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: | Mary Helen Black |
| Institution: | Janssen Pharmaceuticals, Johnson and Johnson |
| Department: | Data Sciences |
| Country: | |
| Proposed Analysis: | Alzheimer’s disease (AD) is a common, progressive, neurodegenerative disorder with a strong genetic component with heritability estimates ranging from 58–79% for late-onset AD and over 90% for early onset AD. Genetic association studies are important to highlight key biological mechanisms contributing to the etiology of AD and provide key insights into potential pathways that can ultimately be targeted for future therapeutic development. The objective of this study is to perform a retrospective analysis of genetic data collected from large-scale population-based and case-control cohorts including the UK Biobank, the Alzheimer’s Disease Sequencing Project (ADSP), Alzheimer’s Disease Neuroimaging Initiative (ADNI), FinnGen and integrate them with publicly available multi-omics datasets including, but not limited to, Genotype-Tissue Expression (GTEx), Microglia Genomic Atlas (MiGA), and neuroimaging data to identify novel and existing evidence for genetic determinants of AD. No attempt will be made to try and identify subjects. Aim 1: Identify novel and replicate existing gene associations for AD. We will perform case-control and family-based genetic analyses with AD diagnosis as the outcome of interest. Covariates include age, sex, and principal components. ADSP/ADNI, UKB, and FinnGen will be analyzed separately and combined with meta-analysis. Biobank cases will be defined using ICD-9/ICD-10 codes, and proxy cases and controls will be carefully defined using questionnaire data on parental history of AD. Both true and proxy cases will be considered to maximize the number of AD cases. Aim 2: Prioritize novel gene associations identified in Aim 1. We will perform genetic fine-mapping and leverage tissue and cell-type specific datasets (e.g. GTEx and MiGA) to prioritize targets for further functional and analytical interrogation. Statistical methods used for target prioritization include colocalization, statistical fine-mapping, and Mendelian randomization. Furthermore, multi-omics-based network approaches will be used to identify disease-related molecular modules and tissue-specific regulatory circuits. |
| Additional Investigators | |
| Investigator's Name: | Ekaterina Khramtsova |
| Proposed Analysis: | Alzheimer’s disease (AD) is a common, progressive, neurodegenerative disorder with a strong genetic component with heritability estimates ranging from 58–79% for late-onset AD and over 90% for early onset AD. Genetic association studies are important to highlight key biological mechanisms contributing to the etiology of AD and provide key insights into potential pathways that can ultimately be targeted for future therapeutic development. The objective of this study is to perform a retrospective analysis of genetic data collected from large-scale population-based and case-control cohorts including the UK Biobank, the Alzheimer’s Disease Sequencing Project (ADSP), Alzheimer’s Disease Neuroimaging Initiative (ADNI), FinnGen and integrate them with publicly available multi-omics datasets including, but not limited to, Genotype-Tissue Expression (GTEx), Microglia Genomic Atlas (MiGA), and neuroimaging data to identify novel and existing evidence for genetic determinants of AD. No attempt will be made to try and identify subjects. Aim 1: Identify novel and replicate existing gene associations for AD. We will perform case-control and family-based genetic analyses with AD diagnosis as the outcome of interest. Covariates include age, sex, and principal components. ADSP/ADNI, UKB, and FinnGen will be analyzed separately and combined with meta-analysis. Biobank cases will be defined using ICD-9/ICD-10 codes, and proxy cases and controls will be carefully defined using questionnaire data on parental history of AD. Both true and proxy cases will be considered to maximize the number of AD cases. Aim 2: Prioritize novel gene associations identified in Aim 1. We will perform genetic fine-mapping and leverage tissue and cell-type specific datasets (e.g. GTEx and MiGA) to prioritize targets for further functional and analytical interrogation. Statistical methods used for target prioritization include colocalization, statistical fine-mapping, and Mendelian randomization. Furthermore, multi-omics-based network approaches will be used to identify disease-related molecular modules and tissue-specific regulatory circuits. |
| Investigator's Name: | Karen He |
