Polygenic Risk Score (PRS) Builder

Build and interpret polygenic risk scores for complex diseases using genome-wide association study (GWAS) data.

Overview

Use Cases:

"Calculate my genetic risk for type 2 diabetes"

"Build a polygenic risk score for coronary artery disease"

"What's my genetic predisposition to Alzheimer's disease?"

"Interpret my PRS percentile for breast cancer risk"

What This Skill Does:

Extracts genome-wide significant variants (p < 5e-8) from GWAS Catalog

Builds weighted PRS models using effect sizes (beta coefficients)

Calculates individual risk scores from genotype data

Interprets PRS as population percentiles and risk categories

What This Skill Does NOT Do:

Diagnose disease (PRS is probabilistic, not deterministic)

Replace clinical assessment or genetic counseling

Account for non-genetic factors (lifestyle, environment)

Provide treatment recommendations

Methodology

PRS Calculation Formula

A polygenic risk score is calculated as a weighted sum across genetic variants:

PRS = Σ (dosage_i × effect_size_i)

Where:

dosage_i

Number of effect alleles at SNP i (0, 1, or 2)

effect_size_i

Beta coefficient or log(odds ratio) from GWAS

Standardization

Raw PRS is standardized to z-scores for interpretation:

z-score = (PRS - population_mean) / population_std

This allows comparison to population distribution and percentile calculation.

Significance Thresholds

Genome-wide significance

p < 5×10⁻⁸ (default threshold)

This corrects for ~1 million independent tests across the genome

Relaxed thresholds (e.g., p < 1×10⁻⁵) can include more SNPs but may add noise

Effect Size Handling

Continuous traits

(e.g., height, BMI): Beta coefficient (units of trait per allele)

Binary traits

(e.g., disease): Odds ratio converted to log-odds (beta = ln(OR))

Missing effect sizes or non-significant SNPs are excluded

Data Sources

This skill uses ToolUniverse GWAS tools to query:

GWAS Catalog

(EMBL-EBI)

Curated GWAS associations

5000+ studies, millions of variants

Tools:

gwas_get_associations_for_trait

,

gwas_get_snp_by_id

Open Targets Genetics

Integrated genetics platform

Fine-mapped credible sets

Tools:

OpenTargets_search_gwas_studies_by_disease

,

OpenTargets_get_variant_info

Key Concepts

Polygenic Risk Scores (PRS)

Polygenic risk scores aggregate the effects of many genetic variants to estimate an individual's genetic predisposition to a trait or disease. Unlike Mendelian diseases caused by single mutations, complex diseases involve hundreds to thousands of variants, each with small effects.

Key Properties:

Continuous distribution

PRS forms a bell curve in populations

Relative risk

Compares individual to population average

Probabilistic

High PRS doesn't guarantee disease, low PRS doesn't guarantee protection

Ancestry-specific

PRS accuracy depends on matching GWAS and target ancestry

GWAS (Genome-Wide Association Studies)

GWAS compare allele frequencies between cases and controls (or correlate with trait values) across millions of SNPs to identify disease-associated variants.

Study Design:

Discovery cohort

Initial identification of associations

Replication cohort

Validation in independent samples

Sample size

Larger studies detect smaller effects (power ∝ √N)

Multiple testing correction

Bonferroni-type correction for ~1M tests

Effect Sizes and Odds Ratios

Beta (β)

Change in trait per copy of effect allele

Example: β = 0.5 kg/m² means each allele increases BMI by 0.5 units

Odds Ratio (OR)

Multiplicative change in disease odds

OR = 1.5 means 50% increased odds per allele

Convert to beta: β = ln(OR)

Linkage Disequilibrium (LD) and Clumping

Nearby variants are often inherited together (LD). To avoid double-counting:

LD clumping

Select independent variants (r² < 0.1 within 1 Mb windows)

Fine-mapping

Statistical methods to identify causal variants

This skill uses raw associations; production PRS should include LD pruning

Population Stratification

GWAS and PRS are most accurate when ancestries match:

Population structure

Different ancestries have different allele frequencies

Transferability

European-trained PRS perform worse in non-European populations

Solution

Train PRS on diverse cohorts or use ancestry-matched references

Applications

Clinical Risk Assessment

PRS can stratify individuals for:

Screening programs

Target high-risk individuals (e.g., mammography, colonoscopy)

Prevention strategies

Lifestyle interventions for high genetic risk

Drug response

Pharmacogenomics based on metabolism genes

Example

Khera et al. (2018) showed PRS identifies 3× more individuals at >3-fold coronary artery disease risk than monogenic mutations.

