Supplement Title: Analyzing the complex genetics of late-onset Alzheimer's disease in human and mouse models We propose to supplement an existing R01 award, Methods and Tools to Analyze Genetic Complexity (R01GM115518), to analyze genetic networks that influence transcriptional patterns relevant to late-onset Alzheimer's disease (LOAD). This analysis will accelerate the creation of new mouse models of LOAD by identifying genetic loci that interactively modify transcriptomes in a way that mimics analogous human signatures derived from postmortem brain samples. We will use the broad library of LOAD-related gene expression changes from a recent meta-analysis from the Accelerating Medicines Partnership for Alzheimer's Disease (AMP-AD). Genetic factors identified will be used as candidate factors in the Model Organism Development and Evaluation for Late-onset Alzheimer's Disease (MODEL-AD) Consortium. This supplemental project will therefore harness the pilot award methods to address the complex genetics of LOAD, drawing from and feeding into existing NIA-funded consortia.
The clinical success of genomic medicine is contingent upon the development of analytical methods to dissect genetic complexity. Large-scale genotype and phenotype data need to be translated into explicit, testable hypotheses of how multiple gene variants interact to affect specific health outcomes. The proposed research addresses this need through the development, application, and validation of novel computational methods to model genetic effects in a complex mammalian system. The computational tools will be implemented in open- source software designed for a broad range of genetic applications, ranging from engineered screens in model organisms to human genome-wide association data.
| Tyler, Anna L; Ji, Bo; Gatti, Daniel M et al. (2017) Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice. Genetics 206:621-639 |
