bio-atac-seq-allele-specific-accessibility

name	bio-atac-seq-allele-specific-accessibility
description	Detect allele-specific chromatin accessibility from ATAC-seq using WASP, GATK ASEReadCounter, or RASQUAL. Use when mapping cis-regulatory genetic variants from heterozygous SNPs, separating cis from trans regulation, building chromatin QTL (caQTL) maps, validating GWAS variant function with allelic imbalance, or detecting reference allele mapping bias before downstream analysis.
origin	openai4s
category	bioskills/atac-seq
metadata	{"tool_type":"mixed","primary_tool":"WASP","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Version Compatibility

Reference examples tested with: WASP 0.3.4+, GATK 4.4+, RASQUAL 1.1+, samtools 1.19+, bcftools 1.19+, vcftools 0.1.16+, plink 2.00+, MatrixEQTL 2.3+, QuASAR 0.1+, bowtie2 2.5+, bwa-mem2 2.2.1+, scipy 1.11+ (false_discovery_control), pandas 2+, pybedtools 0.10+.

Verify before use:

• CLI: <tool> --version then <tool> --help to confirm flags
• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws unexpected errors, introspect the installed package and adapt rather than retrying.

Allele-Specific Accessibility

"Does this heterozygous SNP affect chromatin accessibility on its allele?" -> Count ATAC reads supporting reference vs alternative allele at heterozygous sites in the same individual; significant deviation from 50:50 indicates cis-regulatory effect. Requires careful handling of reference-allele mapping bias (WASP filtering) and within-individual binomial testing.

• CLI: WASP (Geijn 2015) for de-biased reference mapping
• CLI: gatk ASEReadCounter for allele-specific count tables
• CLI: RASQUAL (Kumasaka 2016) for joint cis-mapping with allelic counts
• R: QuASAR (Harvey 2015) for genotype + ASE inference simultaneously

ASE/ASB analysis is fundamentally different from cohort-level differential. Statistical framework is binomial within-individual; sample size is the count of heterozygous SNPs in accessible regions, not the number of individuals.

Algorithmic Taxonomy

Tool	Method	Input	Strength	Fails when
WASP (Geijn 2015)	Realign reads where alt allele swap could change mapping; filter mapping-bias affected sites	BAM + VCF + reference	Mandatory for any ASE/ASB analysis; controls reference-allele bias	Slow on deep coverage; requires re-alignment step
GATK ASEReadCounter	Count REF and ALT reads at known heterozygous sites	BAM + VCF	Mature; integrates with GATK ecosystem; standard counter	Doesn't fix mapping bias (needs WASP first); single-sample
RASQUAL (Kumasaka 2016)	Joint cis-eQTL/caQTL model: total counts + allelic imbalance	BAM + VCF + peak counts	Best statistical power for caQTL when sample size is moderate (N=20-100); models genotype uncertainty	Complex setup; per-feature regression (slow); requires LD computation
QuASAR (Harvey 2015)	Genotype + ASE inference from RNA-seq or ATAC alone	BAM (no VCF needed)	Useful when genotypes are limited; integrates phasing	Less accurate than WASP+GATK when genotypes are known
MatrixEQTL on per-feature counts	Linear model fit on accessibility per peak	Genotypes + peak counts	Standard cohort-level caQTL; well-supported	No allelic imbalance information; needs sample size N >= 50
Allelic Imbalance from Bayesian models (MAJIQ-style)	Bayesian beta-binomial	BAM + VCF	Models overdispersion appropriately	Niche; less mature than WASP+GATK

Methodology evolves; verify against current Geijn 2015, Kumasaka 2016, Buchkovich 2015 before locking pipelines. Modern caQTL studies typically combine WASP + RASQUAL at modest N or WASP + GATK + linear caQTL at large N.

Reference Allele Mapping Bias (The Single Most Important Issue)

When aligning reads to the reference genome, reads carrying the reference allele align with 0 mismatches; reads carrying the alternative allele have 1 mismatch and may fail to align (especially with bwa-mem -k or bowtie2 --very-sensitive thresholds). This inflates the apparent reference-allele frequency at every SNP and confounds ASE.

Trigger: Always (mapping bias is universal at heterozygous SNPs).

Mechanism: Aligners are mismatch-penalized. Without correction, ALT reads systematically under-align. Effect size: 1-5% bias toward reference at typical mismatch penalties.

Symptom: Per-SNP REF allele fraction skews above 50% even at sites without true allelic imbalance.

