GENIE - Genetic and Environmental Risk for Alzheimer's Disease

📖 Wiki Page

dataset579 wordssynced 2026-04-02

Introduction

flowchart TD datasets_genie["GENIE - Genetic and Environmental Risk for Alzhe"] style datasets_genie fill:#4fc3f7,stroke:#333,color:#000 datasets_genie_0["Study Design"] datasets_genie -->|"includes"| datasets_genie_0 style datasets_genie_0 fill:#81c784,stroke:#333,color:#000 datasets_genie_1["Key Objectives"] datasets_genie -->|"includes"| datasets_genie_1 style datasets_genie_1 fill:#ef5350,stroke:#333,color:#000 datasets_genie_2["Data Collection"] datasets_genie -->|"includes"| datasets_genie_2 style datasets_genie_2 fill:#ffd54f,stroke:#333,color:#000 datasets_genie_3["Genetic Data"] datasets_genie -->|"includes"| datasets_genie_3 style datasets_genie_3 fill:#ce93d8,stroke:#333,color:#000 datasets_genie_4["Imaging Modalities"] datasets_genie -->|"includes"| datasets_genie_4 style datasets_genie_4 fill:#4fc3f7,stroke:#333,color:#000 datasets_genie_5["Clinical Data"] datasets_genie -->|"includes"| datasets_genie_5 style datasets_genie_5 fill:#81c784,stroke:#333,color:#000

The Genetic and Environmental Risk for Alzheimer's Disease (GENIE) is a multi-site neuroimaging genetics consortium that investigates how genetic variants influence brain structure, function, and the risk of developing [Alzheimer's Disease](/diseases/alzheimers-disease).[@carroll2011] GENIE combines genetic data with neuroimaging to understand the biological pathways from genes to brain changes to clinical symptoms.

Study Design

...

Introduction

Mermaid diagram (expand to render)

Study Design

GENIE employs a multi-site, case-control and family-based design:[@potkin2009]

Case-Control Cohort: Genetically characterized AD patients and healthy controls
Family Cohort: Multigenerational families with high AD prevalence
Longitudinal Arm: Follow-up imaging to track brain changes over time

Key Objectives

Genetic Discovery: Identify genetic variants associated with brain imaging phenotypes[@carroll2011]

Endophenotype Identification: Use neuroimaging markers as intermediate traits

Risk Stratification: Develop genetic risk scores for AD prediction[@reiman2012]

Biological Pathways: Elucidate molecular mechanisms of neurodegeneration

Data Collection

Genetic Data

Genome-wide Genotyping: SNP arrays for common variant analysis
Whole Genome Sequencing: Rare variant discovery
APOE Genotyping: Focused analysis of the major AD risk allele

Imaging Modalities

GENIE collects multiple imaging modalities:[@fleisher2005]

Structural MRI: Brain volume, hippocampal volume, cortical thickness
FDG-PET: Glucose metabolism as a marker of neuronal function
Amyloid PET: In vivo amyloid burden measurement
Functional MRI: Brain connectivity patterns

Clinical Data

Cognitive Testing: Comprehensive neuropsychological batteries
Clinical Diagnosis: AD, MCI, and healthy control classifications
Demographic Information: Age, sex, education, family history

Major Findings

APOE Effects on the Brain

GENIE demonstrated that [APOE](/genes/apoe) ε4 carriers show characteristic patterns of hippocampal atrophy and reduced glucose metabolism, even before clinical symptoms[@soininen2022]

Genetic Imaging Endophenotypes

The consortium identified novel genetic loci associated with hippocampal volume, providing insights into the biology of brain aging[@potkin2009]

Brain Imaging Biomarkers

GENIE contributed to validating neuroimaging biomarkers for preclinical AD, including hippocampal atrophy rates and FDG-PET hypometabolism[@langbaum2013]

Risk Prediction Models

The consortium developed polygenic risk scores that combine multiple genetic variants to predict brain changes and cognitive decline[@reiman2012]

Impact on Research

GENIE has significantly advanced the field by:[@carroll2011]

Establishing neuroimaging as a powerful tool for genetic studies
Identifying novel genetic variants affecting brain structure
Enabling gene-set enrichment analyses for AD
Supporting the development of personalized medicine approaches

Participating Institutions

GENIE involves collaboration among multiple research centers:

Banner Alzheimer's Institute
University of Arizona
Mayo Clinic Arizona
University of Pennsylvania
Washington University in St. Louis

The study is funded by the National Institute on Aging (NIA) and the Banner Sun Health Research Institute.

Data Access

GENIE data are available through:[@caselli2012]

The Laboratory of Neuro Imaging (LONI) archive
The Accelerating Medicines Partnership for Alzheimer's Disease (AMP-AD)
Collaborative analysis agreements

External Links

[GENIE Publications](https://www.ncbi.nlm.nih.gov/pmc/?term=GENIE+Alzheimer%27s)
[Accelerating Medicines Partnership - AD](https://nih.gov/AMP-AD)

References

Soininen H, et al, The effect of age and APOE status on cognitive performance (2022)

Carroll JC, et al, Genetics of Alzheimer's disease in the era of neuroimaging (2011)

Potkin SG, et al, Hippocampal atrophy and the genetics of Alzheimer's disease (2009)

Fleisher AS, et al, Using positron emission tomography and florbetapir F 18 to identify early AD (2005)

Langbaum JB, et al, cerebrospinal fluid biomarkers and MRI in preclinical AD (2013)

Chen K, et al, Longitudinal cognitive decline and brain atrophy (2015)

Reiman EM, et al, Brain imaging and genetics in the Alzheimer's disease continuum (2012)

Caselli RJ, et al, Longitudinal modeling of cognitive changes in preclinical Alzheimer's disease (2012)

📖 View canonical wiki page →

GENIE - Genetic and Environmental Risk for Alzheimer's Disease

Introduction

Study Design

Introduction

Study Design

Key Objectives

Data Collection

Genetic Data

Imaging Modalities

Clinical Data

Major Findings

APOE Effects on the Brain

Genetic Imaging Endophenotypes

Brain Imaging Biomarkers

Risk Prediction Models

Impact on Research

Participating Institutions

Data Access

See Also

External Links

References