The Genetic Architecture of Alzheimer's Disease Endophenotypes

Abstract

Alzheimer’s Disease (AD) is one of the most common forms of dementia and is known to have a strong genetic component, but known genetic loci do not fully account for the observed genetic heritability of late onset AD. This genetic complexity is further complicated by disease heterogeneity, with non-uniform presentation and progression of AD neuropathology. Endophenotypes lie upstream of observed AD clinical outcomes and downstream of genetic contributors, allowing for a biological understanding of genetic effects. Understanding the genetic architecture of AD endophenotypes can aid in breaking down AD genetic complexity and heterogeneity. In this study we utilized a variety of models to evaluate the genetic contributors to pathological change and heterogeneity in the top markers of AD pathology: amyloid, tau, neurodegeneration, and cerebrovascular (A/T/N/V framework). Additional composite quantitative measures of cognitive performance were used to relate to downstream AD presentation. These biomarkers allow the investigation of genetic effects contributing to the disease over the stages of disease progression from amyloid deposition to neurofibrillary tangle formation, disruption of metabolism, brain atrophy, and finally to clinical outcomes. First, we performed genome-wide association studies (GWAS) for AD endophenotypes at baseline using a cross-sectional regression model. This method identified sixteen novel or replicated loci, with six (SRSF10, MAPT, XKR3, KIAA1671, ZNF826P, and LOC100507506) associated across multiple A/T/N biomarkers. Cross-sectional data was further utilized to identify three genetic loci (BACH2, EP300, PACRG-AS1) that showed disease stage specific interaction effects. We built upon those results by performing a longitudinal association analysis with linear-mixed effects modeling. Gene enrichment analysis of these results identified 19 significant genetic regions associated with linear longitudinal change in AD endophenotypes. To further break down longitudinal heterogeneity, a latent class mixed model approach was utilized to identify subgroups of longitudinal progression within cognitive and MRI measures, with 16 genetic loci associated with membership in different classes. The genetic patterns of these subgroups show biological relevance in AD. The methods and results from this study provide insight into the complex genetic architecture of AD endophenotypes and a foundation to build upon for future studies into AD genetic architecture.