Fix: WASP. Pseudo-alleles every read at heterozygous sites; re-aligns swapped reads; keeps only reads that map identically to both haplotypes. Mandatory for any ASE/ASB analysis.

Goal: Remove reference-allele mapping bias before counting allele-specific ATAC reads.

Approach: Identify reads overlapping heterozygous SNPs, re-align allele-swapped versions, keep only reads consistent across both haplotypes, then count REF/ALT with GATK ASEReadCounter.

# WASP read-correction pipeline (Geijn 2015)
WASP_DIR=/path/to/WASP
PEAKS=peaks.bed                                  # ATAC peaks for filtering
SAMPLE=sample1
OUT=$SAMPLE.wasp_filtered.bam

# 1. Find reads at SNP sites
python $WASP_DIR/mapping/find_intersecting_snps.py \
    --is_paired_end \
    --is_sorted \
    --output_dir wasp_out/ \
    --snp_tab snp_h5/snp_tab.h5 \
    --snp_index snp_h5/snp_index.h5 \
    --haplotype snp_h5/haps.h5 \
    --samples $SAMPLE \
    $SAMPLE.bam

# 2. Re-align flipped-allele reads
bowtie2 -x hg38_idx -1 wasp_out/$SAMPLE.remap.fq1.gz \
                   -2 wasp_out/$SAMPLE.remap.fq2.gz \
                   -S wasp_out/$SAMPLE.remap.sam
samtools view -bS wasp_out/$SAMPLE.remap.sam | samtools sort -o wasp_out/$SAMPLE.remap.bam
samtools index wasp_out/$SAMPLE.remap.bam

# 3. Keep only consistently-mapped reads
python $WASP_DIR/mapping/filter_remapped_reads.py \
    wasp_out/$SAMPLE.to.remap.bam \
    wasp_out/$SAMPLE.remap.bam \
    wasp_out/$SAMPLE.kept.bam

# 4. Merge kept reads with non-overlapping reads
samtools merge $OUT \
    wasp_out/$SAMPLE.kept.bam \
    wasp_out/$SAMPLE.keep.bam

# After WASP filtering, GATK ASEReadCounter is safe
gatk ASEReadCounter \
    -I $OUT \
    -V heterozygous_snps.vcf \
    -R hg38.fa \
    -O $SAMPLE.ase_counts.tsv

Per-Tool Failure Modes

GATK ASEReadCounter without WASP -- Reference bias

Trigger: Running ASEReadCounter directly on a standard ATAC BAM without WASP filtering.

Mechanism: Reference allele over-counts due to alignment bias.

Symptom: Per-SNP reference fraction systematically > 0.5; aggregate plots show ~0.51-0.55 instead of 0.5.

Fix: WASP filter first, ALWAYS. There are no exceptions.

Sample size for ASE per SNP

Trigger: Single-individual ATAC; per-SNP heterozygous coverage typically 10-100 reads.

Mechanism: Per-SNP binomial test has limited power; 10 reads at p=0.5 has 95% CI from 0.18 to 0.82 -- effectively no power for moderate effects.

Fix: Aggregate across many SNPs in the same peak (within-peak ASE); aggregate across replicates at same SNP; combine with cis-caQTL across cohort.

RASQUAL -- LD computation requirement

Trigger: RASQUAL exits or returns NA for a feature.

Mechanism: RASQUAL estimates genotype/allelic correlation internally from the tabix-streamed VCF (no external LD matrix is needed or accepted). Failures instead come from the -l (testing SNP) and -m (feature SNP) counts not matching the SNPs actually present in the cis-window, or a feature with zero fSNPs.

Fix: Compute -l/-m from the actual VCF window rather than a fixed guess, and skip features with no feature SNPs; there is no LD-precompute step.

WASP -- Phased vs unphased genotypes

Trigger: Using unphased genotypes for ASE.

Mechanism: ASE requires knowing which allele is on which haplotype to assign reads. Unphased het sites ambiguously assign reads.

Fix: Phase genotypes with SHAPEIT5, BEAGLE 5.4, or whatshap (read-based) before running WASP/ASE counter.

Cohort-level caQTL without allelic info

Trigger: MatrixEQTL on peak counts without ASE.

Mechanism: MatrixEQTL maps cohort-level associations; misses cis-mode that ASE captures within individual.

Symptom: Power to detect caQTL is low (typical N=50-100 cohort gives ~hundreds of caQTLs vs ASE-augmented can give thousands).

Fix: Use RASQUAL (joint total + ASE) when N <= 100; or combine MatrixEQTL with separate ASE per individual.

