| Autor: |
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 |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
The American Journal of Human Genetics. 109:1986-1997 |
| ISSN: |
0002-9297 |
| DOI: |
10.1016/j.ajhg.2022.09.009 |
| Popis: |
Whole-genome sequencing (WGS) is the gold standard for fully characterizing genetic variation but is still prohibitively expensive for large samples. To reduce costs, many studies sequence only a subset of individuals or genomic regions, and genotype imputation is used to infer genotypes for the remaining individuals or regions without sequencing data. However, not all variants can be well imputed, and the current state-of-the-art imputation quality metric, denoted as standard Rsq, is poorly calibrated for lower-frequency variants. Here, we propose MagicalRsq, a machine-learning-based method that integrates variant-level imputation and population genetics statistics, to provide a better calibrated imputation quality metric. Leveraging WGS data from the Cystic Fibrosis Genome Project (CFGP), and whole-exome sequence data from UK BioBank (UKB), we performed comprehensive experiments to evaluate the performance of MagicalRsq compared to standard Rsq for partially sequenced studies. We found that MagicalRsq aligns better with true R |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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