MagicalRsq: Machine-learning-based genotype imputation quality calibration
Quan Sun, Yingxi Yang, Jonathan D. Rosen, Min-Zhi Jiang, Jiawen Chen, Weifang Liu, Jia Wen, Laura M. Raffield, Rhonda G. Pace, Yi‐Hui Zhou, Fred A. Wright, Scott M. Blackman, Michael J. Bamshad, Ronald L. Gibson, Garry R. Cutting, Michael R. Knowles, Daniel R. Schrider, Christian Fuchsberger, Yun Li
Abstract
than standard Rsq in almost every situation evaluated, for both European and African ancestry samples. For example, when applying models trained from 1,992 CFGP sequenced samples to an independent 3,103 samples with no sequencing but TOPMed imputation from array genotypes, MagicalRsq, compared to standard Rsq, achieved net gains of 1.4 million rare, 117k low-frequency, and 18k common variants, where net gains were gained numbers of correctly distinguished variants by MagicalRsq over standard Rsq. MagicalRsq can serve as an improved post-imputation quality metric and will benefit downstream analysis by better distinguishing well-imputed variants from those poorly imputed. MagicalRsq is freely available on GitHub.