Polygenic risk score prs catalog

Polygenic Risk Score (PGS Catalog)

When to Use This Skill

• You have a trait of interest and want to calculate PRS using published, peer-reviewed weights
• Multi-trait risk profiling (e.g., cardiometabolic panel: CAD, T2D, LDL, BMI, blood pressure)
• Population comparisons of genetic risk across ancestry groups
• No GWAS summary statistics needed — uses pre-computed weights from PGS Catalog
• Quick PRS — minutes per trait (download + score), no LD computation required

For de novo PRS from raw GWAS summary statistics, use the polygenic-risk-score skill (LDpred2-auto) instead.

Installation

install.packages(c("data.table", "ggplot2", "ggprism", "dplyr", "R.utils", "jsonlite", "remotes"))
remotes::install_github("privefl/bigsnpr")

Software	Version	License	Commercial Use	Install
bigsnpr	≥1.12	GPL-3	✅ Permitted	remotes::install_github("privefl/bigsnpr")
data.table	≥1.14	MPL-2.0	✅ Permitted	install.packages('data.table')
ggplot2	≥3.4	MIT	✅ Permitted	install.packages('ggplot2')
ggprism	≥1.0.3	GPL (≥3)	✅ Permitted	install.packages('ggprism')
dplyr	≥1.1	MIT	✅ Permitted	install.packages('dplyr')
jsonlite	≥1.8	MIT	✅ Permitted	install.packages('jsonlite')
R.utils	≥2.12	LGPL (≥2.1)	✅ Permitted	install.packages('R.utils')

Inputs

• Target genotypes: PLINK binary format (.bed/.bim/.fam) — or use 1000 Genomes Phase 3 example data (2,490 individuals, 5 super-populations)
• PGS Catalog score IDs: One or more PGS IDs (e.g., PGS000018 for CAD) — use search_pgs_catalog() to discover available scores
• Genome build: GRCh37 (default, matches 1000 Genomes) or GRCh38

Outputs

Per-trait files:

• prs_scores_<trait>.csv — Individual PRS (z-scores, percentiles, population labels)
• distribution_<trait>.png/svg — PRS distribution histogram
• population_<trait>.png/svg — PRS by super-population boxplot

Combined files:

• combined_prs_scores.csv — All individuals x all traits (wide format) + composite risk
• prs_correlation_matrix.csv — Trait-trait PRS correlation matrix
• population_summary.csv — Mean PRS by super-population per trait
• match_reports.csv — Variant matching summary per trait

Dashboard plots:

• dashboard_correlation_matrix.png/svg — Heatmap of trait PRS correlations
• dashboard_composite_risk.png/svg — Composite risk distribution by population
• dashboard_population_heatmap.png/svg — Mean PRS by trait x super-population

Analysis objects (RDS):

• prs_analysis.rds — Complete analysis object for downstream use
  • Load with: obj <- readRDS('prs_analysis.rds')
  • Contains: combined_scores, per_trait, cor_matrix, match_reports, snp_weights, trait_info

Clarification Questions

1. Input Data (ASK THIS FIRST):

   • Do you have specific genotype files (.bed/.bim/.fam) to score?
   • Or use 1000 Genomes Phase 3 example data? (2,490 individuals, 26 populations, 5 super-populations)

2. Traits to Score:

   • (If using example data) The demo scores 5 cardiometabolic traits (CAD, T2D, LDL, BMI, SBP). Choose analysis mode:
     • a) Full cardiometabolic panel — all 5 traits (recommended)
     • b) Select specific traits from the panel
   • (If using your own data) What traits do you want to score? Use search_pgs_catalog("trait name") to find PGS IDs.

3. Analysis Options:

   • a) Standard analysis with dashboard (recommended)
   • b) Individual trait scoring only (no dashboard)

Standard Workflow

Note: Run from the OmicsClaw root directory and add the workflow scripts to sys.path:

import sys; import os; sys.path.insert(0, os.path.abspath('knowledge_base/scripts/polygenic-risk-score-prs-catalog'))

🚨 MANDATORY: USE SCRIPTS EXACTLY AS SHOWN - DO NOT WRITE INLINE CODE 🚨

Step 1 - Load reference genotypes and PGS weights:

source("scripts/load_reference_data.R")
ref_data <- load_reference_data()

source("scripts/load_pgs_weights.R")
trait_weights <- load_demo_weights()

DO NOT write custom download or parsing code. Use the scripts.

