chipseq-allele-specific-binding

name	chipseq-allele-specific-binding
description	Detects allele-specific transcription factor or histone modification binding from heterozygous-variant ChIP-seq using WASP (reference-bias filter; mandatory upstream), RASQUAL (joint QTL + bias-corrected testing), BaalChIP (Bayesian beta-binomial with copy-number-aware overdispersion), and AlleleSeq (personalized diploid genome). Handles imprinted-locus awareness, X-inactivation artifacts, cancer copy-number imbalance, and integration with downstream caQTL / bQTL mapping. Use when identifying variants with allelic effects on TF binding, fine-mapping causal regulatory variants, validating deep-learning variant predictions, or characterizing cis-acting regulatory effects.
tool_type	mixed
primary_tool	WASP

Version Compatibility

Reference examples tested with: WASP 0.3.4+, RASQUAL 1.1+, BaalChIP 1.30+ (Bioconductor), AlleleSeq 2.0+, samtools 1.19+, bcftools 1.19+, GATK 4.5+, pysam 0.22+.

Allele-Specific Binding (ASB)

"Identify variants that affect transcription factor or histone modification binding in cis" -> Compare ChIP-seq read counts at the reference and alternate alleles of heterozygous variants in a single sample. Differential read counts (ALT vs REF at hetSNPs in peaks) reveal allele-specific binding.

• CLI (mandatory bias filter): WASP mapping pipeline to remove reference-allele mapping bias
• CLI (joint association): RASQUAL with --n-permutations for cis-QTL + ASB
• R (Bayesian beta-binomial): BaalChIP with copy-number-aware overdispersion
• CLI (personalized genome): AlleleSeq with phased diploid genome
• Statistical test: beta-binomial likelihood ratio or chi-squared on count tables

ASB analysis has three universal pitfalls: reference-allele mapping bias (universal across short-read aligners), imprinted loci (constitutively allele-skewed by biology), and copy-number variation (changes effective allele dose). All three must be addressed or results are unreliable.

Method Taxonomy

Method	Year	Approach	Strength	Fails when
WASP (van de Geijn 2015)	2015	Map reads, swap alleles, re-map, drop discordant	Universal first step; aligner-agnostic; mandatory preprocessing	Drops 22-31% of reads; reduces power; not an analysis method itself
RASQUAL (Kumasaka 2016)	2016	Joint genotype-phenotype association with per-feature phi bias parameter	Improves QTL mapping; integrates bias correction; works for ChIP/ATAC/RNA-seq	Computationally intensive; assumes binomial bias structure
BaalChIP (de Santiago 2017)	2017	Bayesian beta-binomial; copy-number-aware overdispersion	Cancer genomes (copy-number imbalance); rigorous inference	Slower; assumes copy-number known
AlleleSeq (Rozowsky 2011)	2011	Personalized diploid genome alignment	Avoids reference bias completely; conceptually cleanest	Requires phased genotype + diploid genome construction; computational cost
MBASED (Mayba 2014)	2014	Meta-analysis-based ASE; gene-level	RNA-seq oriented; adapted for ChIP gene-body binning	Gene-level not peak-level; less precise for narrow TF peaks
AllelicImbalance (R package)	—	Bioconductor multi-method	Easy R workflow	Requires variants and BAM; less rigorous than BaalChIP
deepSEA / chromBPNet variant effects	2015 / 2024	Deep-learning predictions	Sequence-only; no chromatin sample needed	Predictive not measurement; see chip-deep-learning

Universal First Step: WASP Reference-Bias Filter

Goal: Remove reads that show reference-allele mapping bias before any ASB testing.

Approach: Align reads, identify those overlapping heterozygous SNPs, swap alleles and re-align; reads that don't map consistently to the same position with both alleles are discarded. The output is a bias-corrected BAM at the cost of 22-31% read loss.

