{
  "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.\n\nResults: 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.\n\nAvailability and implementation: 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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Psychiatry, Icahn School of Medicine at Mount Sinai , New York, NY,",
    "Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai , New York, NY,",
    "Friedman Brain Institute, Icahn School of Medicine at Mount Sinai , New York, NY,",
    "Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai , New York, NY,",
    "Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center , Bronx, NY,",
    "Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center , Bronx, NY,",
    "School of Medicine, University of Crete , Heraklion,",
    "Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai , New York, NY,"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1,
        2,
        3,
        4,
        5,
        6
      ],
      "name": "Marios Anyfantakis"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4,
        5
      ],
      "name": "Sanan Venkatesh"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4,
        5
      ],
      "name": "Jamie J R Bennett"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4,
        5
      ],
      "name": "Chenyu Liu"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4,
        5
      ],
      "name": "Gabriel E Hoffman",
      "orcid": "0000-0002-0957-0224"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4,
        5
      ],
      "name": "Panos Roussos"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4,
        5,
        7
      ],
      "name": "Georgios Voloudakis",
      "orcid": "0000-0002-5729-632X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/57ztjs18ntry2.bib",
  "cited_by": 0,
  "doi": "10.1093/bioinformatics/btag719",
  "fulltext_url": null,
  "id": "57ztjs18ntry2",
  "issns": [
    "1367-4803",
    "1367-4811"
  ],
  "landing_url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag719/8859108",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1311|Genetic Associations and Epidemiology"
    ]
  ],
  "openalex": "W7215103660",
  "pdf_url": "https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag719/71559977/btag719.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Oxford University Press (OUP)",
  "ris": "https://research.draconitic.com/paper/57ztjs18ntry2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:38Z",
      "native_id": "10.1093/bioinformatics/btag719",
      "source": "crossref",
      "url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag719/8859108"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1311|Genetic Associations and Epidemiology",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "GROMTools: scalable individual-level GReX imputation for mega-biobank-scale cohorts",
  "title_text": "GROMTools: scalable individual-level GReX imputation for mega-biobank-scale cohorts",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:38Z",
  "url": "https://research.draconitic.com/paper/57ztjs18ntry2",
  "venue": "Bioinformatics",
  "versions": []
}