Using a set of GWAS summary statistics of diseases characterized from both European (UK Biobank and FinnGen) and East Asian (Biobank Japan) populations, we dissected latent DeGAs components of multi-ethnic association summary statistics. We annotated each component by pathway and cell-type enrichment as well as projection of metabolomic and biomarker summary statistics. We demonstrate how can we use such trans-ethnic annotated latent components to classify diseases based on their genetic basis.
Citation: S. Sakaue*, M. Kanai*, Y. Tanigawa, J. Karjalainen, M. Kurki, S. Koshiba, A. Narita, T. Konuma, K. Yamamoto, M. Akiyama, K. Ishigaki, A. Suzuki, K. Suzuki, W. Obara, K. Yamaji, K. Takahashi, S. Asai, Y. Takahashi, T. Suzuki, N. Sinozaki, H. Yamaguchi, S. Minami, S. Murayama, K. Yoshimori, S. Nagayama, D. Obata, M. Higashiyama, A. Masumoto, Y. Koretsune, F. Gen, K. Ito, C. Terao, T. Yamauchi, I. Komuro, T. Kadowaki, G. Tamiya, M. Yamamoto, Y. Nakamura, M. Kubo, Y. Murakami, K. Yamamoto, Y. Kamatani, A. Palotie, M. A. Rivas, M. Daly, K. Matsuda, Y. Okada, A global atlas of genetic associations of 220 deep phenotypes. medRxiv, 2020.10.23.20213652 (2020). https://doi.org/10.1101/2020.10.23.20213652