There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Alexander Kulminski |
| Institution: | DUKE UNIVERSITY |
| Department: | Social Sciences Research Institute |
| Country: | |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. |
| Additional Investigators | |
| Investigator's Name: | Yury Loika |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. |
| Investigator's Name: | Alireza Nazarian |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. |
| Investigator's Name: | Konstantin Arbeev |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. |
| Investigator's Name: | Irina Kulminskaya |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. |
| Investigator's Name: | Anatoliy Yashin |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. |
| Investigator's Name: | Fan Feng |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. |
| Investigator's Name: | Elena Loiko |
| Proposed Analysis: | ADNI data will be used to identify personalized genetic profiles of risks and resilience to Alzheimer’s disease (AD) and vascular diseases in the disease-specific and pleiotropic contexts in prioritized loci leveraging information from the AD-centered pleiotropic meta-analysis, conducted using ADNI and other datasets planned in this project, and previous analyses by our and other research groups, and identify the role of AD risk and other factors in these profiles. We will examine associations and interactions of the identified genetic variants with preclinical measures and measures of cognitive health available in ADNI. We will also examine mediating role of AD risk factors available in ADNI in the identified associations. We will use Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) for imputation of genotypes. This server provides a free genotype imputation service. We will upload only genotype data using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Michigan Imputation Server is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Imputed genotype data will be used to harmonize them with other studies for cross-platform analyses on our local servers. |
| Investigator's Name: | Ivanti Galloway |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. UPDATED (2019-08-06) :ADNI data will be used to identify personalized genetic profiles of risks and resilience to Alzheimer’s disease (AD) and vascular diseases in the disease-specific and pleiotropic contexts in prioritized loci leveraging information from the AD-centered pleiotropic meta-analysis, conducted using ADNI and other datasets planned in this project, and previous analyses by our and other research groups, and identify the role of AD risk and other factors in these profiles. We will examine associations and interactions of the identified genetic variants with preclinical measures and measures of cognitive health available in ADNI. We will also examine mediating role of AD risk factors available in ADNI in the identified associations. We will use Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) for imputation of genotypes. This server provides a free genotype imputation service. We will upload only genotype data using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Michigan Imputation Server is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Imputed genotype data will be used to harmonize them with other studies for cross-platform analyses on our local servers. |
| Investigator's Name: | Ethan Jain-Washburn |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. UPDATED (2019-08-06) :ADNI data will be used to identify personalized genetic profiles of risks and resilience to Alzheimer’s disease (AD) and vascular diseases in the disease-specific and pleiotropic contexts in prioritized loci leveraging information from the AD-centered pleiotropic meta-analysis, conducted using ADNI and other datasets planned in this project, and previous analyses by our and other research groups, and identify the role of AD risk and other factors in these profiles. We will examine associations and interactions of the identified genetic variants with preclinical measures and measures of cognitive health available in ADNI. We will also examine mediating role of AD risk factors available in ADNI in the identified associations. We will use Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) for imputation of genotypes. This server provides a free genotype imputation service. We will upload only genotype data using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Michigan Imputation Server is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Imputed genotype data will be used to harmonize them with other studies for cross-platform analyses on our local servers. UPDATED (2020-07-21) :ADNI data will be used to dissect heterogeneity in genetic and non-genetic predisposition to risks, protection, and resilience to Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) and vascular diseases in the disease-specific and pleiotropic contexts. We will examine associations and interactions of the identified genetic profiles with biomarkers, other preclinical measures, diseases, and their risk factors available in ADNI. We will validate associations of these profiles with those in other datasets. UPDATED (2021-01-06) :We will use the Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) and the TOPMED imputation server (https://imputation.biodatacatalyst.nhlbi.nih.gov/#!) for imputation of genotypes. This servers provide a free genotype imputation service. We will upload genotype data from each study using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Imputation Servers is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Genotype data from all datasets will be used to create the same genotyping set in each study for cross-platform analyses on our local servers. We will designate a statistician to handle imputation on the Imputation Servers. |
