# Methods for Genomic Analysis in Heterogeneous Tissues

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2022 · $624,916

## Abstract

Project Summary/Abstract
The vast majority of genomic data are generated for heterogeneous tissues, whereas many genomic
measurements (e.g., gene expression and methylation) are tissue and cell-type specific. Notably, cell-type-
specific analysis can lead to important insights in understanding of underlying biological mechanisms.
Furthermore, analysis that ignores cell-type-specific effects often results in a substantial power loss and false
positive discoveries. Thus, there is a pressing need to develop methods that can facilitate cell-type specific
analysis on existing and future bulk datasets. Existing efforts to address tissue heterogeneity focus on the
inference of cell counts from bulk RNA and methylation, however these approaches do not detect cell-type
specific association but rather are used to avoid false discoveries. By contrast, this proposal will focus on a
novel set of statistical tools for the inference of the cell-type specific expression and methylation signal in each
gene and each individual.
The approach studied in this project will include the development of methods for the imputation of methylation
from single nucleus RNA-seq. These methods will allow to generate reference data for methylation using
publicly available single-nucleus RNA data. In addition, this project will generate single nucleus RNA-seq and
methylation for sorted cells from Mexican and Finnish blood and adipose samples, resulting in the largest
dataset that includes both types of data, particularly on Latinos and on adipose tissue. These reference data
will be used as training data for the developed methods. Finally, the methods developed will be used to search
for cell-type specific associations with obesity, nonalcoholic fatty liver disease, type 2 diabetes, and
dyslipidemias, as well as perform cell-type specific eQTL and mQTL analyses on a large Mexican and Finnish
population. In order to achieve this goal, bulk methylation data will be generated for Mexican and Finnish
adipose samples for which genotypes, bulk RNA-seq, and refined phenotypes are already available.
Importantly, the Latino data will be one of the largest non-European datasets with expression, methylation and
genotype information. This data will be made available to the research community. Thus, accomplishing this
project will advance the understanding of population-specific genetic and epigenetic components of highly
common cardiometabolic disorders with high morbidity and mortality worldwide. Mexicans have the highest
susceptibility of these cardiometabolic disorders, and this study will provide much needed new genomics data
in this admixed minority population to combat cardiometabolic disease in diverse populations.

## Key facts

- **NIH application ID:** 10424484
- **Project number:** 5R01HG010505-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** ERAN HALPERIN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $624,916
- **Award type:** 5
- **Project period:** 2019-09-15 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10424484

## Citation

> US National Institutes of Health, RePORTER application 10424484, Methods for Genomic Analysis in Heterogeneous Tissues (5R01HG010505-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10424484. Licensed CC0.

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