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<p>We present multi-integration of transcriptome-wide association studies and colocalization (Multi-INTACT), an algorithm that models multiple “gene products” (e.g., encoded RNA transcript and protein levels) to implicate causal genes and relevant gene products. In simulations, Multi-INTACT achieves higher power than existing methods, maintains calibrated false discovery rates, and detects the true causal gene product(s). We apply Multi-INTACT to GWAS on 1408 metabolites, integrating the GTEx expression and UK Biobank protein QTL datasets. Multi-INTACT infers 52 to 109% more metabolite causal genes than protein-alone or expression-alone analyses and indicates both gene products are relevant for most gene nominations.</p>

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Multi-INTACT: integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月3日

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  • Jeffrey Okamoto,
  • Xianyong Yin,
  • Brady Ryan,
  • Joshua Chiou,
  • Francesca Luca,
  • Roger Pique-Regi,
  • Hae Kyung Im,
  • Jean Morrison,
  • Charles Burant,
  • Eric B. Fauman,
  • Markku Laakso,
  • Michael Boehnke,
  • Xiaoquan Wen

摘要

We present multi-integration of transcriptome-wide association studies and colocalization (Multi-INTACT), an algorithm that models multiple “gene products” (e.g., encoded RNA transcript and protein levels) to implicate causal genes and relevant gene products. In simulations, Multi-INTACT achieves higher power than existing methods, maintains calibrated false discovery rates, and detects the true causal gene product(s). We apply Multi-INTACT to GWAS on 1408 metabolites, integrating the GTEx expression and UK Biobank protein QTL datasets. Multi-INTACT infers 52 to 109% more metabolite causal genes than protein-alone or expression-alone analyses and indicates both gene products are relevant for most gene nominations.