# Using three-dimensional genome structure to refine eQTL detection

> **NIH NIH R03** · YALE UNIVERSITY · 2021 · $335,000

## Abstract

SUMMARY
 The Genotype-Tissue Expression (GTEx) Program studies the impact of genetic variants
on gene expression in many human cell types and tissues. To identify the expression
quantitative trait loci (eQTLs) of each gene, the genetic variants within one million base pairs (1
Mb) of the transcription start site (TSS) of the gene are considered as the candidates, and then
the GTEx computational pipeline identifies the significant candidates as eQTLs of the gene. This
1 Mb threshold is being widely used as the gold standard in the field to reduce multiple tests.
Using this threshold assumes that genetic variants outside of this distance contribute little to
gene expression, and thus are unlikely to be eQTLs. However, we observed that, on average,
10% of cis-regulatory elements (CREs) are outside of the 1 Mb threshold, herein referred to as
distal CREs. Therefore, the eQTLs in such CREs are missed using the 1 Mb threshold. In
addition, the 1 Mb threshold implicitly assumes that the majority of genomic regions within the
distance to a TSS are CREs that regulate the gene. However, we found that on average CREs
account for only 2.1% of the ±1Mb regions around a TSS. Moreover, it is not uncommon that
CREs skip the closest genes to regulate distal genes. These observations indicate that many
candidate variants within the 1 Mb distance may be noise, and thus impede the detection of
bona fide eQTLs. In line with this, we found that using distance thresholds smaller than 1 Mb
substantially increase the numbers of eQTLs and associated genes. These results together
indicate that the current eQTLs detection can be improved by focusing only on the CREs of
genes. To this end, we will use the genome structure data from 4D Nucleome and other public
data to build CRE-gene linkages. These linkages are expected to detect more eQTLs,
especially the weak ones. The results will enhance the existing GTEx dataset and substantially
improve our understanding of gene expression regulation and human diseases.

## Key facts

- **NIH application ID:** 10356618
- **Project number:** 1R03OD032623-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Jinrui Xu
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $335,000
- **Award type:** 1
- **Project period:** 2021-09-22 → 2023-08-13

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10356618

## Citation

> US National Institutes of Health, RePORTER application 10356618, Using three-dimensional genome structure to refine eQTL detection (1R03OD032623-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10356618. Licensed CC0.

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