Reference-allele mapping bias is systematic: reads with the reference allele align more readily because the reference is the alignment target. This inflates REF allele frequency by 1-5% genome-wide. WASP fixes this:

# WASP mapping pipeline
# 1. Initial alignment
bowtie2 -x hg38 -1 R1.fq -2 R2.fq -S step1.sam
samtools view -bS step1.sam | samtools sort -o step1.bam
samtools index step1.bam

# 2. Identify reads overlapping hetSNPs; swap alleles; re-map
python /path/to/WASP/mapping/find_intersecting_snps.py \
    --is_paired_end \
    --is_sorted \
    --output_dir wasp_out/ \
    --snp_tab snps_tab.h5 \
    --snp_index snps_index.h5 \
    --haplotype haplotypes.h5 \
    --samples sample_list.txt \
    step1.bam

# 3. Re-map swapped reads
bowtie2 -x hg38 -1 wasp_out/step1.remap.fq.gz -S step2.sam
# (process step2.sam similarly)

# 4. Filter reads that don't map back consistently
python /path/to/WASP/mapping/filter_remapped_reads.py \
    step1.to.remap.bam step2.bam step1.keep.bam

# 5. Final WASP-filtered BAM (use this for all downstream ASB analysis)
samtools sort -o step1.wasp.bam step1.keep.bam
samtools index step1.wasp.bam

WASP always drops 22-31% of reads. This is the cost of bias correction; downstream power is reduced but ASB calls are trustworthy.

Alternative to WASP filter: RASQUAL's phi parameter models bias within the test rather than filtering reads. More sophisticated but assumes binomial bias structure.

Workflow: BaalChIP (Recommended for Cancer / Copy-Number-Imbalanced Samples)

library(BaalChIP)
library(BSgenome.Hsapiens.UCSC.hg38)

# Sample metadata
samples <- data.frame(
    SampleID = c('HCC1395_FOXA1_rep1', 'HCC1395_FOXA1_rep2'),
    Tissue = 'TNBC',
    Target = 'FOXA1',
    BAM = c('rep1.wasp.bam', 'rep2.wasp.bam'),
    Peaks = c('rep1_peaks.bed', 'rep2_peaks.bed'),
    Group = 'HCC1395'
)

# hetSNP file: VCF or BED with chrom, pos, ref, alt, allele frequencies
hetSNPs <- 'het_snps.bed'

# CNV file for copy-number-aware overdispersion (critical for cancer)
cnvs <- 'cnvs.bed'

# Initialize BaalChIP object
res <- BaalChIP(samplesheet = samples, hets = hetSNPs)

# Run filters and Bayesian test
res <- alleleCounts(res, min_base_quality = 10, min_mapq = 15)
res <- QCfilter(res, RegionsToFilter = c('blacklist_v2.bed'))
res <- mergePerGroup(res)
res <- filter1allele(res)
res <- getASB(res, Iter = 5000, conf_level = 0.95)
# Verify parameter names against the installed BaalChIP version (`?getASB`); some releases
# use `nIter` instead of `Iter`.

# Results
asb_table <- BaalChIP.report(res)
head(asb_table)

BaalChIP outputs per-hetSNP: allelic ratio, posterior, Bayes factor, ASB call.

Workflow: RASQUAL (Joint cis-QTL + ASB)

# Prepare input
# - BAM filtered by WASP
# - Genotype VCF (phased)
# - Peak BED

# Run RASQUAL
rasqual \
    --y peak_counts.txt \
    --x covariates.txt \
    --k offsets.txt \
    --n N_samples \
    --p N_peaks \
    --j 0 -i 0 \
    --vcf genotypes.vcf \
    --window 250000 \
    --t 8 \
    > rasqual_results.txt

# Output columns: chrom, peak_id, n_RSNPs, n_FSNPs, n_imputed, summarized_phi,
#                 summarized_overdispersion, summarized_pi, beta, log10_BF, ...

RASQUAL's phi parameter is the per-feature bias estimate; pi is the allelic ratio.

