{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T02:39:13Z","timestamp":1792463953710,"version":"4.5.0"},"reference-count":14,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AG078657"],"award-info":[{"award-number":["R01AG078657"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["K08MH122911"],"award-info":[{"award-number":["K08MH122911"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In UK Biobank (UKB), benchmarks on chromosome 1 across 50,000\u2013450,000 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 &amp;gt;0.999 and maximum RMSE &amp;lt;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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>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:\/\/doi.org\/10.5281\/zenodo.21926165.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag719","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T12:00:21Z","timestamp":1790769621000},"source":"Crossref","is-referenced-by-count":0,"title":["GROMTools: scalable individual-level GReX imputation for mega-biobank-scale cohorts"],"prefix":"10.1093","volume":"42","author":[{"given":"Marios","family":"Anyfantakis","sequence":"first","affiliation":[{"name":"Department of Psychiatry, Icahn School of Medicine at Mount Sinai , New York, NY,","place":["United States"]},{"name":"Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai , New York, NY,","place":["United States"]},{"name":"Friedman Brain Institute, Icahn School of Medicine at Mount Sinai , New York, NY,","place":["United States"]},{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai , New York, NY,","place":["United States"]},{"name":"Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center , Bronx, NY,","place":["United States"]},{"name":"Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center , Bronx, NY,","place":["United States"]},{"name":"School of Medicine, University of Crete , Heraklion,","place":["Greece"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanan","family":"Venkatesh","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Icahn School of Medicine at Mount Sinai , New York, NY,","place":["United States"]},{"name":"Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai , New York, NY,","place":["United States"]},{"name":"Friedman Brain Institute, Icahn School of Medicine at Mount Sinai , New York, NY,","place":["United States"]},{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai , New York, NY,","place":["United States"]},{"name":"Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. 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