# Functionally relevant mapping of human GWAS SNPs on model organisms

> **NIH NIH R21** · UNIVERSITY OF WISCONSIN-MADISON · 2020 · $400,465

## Abstract

Project Summary
A large fraction of trait/disease-associated loci from genome-wide association studies
(GWAS) is intronic or intergenic. A major barrier to elucidating the variants responsible
for a given human trait/disease is the lack of understanding of the function of noncoding
genome. While there have been major developments in analytical tools that exploit
GWAS and large-scale epigenome resources to elucidate cell/tissue types and
epigenomic events relevant for the GWAS loci, comparative genomics methods through
mouse engineering approaches are critically lacking. This is a clear hindrance for
leveraging large-scale and well-powered model organism eQTL and QTL studies such
as the ones from diversity outbred mice to understand mechanisms underlying human
diseases. Current practice of moving between human and model organism genomes
solely pertains a sequence similarity-based mapping. However, this approach leads to
60-70% of the SNPs not mapping, and a significant fraction mapping to multiple
locations. This project addresses key difficulties towards this end by developing a
biologically relevant and statistically rigorous method, liftSNP, that goes beyond
sequence similarity and incorporates epigenome and higher order regulatory grammar
into mapping of human GWAS SNPs to model organism genomes. liftSNP will be
developed and evaluated on GWAS SNPs from three diverse disease systems
(hematologic/developmental; obesity, metabolic syndrome, T2D; neurological/autism).
The results of these large-scale applications will be made available through atSNP
Search and will enable researchers to lift over their GWAS SNP harboring genomic loci
to mouse genome in a functionally relevant manner. The aims will be accomplished
through a combination of methodological development, theoretical analysis, data-driven
simulation, computational analysis, and experimental validation. Statistical resources
generated from this project will be disseminated as open-source software. Collectively,
these aims will significantly enhance our comparative genomics interpretation of GWAS
results.

## Key facts

- **NIH application ID:** 10056966
- **Project number:** 1R21HG011371-01
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Sunduz Keles
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $400,465
- **Award type:** 1
- **Project period:** 2020-08-06 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10056966

## Citation

> US National Institutes of Health, RePORTER application 10056966, Functionally relevant mapping of human GWAS SNPs on model organisms (1R21HG011371-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10056966. Licensed CC0.

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