Workflow: AlleleSeq (Personalized Diploid Genome)

# Build personalized diploid genome from phased VCF
vcf2diploid -id SAMPLE -chr hg38.fa -vcf SAMPLE.phased.vcf -outDir personalized/

# Align reads to both maternal and paternal copies
bowtie2-build personalized/maternal.fa maternal_index
bowtie2-build personalized/paternal.fa paternal_index
bowtie2 -x maternal_index -1 R1.fq -2 R2.fq -S maternal.sam
bowtie2 -x paternal_index -1 R1.fq -2 R2.fq -S paternal.sam

# AlleleSeq pipeline
AlleleSeq2.pl SAMPLE maternal.sam paternal.sam genotype.vcf

# Output: per-hetSNP allelic counts and binomial test

Personalized genome avoids reference bias by construction. Cost: per-sample diploid genome generation and indexing.

Three Universal Pitfalls

Pitfall 1: Imprinted Loci Are Constitutively Skewed

Imprinted loci (H19, IGF2, MEG3, MEG8, KCNQ1OT1, etc.) show extreme allele bias by biology, not from differential binding.

# Filter imprinted loci before ASB analysis
wget https://imprintingdiseases.org/data/imprinted_loci_hg38.bed
bedtools intersect -v -a hetSNPs.bed -b imprinted_loci_hg38.bed > hetSNPs.non_imprinted.bed

Pitfall 2: X-Inactivation in Females

In female samples, X-linked genes show extreme allele skew because each cell silences one X chromosome. This appears as ASB at every X-linked hetSNP.

# Filter chrX in female samples
awk '$1 != "chrX"' hetSNPs.bed > hetSNPs.autosomal.bed
# Or analyze chrX separately with imprinting-aware methods

Pitfall 3: Copy-Number Imbalance (Cancer Genomes)

In cancer cells, copy-number gain of one allele alters effective allele dose; raw allelic ratios mix dose and binding effects. BaalChIP's copy-number-aware overdispersion handles this; other methods require pre-filtering CN-altered regions.

# Use ASCAT / Sequenza / FACETS to call allele-specific CNVs
# Exclude CN-altered regions from ASB analysis OR use BaalChIP

Per-Tool Failure Modes

WASP -- Reference panel mismatch

Trigger: Using a WASP SNP file from a different population than the sample.

Mechanism: WASP swaps alleles at known hetSNPs; if the variant isn't in the SNP file, no swap happens; reads retain reference bias.

Symptom: Sample-specific hetSNPs (not in 1KG) still show reference bias after WASP.

Fix: Build WASP SNP file from the sample's own genotype VCF, not a population panel; OR use RASQUAL which handles novel hetSNPs.

WASP -- Excessive read loss

Trigger: WASP filter removes >40% of reads.

Mechanism: Many reads span multiple hetSNPs; each must re-map consistently after every allele swap; combinatorial loss.

Fix: Accept the loss (genuine bias correction) OR switch to AlleleSeq (personalized genome avoids the swap-and-remap step) OR RASQUAL (no read filtering).

RASQUAL -- Convergence failure

Trigger: Sparse data (few hetSNPs per peak); strong copy-number imbalance.

Mechanism: EM convergence requires enough hetSNPs per feature; sparse data underspecifies the model.

Fix: Increase --imputation-r2-cutoff to require well-imputed SNPs; combine replicates; or switch to BaalChIP for sparse-data robustness.

BaalChIP -- CN file mismatch

Trigger: CN BED uses different naming convention (chrX vs X) than BAMs.

Mechanism: BaalChIP silently doesn't apply CN-aware overdispersion if CN positions don't match BAM chromosomes.

Symptom: ASB calls at CN-altered regions look bimodal (one allele appears 100% bound).

Fix: Verify chromosome naming matches across CN file, BAM, hetSNP VCF.

AlleleSeq -- Insufficient phasing

Trigger: Using unphased VCF for diploid genome construction.

