Benchmarking and fine-tuning of genomic foundation models.

Objectives:
To develop and validate methods for assessing how genomic foundation models generalize to neuropsychiatric conditions, specifically Alzheimer's disease and related dementias (ADRD). We aim to:
- Evaluate foundation models' ability to capture disease-relevant genomic patterns
- Develop fine-tuning approaches to improve model performance
- Create benchmarks for assessing model generalization to psychiatric genetics
- Establish reproducible pipelines for model evaluation and optimization

Study Design:
This secondary data analysis study will utilize NIAGADS datasets to:
- Extract embedding representations from pre-trained genomic foundation models
- Analyze model performance across different cohorts and phenotypes
- Develop and test fine-tuning methods
- Create standardized evaluation metrics

Analysis Plan:
Phase 1: Data Preparation
- Harmonize genomic and phenotypic data across cohorts
- Format data for compatibility with foundation models
- Implement quality control measures
- Create validation datasets

Phase 2: Model Evaluation
Phenotypic characteristics to be evaluated:
- Clinical diagnosis status (AD/ADRD vs. control)
- Cognitive performance metrics
- Fluid biomarker levels
- Neuropathological findings
- Cardiovascular risk factors
- Neuroimaging metrics (DTI, FLAIR, PET, T1)
Analysis methods:
- Embedding space analysis
- Layer-wise representation analysis
- Linear separability testing
- Correlation analysis
- Target decoding performance
- Distribution matching assessment

Phase 3: Fine-tuning Development
- Implement distribution matching techniques
- Develop data augmentation strategies
- Create iterative optimization procedures
- Establish performance benchmarks

All data access and analysis will be conducted under approved institutional protocols with appropriate data security measures in place.

Our research aims to improve how artificial intelligence (AI) systems understand genetic information related to Alzheimer's disease and dementia. Using existing, anonymized genetic data from research participants, we will test and enhance AI systems' ability to identify genetic patterns associated with these conditions.

Just as AI has been trained to understand language and images, we will develop methods to help AI better process genetic information. The goal is to create tools that help researchers better understand the genetic factors contributing to Alzheimer's disease, potentially supporting future advances in early detection and treatment.