Localizing risk variants and estimating effects in the Alzheimer's Disease Sequencing Project (ADSP) Data (Update to GRCh38)
- PI
- Fardo, David
- DAR ID
- 10138
- Initial Approval Date
- April 6, 2020
- Current DAR Status
- Active
- PI Institution
- University of Kentucky
- PI Institution Country
- United States
- PI Institution Type
- Non-Profit
Requested Datasets (1)
Research Use Statement
This is an update to a currently-active project through dbGaP in order to garner mapped reads for downstream analysis of regions identified through the scan statistic, endophenotype and causal approaches.
The original edited RUS is copied below:
We aim to better isolate causal variants within putative ADRD disease genes via two primary approaches. First, we will use an empirical Bayes scan
statistic to detect regions of disease variant enrichment. In addition, we will employ novel causal inference methodology to estimate variant-specific
causal risk for ADRD. These complementary approaches will allow for discovery of novel ADRD genes as well as enumeration/localization of important
variants within putative AD risk genes. We will also employ more conventional approaches (e.g., SKAT, endophenotype development) as appropriate.
We have read and approved the Data Use Agreement as signed and submitted on dbGaP and plan to upload results of our findings in a timely
manner.
The original edited RUS is copied below:
We aim to better isolate causal variants within putative ADRD disease genes via two primary approaches. First, we will use an empirical Bayes scan
statistic to detect regions of disease variant enrichment. In addition, we will employ novel causal inference methodology to estimate variant-specific
causal risk for ADRD. These complementary approaches will allow for discovery of novel ADRD genes as well as enumeration/localization of important
variants within putative AD risk genes. We will also employ more conventional approaches (e.g., SKAT, endophenotype development) as appropriate.
We have read and approved the Data Use Agreement as signed and submitted on dbGaP and plan to upload results of our findings in a timely
manner.
Non-Technical Summary
The main goals of the Alzheimer’s Disease Sequence Project (ADSP) include the identification of novel genomic variants contributing to risk of
Late-Onset Alzheimer’s Disease or to protection against Alzheimer’s Disease (AD), as well as providing information as to why at-risk individuals may
not develop AD or related dementias, especially in multi-ethnic populations. The aim of our data analysis aligns with these goals to identify novel genomic variants
associated with AD. We will aim to do so via a scan-based statistic at each variant, where the statistic is specially designed for the analysis of
genomic data. We will also explore alternative methodologies for these discoveries including the calculation of a causal estimate of variants within
putative AD genes.
Late-Onset Alzheimer’s Disease or to protection against Alzheimer’s Disease (AD), as well as providing information as to why at-risk individuals may
not develop AD or related dementias, especially in multi-ethnic populations. The aim of our data analysis aligns with these goals to identify novel genomic variants
associated with AD. We will aim to do so via a scan-based statistic at each variant, where the statistic is specially designed for the analysis of
genomic data. We will also explore alternative methodologies for these discoveries including the calculation of a causal estimate of variants within
putative AD genes.
Reported Publications (4)
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.
- Genetic associations with dementia-related proteinopathy: Application of item response theory. Katsumata Y, et al. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024. PMID: 38460116
- LATE-NC risk alleles (in TMEM106B, GRN, and ABCC9 genes) among persons with African ancestry. Katsumata Y, et al. Journal of neuropathology and experimental neurology. 2023. PMID: 37528055
- Multiple gene variants linked to Alzheimer's-type clinical dementia via GWAS are also associated with non-Alzheimer's neuropathologic entities. Katsumata Y, et al. Neurobiology of disease. 2022. PMID: 36191742
- Region-based analysis of rare genomic variants in whole-genome sequencing datasets reveal two novel Alzheimer's disease-associated genes: DTNB and DLG2. Prokopenko D, et al. Molecular psychiatry. 2022. PMID: 35246634