Scholarlink

<p>Understanding how genetic variation shapes tissue structure is crucial for disease biology, yet scalable, general-purpose frameworks for genetic analysis of histology traits are lacking. We present HistoGWAS, a framework for genome-wide association studies of histology data that leverages foundation models for automated trait definition, variance component models for efficient association testing, and generative models for variant effect interpretation. Applied to 11 tissues from the Genotype-Tissue Expression project, HistoGWAS identifies four genome-wide significant loci associated with tissue histology—tissue quantitative trait loci (tissueQTLs)—which we link to molecular changes and complex traits. Power analyses demonstrate scalability to population-scale histology cohorts.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

HistoGWAS: an AI-enabled framework for automated genetic analysis of tissue phenotypes in histology cohorts

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月31日

查看全文
  • Shubham Chaudhary,
  • Almut Voigts,
  • Michael Bereket,
  • Matthew L. Albert,
  • Kristina Schwamborn,
  • Eleftheria Zeggini,
  • Francesco Paolo Casale

摘要

Understanding how genetic variation shapes tissue structure is crucial for disease biology, yet scalable, general-purpose frameworks for genetic analysis of histology traits are lacking. We present HistoGWAS, a framework for genome-wide association studies of histology data that leverages foundation models for automated trait definition, variance component models for efficient association testing, and generative models for variant effect interpretation. Applied to 11 tissues from the Genotype-Tissue Expression project, HistoGWAS identifies four genome-wide significant loci associated with tissue histology—tissue quantitative trait loci (tissueQTLs)—which we link to molecular changes and complex traits. Power analyses demonstrate scalability to population-scale histology cohorts.