ALZHEIMER’S DISEASE GENETIC LANDSCAPE

Alzheimer’s disease (AD) is the most common neurodegenerative disorder and a leading cause of dementia worldwide. Clinically, AD involves a progressive decline in memory and cognition, but biologically it is a complex, heterogeneous condition shaped by interactions between genetic risk, aging, and molecular pathways in the brain. While rare mutations in APP, PSEN1, and PSEN2 cause familial early-onset AD, most cases are late-onset and polygenic, driven by many risk loci with small effects.

In our AD genetics project, we aim to move beyond association signals to identify causal genes, understand disease mechanisms, and accelerate therapeutic discovery. We integrate large-scale genomic datasets with brain-relevant multi-omics, systems biology, and machine-learning approaches.

Importantly, our team also performs its own genome-wide association (GWAS) analyses and whole-genome (WGS) sequencing across multiple Alzheimer’s disease cohorts to identify novel risk loci and refine existing genetic signals.

Our work also examines the shared and distinct genetic background between AD and related neurodegenerative synucleinopathies such as Parkinson’s disease (PD), dementia with Lewy bodies (DLB), REM sleep behavior disorder (RBD), and multiple system atrophy (MSA).

Main objectives of the project include:

1. Genetic susceptibility in AD

Identify and interpret common and rare variants contributing to AD risk across diverse cohorts and ancestries.

2. From GWAS loci to causal genes

Fine-map AD GWAS signals, prioritize coding and regulatory variants in linkage disequilibrium with known markers, and define the most likely causal genes.

3. Shared genetic architecture with synucleinopathies

Quantify genetic overlap and divergence between AD, PD, DLB, RBD, and MSA to uncover convergent pathways and disease-specific mechanisms.

 4. Genotype–phenotype correlations

Study how genetic profiles relate to age of onset, progression, cognitive trajectories, imaging biomarkers, and clinical subtypes.

5. Epigenetic and regulatory mechanisms

Investigate AD-related changes in DNA methylation, histone modifications, chromatin accessibility, and non-coding RNA expression, with emphasis on brain cell-type specificity.

6. Functional follow-up in cellular models

Prioritize high-confidence genes for experimental validation in patient-derived cellular systems (e.g., iPSC-derived neurons/microglia) and other model platforms.

Our AD genetics project includes multiple sub-projects, for example:

  • In-house GWAS/WGS to identify and refine AD risk loci
  • Genome-wide association meta-analyses
  • Rare-variant burden and SKAT/SKAT-O analyses
  • Multi-omics fine-mapping of novel loci
  • Cell-type-specific regulatory annotation
  • Polygenic risk score modeling
  • Machine-learning pipelines for gene prioritization
  • Systems biology and network crosstalk analysis
  • Drug-target prioritization and repurposing
  • Integration of WES/WGS datasets (ADNI, ROSMAP, ADSP, UKBB, TRIAD, PREVENT-AD, CIMA-Q, etc.)

Ultimately, our goal is to deliver a clearer map of the genetic and molecular landscape of AD, and to translate that knowledge into clinically meaningful pathways and therapeutic opportunities.

From GWAS to Causal Mechanisms in Alzheimer’s Disease

Integrative framework used in our lab to prioritize causal AD genes and identify actionable pathways by combining GWAS, rare-variant signals, and brain-specific multi-omics.