| Investigator's Name: | Olivia Bagley |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. UPDATED (2019-08-06) :ADNI data will be used to identify personalized genetic profiles of risks and resilience to Alzheimer’s disease (AD) and vascular diseases in the disease-specific and pleiotropic contexts in prioritized loci leveraging information from the AD-centered pleiotropic meta-analysis, conducted using ADNI and other datasets planned in this project, and previous analyses by our and other research groups, and identify the role of AD risk and other factors in these profiles. We will examine associations and interactions of the identified genetic variants with preclinical measures and measures of cognitive health available in ADNI. We will also examine mediating role of AD risk factors available in ADNI in the identified associations. We will use Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) for imputation of genotypes. This server provides a free genotype imputation service. We will upload only genotype data using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Michigan Imputation Server is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Imputed genotype data will be used to harmonize them with other studies for cross-platform analyses on our local servers. UPDATED (2020-08-05) :ADNI data will be used to dissect heterogeneity in genetic and non-genetic predisposition to risks, protection, and resilience to Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) and vascular diseases in the disease-specific and pleiotropic contexts. We will examine associations and interactions of the identified genetic profiles with biomarkers, other preclinical measures, diseases, and their risk factors available in ADNI. We will validate associations of these profiles with those in other datasets. |
| Investigator's Name: | Brandon Cook |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. UPDATED (2019-08-06) :ADNI data will be used to identify personalized genetic profiles of risks and resilience to Alzheimer’s disease (AD) and vascular diseases in the disease-specific and pleiotropic contexts in prioritized loci leveraging information from the AD-centered pleiotropic meta-analysis, conducted using ADNI and other datasets planned in this project, and previous analyses by our and other research groups, and identify the role of AD risk and other factors in these profiles. We will examine associations and interactions of the identified genetic variants with preclinical measures and measures of cognitive health available in ADNI. We will also examine mediating role of AD risk factors available in ADNI in the identified associations. We will use Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) for imputation of genotypes. This server provides a free genotype imputation service. We will upload only genotype data using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Michigan Imputation Server is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Imputed genotype data will be used to harmonize them with other studies for cross-platform analyses on our local servers. UPDATED (2020-08-05) :ADNI data will be used to dissect heterogeneity in genetic and non-genetic predisposition to risks, protection, and resilience to Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) and vascular diseases in the disease-specific and pleiotropic contexts. We will examine associations and interactions of the identified genetic profiles with biomarkers, other preclinical measures, diseases, and their risk factors available in ADNI. We will validate associations of these profiles with those in other datasets. |
| Investigator's Name: | Nazmus Salehin |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. UPDATED (2019-08-06) :ADNI data will be used to identify personalized genetic profiles of risks and resilience to Alzheimer’s disease (AD) and vascular diseases in the disease-specific and pleiotropic contexts in prioritized loci leveraging information from the AD-centered pleiotropic meta-analysis, conducted using ADNI and other datasets planned in this project, and previous analyses by our and other research groups, and identify the role of AD risk and other factors in these profiles. We will examine associations and interactions of the identified genetic variants with preclinical measures and measures of cognitive health available in ADNI. We will also examine mediating role of AD risk factors available in ADNI in the identified associations. We will use Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) for imputation of genotypes. This server provides a free genotype imputation service. We will upload only genotype data using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Michigan Imputation Server is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Imputed genotype data will be used to harmonize them with other studies for cross-platform analyses on our local servers. UPDATED (2020-07-21) :ADNI data will be used to dissect heterogeneity in genetic and non-genetic predisposition to risks, protection, and resilience to Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) and vascular diseases in the disease-specific and pleiotropic contexts. We will examine associations and interactions of the identified genetic profiles with biomarkers, other preclinical measures, diseases, and their risk factors available in ADNI. We will validate associations of these profiles with those in other datasets. UPDATED (2021-01-06) :We will use the Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) and the TOPMED imputation server (https://imputation.biodatacatalyst.nhlbi.nih.gov/#!) for imputation of genotypes. This servers provide a free genotype imputation service. We will upload genotype data from each study using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Imputation Servers is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Genotype data from all datasets will be used to create the same genotyping set in each study for cross-platform analyses on our local servers. We will designate a statistician to handle imputation on the Imputation Servers. |
