# Fine-mapping causal tissues at disease-associated loci to infer disease subtypes

> **NIH NIH F32** · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · 2024 · $73,408

## Abstract

Project Summary (Abstract)
Genome-wide association studies (GWAS) have been extremely successful in identifying genetic loci
associated with complex diseases, but the causal tissue and functional mechanism underlying the association
at each locus is generally unknown. Recent work has shown gene expression across tissues can be used as
an intermediate phenotype to fill in these missing links between genotype and disease. An accurate mapping
from disease locus to the causal tissue and gene mediating that disease locus would further our understanding
of disease biology and provide novel insights into tissue-linked mechanisms of disease. However, accurate
inference of the causal tissue and gene driving a disease locus can be obscured by genetic co-regulation
(correlation among variant effects on gene expression) across tissues as well as genes. The first aim of this
proposal is to develop a new statistical method to accurately infer the causal tissue and gene driving a disease
locus, while explicitly modeling genetic co-regulation between tissues and genes. The second aim of this
proposal is to infer disease subtypes linked to specific tissues using tissue-specific polygenic risk scores (PRS)
generated by annotating disease loci according to their causal tissue type, utilizing the method developed in
the first aim. I expect tissue-specific PRS to uncover patterns of tissue-specific genetic risk across individuals
that correspond to different subtypes of the disease. If achieved, this work will refine our knowledge of causal
tissues and genes underlying disease loci, as well as provide a new paradigm for inferring the disease subtype
of each individual in a disease association study. More generally, this work has the potential to refine the set of
drug target candidate disease genes and facilitate the development of patient care and treatments specific to
disease subtypes. In addition, I propose an in-depth training plan that leverages the research community
throughout Harvard to provide me with the training and skills necessary to advance my career from a post-
doctoral fellow to an independent investigator.

## Key facts

- **NIH application ID:** 10833485
- **Project number:** 5F32HG012889-02
- **Recipient organization:** HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH
- **Principal Investigator:** Benjamin Strober
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $73,408
- **Award type:** 5
- **Project period:** 2023-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10833485

## Citation

> US National Institutes of Health, RePORTER application 10833485, Fine-mapping causal tissues at disease-associated loci to infer disease subtypes (5F32HG012889-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10833485. Licensed CC0.

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