Step 2 - Score all traits:

source("scripts/score_traits.R")

DO NOT write inline scoring code (big_prodVec, allele matching, etc.). Just source the script.

Step 3 - Generate visualizations:

source("scripts/generate_plots.R")
generate_all_plots(all_results, output_dir = "results")

🚨 DO NOT write inline plotting code (ggsave, ggplot, geom_tile, etc.). Just use the script. 🚨

The script handles PNG + SVG export with graceful fallback for SVG dependencies.

Step 4 - Export results:

source("scripts/export_results.R")
export_all(all_results, output_dir = "results")

DO NOT write custom export code. Use export_all().

✅ VERIFICATION - You should see:

• After Step 1: "✓ Reference data loaded successfully" and "✓ PGS Catalog weights loaded: 5/5 traits"
• After Step 2: "✓ Multi-trait PRS scoring completed successfully! (5 traits, 2490 individuals)"
• After Step 3: "✓ All plots generated successfully!"
• After Step 4: "=== Export Complete ==="

❌ IF YOU DON'T SEE THESE: You wrote inline code. Stop and use source().

⚠️ CRITICAL - DO NOT:

• ❌ Write inline scoring code → STOP: Use source("scripts/score_traits.R")
• ❌ Write inline plotting code (ggsave, ggplot, etc.) → STOP: Use generate_all_plots()
• ❌ Write custom export code → STOP: Use export_all()
• ❌ Try to install svglite → script handles SVG fallback automatically

⚠️ IF SCRIPTS FAIL - Script Failure Hierarchy:

1. Fix and Retry (90%) — Install missing package, re-run script
2. Modify Script (5%) — Edit the script file itself, document changes
3. Use as Reference (4%) — Read script, adapt approach, cite source
4. Write from Scratch (1%) — Only if genuinely impossible, explain why

NEVER skip directly to writing inline code without trying the script first.

Scoring Custom Traits

To score a single custom trait instead of the demo panel:

source("scripts/load_reference_data.R")
ref_data <- load_reference_data()

source("scripts/load_pgs_weights.R")
# Search for available scores
scores <- search_pgs_catalog("your trait name")
# Download specific score
trait_weights <- list()
tw <- download_pgs_weights("PGS_ID_HERE")
trait_weights[["TRAIT"]] <- list(
    weights = tw$weights, pgs_id = tw$pgs_id, score_meta = tw$score_meta,
    trait_name = "Your Trait", short_name = "TRAIT"
)

# Then continue with Steps 2-4 as above
source("scripts/score_traits.R")

Common Issues

Error	Cause	Solution
"bigsnpr not found"	Missing core dependency	remotes::install_github("privefl/bigsnpr")
Download timeout	Large scoring file or slow connection	Set options(timeout = 900) before running Step 1
Low match rate (<50%)	Genome build mismatch	Ensure PGS weights and genotypes use same build (GRCh37 for 1000G)
PGS ID not found	Wrong or deprecated PGS ID	Use search_pgs_catalog("trait") to find valid IDs
SVG export error	Missing optional dependency	generate_all_plots() handles fallback automatically. DO NOT install svglite manually.
"catalog_data not found"	Wrong script for this skill	Use score_traits.R (not pgs_catalog_scoring.R from the LDpred2 skill)
Memory error during scoring	Very large scoring file	Normal for genome-wide scores. Ensure ≥8GB RAM available.

Suggested Next Steps

After completing multi-trait PRS:

1. Downstream analysis — Load prs_analysis.rds for custom analyses
2. Additional traits — Add more PGS scores to expand the risk panel
3. De novo PRS — Use polygenic-risk-score skill for traits without PGS Catalog scores
4. GWAS interpretation — Pair with functional annotation skills

Related Skills

• polygenic-risk-score — De novo PRS using LDpred2-auto (requires GWAS summary statistics)
• eqtl-colocalization-coloc — Colocalization of GWAS signals with eQTLs

References

1. Lambert SA, et al. (2021). The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nature Genetics, 53(4), 420-425.
2. 1000 Genomes Project Consortium (2015). A global reference for human genetic variation. Nature, 526(7571), 68-74.
3. Privé F, et al. (2022). Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort. AJHG, 109(1), 12-23.
4. Khera AV, et al. (2018). Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nature Genetics, 50(9), 1219-1224.
5. PGS Catalog: https://www.pgscatalog.org/