Modeling Alzheimer’s disease risk and associated molecular phenotypes
- PI
- Konermann, Silvana
- DAR ID
- 11841
- Initial Approval Date
- August 8, 2025
- Current DAR Status
- Expired
- PI Institution
- Arc Institute
- PI Institution Country
- United States
- PI Institution Type
- Non-Profit
Requested Datasets (63)
NG00067Alzheimer's Disease Sequencing Project Umbrella StudyNG00108Profiling microglia expression profiles in AD using single-nuclei RNA-seqNG00105MiGA – Microglia Genomic AtlasNG00106LASI-DAD Genotyping Chip Data (Illumina Infinium GSA-24 v2.0), Imputed Data (1000 Genomes Phase 3 v5 and TOPMed r2)NG00113Metabolomic and lipidomic signatures in Alzheimer disease brainsNG00114DNA Methylation in Alzheimer disease brainsNG00104Dataset from Clinical and Pathological Studies in the Oldest Old (90+ Study)NG00119Health and Retirement Study Genotype Data 2006-2012NG00121Alzheimer’s disease single-neuron whole-genome sequencing - Miller 2022NG00117NCRAD Families GWASNG00127A longitudinal study of Alzheimer's Disease and other dementing illnesses – KnightADRC GWASNG00129NCRAD Families Whole Exome Sequencing
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NG00126A fast and robust strategy to remove variant level artifacts in Alzheimer’s Disease Sequencing Project data (Belloy et al. 2022)NG00132Health and Retirement Study (HRS) APOE and Serotonin Transporter AllelesNG00118AMP-AD SV WGS and SV-xQTLNG00134National Health and Aging Trends Study (NHATS) GWASNG00130Large scale CSF proteogenomic atlasNG00144The Prediction of Alzheimer’s Disease through Multi-Trait Genetic ModelingNG00131Large scale genetics study on CSF and brain metabolite levelsNG00135UAB ADRC - University of Alabama at Birmingham Alzheimer’s Disease Research CenterNG00146Singapore Longitudinal study of Vascular Cognitive Impairment and DementiaNG00102Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disordersNG00154Dementia in Parkinson's Disease(PDD)NG00153Health and Retirement Study (HRS) DNA MethylationNG00160Sex-specific genetic architecture of late-life memory performanceNG00022ADC1 - Alzheimer's Disease Center Dataset 1NG00023ADC2 - Alzheimer's Disease Center Dataset 2NG00024ADC3 - Alzheimer's Disease Center Dataset 3NG00068ADC4 - Alzheimer's Disease Center Dataset 4NG00069ADC5 - Alzheimer's Disease Center Dataset 5NG00070ADC6 - Alzheimer's Disease Center Dataset 6NG00071ADC7 - Alzheimer's Disease Center Dataset 7NG00136ADC8 - Alzheimer's Disease Center Dataset 8NG00137ADC9 - Alzheimer's Disease Center Dataset 9NG00138ADC10 - Alzheimer's Disease Center Dataset 10NG00139ADC11 - Alzheimer's Disease Center Dataset 11NG00140ADC12 - Alzheimer's Disease Center Dataset 12NG00149ADC13 - Alzheimer's Disease Center Dataset 13NG00150ADC14 - Alzheimer's Disease Center Dataset 14NG00151ADC15 - Alzheimer's Disease Center Dataset 15NG00159Longitudinal Change in Memory Performance as a Strong Endophenotype for Alzheimer’s Disease – Archer, et al, 2023NG00116Resolving mutations in challenging genomic regions to test association with disease phenotypesNG00157Sex-specific genetic predictors of Alzheimer's disease biomarkers – Deming, et al. 2018NG00158Sex differences in the genetic predictors of Alzheimer’s pathology – Dumitrescu, et al. 2019NG00161Sex differences in the genetic architecture of cognitive resilience to Alzheimer's disease – Eissman, et al. 2022NG00145Colorado Adoption/Twin Study of Lifespan behavioral development & cognitive aging (CATSLife) APOENG00103Association Between Common Variants in RBFOX1, an RNA-Binding Protein, and Brain Amyloidosis in Early and Preclinical Alzheimer DiseaseNG00156Genetic variants and functional pathways associated with resilience to Alzheimer's diseaseNG00147Amyloid Neuroimaging and Genetics Initiative (ANGI) GWASNG00165CHARGE Association results from the ADSP R1 WGS data setNG00162Oligodendrocytes single-cell whole genome and RNA sequencingNG00142Plasma Cell-Free RNA Transcriptomics for AD and Related DementiasNG00166Association results from the ADSP R3 17k WGS data set - Lee et al., 2023NG00169Progressive Supranuclear Palsy (PSP) Summary Statistics- Farrell et al., 2024NG00141The Sacramento Area Latino Study on AgingNG00172GWAS Summary Statistics of SNVs/INDELs/SVs for Progressive Supranuclear Palsy (PSP) – Wang et al., 2024NG00174Whole Genome Sequencing (WGS) and SNP Array data for the SEA-AD Consortium CohortNG00100Novel Alzheimer Disease Risk Loci and Pathways in African American Individuals Using the African Genome Resources Panel: A Meta-analysis. Kunkle et al. (2021)NG00035GWAS of CSF tau levels identifies risk variants for Alzheimer’s DiseaseNG00050GWAS of CLU, A potential endophenotype for Alzheimer's diseaseNG00082UAB/HudsonAlpha Families with Neurodegenerative DiseasesNG00171Population structure in LASI-DADNG00178Multi-brain region mRNA-seq of sporadic ALS patients with pTDP-43 pathological staging
Research Use Statement
The objective of the proposed research is to determine the relationship between Alzheimer’s disease (AD) genetic risk and associated molecular phenotypes. Genotype data will be used to compute a polygenic risk score (PRS) for disease-affected and control (non-disease-affected) participants. Statistical regression and mediation analyses will be used to model variation of molecular phenotypes with respect to PRS and, where available, pathology stage or cognitive impairment. Molecular phenotypes to be analyzed include bulk/single-cell/single-nucleus transcriptome, epigenome, proteome, metabolome, lipidome, amyloid, and tau. Molecular phenotypes of participants, including controls, will be matched with molecular phenotypes of in vitro cellular models, informing the design of in vitro perturbation experiments that recapitulate the genetic drivers of AD risk.
Non-Technical Summary
Our goal is to determine the relationship between human genetic profiles associated with Alzheimer’s disease (AD) risk and specific measurable characteristics of human cells. Using multiple statistical analysis methods, we will build quantitative models that describe how those characteristics vary as a function of AD genetic risk. The models we build will help us design in vitro cellular systems that reflect different levels of AD risk, enabling experiments that inform new strategies for treating or preventing AD.
Reported Publications
Publications shown are those reported by investigators through NIAGADS DAR reporting and renewal processes and may not represent a complete list of publications associated with the project.
No publications have been reported for this DAR.