Yosuke presents at CGSI 2026

Yosuke presented at CGSI 2026, the Computational Genomics Summer Institute 2026. This talk focused on scalable regression models for genomic prediction on high-dimensional biobank data. During the CGSI 2026 retreat, Yosuke presented a separate talk with a stronger focus on a recent work on incorporating functional genomic annotations in polygenic score models.

We are grateful to the CGSI organizers and participants for the opportunity to share our recent work.

Slides

Related Papers

• Tanigawa, Y., & Kellis, M. (2023). Power of inclusion: Enhancing polygenic prediction with admixed individuals. American Journal of Human Genetics, 110(11), 1888–1902. https://doi.org/10.1016/j.ajhg.2023.09.013
• Kachuri, L., Chatterjee, N., Hirbo, J., Schaid, D. J., Martin, I., Kullo, I. J., Kenny, E. E., Pasaniuc, B., Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium Methods Working Group, Witte, J. S., & Ge, T. (2024). Principles and methods for transferring polygenic risk scores across global populations. Nature Reviews. Genetics, 25(1), 8–25. https://doi.org/10.1038/s41576-023-00637-2
• Qian, J., Tanigawa, Y., Du, W., Aguirre, M., Chang, C., Tibshirani, R., Rivas, M. A., & Hastie, T. (2020). A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank. PLoS Genetics, 16(10), e1009141. https://doi.org/10.1371/journal.pgen.1009141
• Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1–22. https://doi.org/10.18637/jss.v033.i01
• Tibshirani, R., Bien, J., Friedman, J., Hastie, T., Simon, N., Taylor, J., & Tibshirani, R. J. (2012). Strong rules for discarding predictors in lasso-type problems. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 74(2), 245–266. https://doi.org/10.1111/j.1467-9868.2011.01004.x
• Tanigawa, Y., Qian, J., Venkataraman, G., Justesen, J. M., Li, R., Tibshirani, R., Hastie, T., & Rivas, M. A. (2022). Significant sparse polygenic risk scores across 813 traits in UK Biobank. PLoS Genetics, 18(3), e1010105. https://doi.org/10.1371/journal.pgen.1010105
• Ohta, R., Tanigawa, Y., Suzuki, Y., Kellis, M., & Morishita, S. (2024). A polygenic score method boosted by non-additive models. Nature Communications, 15(1), 4433. https://doi.org/10.1038/s41467-024-48654-x