MagicalRsq: Machine-learning-based genotype imputation quality calibration Article Swipe
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
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.ajhg.2022.09.009
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.ajhg.2022.09.009
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 R2 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.
Related Topics
Concepts
Imputation (statistics)
Exome
Computer science
Whole genome sequencing
Metric (unit)
Statistics
Artificial intelligence
Biology
Data mining
Machine learning
Genome
Exome sequencing
Missing data
Genetics
Mathematics
Mutation
Gene
Economics
Operations management
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ajhg.2022.09.009
- http://www.cell.com/article/S0002929722004128/pdf
- OA Status
- hybrid
- Cited By
- 17
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4300979371
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4300979371Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.ajhg.2022.09.009Digital Object Identifier
- Title
-
MagicalRsq: Machine-learning-based genotype imputation quality calibrationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-04Full publication date if available
- Authors
-
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 LiList of authors in order
- Landing page
-
https://doi.org/10.1016/j.ajhg.2022.09.009Publisher landing page
- PDF URL
-
https://www.cell.com/article/S0002929722004128/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://www.cell.com/article/S0002929722004128/pdfDirect OA link when available
- Concepts
-
Imputation (statistics), Exome, Computer science, Whole genome sequencing, Metric (unit), Statistics, Artificial intelligence, Biology, Data mining, Machine learning, Genome, Exome sequencing, Missing data, Genetics, Mathematics, Mutation, Gene, Economics, Operations managementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 8, 2023: 5Per-year citation counts (last 5 years)
- References (count)
-
56Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.ancestry | 157 |
| abstract_inverted_index.applying | 162 |
| abstract_inverted_index.compared | 127, 185 |
| abstract_inverted_index.evaluate | 122 |
| abstract_inverted_index.example, | 160 |
| abstract_inverted_index.genetics | 89 |
| abstract_inverted_index.genotype | 35 |
| abstract_inverted_index.improved | 222 |
| abstract_inverted_index.imputed, | 58 |
| abstract_inverted_index.imputed. | 239 |
| abstract_inverted_index.regions, | 33 |
| abstract_inverted_index.samples. | 19, 158 |
| abstract_inverted_index.sequence | 25, 111 |
| abstract_inverted_index.standard | 6, 68, 129, 145, 187, 215 |
| abstract_inverted_index.studies. | 134 |
| abstract_inverted_index.variants | 54, 211, 235 |
| abstract_inverted_index.available | 243 |
| abstract_inverted_index.correctly | 209 |
| abstract_inverted_index.expensive | 16 |
| abstract_inverted_index.genotypes | 41 |
| abstract_inverted_index.partially | 132 |
| abstract_inverted_index.performed | 118 |
| abstract_inverted_index.remaining | 44 |
| abstract_inverted_index.sequenced | 133, 168 |
| abstract_inverted_index.situation | 150 |
| abstract_inverted_index.variants, | 201 |
| abstract_inverted_index.variants. | 75 |
| abstract_inverted_index.variation | 11 |
| abstract_inverted_index.Leveraging | 99 |
| abstract_inverted_index.MagicalRsq | 126, 138, 213, 217, 240 |
| abstract_inverted_index.calibrated | 72, 95 |
| abstract_inverted_index.downstream | 229 |
| abstract_inverted_index.evaluated, | 151 |
| abstract_inverted_index.genotypes, | 183 |
| abstract_inverted_index.imputation | 36, 63, 86, 96, 180 |
| abstract_inverted_index.integrates | 84 |
| abstract_inverted_index.population | 88 |
| abstract_inverted_index.sequencing | 1, 49, 177 |
| abstract_inverted_index.MagicalRsq, | 79, 184 |
| abstract_inverted_index.experiments | 120 |
| abstract_inverted_index.independent | 172 |
| abstract_inverted_index.individuals | 30, 45 |
| abstract_inverted_index.performance | 124 |
| abstract_inverted_index.statistics, | 90 |
| abstract_inverted_index.whole-exome | 110 |
| abstract_inverted_index.Whole-genome | 0 |
| abstract_inverted_index.well-imputed | 234 |
| abstract_inverted_index.R<sup>2</sup> | 143 |
| abstract_inverted_index.comprehensive | 119 |
| abstract_inverted_index.distinguished | 210 |
| abstract_inverted_index.prohibitively | 15 |
| abstract_inverted_index.variant-level | 85 |
| abstract_inverted_index.characterizing | 9 |
| abstract_inverted_index.distinguishing | 233 |
| abstract_inverted_index.low-frequency, | 197 |
| abstract_inverted_index.lower-frequency | 74 |
| abstract_inverted_index.post-imputation | 223 |
| abstract_inverted_index.state-of-the-art | 62 |
| abstract_inverted_index.machine-learning-based | 81 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5100369226, https://openalex.org/A5034697802 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 19 |
| corresponding_institution_ids | https://openalex.org/I114027177, https://openalex.org/I1319360392, https://openalex.org/I4210118299 |
| citation_normalized_percentile.value | 0.91475805 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |