Brain imaging genetics in Alzheimer’s Disease: linking genetics and imaging-derived endophenotypes

Brain imaging genomics is an emerging scientific field employing advanced computational statistical genomics and artificial intelligence to derive genomics and imaging-derived endophenotypes. The underlying hypothesis posits that these endophenotypes reside within the causal pathway, acting as more effective instruments — enhancing statistical power — to decipher the multifaceted pathogenesis of brain disorders and the mechanisms of the human brain. Genetics and imaging genetic studies have the potential to unravel the genetic basis of the brain, providing a molecular-level understanding of biological processes. Additionally, the integration of brain imaging technologies with genetic analyses enables researchers to visualize and comprehend the structural and functional consequences of genetic variations in the brain. This interdisciplinary approach not only enhances our understanding of the normal biological functions of the brain but also significantly contributes to identifying genetic factors associated with various brain pathological conditions and biological brain aging processes.
This talk aims to present an overview of the latest research studies and methodologies in the field of Brain Imaging Genetics. These topics will be further expanded upon by the presenters. Key areas covered include deep learning, the polygenicity of multimodal imaging-derived phenotypes, and the multivariate assessment of genetics and brain imaging features.
 

Little is known about the genetic factors and the downstream molecular pathways determining individual variability in MRI-derived endophenotypes. Unraveling how genetic variability is related to neuropathological heterogeneity, and whether this occurs through specific biological pathways, are key steps towards precision medicine for several polygenic diseases, such as Alzheimer’s disease (AD). This talk will demonstrate the use of pathways and functional enrichment analysis to biologically characterize multimodal imaging-derived metrics. We studied AD polygenic risk score and performed functional enrichment analysis to measure genetic risk related to specific biological pathways, such as neuroinflammation, immune activation and amyloid clearance. We further used pathway-specific PRS to biologically characterize multimodal imaging biomarkers including gray matter volumes, white matter hyperintensities (WMH) volumes, fractional anisotropy from diffusion tensor imaging, and resting-state network functional connectivity. This study reveals distinct genetic risk-profiles in association with specific AD pathophysiological aspects, unraveling the biological substrates of the heterogeneity of AD-associated endophenotypes.
 

Alzheimer’s disease (AD) is a major public health crisis, affecting millions worldwide, with a substantial social and economic burden. Effective strategies are urgently needed to discover new AD genes for disease modeling and drug development. Studying AD genetics using multimodal imaging and multi-omics data is becoming a rapidly growing field with distinct advantages in power over categorical diagnosis under imaging and omics traits as well as in capturing new insights into disease mechanism and heterogeneity from genetic determinants to omics-level molecular signatures, to brain imaging biomarkers, and to AD outcomes. In this talk, we will discuss AI and informatics strategies for discovering AD risk and protective genes through analyzing multidimensional genetics, omics, imaging and outcome data from landmark and local AD biobanks. We show that the wide availability of these rich biobank data, coupled with advances in trustworthy AI and informatics, provides enormous opportunities to contribute significantly to gene discovery in AD and to impact the development of new diagnostic, therapeutic and preventative approaches.
 

Brain Imaging Genetic studies investigate genetic influences on brain structure and function by integrating neuroimaging-based features and genetic data. While many studies focus on individual genetic correlations with single brain measurements, the field emphasizes the necessity of multivariate methods. This presentation will highlight the significance of multivariate methodologies in the realm of brain Imaging Genetic studies. As a proof of concept, we will show a detailed analysis showcasing how genetic predisposition to Alzheimer's disease impacts the joint modulation of hippocampal subfields volumes. A comparative analysis with results from univariate approaches will underscore the pivotal role of multivariate methodologies in unraveling the genetic influences on brain imaging.