# Apply novel pathogenomic approaches to identify interpretable image QTLs for multiple normal tissues

> **NIH NIH R01** · INDIANA UNIVERSITY INDIANAPOLIS · 2024 · $412,613

## Abstract

ABSTRACT
The overarching goal of this project is to identify tissue-specific genetic variants and genes that
are associated with cellular and tissue morphology in normal tissues. We plan to apply advanced
computational pathology methods based on machine learning and computer vision to analyze
normal tissue histology H&E images from Genotype-Tissue Expression Project (GTEx), extracting
interpretable image features as quantitative traits. Next, we will apply bioinformatic and statistical
genetic methods to identify the morphological traits that are correlated with eQTLs in donor
population, thus generate image QTLs (a.k.a. imQTLs) for all normal tissue types in GTEx with
over 100 samples. In addition, we will apply advanced functionally informed GWAS (FiGWAS),
which has been successfully applied to eQTL research and significantly boosted the detection of
rare variants in genomic association study, to further investigate the association of non-eQTL
genetic variants with the interpretable quantitative morphology features described above and to
generate supplementary imQTLs. Neither of these two approaches has previously been applied
to identify imQTLs. The workflow will initially focus on cell type morphological features, then
expand to features related to tissue development and cell-cell interactions. The identified imQTLs
(or the genes/image traits associated with them) will be further tested in histopathological images
in corresponding tissues from The Cancer Genome Atlas (TCGA) for any difference in terms of
imQTL presence, abnormality in the associated image traits, or expression in the associated
genes. The identified imQTLs will not only generate new insights about the tissue differentiation,
development, and morphological variations in the normal population, but also will provide a solid
basis for comparing pathological changes in many types of diseases and help quantify the level
of the corresponding histopathological changes. The resulted image features and imQTLs will be
made available through a web portal called PathoGenome Viewer for general public query and
use.

## Key facts

- **NIH application ID:** 10820326
- **Project number:** 1R01GM153028-01
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** Kun Huang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $412,613
- **Award type:** 1
- **Project period:** 2024-09-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10820326

## Citation

> US National Institutes of Health, RePORTER application 10820326, Apply novel pathogenomic approaches to identify interpretable image QTLs for multiple normal tissues (1R01GM153028-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10820326. Licensed CC0.

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