Genetic risk prediction using genome-wide association studies (GWAS) data

* Wu, T., Liu, Z., Mak, T., and Sham, P.C. (2022). Polygenic power calculator: Statistical power and polygenic prediction accuracy of genome-wide association studies of complex traits. Accepted. * Liu, Z., Qin, Y., Wu, T., Tubbs, J., Baum, L., Mak, T., Li, M., Zhang, Y., and Sham, P.C. (2022). Reciprocal causation mixture model for robust mendelian randomization analysis using genome-scale summary data. Accepted. * Boer, C.G., Hatzikotoulas, K., Southam, L., Stefansdottir, L., Zhang, Y., Coutinho de Almeida, R., Wu, T.T., Zheng, J., Hartley, A., Teder-Laving, M., et al. (2021). Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations. Cell. 10.1016/j.cell.2021.07.038. * Wu, T., and Sham, P.C. (2021). On the transformation of genetic effect size from logit to liability scale. Behav Genet 51, 215-222. 10.1007/s10519-021-10042-2.