Research Applications

Gene discovery

PRS-based phenome-wide association studies (PheWAS)

Genetic correlation

Compare PRS across traits

Causal inference

Mendelian randomization using PRS as instruments

Simulation studies

Model polygenic architecture

Personal Genomics

Consumer genetic testing (23andMe, Ancestry DNA) provides raw genotypes. Users can:

Calculate PRS for traits not reported

Compare to published PRS models

Understand genetic contribution vs. lifestyle factors

Caution

Personal PRS should not replace medical advice. Results may cause anxiety if not properly contextualized.

Limitations and Considerations

Scientific Limitations

Heritability Gap

PRS explains a fraction of genetic heritability

Type 2 diabetes: ~50% heritable, PRS explains ~10-20%

Rare variants, epistasis, and gene-environment interactions not captured

Ancestry Bias

Most GWAS are European ancestry

PRS accuracy drops in non-European populations

Need for diverse cohort recruitment

Winner's Curse

Discovery effect sizes often overestimated

Replication studies show smaller effects

Meta-analyses provide better estimates

Missing Heritability

Unexplained genetic contribution from:

Rare variants not captured by SNP arrays

Structural variants (CNVs, inversions)

Epigenetic factors

Clinical Limitations

Not Diagnostic

PRS is probabilistic, not deterministic

High PRS doesn't mean you will get disease

Low PRS doesn't mean you won't get disease

Environmental Factors

Many complex diseases are 50%+ environmental

Smoking, diet, exercise, stress, pollution

PRS doesn't account for these

Pleiotropy

Same variants affect multiple traits

Genetic correlation between diseases

Risk for one may protect against another

Actionability

Not all high-risk predictions have interventions

Alzheimer's PRS has limited actionability currently

Ethical considerations for testing

Ethical Considerations

Privacy

Genetic data is identifiable and permanent

Can't be changed like passwords

Familial implications (relatives share genetics)

Discrimination

Potential for genetic discrimination

GINA protects against health/employment discrimination (US)

Life insurance and long-term care not protected

Psychological Impact

Knowledge of high risk can cause anxiety

Need for genetic counseling

Risk communication training

Equity

Ancestry bias means unequal benefits

Europeans benefit most from current PRS

Exacerbates health disparities

References

Key Publications

Lambert et al. (2021)

"The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation"

PGS Catalog:

https://www.pgscatalog.org/

Repository of published PRS models

Khera et al. (2018)

"Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations"

Nature Genetics, 50:1219–1224

Demonstrated clinical utility of PRS

Torkamani et al. (2018)

"The personal and clinical utility of polygenic risk scores"

Nature Reviews Genetics, 19:581–590

Comprehensive review of PRS applications

Martin et al. (2019)

"Clinical use of current polygenic risk scores may exacerbate health disparities"

Nature Genetics, 51:584–591

Addresses ancestry bias and equity concerns

Choi et al. (2020)

"Tutorial: a guide to performing polygenic risk score analyses" Nature Protocols, 15:2759–2772 Practical guide to PRS calculation and evaluation Resources PGS Catalog : https://www.pgscatalog.org/ - Published PRS models LD Hub : http://ldsc.broadinstitute.org/ - Genetic correlations PRSice : https://www.prsice.info/ - PRS calculation software GWAS Catalog : https://www.ebi.ac.uk/gwas/ - Association database Workflow 1. Trait Selection Identify the disease or trait of interest: Use standard terminology (e.g., "type 2 diabetes" not "T2D") Check GWAS Catalog for availability Verify sufficient GWAS studies exist (n > 10,000 samples ideal) 2. Association Collection Query GWAS databases for genome-wide significant associations: prs = build_polygenic_risk_score ( trait = "coronary artery disease" , p_threshold = 5e-8 ,

