A cloud-based imputation service built on a multi-ancestry reference panel derived from 515,579 jointly phased genomes from the All of Us and AnVIL datasets, establishing a new foundation for genome-wide association studies (GWAS) and fine-mapping, especially in previously underrepresented populations.
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.
Polygenic risk scores (PGS) for four most common cancers showed predictive performance broadly consistent with previous studies, including association between PGS and earlier age at prostate cancer diagnosis, and no meaningful differences in cancer stage at diagnosis by PGS.
H. M. Tanha, D. Goldsbury, R. Parker et al.· medRxiv· 0 citations
Background Ancient DNA datasets are often characterized by low coverage and high levels of missing data, which limit the use of diploid-based analyses and constrain population genetic inference. Although genotype imputation is increasingly used to overcome these limitations, its performance depends strongly on the comp...
Erkin Alaçamlı, Stefania Sasso, Roberto Didonna et al.· bioRxiv· 0 citations
This work describes the full spectrum of genetic variation and shows that while 99% of the variants between any two genomes are single base-pair substitutions, 88% of the euchromatic variant base pairs are SVs, including insertions, deletions, duplications, and inversions.
J. Lin, J. Gustafson, J. Wertz et al.· medRxiv· 1 citation
The 8q24 region is a major prostate cancer susceptibility locus, but variant effects may differ across ancestry-associated haplotype contexts. We analyzed whole genome sequencing and electronic health record data from 166,155 male participants in the NIH All of Us Research Program (10,166 cases and 155,989 controls). A...
The NHRI-RP-1 demonstrated a superior F1 score on local match, genotype concordance and r-squared, as compared to those using worldwide reference genomes, particularly for rare MAFs, particularly for rare MAFs.
Kuang-Huan Cheng, Yi-Rong Chen, Ren-Hua Chung et al.· Journal of Biomedical Scienc...· 0 citations
Type 2 diabetes (T2D) is a highly heterogeneous metabolic trait, with a higher prevalence in East Asians. Despite extensive genetic studies focusing mainly on Europeans, how genetic risk for T2D is distributed across biological pathways in East Asians in comparison with Europeans remains incompletely understood. Here,...