| Proposed Analysis: | Alzheimer’s disease (AD) is a common, progressive, neurodegenerative disorder with a strong genetic component with heritability estimates ranging from 58–79% for late-onset AD and over 90% for early onset AD. Genetic association studies are important to highlight key biological mechanisms contributing to the etiology of AD and provide key insights into potential pathways that can ultimately be targeted for future therapeutic development. The objective of this study is to perform a retrospective analysis of genetic data collected from large-scale population-based and case-control cohorts including the UK Biobank, the Alzheimer’s Disease Sequencing Project (ADSP), Alzheimer’s Disease Neuroimaging Initiative (ADNI), FinnGen and integrate them with publicly available multi-omics datasets including, but not limited to, Genotype-Tissue Expression (GTEx), Microglia Genomic Atlas (MiGA), and neuroimaging data to identify novel and existing evidence for genetic determinants of AD. No attempt will be made to try and identify subjects. Aim 1: Identify novel and replicate existing gene associations for AD. We will perform case-control and family-based genetic analyses with AD diagnosis as the outcome of interest. Covariates include age, sex, and principal components. ADSP/ADNI, UKB, and FinnGen will be analyzed separately and combined with meta-analysis. Biobank cases will be defined using ICD-9/ICD-10 codes, and proxy cases and controls will be carefully defined using questionnaire data on parental history of AD. Both true and proxy cases will be considered to maximize the number of AD cases. Aim 2: Prioritize novel gene associations identified in Aim 1. We will perform genetic fine-mapping and leverage tissue and cell-type specific datasets (e.g. GTEx and MiGA) to prioritize targets for further functional and analytical interrogation. Statistical methods used for target prioritization include colocalization, statistical fine-mapping, and Mendelian randomization. Furthermore, multi-omics-based network approaches will be used to identify disease-related molecular modules and tissue-specific regulatory circuits. |
| Investigator's Name: | Antonio Parrado |
| Proposed Analysis: | Alzheimer’s disease (AD) is a common, progressive, neurodegenerative disorder with a strong genetic component with heritability estimates ranging from 58–79% for late-onset AD and over 90% for early onset AD. Genetic association studies are important to highlight key biological mechanisms contributing to the etiology of AD and provide key insights into potential pathways that can ultimately be targeted for future therapeutic development. The objective of this study is to perform a retrospective analysis of genetic data collected from large-scale population-based and case-control cohorts including the UK Biobank, the Alzheimer’s Disease Sequencing Project (ADSP), Alzheimer’s Disease Neuroimaging Initiative (ADNI), FinnGen and integrate them with publicly available multi-omics datasets including, but not limited to, Genotype-Tissue Expression (GTEx), Microglia Genomic Atlas (MiGA), and neuroimaging data to identify novel and existing evidence for genetic determinants of AD. No attempt will be made to try and identify subjects. Aim 1: Identify novel and replicate existing gene associations for AD. We will perform case-control and family-based genetic analyses with AD diagnosis as the outcome of interest. Covariates include age, sex, and principal components. ADSP/ADNI, UKB, and FinnGen will be analyzed separately and combined with meta-analysis. Biobank cases will be defined using ICD-9/ICD-10 codes, and proxy cases and controls will be carefully defined using questionnaire data on parental history of AD. Both true and proxy cases will be considered to maximize the number of AD cases. Aim 2: Prioritize novel gene associations identified in Aim 1. We will perform genetic fine-mapping and leverage tissue and cell-type specific datasets (e.g. GTEx and MiGA) to prioritize targets for further functional and analytical interrogation. Statistical methods used for target prioritization include colocalization, statistical fine-mapping, and Mendelian randomization. Furthermore, multi-omics-based network approaches will be used to identify disease-related molecular modules and tissue-specific regulatory circuits. |
| Investigator's Name: | Dongnhu Truong |
| Proposed Analysis: | Alzheimer’s disease (AD) is a common, progressive, neurodegenerative disorder with a strong genetic component with heritability estimates ranging from 58–79% for late-onset AD and over 90% for early onset AD. Genetic association studies are important to highlight key biological mechanisms contributing to the etiology of AD and provide key insights into potential pathways that can ultimately be targeted for future therapeutic development. The objective of this study is to perform a retrospective analysis of genetic data collected from large-scale population-based and case-control cohorts including the UK Biobank, the Alzheimer’s Disease Sequencing Project (ADSP), Alzheimer’s Disease Neuroimaging Initiative (ADNI), FinnGen and integrate them with publicly available multi-omics datasets including, but not limited to, Genotype-Tissue Expression (GTEx), Microglia Genomic Atlas (MiGA), and neuroimaging data to identify novel and existing