Read-deep peak coverage required

Trigger: Per-peak coverage < 30 reads at SNP site.

Mechanism: Binomial test power at p=0.5, n=30 yields detectable shifts only at |delta_p| >= 0.2.

Fix: Pool replicates if available; or restrict to peaks with sufficient coverage; or aggregate to per-individual peak-level ASE rather than per-SNP.

Decision Tree by Setting

Setting	Recommended pipeline
Single individual, ATAC + genotypes	WASP + GATK ASEReadCounter -> per-SNP and per-peak ASE; within-peak aggregation
Cohort N >= 100, want caQTL	WASP + GATK + MatrixEQTL on peak counts; supplement with ASE for cis-effects
Cohort N = 20-100	WASP + RASQUAL (joint total + ASE) for max power
Cohort with no genotypes	QuASAR (infers genotypes from data)
Validating GWAS variant function	Look up het samples in cohort; aggregate ASE at the variant; ASB ratio
Trios or quartets	Per-trio phasing then ASE per individual
iPSC line genotype validation	Single-individual ASE at known SNPs

Cohort caQTL Pipeline

Goal: Build cohort-level chromatin QTLs by combining WASP-corrected per-sample counts with cis-genotype association.

Approach: WASP-correct each BAM, build consensus peakset, count reads in peaks per sample, then test cis-genotype association via MatrixEQTL (cohort) or RASQUAL (joint total + allelic).

# 1. WASP-correct each individual's BAM
for sample in $(cat samples.txt); do
    bash wasp_pipeline.sh $sample.bam $sample.vcf
done

# 2. Build consensus peakset (atac-seq/consensus-peakset)
# 3. Count reads in peaks per sample (featureCounts)
featureCounts -F SAF -a consensus.saf -o counts.tsv -p --countReadPairs *.wasp.bam

# 4. Cohort-level caQTL via MatrixEQTL
# (see R script in examples/)

# 5. Per-individual ASE (RASQUAL alternative)
# Parallel: gatk ASEReadCounter per sample, then merge for QuASAR meta-analysis

Within-Peak ASE Aggregation

Goal: Boost per-SNP ASE power by pooling allele counts across heterozygous SNPs in the same peak.

Approach: Map each het SNP to its containing peak, sum REF and ALT counts per peak, run pooled binomial test against 50:50, apply BH FDR, and threshold on effect size.

import pandas as pd, numpy as np
from scipy import stats

# Per-SNP allele counts at heterozygous sites
ase = pd.read_csv('sample.ase_counts.tsv', sep='\t')
ase = ase.rename(columns={'refCount': 'REF', 'altCount': 'ALT'})
ase['totalCount'] = ase['REF'] + ase['ALT']

# Map each SNP to its containing peak
ase['peak'] = map_snps_to_peaks(ase, 'consensus_peaks.bed')

# Aggregate within peak: pooled binomial test
def peak_ase(group):
    ref = group['REF'].sum()
    total = group['totalCount'].sum()
    if total < 30: return pd.Series({'ref_frac': np.nan, 'p_value': np.nan})
    p = stats.binomtest(ref, total, p=0.5).pvalue
    return pd.Series({'ref_frac': ref / total, 'p_value': p, 'snp_count': len(group)})

peak_ase_df = ase.groupby('peak').apply(peak_ase)
peak_ase_df['adj_p'] = stats.false_discovery_control(peak_ase_df['p_value'].fillna(1.0))
sig_ase = peak_ase_df[(peak_ase_df['adj_p'] < 0.05) & (abs(peak_ase_df['ref_frac'] - 0.5) >= 0.2)]

|ref_frac - 0.5| >= 0.2 is a 30:70 effect; smaller imbalances are detectable but biologically minor. Per-peak SNP count >= 2 strengthens the call.

RASQUAL Joint Modeling

RASQUAL uses a non-standard CLI: it reads the VCF from stdin via tabix and uses single-letter flags. The canonical invocation pattern is:

Goal: Combine total accessibility counts and allele-specific counts into one joint cis-caQTL test per peak.

Approach: Pre-build binary count and offset files via rasqualTools, then iterate per-feature, streaming the cis-window VCF through tabix into RASQUAL with feature coordinates and SNP counts.

