Quan Sun, Yingxi Yang, Jonathan D Rosen, Jiawen Chen, Xihao Li, Wyliena Guan, Min-Zhi Jiang, Jia Wen, Rhonda G Pace, Scott M Blackman, Michael J Bamshad, Ronald L Gibson, Garry R Cutting, Wanda K O'Neal, Michael R Knowles, Charles Kooperberg, Alexander P Reiner, Laura M Raffield, April P Carson, Stephen S Rich, Jerome I Rotter, Ruth J F Loos, Eimear Kenny, Byron C Jaeger, Yuan-I Min, Christian Fuchsberger, Yun Li
Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X...
April 9, 2024: American Journal of Human Genetics