Polygenic Risk Scores in Neurodegenerative Disease

Polygenic Risk Scores in Neurodegenerative Disease

Introduction

Polygenic risk scores (PRS) aggregate the effects of thousands to millions of genetic variants to quantify an individual's genetic predisposition to complex diseases. In neurodegenerative disease research, PRS have emerged as powerful tools for identifying high-risk individuals, understanding disease heterogeneity, and informing clinical trial design. The development of robust PRS for conditions like Alzheimer's disease (AD) and Parkinson's disease (PD) represents a major advance in precision medicine approaches to neurodegeneration. [@karch2022][@nalls2019]

Overview

Unlike monogenic disorders caused by variants in single genes, neurodegenerative diseases like Alzheimer's Disease and Parkinson's Disease are influenced by hundreds of genetic variants, each with small effect sizes. PRS integrate these variants to generate a single quantitative risk score. The statistical framework for PRS construction involves careful consideration of linkage disequilibrium structure, effect size estimation, and appropriate validation in independent cohorts. [@wightman2021] [@nalls2019]

Key Concepts

Polygenic Risk Scores in Neurodegenerative Disease

Introduction

Polygenic risk scores (PRS) aggregate the effects of thousands to millions of genetic variants to quantify an individual's genetic predisposition to complex diseases. In neurodegenerative disease research, PRS have emerged as powerful tools for identifying high-risk individuals, understanding disease heterogeneity, and informing clinical trial design. The development of robust PRS for conditions like Alzheimer's disease (AD) and Parkinson's disease (PD) represents a major advance in precision medicine approaches to neurodegeneration. [@karch2022][@nalls2019]

Overview

Unlike monogenic disorders caused by variants in single genes, neurodegenerative diseases like Alzheimer's Disease and Parkinson's Disease are influenced by hundreds of genetic variants, each with small effect sizes. PRS integrate these variants to generate a single quantitative risk score. The statistical framework for PRS construction involves careful consideration of linkage disequilibrium structure, effect size estimation, and appropriate validation in independent cohorts. [@wightman2021] [@nalls2019]

Key Concepts

• Linkage Disequilibrium (LD): Non-random association between genetic variants that must be accounted for in PRS construction
• Genome-Wide Association Studies (GWAS): Large-scale studies that identify genetic variants associated with disease risk
• PRS Performance: Typically measured by area under the receiver operating characteristic curve (AUC) and odds ratios between risk strata
• Heritability: The proportion of disease variance explained by genetic factors
• P-value Thresholding: Selecting variants below a specific significance threshold for inclusion
• LD Clumping: Removing correlated variants to reduce redundancy

Construction Methods

Clumping and Thresholding (C+T):

• Standard approach using P-value thresholds
• Fast and computationally efficient
• Limited by single threshold selection

• Bayesian method accounting for LD structure
• More accurate effect size estimation
• Computationally intensive

• Continuous shrinkage method
• Automatically learns LD architecture
• Superior performance in diverse populations

PRS Workflow in Neurodegenerative Disease

PRS Components in Neurodegeneration

| Component | Description | Application |
|-----------|-------------|-------------|
| GWAS | Genome-wide association study | Identify risk variants |
| LD Clumping | Linkage disequilibrium pruning | Reduce collinearity |
| P-value Threshold | Significance cutoff | Optimize discovery |
| Effect Size Weighting | Meta-analysis priors | Improve accuracy |
| Risk Stratification | Categorize individuals | Clinical decision-making |

Alzheimer's Disease

Genetic Architecture

Alzheimer's Disease has a highly polygenic architecture, with over 40 genetic risk loci identified through GWAS. The largest GWAS meta-analysis to date included over 60,000 cases and 500,000 controls, revealing novel risk loci and refining effect size estimates for known variants. Key risk genes include: [@wightman2021]

• [APOE](/genes/apoe) — strongest genetic risk factor (OR ~3-4 for heterozygotes)
• [CLU](/genes/clu) — clusterin, involved in amyloid clearance
• [PICALM](/genes/picalm) — phosphatidylinositol binding clathrin assembly protein
• [BIN1](/genes/bin1) — bridging integrator 1, tau pathology modifier
• [TREM2](/genes/trem2) — triggering receptor expressed on myeloid cells 2
• [CD2AP](/genes/cd2ap) — cell adhesion molecule
• [ABCA7](/genes/abca7) — ATP-binding cassette transporter
• [MS4A](/genes/ms4a) — membrane-spanning 4-domains subfamily A

PRS Performance in AD

Current AD PRS explain approximately 10-15% of disease variance, with odds ratios of 3-4 between highest and lowest risk deciles. This represents a significant improvement over single genetic markers but remains insufficient for standalone clinical prediction. PRS performance is limited by: [@jansen2019]

• Incomplete capture of rare variants
• Population-specific effects
• Gene-environment interactions
• Phenotypic heterogeneity
• Missing heritability

Clinical Applications

• Risk stratification: Identifying individuals for prevention trials
• Diagnostic support: Enhancing probabilistic assessment
• Trial enrichment: Selecting high-risk participants for clinical trials
• Disease modification timing: Guiding intervention timing

APOE-PRS Interactions

APOE ε4 carrier status significantly modifies the predictive power of PRS. Studies show that combining APOE genotype with PRS improves discrimination beyond either alone. The interaction suggests that APOE may amplify the effect of common polygenic variation, creating a subpopulation with particularly high risk.

