bio-clinical-databases-polygenic-risk

name	bio-clinical-databases-polygenic-risk
description	Calculate polygenic risk scores using PRSice-2, LDpred2, or PRS-CS from GWAS summary statistics. Use when predicting disease risk from genome-wide genetic variants.
tool_type	mixed
primary_tool	PRSice-2

Version Compatibility

Reference examples tested with: LDpred2 1.14+, PRSice-2 2.3+, numpy 1.26+, scipy 1.12+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Polygenic Risk Scores

"Calculate polygenic risk scores for my cohort" → Compute genome-wide risk scores from GWAS summary statistics and individual genotypes to predict disease susceptibility.

• CLI: PRSice_linux --base gwas.txt --target genotypes --out prs_results
• R: bigsnpr::snp_ldpred2_auto() for LDpred2 Bayesian PRS

PRSice-2 Workflow

Goal: Calculate polygenic risk scores from GWAS summary statistics using clumping and thresholding.

Approach: Run PRSice-2 with GWAS summary stats and target genotypes, applying LD clumping and multiple p-value thresholds.

Basic PRS Calculation

# PRSice-2 with clumping and thresholding
PRSice_linux \
    --base gwas_summary.txt \
    --target genotypes \
    --snp SNP \
    --chr CHR \
    --bp BP \
    --A1 A1 \
    --A2 A2 \
    --pvalue P \
    --beta BETA \
    --clump-kb 250 \
    --clump-r2 0.1 \
    --bar-levels 5e-8,1e-5,1e-3,0.01,0.05,0.1,0.5,1 \
    --fastscore \
    --all-score \
    --out prs_results

PRSice-2 with Covariates

PRSice_linux \
    --base gwas_summary.txt \
    --target genotypes \
    --pheno phenotype.txt \
    --cov covariates.txt \
    --cov-col @PC[1-10],Age,Sex \
    --binary-target T \
    --clump-kb 250 \
    --clump-r2 0.1 \
    --out prs_with_cov

GWAS Summary Statistics Format

SNP          CHR  BP        A1  A2  BETA    SE      P
rs12345      1    10000     A   G   0.05    0.01    1e-8
rs67890      1    20000     T   C   -0.03   0.02    0.001

LDpred2 (R)

Goal: Compute Bayesian polygenic risk scores with automatic hyperparameter tuning via LDpred2-auto.

Approach: Load genotypes with bigsnpr, match GWAS variants, compute LD matrix, estimate heritability with LD score regression, then run LDpred2-auto.

Setup and Run

library(bigsnpr)
library(data.table)

# Load genotype data (plink bed/bim/fam)
obj.bigsnp <- snp_attach('genotypes.rds')
G <- obj.bigsnp$genotypes
map <- obj.bigsnp$map

# Load and format GWAS summary stats
sumstats <- fread('gwas_summary.txt')

# Match variants
df_beta <- snp_match(sumstats, map, strand_flip = TRUE)

# Compute LD matrix (correlation)
# Uses reference panel or in-sample LD
corr <- snp_cor(G, ind.col = df_beta$`_NUM_ID_`)

# LDpred2-auto (recommended - automatic hyperparameter tuning)
ldsc <- snp_ldsc2(corr, df_beta)
h2_est <- ldsc[['h2']]

multi_auto <- snp_ldpred2_auto(
    corr,
    df_beta,
    h2_init = h2_est,
    vec_p_init  seq_log  
    ncores  



beta_auto  sapplymulti_auto x xbeta_est
pred_auto  big_prodMatG beta_auto

LDpred2 Grid Model

# Grid of hyperparameters
h2_seq <- round(h2_est * c(0.7, 1, 1.4), 4)
p_seq <- signif(seq_log(1e-5, 1, 21), 2)
params <- expand.grid(p = p_seq, h2 = h2_seq, sparse = c(FALSE, TRUE))

# Run LDpred2-grid
beta_grid <- snp_ldpred2_grid(corr, df_beta, params, ncores = 4)
pred_grid <- big_prodMat(G, beta_grid)

# Select best parameters by validation R2
auc_grid  applypred_grid  x 
    AUCx obj.bigsnpfamaffection  

best_params  paramswhich.maxauc_grid 

PRS-CS

Goal: Compute PRS using continuous shrinkage priors with an external LD reference panel.

