# Statistical analysis of large genomic data sets

> **NIH NIH R01** · CASE WESTERN RESERVE UNIVERSITY · 2022 · $350,917

## Abstract

Project Summary/Abstract
Alzheimer's disease (AD) is a highly heritable, common and fatal neurodegenerative disease
among older people. There have been significant advances in genomic studies of risk factors,
volumetric variations of the human brain and AD, however, there is a lack of systemic analysis of
these traits together. Single trait analysis is not only less powerful than multiple trait analysis in
searching for disease associated variants but also misses the opportunity to identify novel
pleiotropic variants and to understand the biological mechanism among them. Since risk factors
and brain function and structure all contribute to AD, an AD polygenic risk score incorporating
genetic components obtained from risk factors and brain imaging data may substantially improve
its. But how to incorporate this information has not been studied. Furthermore, searching for rare
variants contributing to AD is extremely challenged and requires a large sample size. Novel
analysis approaches are necessary to improve the identification of AD associated rare variants.
Our NHGRI funded project R01 HG011052 entitled “Statistical analysis of large genomic data
sets” is developing statistical methods and software for estimating causal effects among traits
and searching for pleiotropic variants, as well as testing rare variant associations by incorporating
linkage evidence and natural selection. We believe these novel methods can contribute AD
research and move the field forward. In this project we propose two specific aims using the
methods and software we developed in HG011052. In Aim 1, we will construct a genetic network,
identify genetic variants with and without pleiotropy effects and construct a composite polygenic
risk score for AD. In Aim 2, we will identify rare genetic variants associated with AD by
incorporating family information and natural selection. Our proposed work focuses on
understanding the mechanism of AD and the project HG011052 is the foundation of this
administrative supplement.

## Key facts

- **NIH application ID:** 10497922
- **Project number:** 3R01HG011052-03S1
- **Recipient organization:** CASE WESTERN RESERVE UNIVERSITY
- **Principal Investigator:** XIAOFENG ZHU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $350,917
- **Award type:** 3
- **Project period:** 2020-05-08 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10497922

## Citation

> US National Institutes of Health, RePORTER application 10497922, Statistical analysis of large genomic data sets (3R01HG011052-03S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10497922. Licensed CC0.

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