A 515,579-Genome Reference Panel Improves Rare-Variant Imputation Across Multiple Underrepresented Populations
Ivankovic, F.; Ko, A.; Aster, M. M.; Balaconis, M. K.; Banks, E.; Bemis, M.; Cibulskis, K. R.; Degatano, K.; Gauthier, L. D.; Grant, G.; Hatcher, A.; Kachulis, C.; Karczewski, K. J.; Labrecque, S. M.; Lawson, J.; Liao, C.; Magner, R.; Munshi, R.; Schatz, M. C.; Schultz, P. M.; Shah, S. P.; Sheets, E. A.; Tibbetts, K.; Vernest, K. A.; Ye, R.; Gabriel, S.; Lennon, N. J.; Neale, B. M.; Browning, B. L.; Lichtenstein, L. T.
Show abstract
Genotype imputation remains essential for large-scale human genetics studies, but its performance is limited by the size and ancestral diversity of available reference panels, reducing accuracy for rare variants and underrepresented populations. Here, we present a cloud-based imputation service built on a multi-ancestry reference panel derived from 515,579 jointly phased genomes from the All of Us (N=414,830) and National Human Genome Research Institute's Analysis, Visualization, and Informatics Lab-space (AnVIL, N=100,749) datasets. The All of Us + AnVIL reference panel is highly diverse and includes 261,163 participants most genetically similar to non-European reference populations, spanning 665,398,839 high-quality autosomal sites, representing a nearly 50% increase over TOPMed, the previous largest imputation service. Across multiple ancestry groups, the panel enables accurate imputation (empirical R2 0.8) for variants with allele frequencies as low as 0.2%, extending reliable imputation into the rare-variant frequency spectrum, including allele frequencies down to 0.002% and 0.006% for samples with European ancestry and African ancestry in the United States, respectively. Compared with TOPMed, the panel improves imputation accuracy across all ancestry groups except Africans, and recovers additional trait-associated variants not represented in existing reference panels. To facilitate broad community access while preserving participant privacy, we deploy the panel through a secure cloud-based imputation platform using privacy-preserving recombined haplotypes. This resource establishes a new foundation for genome-wide association studies (GWAS) and fine-mapping, especially in previously underrepresented populations.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Streamlining Large-Scale Genomic Data Management: Insights from the UK Biobank Whole-Genome Sequencing Data 95%
- Fast and powerful statistical method for context-specific QTL mapping in multi-context genomic studies 95%
- Polymorphic short tandem repeats make widespread contributions to blood and serum traits 93%
Similar papers in this journal
Similar papers in this journal
- Evaluating genetic-ancestry inference from single-cell transcriptomic datasets 93%
- Leveraging TOPMed Imputation Server and Constructing a Cohort-Specific Imputation Reference Panel to Enhance Genotype Imputation among Cystic Fibrosis Patients 93%
- A simple approach for multiple observations improves power to detect genetic effects and genomic prediction accuracy. 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.