| Investigator's Name: | Stephanie Webster |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. UPDATED (2019-08-06) :ADNI data will be used to identify personalized genetic profiles of risks and resilience to Alzheimer’s disease (AD) and vascular diseases in the disease-specific and pleiotropic contexts in prioritized loci leveraging information from the AD-centered pleiotropic meta-analysis, conducted using ADNI and other datasets planned in this project, and previous analyses by our and other research groups, and identify the role of AD risk and other factors in these profiles. We will examine associations and interactions of the identified genetic variants with preclinical measures and measures of cognitive health available in ADNI. We will also examine mediating role of AD risk factors available in ADNI in the identified associations. We will use Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) for imputation of genotypes. This server provides a free genotype imputation service. We will upload only genotype data using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Michigan Imputation Server is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Imputed genotype data will be used to harmonize them with other studies for cross-platform analyses on our local servers. UPDATED (2020-08-05) :ADNI data will be used to dissect heterogeneity in genetic and non-genetic predisposition to risks, protection, and resilience to Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) and vascular diseases in the disease-specific and pleiotropic contexts. We will examine associations and interactions of the identified genetic profiles with biomarkers, other preclinical measures, diseases, and their risk factors available in ADNI. We will validate associations of these profiles with those in other datasets. |
| Investigator's Name: | Tatiana Prytkova |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. UPDATED (2019-08-06) :ADNI data will be used to identify personalized genetic profiles of risks and resilience to Alzheimer’s disease (AD) and vascular diseases in the disease-specific and pleiotropic contexts in prioritized loci leveraging information from the AD-centered pleiotropic meta-analysis, conducted using ADNI and other datasets planned in this project, and previous analyses by our and other research groups, and identify the role of AD risk and other factors in these profiles. We will examine associations and interactions of the identified genetic variants with preclinical measures and measures of cognitive health available in ADNI. We will also examine mediating role of AD risk factors available in ADNI in the identified associations. We will use Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) for imputation of genotypes. This server provides a free genotype imputation service. We will upload only genotype data using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Michigan Imputation Server is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Imputed genotype data will be used to harmonize them with other studies for cross-platform analyses on our local servers. UPDATED (2020-08-05) :ADNI data will be used to dissect heterogeneity in genetic and non-genetic predisposition to risks, protection, and resilience to Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) and vascular diseases in the disease-specific and pleiotropic contexts. We will examine associations and interactions of the identified genetic profiles with biomarkers, other preclinical measures, diseases, and their risk factors available in ADNI. We will validate associations of these profiles with those in other datasets. UPDATED (2021-09-23) :We will use the Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) and the TOPMED imputation server (https://imputation.biodatacatalyst.nhlbi.nih.gov/#!) for imputation of genotypes. This servers provide a free genotype imputation service. We will upload genotype data from each study using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Imputation Servers is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Genotype data from all datasets will be used to create the same genotyping set in each study for cross-platform analyses on our local servers. We will designate a statistician to handle imputation on the Imputation Servers. |
| Investigator's Name: | Diana Gerardo |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. UPDATED (2019-08-06) :ADNI data will be used to identify personalized genetic profiles of risks and resilience to Alzheimer’s disease (AD) and vascular diseases in the disease-specific and pleiotropic contexts in prioritized loci leveraging information from the AD-centered pleiotropic meta-analysis, conducted using ADNI and other datasets planned in this project, and previous analyses by our and other research groups, and identify the role of AD risk and other factors in these profiles. We will examine associations and interactions of the identified genetic variants with preclinical measures and measures of cognitive health available in ADNI. We will also examine mediating role of AD risk factors available in ADNI in the identified associations. We will use Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) for imputation of genotypes. This server provides a free genotype imputation service. We will upload only genotype data using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Michigan Imputation Server is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Imputed genotype data will be used to harmonize them with other studies for cross-platform analyses on our local servers. UPDATED (2020-07-21) :ADNI data will be used to dissect heterogeneity in genetic and non-genetic predisposition to risks, protection, and resilience to Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) and vascular diseases in the disease-specific and pleiotropic contexts. We will examine associations and interactions of the identified genetic profiles with biomarkers, other preclinical measures, diseases, and their risk factors available in ADNI. We will validate associations of these profiles with those in other datasets. UPDATED (2021-01-06) :We will use the Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) and the TOPMED imputation server (https://imputation.biodatacatalyst.nhlbi.nih.gov/#!) for imputation of genotypes. This servers provide a free genotype imputation service. We will upload genotype data from each study using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Imputation Servers is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Genotype data from all datasets will be used to create the same genotyping set in each study for cross-platform analyses on our local servers. We will designate a statistician to handle imputation on the Imputation Servers. |
