# Integrating Neuroimaging, Multi-omics, and Clinical Data in Complex Disease

> **NIH NIH R01** · INDIANA UNIVERSITY INDIANAPOLIS · 2021 · $341,300

## Abstract

ABSTRACT
Rapid progress in biomedical informatics has generated massive high-dimensional data sets (“big data”),
ranging from clinical information and medical imaging to genomic sequence data. The scale and complexity
of these data sets hold great promise, yet present substantial challenges. To fully exploit the potential
informativeness of big data, there is an urgent need to find effective ways to integrate diverse data from
different levels of informatics technologies. Existing approaches and methods for data integration to date
have several important limitations. In this project, we propose novel statistical methods and strategies to
integrate neuroimaging, multi-omics, and clinical/behavioral data sets. To increase power for association
analysis compared to existing methods, we propose a novel multi-phenotype multi-variant association
method that can evaluate the cumulative effect of common and rare variants in genes or regions of interest,
incorporate prior biological knowledge on the multiple phenotype structure, identify associated phenotypes
among multiple phenotypes, and be computationally efficient for high-dimensional phenotypes. To improve
the prediction of clinical outcomes, we propose a novel machine learning strategy that can integrate
multimodal neuroimaging and multi-omics data into a mathematical model and can incorporate prior
biological knowledge to identify genomic interactions associated with clinical outcomes. The ongoing
Alzheimer's Disease Neuroimaging Initiative (ADNI) and Indiana Memory and Aging Study (IMAS) projects
as a test bed provide a unique opportunity to evaluate/validate the proposed methods. Specific Aims: Aim 1:
to develop powerful statistical methods for multivariate tests of associations between multiple phenotypes
and a single genetic variant or set of variants (common and rare) in regions of interest, and to develop
methods for mediation analysis to integrate neuroimaging, genetic, and clinical data to test for direct and
indirect genetic effects mediated through neuroimaging phenotypes on clinical outcomes; Aim 2: to develop
a novel multivariate model that combines multi-omics and neuroimaging data using a machine learning
strategy to predict individuals with disease or those at high-risk for developing disease, and to develop a
novel multivariate model incorporating prior biological knowledge to identify genomic interactions associated
with clinical outcomes; Aim 3: to evaluate and validate the proposed methods using real data from the ADNI
and IMAS cohorts; and Aim 4: to disseminate and support publicly available user-friendly software that
efficiently implements the proposed methods. RELEVANCE TO PUBLIC HEALTH: Alzheimer's disease
(AD) as an exemplar is an increasingly common progressive neurodegenerative condition with no validated
disease modifying treatment. The proposed multivariate methods are likely to help identify novel diagnostic
biomarkers and therapeutic targets for AD. Identifying new su...

## Key facts

- **NIH application ID:** 10139101
- **Project number:** 5R01LM012535-05
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** Kwangsik Timothy Nho
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $341,300
- **Award type:** 5
- **Project period:** 2017-07-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10139101

## Citation

> US National Institutes of Health, RePORTER application 10139101, Integrating Neuroimaging, Multi-omics, and Clinical Data in Complex Disease (5R01LM012535-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10139101. Licensed CC0.

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