Mechanism: AlleleSeq requires phased genotypes; without phasing, maternal and paternal genomes are randomly assigned.

Fix: Use trio or read-based phasing (HapCUT2, WhatsHap) before AlleleSeq.

Imprinted loci not filtered

Trigger: Reporting ASB at H19 or IGF2.

Mechanism: These loci are biologically allele-skewed; the "ASB" call is correct but uninformative.

Fix: Always filter imprinted loci before reporting / interpreting ASB.

Female chrX ASB artifacts

Trigger: Reporting ASB at chrX in female samples without X-inactivation correction.

Mechanism: Random X-inactivation silences one X per cell; population of cells shows extreme allele bias at any X-linked variant.

Fix: Filter chrX in female samples OR use methods that model X-inactivation (rare in standard ASB pipelines).

Reference allele bias not corrected

Trigger: Running BaalChIP / chi-squared test directly without WASP or RASQUAL bias handling.

Mechanism: 1-5% genome-wide REF allele over-representation produces false-positive REF-favoring ASB calls.

Symptom: ASB calls skewed toward REF allele.

Fix: Always apply WASP (or RASQUAL's phi parameter) before testing.

Reconciliation

Pattern	Likely cause	Action
WASP filter applied; still REF-biased	Sample-specific hetSNPs not in WASP SNP file	Use sample's own genotype VCF for WASP
BaalChIP and RASQUAL disagree at sparse hetSNPs	Different sparse-data behavior	BaalChIP Bayesian more conservative for sparse; check posterior
ASB call at imprinted locus	Biology, not differential binding	Filter imprinted loci
ASB at chrX in female	X-inactivation	Filter chrX
ASB call where copy-number altered	Cancer dose effect	Use BaalChIP with CN file OR exclude CN-altered regions
chromBPNet predicts strong variant effect; ASB doesn't	Sample has low coverage at variant; chromBPNet predicts in counterfactual	Increase depth; ASB requires actual chromatin sample

Common Errors

Error / symptom	Cause	Solution
WASP find_intersecting_snps.py fails	h5 SNP table format wrong	Convert from VCF via WASP's extract_vcf.py
BaalChIP "no overlap with peaks"	hetSNP and peak chrom naming mismatch	Standardize chrom prefixes
RASQUAL OOM	Window too large; too many features	Reduce --window; chunk feature list
AlleleSeq "diploid genome too large"	Many SVs in genome	Use small-variant only VCF; exclude SV-rich regions
ASB calls cluster at REF allele	WASP not applied OR insufficient	Re-run WASP with sample-specific SNP file
Many ASB at chrX in female	X-inactivation	Filter chrX
All "ASB" calls are at imprinted loci	Imprinting not filtered	Apply imprinted-loci BED

References

• Rozowsky J et al 2011 Mol Syst Biol 7:522 (AlleleSeq)
• van de Geijn B et al 2015 Nat Methods 12:1061 (WASP)
• Kumasaka N et al 2016 Nat Genet 48:206 (RASQUAL)
• de Santiago I et al 2017 Genome Biol 18:39 (BaalChIP)
• Mayba O et al 2014 Genome Biol 15:405 (MBASED)
• Chen J et al 2016 Nat Commun 7:11101 (1000 Genomes ASB / ASE survey)

Related Skills

• chip-seq/peak-calling - Peak calling upstream
• chip-seq/chipseq-qc - QC before ASB analysis
• chip-seq/chip-deep-learning - Validate DL variant predictions against ASB
• chip-seq/peak-annotation - Annotate ASB variants to genes / cCREs
• atac-seq/allele-specific-accessibility - Parallel ATAC ASB workflow
• causal-genomics/fine-mapping - ASB as fine-mapping orthogonal evidence
• variant-calling/variant-annotation - Annotate hetSNPs before ASB
• phasing-imputation/haplotype-phasing - Required for AlleleSeq