Characterizing the progression of Alzheimer's disease with multi-omic genetic and imaging data

Indiana University Indianapolis, Indianapolis IN

Investigators

Abstract

Alzheimer’s Disease (AD) is an irreversible neurodegenerative disorder characterized by progressive impairment in brain structures and functions. Early biomarkers are believed crucial to AD, making it possible to identify and treat AD patients before evident symptoms. Established AD biomarkers are grouped into β amyloid deposition (A), pathologic tau (T), and neurodegeneration (N), including neuroimaging and cerebrospinal fluid (CSF). However, they fall short in explaining the heterogeneity of individual clinical trajectories. In this project, we aim to develop novel computational approaches to identify genetic biomarkers related to AD progression. Leveraging the multi-omic genetic data and multi-modal brain imaging data in AD (e.g., AMP-AD, ADNI), We will 1) identify stage-specific genetic biomarkers of AD with known downstream effect on transcriptome and proteome layers, and 2) identity genetic biomarkers that can help differentiate distinct phenotype trajectories. These results can help with candidate screening in clinical trials and provide stratified risk groups to facilitate the development of therapeutic intervention. Alzheimer’s Disease (AD) is an irreversible neurodegenerative disorder with a long prodromal phase and no clinically validated cure. Detecting when and how molecular and phenotype marker develop along AD progression will provide a template for understanding the underlying etiology of clinical syndromes and for improving early diagnosis, clinical trial recruitment and treatment assessment. Established AD biomarkers can be grouped into β amyloid deposition (A), pathologic tau (T), and neurodegeneration (N), captured from neuroimaging and cerebrospinal fluid (CSF). Despite some applications in early detection, the ATN framework relies on the dichotomous classification of individuals and cannot capture the full spectrum of AD-related pathologies. It could be supplemented with the addition of stage-specific markers or a severity staging scheme. In this project, we will develop novel computational approaches for subject-level stage-specific markers and severity staging scores. We will leverage major multi-omic genetic data and multi-modal brain imaging data in AD, and propose the following two aims, 1) Identify subject-level stage-specific disease modules using multi- omic data, and 2) subject-specific severity staging with longitudinal imaging data based pseudotime. These methods and tools will have considerable potential for improved understanding of disease progression and discovery of associated neuroimaging and genetic markers. These results can help with candidate screening in clinical trials and provide stratified risk groups to facilitate the development of therapeutic intervention.