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Bio phasing imputation reference panels

Skill FridrichMethod/awesome-skills/skills/bio-phasing-imputation-reference-panels

Download, prepare, and manage reference panels for phasing and imputation. Covers 1000 Genomes, HRC, and TOPMed panels. Use when setting up imputation infrastructure or selecting appropriate reference panels for target populations.From its SKILL.md

Install
npx -y skills add FridrichMethod/awesome-skills --skill bio-phasing-imputation-reference-panels

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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SKILL.md

5.2 KB, ~1.5k tokens by cl100k_base, as published. Nobody here has run it

<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <[email protected]> # All Rights Reserved. # # This code is proprietary and confidential. # Unauthorized copying of this file, via any medium is strictly prohibited. # # Provenance: Authenticated by MD BABU MIA -->

Reference Panels

1000 Genomes Phase 3 (GRCh38)

# Download from IGSR
BASE_URL="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/working/20201028_3202_phased"

for chr in {1..22}; do
    wget ${BASE_URL}/CCDG_14151_B01_GRM_WGS_2020-08-05_chr${chr}.filtered.shapeit2-duohmm-phased.vcf.gz
    wget ${BASE_URL}/CCDG_14151_B01_GRM_WGS_2020-08-05_chr${chr}.filtered.shapeit2-duohmm-phased.vcf.gz.tbi
done

Subset by Population

# Download sample info
wget http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/1000G_2504_high_coverage.sequence.index

# Create population sample lists
grep "EUR" samples.txt | cut -f1 > european_samples.txt
grep "AFR" samples.txt | cut -f1 > african_samples.txt
grep "EAS" samples.txt | cut -f1 > east_asian_samples.txt

# Subset reference to specific population
bcftools view -S european_samples.txt \
    1000GP.chr22.vcf.gz \
    -Oz -o 1000GP_EUR.chr22.vcf.gz

Convert to Beagle Format

# Beagle uses VCF directly, but ensure proper format
bcftools view -m2 -M2 -v snps reference.vcf.gz | \
    bcftools annotate --set-id '%CHROM:%POS:%REF:%ALT' | \
    bgzip > reference_beagle.vcf.gz
bcftools index reference_beagle.vcf.gz

Convert to IMPUTE5 Format

# IMPUTE5 uses its own format
imp5Converter \
    --h reference.vcf.gz \
    --r chr22 \
    --o reference.chr22.imp5

HRC Reference Panel

# HRC requires registration at EGA
# After access granted:

# Download from EGA using pyega3
pip install pyega3
pyega3 -cf credentials.json fetch EGAD00001002729

# HRC contains 32,470 samples (mostly European)

TOPMed Reference Panel

# TOPMed available through imputation servers
# Or download from dbGaP with appropriate access

# Use via Michigan Imputation Server:
# 1. Upload study VCF
# 2. Select "TOPMed r2" as reference
# 3. Download imputed results

Genetic Maps

# Beagle format (GRCh38) - from Browning lab
wget https://faculty.washington.edu/browning/beagle/genetic_maps/plink.GRCh38.map.zip
unzip plink.GRCh38.map.zip -d genetic_maps/

# SHAPEIT5 format (recommended for SHAPEIT5)
wget https://github.com/odelaneau/shapeit5/raw/main/maps/genetic_maps.b38.tar.gz
tar xzf genetic_maps.b38.tar.gz

Check Reference Panel

# Basic stats
bcftools stats reference.vcf.gz | head -50

# Sample count
bcftools query -l reference.vcf.gz | wc -l

# Variant count
bcftools view -H reference.vcf.gz | wc -l

# Check chromosomes
bcftools index -s reference.vcf.gz

Lift Over Reference Panel

# GRCh37 to GRCh38
# Using Picard
java -jar picard.jar LiftoverVcf \
    I=reference_hg19.vcf.gz \
    O=reference_hg38.vcf.gz \
    CHAIN=hg19ToHg38.over.chain.gz \
    REJECT=rejected.vcf \
    R=hg38.fa

# Or using CrossMap
CrossMap.py vcf hg19ToHg38.chain reference_hg19.vcf hg38.fa reference_hg38.vcf

Align Study to Reference

# Check strand concordance
bcftools +fixref study.vcf.gz -Oz -o study_fixed.vcf.gz -- \
    -f reference.fa \
    -i reference_panel.vcf.gz \
    -m flip

# Statistics on fixes
bcftools +fixref study.vcf.gz -- -f reference.fa -m stats

Filter Reference Panel

# Remove singletons (appear in only 1 sample)
bcftools view -c 2 reference.vcf.gz -Oz -o reference_no_singletons.vcf.gz

# Filter by MAF
bcftools view -q 0.001:minor reference.vcf.gz -Oz -o reference_maf001.vcf.gz

# Remove indels (SNPs only)
bcftools view -v snps reference.vcf.gz -Oz -o reference_snps.vcf.gz

Merge Custom Panel with 1000G

# If you have additional reference samples
bcftools merge \
    1000GP.chr22.vcf.gz \
    custom_reference.chr22.vcf.gz \
    -Oz -o combined_reference.chr22.vcf.gz

# Ensure matching variants first
bcftools isec -n=2 \
    1000GP.chr22.vcf.gz \
    custom_reference.chr22.vcf.gz \
    -p isec_output

Reference Panel Comparison

PanelSamplesVariantsPopulations
1000G Phase 32,50488M26 global
HRC r1.132,47040MEuropean-heavy
TOPMed r297,256308M60% European, diverse
UK10K3,78142MBritish

Related Skills

  • phasing-imputation/haplotype-phasing - Use panels for phasing
  • phasing-imputation/genotype-imputation - Use panels for imputation
  • variant-calling/vcf-manipulation - VCF file operations
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

What ships with it: 2 files

6.8 KB alongside SKILL.md, 1 of them executable

examples/

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