Prioritization of Genetic Variants Contributing to Late-Onset Alzheimer’s Disease

The goal of this collaborative project is to identify and prioritize genetic variations that contribute to late-onset Alzheimer’s disease (AD), with a particular focus on developing experimental models to understand the biology and of and develop new treatments for AD. We use a variety of approaches to analyze high-throughput genomic and genetic data, including statistical methods for identifying causal variants and genetic interaction networks, as well as epigenetic analyses of cellular regulation. By investigating and quantifying the effects of these mutations individually and interactively, we hope to begin to understand the biology of AD and unravel the complexity of AD genetic risk.
Our analysis will use the human genetic data in the Alzheimer’s disease sequencing project (ADSP), from which we will identify high-priority candidate genes and variants for further study in experimental models. Thus the goals of this work are twofold: to understand how brain irregularities progress into full AD, and provide the research community with a valuable mouse model for further studies and therapeutic testing. The described research will pilot novel analytical tools for the study of the biology of AD and provide insights into the genetics of neurodegeneration.
We will use independent evidence to identify candidates with putative functional roles. Transcriptome data from Alzheimer’s studies will be used to identify candidate genes and ENCODE and other regulatory data will be used to identify putative regulatory regions. Variants will be ranked based on computationally predicted mutation severity and differential expression in AD. Candidates in regulatory regions will be prioritized based on expression differences in nearby genes. Finally, functional data sources such as Gene Ontology, Allen Brain Atlas, and mouse phenotypes will be used to determine the potential role in neurodegeneration. The result will be a list of candidate variants prioritized for study in models. Since these data will be integrated with a focus on the role of individual genes generated from a population-wide analysis, we do not foresee creating any additional risk to individual participant

While many genetic loci have been identified as contributing to the risk of late-onset Alzheimer’s disease, the biological underpinnings and interdependence of these mutations are generally poorly understood. By investigating and quantifying the effects of these mutations individually and interactively, we hope to begin to unravel the complexity of genetic risk. Data from the Alzheimer’s Disease Sequencing Project will be essential in this approach, allowing us to identify the best candidate genes and perform computational analysis to design advanced experimental models of AD. These models will be an experimental basis for understanding the biology of Alzheimer’s and performing early-stage testing of candidate therapeutics.