evidence for genetic determinants of AD. No attempt will be made to try and identify subjects. Aim 1: Identify novel and replicate existing gene associations for AD. We will perform case-control and family-based genetic analyses with AD diagnosis as the outcome of interest. Covariates include age, sex, and principal components. ADSP/ADNI, UKB, and FinnGen will be analyzed separately and combined with meta-analysis. Biobank cases will be defined using ICD-9/ICD-10 codes, and proxy cases and controls will be carefully defined using questionnaire data on parental history of AD. Both true and proxy cases will be considered to maximize the number of AD cases. Aim 2: Prioritize novel gene associations identified in Aim 1. We will perform genetic fine-mapping and leverage tissue and cell-type specific datasets (e.g. GTEx and MiGA) to prioritize targets for further functional and analytical interrogation. Statistical methods used for target prioritization include colocalization, statistical fine-mapping, and Mendelian randomization. Furthermore, multi-omics-based network approaches will be used to identify disease-related molecular modules and tissue-specific regulatory circuits. |
| Investigator's Name: | Brice Sarver |
| Proposed Analysis: | Alzheimer’s disease (AD) is a common, progressive, neurodegenerative disorder with a strong genetic component with heritability estimates ranging from 58–79% for late-onset AD and over 90% for early onset AD. Genetic association studies are important to highlight key biological mechanisms contributing to the etiology of AD and provide key insights into potential pathways that can ultimately be targeted for future therapeutic development. The objective of this study is to perform a retrospective analysis of genetic data collected from large-scale population-based and case-control cohorts including the UK Biobank, the Alzheimer’s Disease Sequencing Project (ADSP), Alzheimer’s Disease Neuroimaging Initiative (ADNI), FinnGen and integrate them with publicly available multi-omics datasets including, but not limited to, Genotype-Tissue Expression (GTEx), Microglia Genomic Atlas (MiGA), and neuroimaging data to identify novel and existing evidence for genetic determinants of AD. No attempt will be made to try and identify subjects. Aim 1: Identify novel and replicate existing gene associations for AD. We will perform case-control and family-based genetic analyses with AD diagnosis as the outcome of interest. Covariates include age, sex, and principal components. ADSP/ADNI, UKB, and FinnGen will be analyzed separately and combined with meta-analysis. Biobank cases will be defined using ICD-9/ICD-10 codes, and proxy cases and controls will be carefully defined using questionnaire data on parental history of AD. Both true and proxy cases will be considered to maximize the number of AD cases. Aim 2: Prioritize novel gene associations identified in Aim 1. We will perform genetic fine-mapping and leverage tissue and cell-type specific datasets (e.g. GTEx and MiGA) to prioritize targets for further functional and analytical interrogation. Statistical methods used for target prioritization include colocalization, statistical fine-mapping, and Mendelian randomization. Furthermore, multi-omics-based network approaches will be used to identify disease-related molecular modules and tissue-specific regulatory circuits. |
| Investigator's Name: | Qingqin Li |
| Proposed Analysis: | Alzheimer’s disease (AD) is a common, progressive, neurodegenerative disorder with a strong genetic component with heritability estimates ranging from 58–79% for late-onset AD and over 90% for early onset AD. Genetic association studies are important to highlight key biological mechanisms contributing to the etiology of AD and provide key insights into potential pathways that can ultimately be targeted for future therapeutic development. The objective of this study is to perform a retrospective analysis of genetic data collected from large-scale population-based and case-control cohorts including the UK Biobank, the Alzheimer’s Disease Sequencing Project (ADSP), Alzheimer’s Disease Neuroimaging Initiative (ADNI), FinnGen and integrate them with publicly available multi-omics datasets including, but not limited to, Genotype-Tissue Expression (GTEx), Microglia Genomic Atlas (MiGA), and neuroimaging data to identify novel and existing evidence for genetic determinants of AD. No attempt will be made to try and identify subjects. Aim 1: Identify novel and replicate existing gene associations for AD. We will perform case-control and family-based genetic analyses with AD diagnosis as the outcome of interest. Covariates include age, sex, and principal components. ADSP/ADNI, UKB, and FinnGen will be analyzed separately and combined with meta-analysis. Biobank cases will be defined using ICD-9/ICD-10 codes, and proxy cases and controls will be carefully defined using questionnaire data on parental history of AD. Both true and proxy cases will be considered to maximize the number of AD cases. Aim 2: Prioritize novel gene associations identified in Aim 1. We will perform genetic fine-mapping and leverage tissue and cell-type specific datasets (e.g. GTEx and MiGA) to prioritize targets for further functional and analytical interrogation. Statistical methods used for target prioritization include colocalization, statistical fine-mapping, and Mendelian randomization. Furthermore, multi-omics-based network approaches will be used to identify disease-related molecular modules and tissue-specific regulatory circuits. |
