{
  "abstract": "Unmeasured confounding threatens causal inference and replicability in observational multi-omic studies across variable environments. Genetic instrumental variables (Mendelian randomization) and negative-control calibration each address complementary sources of unmeasured confounding, yet no existing framework unifies them for omics-scale mediation analysis. We introduce ICONIC, an R package that embeds genetic instruments and negative controls within a proximal causal inference framework for total-effect and mediation analysis. ICONIC implements eight estimators spanning five confounding-control strategies, supports continuous, binary, and time-to-event outcomes, and provides extensive diagnostics including sensitivity analyses that map estimator performance across plausible assumptions. Ground-truth benchmarks are calibrated to real-omics covariance structures via a hybrid generative model (GAN + feature-level Gaussian copula) rather than parametric simulation, and a companion planning tool predicts performance gains from collecting additional omic data. We demonstrate ICONIC in two case studies: screening for placental transcriptomic mediators of gestational diabetes on birth weight (n = 164), and tumor-expression mediators of smoking intensity on lung cancer survival (n = 494). Notably, ICONICs diagnostics recommended different estimation strategies across the two scenarios, reflecting differences in the likely influence of unmeasured confounding. ICONIC is freely available at https://github.com/sbresnahan/iconic/.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. T. Bresnahan"
    },
    {
      "name": "C. Xiong"
    },
    {
      "name": "T. Head"
    },
    {
      "name": "Y.-H. Chang"
    },
    {
      "name": "A. Bhattacharya"
    },
    {
      "name": "J. Y. Huang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d664rnk7wzv5k.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.26.26361466",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.08.26.26361466v1.full",
  "id": "d664rnk7wzv5k",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.08.26.26361466v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "genetic and genomic medicine"
    ],
    [
      "openalex",
      "2613|Advanced Causal Inference Techniques"
    ]
  ],
  "openalex": "W7204854507",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.08.26.26361466v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d664rnk7wzv5k.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.64898/2026.08.26.26361466",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.08.26.26361466v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:genetic and genomic medicine",
      "subfield": "human-genetics"
    }
  ],
  "title": "ICONIC: An R Package for Integrating Instrumental Variable- and Negative-Control-Informed Causal Discovery and Diagnostics in Multiomic Studies",
  "title_text": "ICONIC: An R Package for Integrating Instrumental Variable- and Negative-Control-Informed Causal Discovery and Diagnostics in Multiomic Studies",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/d664rnk7wzv5k",
  "venue": "medRxiv",
  "versions": []
}