Identification of genetic risks and potential target for stratified Alzheimer's disease patient groups
- PI
- Lee, Jonghun
- DAR ID
- 10290
- Initial Approval Date
- March 16, 2021
- Current DAR Status
- Active
- PI Institution
- Takeda pharmaceutical company
- PI Institution Country
- United States
- PI Institution Type
- For-Profit
Requested Datasets (5)
NG00067Alzheimer's Disease Sequencing Project Umbrella StudyNG00108Profiling microglia expression profiles in AD using single-nuclei RNA-seqNG00105MiGA – Microglia Genomic AtlasNG00127A longitudinal study of Alzheimer's Disease and other dementing illnesses – KnightADRC GWASNG00154Dementia in Parkinson's Disease(PDD)
Research Use Statement
The goal of analyzing ADSP umbrella cohort data is identifying variants, genes and pathways associated to Alzheimer’s disease (AD), and stratifying patients by genetic risks. Following describes procedure.
1) Identification and validation of genetic risks
The whole genome and whole exome sequencing data will be analyzed to identify genetic variants or genes associated to phenotypes in case-control cohort, such as AD status and Braak stages. Several methods will be applied, such as VEP [William McLaren et al, 2016], LOFTEE [Karczewski, 2015] and PEXT scoring [Beryl B.C et al., 2020] for variant annotation and SAIGE-GENE [Wei Zhou et al., 2020], and KGWAS [Kexin Huang 2024] for the association test. The association will be tested for other endophenotypes such as cognitive scores and brain volumes that available in subset of the cohort. Replication and meta-analysis will be conducted on UK biobank and Tohoku medical megabank organization (ToMMo) cohort data.
2) Patient stratification in ADSP cohort
Leveraging the increased sample size, we will stratify the cohort by genetic risks such as ApoE types, or phenotypes such as Braak stages, and compare the effect size of variants or genes among the patient groups. In addition, the genetic risk score (GRS) will be calculated using LDpred2 [Florian Prive, 2020], RapidoPGS [Guillermo Reales, 2020], and PRSice2 [Choi, S.W., 2020], and validated in independent cohorts and compared to available clinical endophenotypes. Then we will search the effect of the GRS to extensive phenotypes in UK biobank and ToMMo. Last, the NG00130 proteome will be used for unsupervised classification of patients. Overlapping the protein signature-based groups with genetic signals, we’ll find casual pathologic pathways and targets for each subgroups.
3) Identify CSF proteomic biomarker for AD patient stratification
The large CSF proteomic data (NG00130) will be used to identify composition of proteins classifying patients into AD subtypes. In addition, downstream pathway engagement of relevant pathologies of the subtypes will be analyzed
1) Identification and validation of genetic risks
The whole genome and whole exome sequencing data will be analyzed to identify genetic variants or genes associated to phenotypes in case-control cohort, such as AD status and Braak stages. Several methods will be applied, such as VEP [William McLaren et al, 2016], LOFTEE [Karczewski, 2015] and PEXT scoring [Beryl B.C et al., 2020] for variant annotation and SAIGE-GENE [Wei Zhou et al., 2020], and KGWAS [Kexin Huang 2024] for the association test. The association will be tested for other endophenotypes such as cognitive scores and brain volumes that available in subset of the cohort. Replication and meta-analysis will be conducted on UK biobank and Tohoku medical megabank organization (ToMMo) cohort data.
2) Patient stratification in ADSP cohort
Leveraging the increased sample size, we will stratify the cohort by genetic risks such as ApoE types, or phenotypes such as Braak stages, and compare the effect size of variants or genes among the patient groups. In addition, the genetic risk score (GRS) will be calculated using LDpred2 [Florian Prive, 2020], RapidoPGS [Guillermo Reales, 2020], and PRSice2 [Choi, S.W., 2020], and validated in independent cohorts and compared to available clinical endophenotypes. Then we will search the effect of the GRS to extensive phenotypes in UK biobank and ToMMo. Last, the NG00130 proteome will be used for unsupervised classification of patients. Overlapping the protein signature-based groups with genetic signals, we’ll find casual pathologic pathways and targets for each subgroups.
3) Identify CSF proteomic biomarker for AD patient stratification
The large CSF proteomic data (NG00130) will be used to identify composition of proteins classifying patients into AD subtypes. In addition, downstream pathway engagement of relevant pathologies of the subtypes will be analyzed
Non-Technical Summary
The aim of our study is identifying variants or genes potentially causal of the Alzheimer’s disease in whole or subset of patients. To be specific, WES and WGS data will be analyzed to investigate common and rare variants associated with disease status and intermediate phenotypes. In addition, the patients will be stratified and sub-grouped by their genetic and proteomic signatures. Last, we will incorporate other large biobanks such as UK biobank or ToMMo to investigate the genetic effects to extensive phenotypes potentially linked to symptoms appearing in sub patient groups.
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.