# meQTL Discovery in Admixed Human Genomes Facilitates Estimates of Epistasis

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2024 · $281,751

## Abstract

Personalized genetics is poised to revolutionize healthcare; however, despite advances, genetic
prediction and portability of critical variants remains too limited for clinical use. Genetic
prediction is error-prone when applied to individuals with genetic ancestries different from
discovery cohorts, often predicting disease risk little better than random in non-European
samples. We propose understanding an underlying cause of this loss of prediction accuracy by
assessing the extent of GxG interaction effects across ancestries. Using an innovative
approach, we tackle this statistically challenging problem by 1) modeling effect sizes of meQTLs
on different ancestry haplotypes in an admixed African sample from the same population --
thereby controlling for GxE, and 2) using highly heritable genome-wide methylation phenotypes,
affording us thousands of observations per participant rather than a single phenotype (e.g.
presence of cardiovascular disease). Our study design allows us to assess whether the
presence of ancestry-dependent interactions is a common factor in the variability of SNP effect
sizes across populations. Outcomes of this grant include: generating a large genome-wide
methylation dataset from 500 admixed South Africans, paired with underlying genome-wide
DNA variation. We will further estimate the fraction of meQTLs with ancestry-specific effects and
thereby comprehensively provide a snapshot of the frequency of GxG interactions in the human
genome. These results will motivate investigation of GxG effects in a broader set of biomedical
phenotypes and the extent to which they contribute to poor portability of polygenic risk across
populations.

## Key facts

- **NIH application ID:** 10871292
- **Project number:** 1R21HG013565-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Brenna M Henn
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $281,751
- **Award type:** 1
- **Project period:** 2024-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10871292

## Citation

> US National Institutes of Health, RePORTER application 10871292, meQTL Discovery in Admixed Human Genomes Facilitates Estimates of Epistasis (1R21HG013565-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10871292. Licensed CC0.

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