, J. R.; Palmer, N. D.; Peyser, P. A.; Psaty, B. M.; Raff

doi:10.1101/2021.11.05.467531

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bioRxiv · 10.1101/2021.11.05.467531

A framework for detecting noncoding rare variant associations of large-scale whole-genome sequencing studies

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Abstract

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare variants (RVs) associations with complex human traits. Variant set analysis is a powerful approach to study RV association, and a key component of it is constructing RV sets for analysis. However, existing methods have limited ability to define analysis units in the noncoding genome. Furthermore, there is a lack of robust pipelines for comprehensive and scalable noncoding RV association analysis. Here we propose a computationally-efficient noncoding RV association-detection framework that uses STAAR (variant-set test for association using annotation information) to group noncoding variants in gene-centric analysis based on functional categories. We also propose SCANG (scan the genome)-STAAR, which uses dynamic window sizes and incorporates multiple functional annotations, in a non-gene-centric analysis. We furthermore develop STAARpipeline to perform flexible noncoding RV association analysis, including gene-centric analysis as well as fixed-window-based and dynamic-window-based non-gene-centric analysis. We apply STAARpipeline to identify noncoding RV sets associated with four quantitative lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several noncoding RV associations in an additional 9,123 TOPMed samples.

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Li, Z., Li, X., Zhou, H., Gaynor, S. M., Selvaraj, M. S., Arapoglou, T., Quick, C., Liu, Y., Chen, H., Sun, R., Dey, R., Arnett, D. K., Bielak, L. F., Bis, J. C., Blackwell, T., Blangero, J., Boerwinkle, E., Bowden, D. W., Brody, J. A., Cade, B. E., Conomos, M., Correa, A., Cupples, L. A., Curran, J. E., de Vries, P. S., Duggirala, R., Freedman, B. I., Goring, H. H. H., Guo, X., Kalyani, R. R., Kooperberg, C., Kral, B. G., Lange, L. A., Manichaikul, A. W., Martin, L. W., Mitchell, B. D., Montasser, M., Morrison, A., Naseri, T., O'Connell, J. R., Palmer, N. D., Peyser, P. A., Psaty, B. M., Raff. 2021-11-08. A framework for detecting noncoding rare variant associations of large-scale whole-genome sequencing studies. https://doi.org/10.1101/2021.11.05.467531

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