# Detecting pleiotropic effects through integration of omics data

> **NIH NIH R01** · YALE UNIVERSITY · 2021 · $689,993

## Abstract

Vast amounts of whole genome sequence and imputed sequence data are being generated for
many complex traits and diseases. Most studies, e.g. UK10K, National Heart, Lung and Blood
Institute-Exome Sequencing Project, have concentrated on detecting main effects. Pleiotropy,
although an important phenomenon in genetic etiology, has not been adequately studied and
methods are limited to detect pleiotropy for rare and imputed variants. Additionally, although
there have been reports of pleiotropic loci it has been difficult to elucidate if these effects
underlie disease etiology or are false positives. We will tackle this problem using a multi-prong
approach that utilizes pleiotropic association testing, estimating tissue-specific disease
heritability and detecting tissue-specific pleiotropy. To meet the goals of this study we will use
omics data, implement previously developed methods and extend existing methods to analyze
imputed and rare variants. To ensure discoveries for a large variety of complex diseases and
traits e.g. asthma, type 2 diabetes, adiposity, and lipids, and to demonstrate that these methods
are an effective approach to study pleiotropy, data from the UK Biobank (500,000 study
subjects) will be analyzed. A split sample design will be employed in which 350,000 subjects
(Release 2) for Discovery and 150,000 subjects (Release 1) for Replication. Secondary
replication and fine mapping will be performed using TOPMed data which will have >150,000
individuals with whole genome sequence data with 26% of these individuals being African-
American, 10% Hispanic, and 7% Asian. All methods will be implemented in our SEQSpark
software which uses parallel processing to make it feasible to analyze hundreds of thousands of
samples efficiently and quickly. Not only is this study expected to improve our understanding of
the genetic etiology for complex diseases and traits, but it also has high public health
significance; understanding pleiotropic effects will improve our ability to estimate genetic risk
and provide insight into drug targets for the development of treatments of multiple diseases due
to shared genetic architecture. The framework and software developed in this proposal will be
available to the scientific community to apply to other large datasets for the identification of
pleiotropic loci beyond those phenotypes described here.

## Key facts

- **NIH application ID:** 10117042
- **Project number:** 5R01HL145660-03
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Andrew DeWan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $689,993
- **Award type:** 5
- **Project period:** 2019-03-15 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10117042

## Citation

> US National Institutes of Health, RePORTER application 10117042, Detecting pleiotropic effects through integration of omics data (5R01HL145660-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10117042. Licensed CC0.

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