| Investigator's Name: | Marissa Morado |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. UPDATED (2019-08-06) :ADNI data will be used to identify personalized genetic profiles of risks and resilience to Alzheimer’s disease (AD) and vascular diseases in the disease-specific and pleiotropic contexts in prioritized loci leveraging information from the AD-centered pleiotropic meta-analysis, conducted using ADNI and other datasets planned in this project, and previous analyses by our and other research groups, and identify the role of AD risk and other factors in these profiles. We will examine associations and interactions of the identified genetic variants with preclinical measures and measures of cognitive health available in ADNI. We will also examine mediating role of AD risk factors available in ADNI in the identified associations. We will use Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) for imputation of genotypes. This server provides a free genotype imputation service. We will upload only genotype data using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Michigan Imputation Server is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Imputed genotype data will be used to harmonize them with other studies for cross-platform analyses on our local servers. UPDATED (2020-07-21) :ADNI data will be used to dissect heterogeneity in genetic and non-genetic predisposition to risks, protection, and resilience to Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) and vascular diseases in the disease-specific and pleiotropic contexts. We will examine associations and interactions of the identified genetic profiles with biomarkers, other preclinical measures, diseases, and their risk factors available in ADNI. We will validate associations of these profiles with those in other datasets. UPDATED (2021-01-06) :We will use the Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) and the TOPMED imputation server (https://imputation.biodatacatalyst.nhlbi.nih.gov/#!) for imputation of genotypes. This servers provide a free genotype imputation service. We will upload genotype data from each study using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Imputation Servers is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Genotype data from all datasets will be used to create the same genotyping set in each study for cross-platform analyses on our local servers. We will designate a statistician to handle imputation on the Imputation Servers. UPDATED (2022-03-16) :Protocol ID Pro00105247 was updated covering the period until 03/07/2023 |
| Investigator's Name: | Evan Knox |
| Proposed Analysis: | ADNI data will be used to identify personalized polygenic profiles, comprised of the APOE e2 allele, other polymorphisms in the APOE region, and polymorphisms spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles. We will examine interaction of the identified polygenic profiles with preclinical measures available in ADNI and validate associations of polygenic profiles with these measures. UPDATED (2019-08-06) :ADNI data will be used to identify personalized genetic profiles of risks and resilience to Alzheimer’s disease (AD) and vascular diseases in the disease-specific and pleiotropic contexts in prioritized loci leveraging information from the AD-centered pleiotropic meta-analysis, conducted using ADNI and other datasets planned in this project, and previous analyses by our and other research groups, and identify the role of AD risk and other factors in these profiles. We will examine associations and interactions of the identified genetic variants with preclinical measures and measures of cognitive health available in ADNI. We will also examine mediating role of AD risk factors available in ADNI in the identified associations. We will use Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) for imputation of genotypes. This server provides a free genotype imputation service. We will upload only genotype data using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Michigan Imputation Server is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Imputed genotype data will be used to harmonize them with other studies for cross-platform analyses on our local servers. UPDATED (2020-07-21) :ADNI data will be used to dissect heterogeneity in genetic and non-genetic predisposition to risks, protection, and resilience to Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) and vascular diseases in the disease-specific and pleiotropic contexts. We will examine associations and interactions of the identified genetic profiles with biomarkers, other preclinical measures, diseases, and their risk factors available in ADNI. We will validate associations of these profiles with those in other datasets. UPDATED (2021-01-06) :We will use the Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html) and the TOPMED imputation server (https://imputation.biodatacatalyst.nhlbi.nih.gov/#!) for imputation of genotypes. This servers provide a free genotype imputation service. We will upload genotype data from each study using de-identified individuals’ IDs through secure protocol provided by this service. We will receive phased and imputed genomes in return. All uploaded information on the Imputation Servers is permanently deleted after the imputation. We will use these de-identified IDs to link imputed genomes to the phenotypic information on our local servers under the same protocol as for the data analyses. Genotype data from all datasets will be used to create the same genotyping set in each study for cross-platform analyses on our local servers. We will designate a statistician to handle imputation on the Imputation Servers. UPDATED (2022-03-16) :Protocol ID Pro00105247 was updated covering the period until 03/07/2023 UPDATED (2023-02-27) :Protocol ID Pro00105247 was updated covering the period until 03/07/2024 |
