GROMTools: scalable individual-level GReX imputation for mega-biobank-scale cohorts

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ID: 330807
2026
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Abstract
Abstract Motivation The computational burden of individual-level genetically regulated gene expression (GReX) imputation has risen sharply with the growth of human mega-biobanks and the rapid expansion of transcriptomic imputation models across tissues and single-cell hierarchies. Existing tools were not designed for this setting and require complex, memory-intensive workflows that are poorly matched to shared and cloud-based compute environments, where runtime, memory, and I/O directly determine cost and throughput. GROMTools is an open-source C ++ engine with an R interface that exploits sparse prediction weights, streams PLINK2 genotypes, and writes compact binary outputs for scalable individual-level GReX imputation. Results In UK Biobank (UKB), benchmarks on chromosome 1 across 50,000-450,000 individuals, 388,017 variants, and 11,724 gene-model pairs from 32 single-nucleus transcriptome imputation models, GROMTools produced near-identical predictions to PrediXcan and PLINK2, with minimum Pearson correlation >0.999 and maximum RMSE <0.001 across all of the imputed genes, while reducing CPU time by about 100-fold and peak memory by about 33-fold. These gains make routine biobank-scale individual-level GReX imputation practical and cost-efficient on standard CPU infrastructure. Availability GROMTools is freely available at https://github.com/voloudakislab/gromtools under the GPL v3 license. Documentation is available at https://voloudakislab.github.io/gromtools/ .All scripts used for data quality control and benchmarking, together with log files and a snapshot of the GROMTools code version used for all of our analyses, are archived at DOI:https://zenodo.org/records/21926165. Supplementary information Supplementary data are available at Bioinformatics online.
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Authors Marios Anyfantakis, Sanan Venkatesh, Jamie J. R. Bennett, Chenyu Liu, Gabriel E. Hoffman, Panos Roussos, Georgios Voloudakis
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag719
URL
https://doi.org/10.1093/bioinformatics/btag719
Keywords Keywords not found

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