# Novel integrative imaging genetics analysis for Alzheimer's disease riskand progression > **NIH NIH RF1** · YALE UNIVERSITY · 2021 · $1,874,912 ## Abstract Abstract Our overarching goal is to construct heritability enhanced multimodal imaging endophenotypes, dissect their associated genetic underpinnings, and characterize the pathological mechanism along with clinical profiles for Alzheimer’s disease (AD) susceptibility and progression through a suite of statistically powerful, biologically plausible and computationally efficient Bayesian models. AD is complex: its etiology is in no way clear and with no available cure. Genetics play a dominate role in AD. However, genome wide association studies based on AD susceptibility have only discovered a small number of independent genetic factors. Compared with categorical diagnoses, imaging quantitative trait (QT) has distinct advantages to capture disease etiology and their association on genetic variants has yielded some prominent finding. However, existing imaging genetics studies suffer with i) inadequate power under current atlas-based imaging traits, ii) oversight of genetic underpinnings across different brain processes and potential role of SNP-SNP interactions, and iii) unclear pathological mechanism among genetics, imaging and clinical profiles, which limit their further advances to AD research. To overcome these barriers, this project proposes the following four aims: 1) construct novel multimodal imaging endophenotypes under “brain heritability parcellation'” for AD risk and progression; 2) identify pleiotropic SNPs and SNP-SNP interactions under constructed multimodal brain biomarkers; 3) investigate the fundamental pathological mechanism between genetic variations and AD pathology mediated by multimodal imaging endophenotypes; and 4) perform systematic evaluation of the proposed methods through real data analyses and simulations, and develop user-friendly analytical pipelines. Our proposed methods are innovative in multiple aspects for and beyond AD biomedical research including a) to construct a novel brain heritability parcellation, b) to integrate multimodal imaging on their association to genetic bases, c) to explore risk SNP- SNP interaction along with common genetic variants in a coherent and scalable way, d) to consider genetic pleiotrophy in a fine mapping framework, e) to establish pathological mechanism among individual’s genetic variants and polygenic profile, brain activities and disease symptoms, f) to extend the proposed methods to longitudinal settings with respect to AD progression, and g) to develop efficient and user friendly pipelines for our products. A successful completion of this proposal will facilitate the identification of novel genetic variants and characterize their impacts on AD pathogenesis, mediated by the constructed multimodal imaging biomarkers. The development of innovative statistical methods and computational tools, and their implementation on the ADNI and Yale ADRC cohort will offer a great potential for understanding the genetic and neurobiological mechanisms of AD and advancing targeted prevention and t... ## Key facts - **NIH application ID:** 10210551 - **Project number:** 1RF1AG068191-01A1 - **Recipient organization:** YALE UNIVERSITY - **Principal Investigator:** Yize Zhao - **Activity code:** RF1 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $1,874,912 - **Award type:** 1 - **Project period:** 2021-05-15 → 2024-04-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10210551 ## Citation > US National Institutes of Health, RePORTER application 10210551, Novel integrative imaging genetics analysis for Alzheimer's disease riskand progression (1RF1AG068191-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10210551. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*