A longitudinal study of Alzheimer’s Disease and other dementing illnesses – KnightADRC GWAS
- PI
- Shelton, Janie
- DAR ID
- 11965
- Initial Approval Date
- January 30, 2026
- Current DAR Status
- Active
- PI Institution
- Bristol Myers Squibb
- PI Institution Country
- United States
- PI Institution Type
- For-Profit
Requested Datasets (10)
NG00067Alzheimer's Disease Sequencing Project Umbrella StudyNG00108Profiling microglia expression profiles in AD using single-nuclei RNA-seqNG00113Metabolomic and lipidomic signatures in Alzheimer disease brainsNG00114DNA Methylation in Alzheimer disease brainsNG00127A longitudinal study of Alzheimer's Disease and other dementing illnesses – KnightADRC GWASNG00128Proteomic profiling identified plasma biomarkers for SARS-CoV-2 infection and severity of COVID-19 patientsNG00102Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disordersNG00142Plasma Cell-Free RNA Transcriptomics for AD and Related DementiasNG00035GWAS of CSF tau levels identifies risk variants for Alzheimer’s DiseaseNG00050GWAS of CLU, A potential endophenotype for Alzheimer's disease
Research Use Statement
Recently approved Alzheimer’s disease (AD) therapies, such as lecanemab (Leqembi) and donanemab (Kisunla), represent a significant advancement toward disease-modifying treatment. However, their impact on cognitive decline remains modest, and both are associated with potentially serious adverse events, including amyloid-related imaging abnormalities (ARIA). These limitations underscore the urgent need for additional therapeutic strategies to reduce disease burden.
Genetic approaches offer a powerful avenue for drug target discovery, with evidence suggesting that genetically supported targets are at least twice as likely to progress successfully through clinical development to FDA approval (Nelson et al., 2015, Nat Genet; King et al., 2019, PLoS Genet; Minikel et al., 2024, Nature). To date, most genetic studies in AD have focused on identifying loci associated with disease risk. Large-scale genome-wide association studies (GWAS) have uncovered approximately 75 risk loci (Bellenguez et al., 2022, Nat Genet), providing valuable insights into disease etiology.
However, therapeutic interventions are typically aimed at individuals already diagnosed with AD, making the genetics of disease progression a critical—yet underexplored—complementary approach for target discovery. Progression-focused genetic studies face challenges due to limited availability of longitudinal phenotypic data. To address this, meta-analysis of multiple GWAS datasets offers a practical strategy to increase statistical power and detect robust associations.
We propose to incorporate summary statistics from the Knight Alzheimer Disease Research Center (Knight-ADRC) AD progression GWAS into a meta-analysis alongside several publicly available and proprietary datasets. Our objective is to identify novel genetic drivers of AD progression, prioritize new therapeutic targets, and assess the impact of existing pipeline candidates on disease trajectory.
Genetic approaches offer a powerful avenue for drug target discovery, with evidence suggesting that genetically supported targets are at least twice as likely to progress successfully through clinical development to FDA approval (Nelson et al., 2015, Nat Genet; King et al., 2019, PLoS Genet; Minikel et al., 2024, Nature). To date, most genetic studies in AD have focused on identifying loci associated with disease risk. Large-scale genome-wide association studies (GWAS) have uncovered approximately 75 risk loci (Bellenguez et al., 2022, Nat Genet), providing valuable insights into disease etiology.
However, therapeutic interventions are typically aimed at individuals already diagnosed with AD, making the genetics of disease progression a critical—yet underexplored—complementary approach for target discovery. Progression-focused genetic studies face challenges due to limited availability of longitudinal phenotypic data. To address this, meta-analysis of multiple GWAS datasets offers a practical strategy to increase statistical power and detect robust associations.
We propose to incorporate summary statistics from the Knight Alzheimer Disease Research Center (Knight-ADRC) AD progression GWAS into a meta-analysis alongside several publicly available and proprietary datasets. Our objective is to identify novel genetic drivers of AD progression, prioritize new therapeutic targets, and assess the impact of existing pipeline candidates on disease trajectory.
Non-Technical Summary
New Alzheimer’s treatments like lecanemab (Leqembi) and donanemab (Kisunla) are an important step forward in the search for ways to help patients, but these drugs have only moderate benefits and can come with serious side effects. Better therapies are still needed to reduce the impact of the disease. Genetics offers a powerful way to discover new drugs—studies show that treatments based on genetic findings are more likely to succeed. So far most genetic research has focused on the genes which increase the risk of developing Alzheimer’s, but understanding genes that drive how the disease progresses in Alzheimer’s patients may be even more beneficial. However this type of data, which involves following participants over time, is limited, combining results from multiple smaller studies (a meta-analysis) can help uncover important patterns. We plan to add data from the Knight Alzheimer Disease Research Center to a larger analysis to find new genetic clues, identify better treatment targets, and evaluate how current and future drugs may slow disease progression.
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.