Version Compatibility

Reference examples tested with: bcftools 1.19+

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

• 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.

Haplotype Phasing

"Phase my genotypes into haplotypes" → Resolve which alleles are inherited together on each chromosome for downstream imputation, HLA typing, or population genetic analyses.

• CLI:

  java -jar beagle.jar gt=input.vcf out=phased

  (Beagle 5.4)
• CLI:

  shapeit4 --input input.vcf --output phased.vcf

  (SHAPEIT)

Beagle 5.4 Phasing (Recommended)

# Download Beagle 5.4
wget https://faculty.washington.edu/browning/beagle/beagle.22Jul22.46e.jar

# Basic phasing
java -jar beagle.22Jul22.46e.jar \
    gt=input.vcf.gz \
    out=phased

# Output: phased.vcf.gz (phased genotypes)

# With genetic map (improves accuracy)
java -jar beagle.22Jul22.46e.jar \
    gt=input.vcf.gz \
    map=plink.chr22.GRCh38.map \
    out=phased

Beagle Options

java -jar beagle.22Jul22.46e.jar \
    gt=input.vcf.gz \
    out=phased \
    map=genetic_map.txt \
    nthreads=8 \
    window=40 \
    overlap=4 \
    ne=20000 \              # Effective population size
    seed=12345              # For reproducibility

Phase Per Chromosome

# Process each chromosome separately
for chr in {1..22}; do
    java -Xmx16g -jar beagle.jar \
        gt=input.chr${chr}.vcf.gz \
        map=genetic_maps/plink.chr${chr}.GRCh38.map \
        out=phased.chr${chr} \
        nthreads=8
done

# Concatenate chromosomes
bcftools concat phased.chr*.vcf.gz -Oz -o phased.all.vcf.gz
bcftools index phased.all.vcf.gz

SHAPEIT5 Phasing (for Large Datasets)

Goal: Phase large biobank-scale datasets using SHAPEIT5's two-stage approach for accurate haplotype resolution of both common and rare variants.

Approach: First phase common variants to build a haplotype scaffold, then phase rare variants onto that scaffold using the common-variant structure as a guide.

# Phase common variants first
shapeit5_phase_common \
    --input input.vcf.gz \
    --map genetic_map.txt \
    --output phased_common.bcf \
    --thread 8 \
    --log phased.log

# Then phase rare variants
shapeit5_phase_rare \
    --input input.vcf.gz \
    --scaffold phased_common.bcf \
    --map genetic_map.txt \
    --output phased.bcf \
    --thread 8

SHAPEIT5 with Reference Panel

# Improves phasing using reference haplotypes
shapeit5_phase_common \
    --input input.vcf.gz \
    --reference reference_panel.bcf \
    --map genetic_map.txt \
    --output phased.bcf \
    --thread 8

Beagle with Reference Panel

# Use reference panel for better phasing
java -jar beagle.22Jul22.46e.jar \
    gt=input.vcf.gz \
    ref=reference.vcf.gz \
    map=genetic_map.txt \
    out=phased \
    nthreads=8

Input Preparation

# Filter variants before phasing
bcftools view -m2 -M2 -v snps input.vcf.gz -Oz -o biallelic_snps.vcf.gz

# Remove missing genotypes (optional)
bcftools view -g ^miss biallelic_snps.vcf.gz -Oz -o no_missing.vcf.gz

# Normalize (important!)
bcftools norm -f reference.fa -Oz -o normalized.vcf.gz input.vcf.gz

Check Phasing Results

# View phased genotypes (| instead of /)
bcftools query -f '%CHROM\t%POS\t[%GT\t]\n' phased.vcf.gz | head

# Unphased: 0/1
# Phased: 0|1 or 1|0

# Count phased vs unphased
bcftools query -f '[%GT\n]' phased.vcf.gz | grep -c '|'

Genetic Maps

# Download genetic maps (GRCh38)
wget https://faculty.washington.edu/browning/beagle/genetic_maps/plink.GRCh38.map.zip
unzip plink.GRCh38.map.zip

# Format: chromosome position rate(cM/Mb) genetic_position(cM)
# chr1 55550 2.981822 0.000000

Key Parameters

Parameter	Beagle	SHAPEIT5	Description
Threads	nthreads	--thread	CPU threads
Window	window	--window	Analysis window size
Eff. pop size	ne	--effective-size	For LD modeling
Seed	seed	--seed	Random seed

Memory Requirements

Dataset Size	Beagle Memory	SHAPEIT5 Memory
1,000 samples	8 GB	4 GB
10,000 samples	32 GB	16 GB
100,000 samples	64+ GB	32 GB

Phasing Accuracy Metrics

• Switch error rate: Rate of phase switches vs truth
• Mismatch error rate: Overall haplotype differences
• Measure using trio data or known haplotypes

Related Skills

• phasing-imputation/genotype-imputation - Impute after phasing
• phasing-imputation/reference-panels - Get reference data
• variant-calling/filtering-best-practices - Prepare input VCF
• population-genetics/linkage-disequilibrium - LD analysis