# 1. Pre-compute genotype offsets and binary count files (rasqualTools R package)
# (see rasqualTools::saveRasqualMatrices; produces .bin files for -y, -k)

# 2. Per-feature (peak) RASQUAL call: pipe tabix VCF in via stdin
# Per-line meaning: feature name, chromosome, start, end, n_testing_SNPs (cis-window rSNP candidates), n_feature_SNPs (fSNPs in the peak)
while IFS=$'\t' read -r name chr start end n_testing n_feature; do
    tabix cohort.vcf.gz $chr:$((start-500000))-$((end+500000)) | \
        rasqual -y counts.bin \
                -k offsets.bin \
                -n $N_SAMPLES \
                -j $FEATURE_INDEX \
                -l $n_testing -m $n_feature \
                -s $start -e $end \
                -f $name \
                > $name.rasqual.txt
done < features.tsv

-y is the binary count file; -k is the binary size-factor / offset file (both produced by rasqualTools::saveRasqualMatrices from R); -j is the row index of the feature; -l is the number of testing SNPs (cis-window rSNP candidates) and -m the number of feature SNPs (fSNPs overlapping the peak). RASQUAL does NOT use --features, --counts, --vcf flags; those are common in newer caQTL wrappers but not in stock RASQUAL.

For modern usage, the rasqualTools R wrapper (Kumasaka GitHub) handles this orchestration. Verify against rasqual --help because the flag set is unusual.

RASQUAL output includes joint p-values, total-only and ASE-only sub-tests; the joint test typically gains 1.5-3x power vs MatrixEQTL alone.

Reconciliation

Pattern	Likely cause	Action
GATK ASE shows REF > ALT systematically	WASP not run	Re-run with WASP filter; bias fixed
RASQUAL joint p << ASE-only p	Cis effect dominated by allelic component	Confirms cis-regulatory mechanism
ASE detected at SNP not in peak	Possible coding splice or 3' UTR effect	Check annotation; may not be regulatory
Cohort caQTL doesn't replicate per-individual ASE	Trans effect or technical artifact	Consider trans-effect; or single-individual outliers

Operational rule for high-confidence reporting: A cis-regulatory variant must show (a) WASP-filtered allelic imbalance with adjusted p < 0.05 and effect size >= 0.2, AND (b) cohort-level caQTL p < 1e-5 (or RASQUAL joint p < 1e-5), AND (c) accessibility peak overlap. Validation against MPRA or CRISPRi-FlowFISH increases confidence.

Common Errors

Error / symptom	Cause	Solution
Reference allele systematically over-represented	WASP not run	Mandatory WASP filter
ASE per-SNP coverage too low	Sparse coverage; rare alleles	Aggregate across SNPs in peak; or filter SNPs with allele freq 0.1-0.9
RASQUAL crashes/NA on small features	Feature has no fSNPs, or -l/-m counts mismatch the VCF window	Skip features with 0 feature-SNPs; compute -l/-m from the actual window
caQTL replication low	Cohort-effect vs cis-effect confusion	Use RASQUAL joint or replicate ASE separately
Phased vs unphased confusion	Different software expectations	Phase with SHAPEIT5/BEAGLE before any ASE
GATK ASEReadCounter fails on multiallelic sites	Multi-allelic complications	Pre-filter VCF to biallelic only with bcftools
WASP runs slow	Re-alignment step is dominant	Parallelize per-chromosome; or use samtools faidx + region-based parallelism

References

• van de Geijn B et al 2015 Nat Methods 12:1061 (WASP; reference allele bias correction)
• Castel SE et al 2015 Genome Biol 16:195 (GATK ASEReadCounter; ASE framework)
• Kumasaka N et al 2016 Nat Genet 48:206 (RASQUAL; joint total + ASE caQTL)
• Harvey CT et al 2015 Bioinformatics 31:1235 (QuASAR)
• Buchkovich ML et al 2015 BMC Med Genomics 8:43 (reference mapping-bias correction with limited/no genotype data; AA-ALIGNER)
• Browning SR & Browning BL 2007 Am J Hum Genet 81:1084 (BEAGLE phasing)
• Patterson M et al 2015 J Comput Biol 22:498 (whatshap; read-based phasing)

Related Skills

• atac-seq/atac-peak-calling - Generate peaks for ASE within-peak aggregation
• atac-seq/consensus-peakset - Cohort consensus peakset
• atac-seq/differential-accessibility - Cohort-level (cis + trans) differential
• atac-seq/deep-learning-atac - Predicted variant effects vs observed allelic imbalance
• atac-seq/enhancer-gene-linking - Map ASE-supported variants to target genes
• variant-calling/vcf-basics - VCF input
• variant-calling/joint-calling - Cohort genotype inputs
• phasing-imputation/haplotype-phasing - Phasing before WASP
• causal-genomics/fine-mapping - Use caQTL for fine-mapping
• population-genetics/association-testing - GWAS context