Approach: Run PRS-CS to estimate posterior effect sizes, then score with plink.

# PRS-CS with external LD reference
python PRScs.py \
    --ref_dir=ldblk_1kg_eur \
    --bim_prefix=target \
    --sst_file=gwas_summary.txt \
    --n_gwas=100000 \
    --out_dir=prscs_output

# Score with plink
plink --bfile target \
    --score prscs_output_pst_eff_a1_b0.5_phi1e-02.txt 2 4 6 \
    --out prs_scores

Score Normalization

Goal: Normalize raw PRS values to Z-scores and population percentiles for interpretable reporting.

Approach: Z-score normalize against a reference distribution, then convert to percentiles via the normal CDF.

import numpy as np
from scipy import stats

def normalize_prs(scores, reference_scores=None):
    '''Z-score normalize PRS

    Args:
        scores: Array of PRS values
        reference_scores: Population reference (if None, use scores)

    Returns:
        Z-scored PRS values
    '''
    if reference_scores is None:
        reference_scores = scores
    mean = np.mean(reference_scores)
    std = np.std(reference_scores)
    return (scores - mean) / std

def prs_to_percentile(z_score):
    '''Convert Z-scored PRS to population percentile'''
    return stats.norm.cdf(z_score) * 100

# Example
prs_raw = np.array([0.5, 1.2, -0.3, 2.1, 0.8])
prs_z = normalize_prs(prs_raw)
percentiles = prs_to_percentile(prs_z)

Risk Stratification

Goal: Categorize individuals into clinical risk groups based on their Z-scored PRS.

Approach: Apply population-distribution-based thresholds to assign Low/Average/High/Very High risk tiers.

def stratify_risk(prs_z, thresholds=None):
    '''Categorize PRS into risk groups

    Default thresholds based on population distribution:
    - Low: < -1 SD (bottom 16%)
    - Average: -1 to 1 SD (middle 68%)
    - High: > 1 SD (top 16%)
    - Very high: > 2 SD (top 2.5%)
    '''
    if thresholds is None:
        thresholds = {'low': -1, 'high': 1, 'very_high': 2}

    if prs_z > thresholds['very_high']:
        return 'Very High Risk'
    elif prs_z > thresholds['high']:
        return 'High Risk'
    elif prs_z < thresholds['low']:
        return 'Low Risk'
    else:
        return 'Average Risk'

PGS Catalog Integration

Goal: Download pre-computed PRS weights from the PGS Catalog for published scores.

Approach: Query the PGS Catalog REST API by score ID and retrieve the scoring file URL.

def download_pgs_weights(pgs_id):
    '''Download PRS weights from PGS Catalog

    Args:
        pgs_id: PGS ID (e.g., 'PGS000001')
    '''
    import requests
    url = f'https://www.pgscatalog.org/rest/score/{pgs_id}'
    response = requests.get(url)
    score_info = response.json()

    # Download scoring file
    ftp_url = score_info['ftp_scoring_file']
    # Use wget or requests to download

    return score_info

Validation Metrics

Goal: Evaluate PRS predictive performance using discrimination and effect size metrics.

Approach: Compute Nagelkerke R-squared, AUC, and odds ratio per standard deviation from logistic regression models.

# Nagelkerke's R2 for case-control
library(rms)
mod <- lrm(case ~ prs + age + sex + PC1 + PC2, data = df)
r2 <- mod$stats['R2']

# AUC
library(pROC)
auc_result <- auc(case ~ prs, data = df)

# Odds ratio per SD
mod <- glm(case ~ scale(prs), data = df, family = 'binomial')
or_per_sd <- exp(coef(mod)['scale(prs)'])

Related Skills

• population-genetics/gwas-analysis - GWAS input
• population-genetics/population-structure - Population matching
• clinical-databases/variant-prioritization - Clinical filtering