# Integrative approaches for mapping the genetic risk of complex traits

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $455,271

## Abstract

PROJECT SUMMARY/ABSTRACT
Although genome-wide association studies (GWAS) have been extremely successful in identifying numerous
risk loci for complex traits and diseases, at the vast majority of these loci, the causal mechanism between
genetic variation and disease risk remains largely unknown. This prohibits the development of novel drug
targets, personalized treatments or accurate prediction of high-risk individuals. In the quest to address this gap,
post-GWAS studies are experiencing a “big data” revolution driven by the exponentially decreasing costs of
high-throughput genomic assays. Multiple layers of data (genetic variation, transcriptome levels, epigenetic
modifications, localization of tissue-specific regulatory sites, etc.) are routinely collected in increasingly large
cohorts of individuals. This raises the need for new computational and statistical methods that are able to
integrate various types of data (genetic, epigenetic, transcriptomic) to understand the causal mechanism of
disease at GWAS risk loci. Here we propose to develop new methods and techniques and to apply them to
gain insights to the genetic basis of common disease and traits. Importantly, we aim to circumvent genomic
privacy issues (that often prohibit access to large-scale GWAS data) by proposing techniques that operate
directly at the summary statistic level (e.g. variant effect sizes). We will apply existing and newly developed
methods on GWAS summary data sets over 30 traits and diseases spanning more than 1,000,000 phenotype
measurements, joint with a catalogue of over 7,000 biochemical and evolutionary genetic metrics of
functionality as well as over 10,000 individuals for which genetic variation, gene expression and disease status
has been measured.

## Key facts

- **NIH application ID:** 10112280
- **Project number:** 5R01HG009120-05
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Bogdan Pasaniuc
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $455,271
- **Award type:** 5
- **Project period:** 2017-03-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10112280

## Citation

> US National Institutes of Health, RePORTER application 10112280, Integrative approaches for mapping the genetic risk of complex traits (5R01HG009120-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10112280. Licensed CC0.

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