"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 for cis-QTL + ASB (genotype VCF piped in via tabix)
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
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 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 =list(blacklist = rtracklayer::import('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 (AR), bias-corrected ratio (Corrected.AR), a Bayesian credible interval (Bayes_lower/Bayes_upper), and the ASB call (isASB).
Workflow: RASQUAL (Joint cis-QTL + ASB)
# Prepare input# - BAM filtered by WASP# - Genotype VCF (phased)# - Peak BED# Run RASQUAL. The genotype VCF is piped in from tabix (there is NO --vcf flag);# options are single-dash. Inputs -y/-k/-x are BINARY files built by RASQUAL's# txt2bin utilities (not .txt). One run per feature: -j selects the feature row,# -l = number of test (cis) SNPs, -m = number of feature SNPs in the window.
tabix genotypes.vcf.gz chr:start-end | \
rasqual -y Y.bin -k K.bin -x X.bin \
-n N_samples -j FEATURE_INDEX -l N_TEST_SNPS -m N_FEATURE_SNPS \
-s EXON_STARTS -e EXON_ENDS -f PEAK_ID \
> 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
java -jar vcf2diploid.jar -id SAMPLE -chr hg38.fa -vcf SAMPLE.phased.vcf # per-haplotype FASTAs written to CWD (no -outDir option)# 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
# AlleleSeq2 pipeline (Makefile-based; there is no AlleleSeq2.pl entry point)
make -f PIPELINE.mk PGENOME_DIR=personalized/ REFGENOME_VERSION=GRCh38 ALIGNMENT_MODE=ASB NTHR=8
# 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# Imprinted-gene coordinates are derived from a catalog (geneimprint.com or the# Otago Imprinted Gene Catalogue, igc.otago.ac.nz) mapped to hg38; there is no# canonical hosted hg38 BED. Given 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: Require well-imputed SNPs (--imputation-quality-fsnp); 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).
