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Machine Learning Models Based on Histological Images from Healthy Donors Identify ImageQTLs and Predict Chronological

Ran Meng1,2, William Zhu3, Christopher Jf Cameron2,4

  • 1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.

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|September 18, 2025
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Summary
This summary is machine-generated.

This study links histological image features to genotype and gene expression, identifying 906 image quantitative trait loci (imageQTLs). A deep learning model predicts gene expression and chronological age from tissue images, aiding disease diagnosis.

Keywords:
Biological Sciences/GeneticsComputational biology and bioinformaticsHistological imageImageQTLs (Image quantitative trait loci)deep learning modelsimage-based age predictiontranscriptome prediction

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Bioinformatics

Background:

  • Histological images contain rich data for disease diagnosis and prognosis.
  • Challenges exist in extracting comprehensive biological insights, particularly in non-cancerous tissues.
  • Integrating imaging data with genomic and transcriptomic information is crucial.

Purpose of the Study:

  • To develop a statistical framework linking histological image features to genotype, transcriptome, and chronological age.
  • To identify genetic loci associated with tissue morphology.
  • To build predictive models for gene expression and age from histological images.

Main Methods:

  • Developed a statistical framework to associate image features with genotypes, identifying image quantitative trait loci (imageQTLs).
  • Stratified samples into image-similar groups to identify differentially expressed (DE) genes.
  • Utilized deep learning models for predicting gene expression and chronological age from raw histological images and derived features.
  • Employed a computational approach for compressing gigapixel whole-slide images and extracting interpretable nucleus features.

Main Results:

  • Identified 906 imageQTLs significantly associated with histological image features.
  • Discovered differentially expressed genes by comparing image-similar sample groups.
  • Developed a deep learning model that accurately predicts gene expression from tissue images, linking morphology to gene sets.
  • Created a deep learning model predicting chronological age from images, highlighting nucleus features' role in age-related morphology.
  • Made interpretable nucleus features, imageQTLs, DE genes, and models available as online resources.

Conclusions:

  • The developed framework successfully integrates large-scale tissue morphology with genomic and transcriptomic data.
  • Predictive models demonstrate the potential of histological images in inferring biological attributes like gene expression and age.
  • The study provides valuable resources for advancing computational pathology and understanding tissue biology.