Parkinson's Disease

Genetic Architecture

Parkinson's Disease shows a complex genetic landscape with both monogenic and polygenic contributions. While LRRK2, GBA, SNCA, and PARK genes cause familial forms, common variants contribute to sporadic PD risk. The largest PD GWAS has identified over 90 risk loci, explaining approximately 16-22% of heritability. [@blauwendraat2020] [@schwartzentruber2021]

Monogenic Forms:

• [LRRK2](/genes/lrrk2) — most common genetic cause
• [GBA](/genes/gba) — glucocerebrosidase, significant risk modifier
• [SNCA](/genes/snca) — alpha-synuclein, Lewy body component
• [PRKN](/genes/prkn) — parkin, autosomal recessive PD
• [PINK1](/genes/pink1) — mitophagy pathway
• [DJ-1](/genes/park7) — oxidative stress response

PD PRS Development

Recent studies have developed PD PRS using 90+ risk loci, demonstrating:

• 2-3x odds ratio between top and bottom risk quintiles
• Potential for identifying prodromal individuals
• Integration with environmental risk factors
• Utility in predicting progression and subtype

PD Subtype Prediction

PRS may help distinguish PD clinical subtypes:

• Tremor-dominant: Associated with certain genetic profiles
• Postural instability gait difficulty: Different genetic associations
• Cognitive phenotype: Risk of dementia development

Amyotrophic Lateral Sclerosis

ALS demonstrates both rare highly-penetrant variants and common polygenic contributions. PRS for ALS face challenges due to: [@van2022]

• Strong founder effects in some populations
• Genetic heterogeneity between familial and sporadic cases
• Limited GWAS sample sizes
• Rapid disease progression affecting recruitment

ALS Genetics

• [C9orf72](/genes/c9orf72) — hexanucleotide repeat expansion (most common)
• [SOD1](/genes/sod1) — superoxide dismutase mutations
• [FUS](/genes/fus) — RNA-binding protein
• [TARDBP](/genes/tardbp) — TDP-43 protein
• ANG — angiogenin

PRS Challenges in ALS

• 10-15% of heritability captured by common variants
• Rare variants contribute significantly
• Phenotypic variability within genetic subtypes

Frontotemporal Dementia

FTD Genetic Architecture

FTD shows significant genetic heterogeneity with three major genes accounting for most familial cases:

• [MAPT](/genes/mapt) — microtubule-associated protein tau
• [GRN](/genes/grn) — progranulin
• [C9orf72](/genes/c9orf72) — hexanucleotide repeat expansion

FTD PRS Development

• Less mature than AD/PD PRS
• GWAS sample sizes smaller
• Clinical heterogeneity challenging

Methodological Considerations

PRS Construction Pipeline

Limitations

• Population stratification: Effects must be calibrated within ancestry groups
• Missing heritability: Much of genetic architecture remains unexplained
• Environmental interactions: Gene-environment effects not captured
• Clinical utility: Limited evidence for individual-level prediction
• Phenotypic quality: Diagnostic accuracy affects GWAS results
• Winner's curse: Overestimation of effect sizes in discovery cohorts

Validation Requirements

• Internal validation: Cross-validation within discovery cohort
• External validation: Test in independent populations
• Transferability: Assess across ancestry groups
• Calibration: Probability predictions match observed rates

Clinical Implementation

Risk Communication

• Framing PRS as probability estimates
• Explaining uncertainty ranges
• Integrating with clinical factors
• Considering life expectancy and competing risks

Integration with Clinical Assessment

• Family history integration
• Age of onset weighting
• Environmental exposure consideration
• Biomarker combination

Ethical Considerations

• Genetic discrimination concerns
• Psychological impact of risk knowledge
• Informed consent for PRS testing
• Equity in access to testing

Future Directions

Emerging Approaches

• Multi-ancestry PRS: Improving cross-population transferability
• Functional PRS: Incorporating functional genomics data
• Machine learning integration: Combining PRS with biomarkers
• Dynamic PRS: Modeling risk changes over time
• Single-cell PRS: Cell-type specific genetic effects

Research Priorities

Recent Research (2024-2026)

Recent advances in polygenic risk scores for neurodegeneration:

• AD PGS Validation: Polygenic risk scores for Alzheimer's have been validated in diverse populations, improving risk prediction[@karch2022].
• PD PGS Development: New polygenic risk scores for Parkinson's incorporate rare variants and improve predictive accuracy[@nalls2019].
• Clinical Implementation: Studies are evaluating PGS integration into clinical practice for early intervention.
• Multi-ancestry PRS: Development of PRS that transfer across populations with different genetic backgrounds.
• Machine Learning Integration: Combining PRS with neuroimaging and fluid biomarkers for improved prediction.

See Also

• [Alzheimer's Disease](/diseases/alzheimers-disease)
• [Parkinson's Disease](/diseases/parkinsons-disease)
• [APOE Gene](/genes/apoe)
• [CLU Gene](/genes/clu)
• [PICALM Gene](/genes/picalm)
• [BIN1 Gene](/genes/bin1)
• [TREM2 Gene](/genes/trem2)
• [ALS](/diseases/amyotrophic-lateral-sclerosis)
• [Frontotemporal Dementia](/diseases/frontotemporal-dementia)

External Links

• [International Parkinson's Disease Genomics Consortium](https://ipdgc.braininitiative.org/)
• [APOE Gene and PRS](https://www.ncbi.nlm.nih.gov/gene/348)
• [KEGG Pathways](https://www.genome.jp/kegg/pathway.html)

References