Projects

Alzheimer disease is the primary focus of the research done within the NGI. We have several ongoing, funded projects dedicated to understanding more about what causes Alzheimer disease and what we can do to detect it earlier and potentially treat it.

Alzheimer Disease Endophenotypes Genetics	The Familial Alzheimer Sequencing (FASe) project
Recent genetic studies of complex traits and diseases have focused on the identification of common variants associated with risk through genome-wide association studies (GWAS). In this study, we will use GWAS and sequence data data to identify variants, genes and pathways associated with cerebrospinal fluid levels of known AD biomarkers (tau, ptau, Aβ, YKL40, VILIP1) and other AD-related proteins (CLU, APOE, TREM2), and other AD-related endophenotypes Funded by the NIA	The aim of this research is to identify rare functional variants with large effect size on risk for Alzheimer’s disease (AD). We hypothesize that families with extensive history of dementia are enriched for genetic risk factors and that by sequencing those families, we will identify rare variants with large effect size. In this project, we will combine sequencing data in families with late-onset AD and genotyping data in large case-control series. FASe Funded by the NIA
Early Onset Alzheimer Disease (EOAD)	Amyloid PET Neuroimaging
Mutations in the APP, PSEN1, and PSEN2 genes, which follow Mendelian inheritance patterns explain only 10% of EOAD cases. Large-scale, whole-genome sequencing is needed to identify additional EOAD-associated variants that explain the remaining 90% of cases. We are collaborating with investigators at Columbia University and University of Miami to perform a large, whole-genome sequencing study including thousands of EOAD cases and controls. We expect to identify novel genetic contributors to EOAD, uncover mechanisms of AD, and provide a basis for the development of novel therapeutics. Funded by the NIA	Neuroimaging, using positron emission tomography (PET), has emerged as a non-invasive way to measure the accumulation of amyloid and hyperphosphorylated tau in the brain at early stages of the Alzheimer disease process. We work with other researchers at WashU to use amyloid imaging data from multiple, multiethnic cohorts to identify genes and variants associated with amyloidosis. With biomarkers, detectible from early disease stages, we can identify the genetic variations that contribute to these early warning signs, form prediction models based on these variants, and possibly identify drug targets to slow or reverse these early changes.
TREM2 variants	Multi-tissue Multi-omics
While 1-2% of AD cases are inherited dominantly with mutations in APP, PSEN1, and PSEN2, most cases occur sporadically. One goal of our research has been to identify genetic changes that explain some of the heterogeneous nature of sporadic AD cases. To this end, we have identified variants in TREM2 that are associated with increased risk of AD. The effect of these variants impacts the interaction between TREM2 and MS4A4A proteins. Using this knowledge, we are able to add another level of stratification in our other studies based on TREM2 status. This enables us to identify other differences between seemingly similar AD cases based on a patient’s genetic background. Funded by the NIA	To completely understand a disease, such as Alzheimer disease, it is not enough to study it from a single angle. only the relationship between gene variants and disease status in a single tissue, even if it clinically relevant (e.g. brain for AD). For this reason, we have committed to the study of multiple “omic” layers, including transcriptomics, proteomics, lipidomics, and metabolomics in multiple tissue that are clinically relevant to AD diagnosis and treatment. We are leveraging our access to large, well-characterized AD and control cohorts with brain, CSF, and plasma tissue samples. We plan to use high-throughput, multi-comic methods and advanced computational approaches, such as machine learning, to identify genes, pathways, molecular signatures, and potential biomarkers and drug targets for AD. This knowledge will lead to better informed risk prediction, diagnostics, and treatment of AD. Multi-tissue Multi-omics