Genome-wide significance

max_snps

1000

)

Considerations:

P-value threshold: 5e-8 is conservative, 1e-5 includes more variants

LD clumping: Production systems should prune correlated SNPs

Study quality: Prefer large meta-analyses over small studies

3. Effect Size Extraction

Extract beta coefficients or odds ratios:

Beta for continuous traits (direct use)

OR for binary traits (convert to log-odds)

Handle missing values (exclude or impute from meta-analysis)

4. SNP Filtering

Quality control filters:

MAF filter

Exclude rare variants (MAF < 0.01) for robustness

Genotype QC

Remove SNPs with high missingness (> 10%)

Hardy-Weinberg

Exclude SNPs violating HWE (p < 1e-6)

Ambiguous SNPs

Remove A/T and G/C SNPs (strand ambiguity)

5. Score Calculation

Calculate weighted sum of genotype dosages:

result

=

calculate_personal_prs

(

prs_weights

=

prs

,

genotypes

=

my_genotypes

,

population_mean

=

0.0

,

population_std

=

1.0

)

Genotype Sources:

23andMe raw data export

Ancestry DNA raw data

Whole genome sequencing (VCF files)

SNP array data (Illumina, Affymetrix)

6. Risk Interpretation

Convert to percentiles and risk categories:

result

=

interpret_prs_percentile

(

result

)

print

(

f"Percentile:

{

result

.

percentile

:

.1f

}

%"

)

print

(

f"Risk:

{

result

.

risk_category

}

"

)

Risk Categories:

Low risk

< 20th percentile (genetic protection)

Average risk

20-80th percentile (typical genetic predisposition)

Elevated risk

80-95th percentile (moderately increased risk)

High risk

95th percentile (substantially increased risk) Clinical Interpretation: Percentiles assume normal distribution Relative risk vs. average (not absolute risk) Combine with family history, clinical risk factors PRS is NOT diagnostic - many high-risk individuals never develop disease Best Practices PRS Construction Use validated PRS from PGS Catalog when available Published models have been externally validated Include LD clumping and ancestry-specific weights Match ancestries between GWAS and target population European GWAS for European individuals Use multi-ancestry GWAS when available Include as many SNPs as practical More SNPs = better prediction (up to a point) Balance between coverage and genotyping cost Consider trait architecture Highly polygenic traits (height, education): benefit from relaxed thresholds Oligogenic traits (IBD, T1D): few large-effect variants, strict thresholds Clinical Use Combine with clinical risk scores Add PRS to Framingham Risk Score, QRISK, etc. Integrated models improve prediction Stratify screening and prevention Intensify surveillance for high PRS (e.g., earlier mammography) Lifestyle interventions for modifiable risk Provide genetic counseling Explain probabilistic nature of PRS Discuss limitations and uncertainty Address psychological impact Consider actionability Is there an intervention for high risk? Benefits vs. harms of knowing genetic risk Research Use Report methods transparently Document SNP selection criteria Report LD clumping parameters Specify ancestry of GWAS and target Validate in held-out cohorts Split data: training vs. testing Report out-of-sample prediction accuracy (R², AUC) Compare to existing PRS Benchmark against PGS Catalog models Report incremental improvement Test across ancestries Evaluate transferability to non-European populations Report performance stratified by ancestry Disclaimer This skill is for educational and research purposes only. Not for clinical diagnosis or treatment decisions Not validated for clinical use - use PGS Catalog models for clinical-grade PRS Requires genetic counseling - interpretation requires expertise Does not account for family history, environment, or lifestyle factors Ancestry-specific - accuracy depends on matching GWAS ancestry For clinical genetic testing, consult: Genetic counselors (certified by ABGC/ABMGG) Medical geneticists Healthcare providers with genomics training PRS is a rapidly evolving field. Guidelines and best practices will continue to change as research progresses. Regulatory Status: FDA does not currently regulate PRS (as of 2024) Some countries restrict direct-to-consumer genetic risk reporting Check local regulations before clinical implementation