# New Computational Tools for Advanced Analytics in Genome-wide Association Studies

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $312,817

## Abstract

Project Summary
Many genome-wide association studies (GWAS) have been successfully carried out, identifying numerous
genetic variants associated with common diseases and disease related complex traits. The identified genetic
associations can now explain a large fraction of the genetic contribution and trait heritability, revealing the
genetic architecture underlying common diseases. The success of GWAS in the past few years have laid down
a solid foundation both for pursuing the mechanistic insights towards the biology of disease and for
transitioning towards new diagnostics and therapeutics in clinical settings. Advancing GWAS towards
mechanistic insights and clinical translations, however, urgently requires the development of advanced
computational methods that can take advantage of the unique data features and increased data complexity of
GWAS as well as the data available from parallel genomics studies.
 Here, we propose to develop a set of new computational methods to advance GWAS analytics beyond
simple variant association analysis and move towards the understanding of the biology of disease and enable
potential clinical translations. Specifically, in Aim 1, we will develop integrative methods to integrate GWAS
with multiple gene expression mapping studies of distinct genetic ancestries to investigate the molecular
mechanisms underlying the variant-trait associations and interrogate the contribution of ancestry specific
genetic architecture underlying expression towards gene-trait associations. In Aim 2, we will develop causal
inference methods to leverage the genetic associations to improve our understanding of the causal relationship
among complex traits and to identify causal risk factors that underlie disease etiology. In Aim 3, we will develop
prediction methods to make use of the genetic associations and take advantage of the genetic and
environmental correlation among multiple complex traits to facilitate the genetic prediction of disease risk,
aiding disease diagnosis and clinical applications. All methods will be implemented in user-friendly open-
source software and disseminated to the scientific community. At its conclusion, the proposed study will
provide a comprehensive suite of computational methods and software tools for advanced analytics in GWAS.
These methods are essential for understanding the transcriptomic and causal mechanism underlying disease
etiology, enabling accurate and robust genetic prediction of disease risks, and facilitating biological discoveries
and insights.

## Key facts

- **NIH application ID:** 10833606
- **Project number:** 5R01HG009124-07
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Xiang Zhou
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $312,817
- **Award type:** 5
- **Project period:** 2017-06-14 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10833606

## Citation

> US National Institutes of Health, RePORTER application 10833606, New Computational Tools for Advanced Analytics in Genome-wide Association Studies (5R01HG009124-07). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10833606. Licensed CC0.

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