ArticleiScience2018
Histopathological Image QTL Discovery of Immune Infiltration Variants.
Article in iScience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- HistoGWAS: an AI-enabled framework for automated genetic analysis of tissue phenotypes in histology cohorts.Genome biology · 2026Article
- Machine-learning models based on histological images from healthy donors identify imageQTLs and predict chronological age.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Machine Learning Models Based on Histological Images from Healthy Donors Identify ImageQTLs and Predict Chronological Age.bioRxiv : the preprint server for biology · 2025Article
- Thioredoxin Domain Containing 5 (TXNDC5): Friend or Foe?Current issues in molecular biology · 2024Review
- Development and validation of epigenetic modification-related signals for the diagnosis and prognosis of colorectal cancer.BMC genomics · 2024Article
- The landscape of expression and alternative splicing variation across human traits.Cell genomics · 2023Article
- Role of Artificial Intelligence in Video Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2021Review
- Demographic and genetic factors influence the abundance of infiltrating immune cells in human tissues.Nature communications · 2020Article
- QTL Mapping of Intestinal Neutrophil Variation in Threespine Stickleback Reveals Possible Gene Targets Connecting Intestinal Inflammation and Systemic Health.G3 (Bethesda, Md.) · 2020Article
- Exploring the Limits of Combined Image/'omics Analysis for Non-cancer Histological Phenotypes.Frontiers in genetics · 2020Article
- Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives.Genetics · 2019Article
- Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis.Advances in bioinformatics · 2019Review
- Machine Learning Methods for Histopathological Image Analysis.Computational and structural biotechnology journal · 2018Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Genotype-to-phenotype association studies typically use macroscopic physiological measurements or molecular readouts as quantitative traits. There are comparatively few suitable quantitative traits available between cell and tissue length scales, a limitation that hinders our ability to identify variants affecting phenotype at many clinically informative levels. Here we show that quantitative image features, automatically extracted from histopathological imaging data, can be used for image quantitative trait loci (iQTLs) mapping and variant discovery. Using thyroid pathology images, clinical metadata, and genomics data from the Genotype-Tissue Expression (GTEx) project, we establish and validate a quantitative imaging biomarker for immune cell infiltration. A total of 100,215 variants were selected for iQTL profiling and tested for genotype-phenotype associations with our quantitative imaging biomarker. Significant associations were found in HDAC9 and TXNDC5. We validated the TXNDC5 association using GTEx cis-expression QTL data and an independent hypothyroidism dataset from the Electronic Medical Records and Genomics network.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.