# Integrative Approaches to Understanding Genetic Basis of Neuropsychiatric Diseases

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2022 · $503,169

## Abstract

Project Summary
Identifying the susceptibility genes and variants of neuro-psychiatric diseases will not only contribute to our
understanding of these diseases, but also point to potential therapeutic targets. Genome-wide association
studies (GWAS) are commonly used to study complex diseases, and have been highly successful in a range of
disorder, for instance, more than 100 loci have been associated with the risk of Schizophrenia through GWAS.
Nevertheless, in most cases, we do not know the biological mechanisms underlying disease associated loci,
because the causal variants and genes are obscured by linkage disequilibrium (LD) and by the difficulty of
interpreting functional effects of most genetic variants.
 The goal of this project is to develop novel statistical methods for integrative analysis of genetic data of
neuropsychiatric diseases to better understand the underlying genes and biological processes. (1) We will
develop a method to integrate expression QTL (eQTL) data with GWAS. Our method extends the popular
Transcriptome-Wise Association Studies (TWAS). TWAS aims to discover risk genes, by effectively assessing
the correlation of eQTLs of a gene with the phenotype of interest. TWAS has many advantages over standard
single variant-based analysis, e.g. it reduces multiple testing burden and provides biological contexts of
associations. However, current TWAS methods are susceptible to false positive findings. We will develop a
rigorous statistical framework to control false discoveries by accounting for pleiotropic effects of variants. (2)
Fine-mapping is the statistical approach to identifying causal variants in disease-associated loci. Current fine-
mapping methods, however, are often not able to narrow down specific causal variants. Our approach is based
on the observation that allelic heterogeneity (AH), i.e. many variants disrupting the same gene, is common. So
we can leverage AH to identify risk genes, borrowing the statistical framework of fine-mapping. (3)
Researchers have developed tools to joint analyze multiple traits to improve the power of gene discovery and
to identify causal risk factors of diseases. Existing approaches, however, are often based on pair-wise
analysis. We will develop a powerful statistical framework to better understand common biological processes
driving genetic relationships among multiple traits. Additionally, we will develop more accurate Mendelian
Randomization (MR) method to identify causal relationship among traits. (4) A key component of our effort is
the development of user-friendly software that could benefit the broad psychiatric genetics community.

## Key facts

- **NIH application ID:** 10413982
- **Project number:** 5R01MH110531-06
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Xin He
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $503,169
- **Award type:** 5
- **Project period:** 2017-05-17 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10413982

## Citation

> US National Institutes of Health, RePORTER application 10413982, Integrative Approaches to Understanding Genetic Basis of Neuropsychiatric Diseases (5R01MH110531-06). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10413982. Licensed CC0.

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