| Investigator's Name: | Hussein el abbass Hijazi |
| Proposed Analysis: | Alzheimer’s disease (AD) is a common, progressive, neurodegenerative disorder with a strong genetic component with heritability estimates ranging from 58–79% for late-onset AD and over 90% for early onset AD. Genetic association studies are important to highlight key biological mechanisms contributing to the etiology of AD and provide key insights into potential pathways that can ultimately be targeted for future therapeutic development. The objective of this study is to perform a retrospective analysis of genetic data collected from large-scale population-based and case-control cohorts including the UK Biobank, the Alzheimer’s Disease Sequencing Project (ADSP), Alzheimer’s Disease Neuroimaging Initiative (ADNI), FinnGen and integrate them with publicly available multi-omics datasets including, but not limited to, Genotype-Tissue Expression (GTEx), Microglia Genomic Atlas (MiGA), and neuroimaging data to identify novel and existing evidence for genetic determinants of AD. No attempt will be made to try and identify subjects. Aim 1: Identify novel and replicate existing gene associations for AD. We will perform case-control and family-based genetic analyses with AD diagnosis as the outcome of interest. Covariates include age, sex, and principal components. ADSP/ADNI, UKB, and FinnGen will be analyzed separately and combined with meta-analysis. Biobank cases will be defined using ICD-9/ICD-10 codes, and proxy cases and controls will be carefully defined using questionnaire data on parental history of AD. Both true and proxy cases will be considered to maximize the number of AD cases. Aim 2: Prioritize novel gene associations identified in Aim 1. We will perform genetic fine-mapping and leverage tissue and cell-type specific datasets (e.g. GTEx and MiGA) to prioritize targets for further functional and analytical interrogation. Statistical methods used for target prioritization include colocalization, statistical fine-mapping, and Mendelian randomization. Furthermore, multi-omics-based network approaches will be used to identify disease-related molecular modules and tissue-specific regulatory circuits. |
| Investigator's Name: | Shicheng Guo |
| Proposed Analysis: | Alzheimer’s disease (AD) is a common, progressive, neurodegenerative disorder with a strong genetic component with heritability estimates ranging from 58–79% for late-onset AD and over 90% for early onset AD. Genetic association studies are important to highlight key biological mechanisms contributing to the etiology of AD and provide key insights into potential pathways that can ultimately be targeted for future therapeutic development. The objective of this study is to perform a retrospective analysis of genetic data collected from large-scale population-based and case-control cohorts including the UK Biobank, the Alzheimer’s Disease Sequencing Project (ADSP), Alzheimer’s Disease Neuroimaging Initiative (ADNI), FinnGen and integrate them with publicly available multi-omics datasets including, but not limited to, Genotype-Tissue Expression (GTEx), Microglia Genomic Atlas (MiGA), and neuroimaging data to identify novel and existing evidence for genetic determinants of AD. No attempt will be made to try and identify subjects. Aim 1: Identify novel and replicate existing gene associations for AD. We will perform case-control and family-based genetic analyses with AD diagnosis as the outcome of interest. Covariates include age, sex, and principal components. ADSP/ADNI, UKB, and FinnGen will be analyzed separately and combined with meta-analysis. Biobank cases will be defined using ICD-9/ICD-10 codes, and proxy cases and controls will be carefully defined using questionnaire data on parental history of AD. Both true and proxy cases will be considered to maximize the number of AD cases. Aim 2: Prioritize novel gene associations identified in Aim 1. We will perform genetic fine-mapping and leverage tissue and cell-type specific datasets (e.g. GTEx and MiGA) to prioritize targets for further functional and analytical interrogation. Statistical methods used for target prioritization include colocalization, statistical fine-mapping, and Mendelian randomization. Furthermore, multi-omics-based network approaches will be used to identify disease-related molecular modules and tissue-specific regulatory circuits. |
