Orchestrates the GWAS pipeline from genotypes to association results, chaining PLINK2 QC (variant-then-sample missingness, controls-only HWE, KING relatedness), panel harmonization + joint phasing/imputation to dosages, long-range-LD-excluded PCA, and an engine chosen by sample structure (PLINK2-GLM / regenie / SAIGE / BOLT-LMM), with LDSC-intercept diagnostics. Use when committing the genome build + ancestry-matched imputation panel once (ancestry match > panel size), running the strand/allele harmonization gate (drop intermediate-frequency palindromes), imputing cases+controls TOGETHER on dosages, excluding long-range-LD regions before PCA, choosing an LMM when relatedness/structure is present (PCs cannot remove a covariance), or separating polygenicity from confounding via the LDSC intercept. Hands mechanism to the population-genetics and phasing-imputation component skills; not a re-teach of any single step.
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Orchestrates the GWAS pipeline from genotypes to association results, chaining PLINK2 QC (variant-then-sample missingness, controls-only HWE, KING relatedness), panel harmonization + joint phasing/imputation to dosages, long-range-LD-excluded PCA, and an engine chosen by sample structure (PLINK2-GLM / regenie / SAIGE / BOLT-LMM), with LDSC-intercept diagnostics. Use when committing the genome build + ancestry-matched imputation panel once (ancestry match > panel size), running the strand/allele harmonization gate (drop intermediate-frequency palindromes), imputing cases+controls TOGETHER on dosages, excluding long-range-LD regions before PCA, choosing an LMM when relatedness/structure is present (PCs cannot remove a covariance), or separating polygenicity from confounding via the LDSC intercept. Hands mechanism to the population-genetics and phasing-imputation component skills; not a re-teach of any single step.
Before using code patterns, verify installed versions match. If versions differ:
R: packageVersion('<pkg>') then ?function_name to verify parameters
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.
Note: regenie/SAIGE run a two-step design (step 1 whole-genome ridge / null GLMM on LD-pruned common markers; step 2 tests the imputed set with LOCO) — step-1 and step-2 sample IDs + variance-ratio file MUST match or calibration silently fails. Binary PLINK2 --glm defaults to firth-fallback (output .glm.logistic.hybrid). Sequencing/WGS and non-EUR LD shift the genome-wide threshold off the 5e-8 EUR-array folklore. Confirm in-tool before quoting.
GWAS Pipeline
"Run a GWAS from my genotype data" -> Orchestrate sample/variant QC (PLINK2), population stratification (PCA), association testing (linear/logistic regression), multiple testing correction, and Manhattan/QQ plot visualization.
This is a workflow skill: it owns the chaining decisions and hand-offs, not the internals of any one step. Every step below cross-references the component skill that teaches its mechanism.
The governing principle
A GWAS result is decided at four seams, not inside the association test.
The genome build + ancestry-matched imputation panel is a made-once commitment. The panel fixes what can be imputed (HRC MAF floor ~5e-4; TOPMed imputes far rarer) AND the build (HRC/1000G-P3 are GRCh37; TOPMed/1000G-NYGC are GRCh38). Ancestry match > panel size: a bigger ancestry-mismatched panel imputes WORSE because there are no matching haplotypes to copy.
Strand/allele harmonization is a made-once GATE, and the silent corruptor. Align every study variant's alleles to the panel REF/ALT, fix strand, and DROP unresolvable palindromes (A/T, C/G at MAF>0.4). A flipped palindrome or a build mismatch flips BETA and cancels/manufactures signal WITHOUT erroring — caught only on the AF-concordance plot (points on the y=1-x anti-diagonal), not by a crash.
QC runs before association and in a defensible internal order; the engine is chosen by structure, not convenience. Variant missingness before sample missingness; HWE in CONTROLS only (a true non-additive risk variant deviates in cases); impute cases+controls TOGETHER; exclude long-range-LD regions before PCA. PC-covariate GLM is valid only for unrelated, continuous-ancestry cohorts — relatedness/structure needs an LMM (regenie/SAIGE/BOLT) with LOCO, because PCs remove a mean shift, not a covariance.
Refuse the bare lambda. lambda_GC alone cannot separate polygenicity from confounding — read it WITH the LDSC intercept (~1 = polygenic, fine; >1 = confounding). Do not reflexively genomic-control (it over-corrects true signal). Filter INFO/R2 MAF-stratified (a flat cutoff is a hidden rare-variant filter).
What can be imputed + the build; ancestry mismatch imputes worse regardless of size
Strand/allele harmonization
A flipped palindrome / build mismatch flips BETA silently; drop MAF>0.4 palindromes
Ancestry/LD reference for structure
Flows into PCA and (if LMM) the GRM; must match the cohort
Association engine (by structure)
PC-GLM (unrelated) vs LMM+LOCO (related/structured); SPA/Firth for imbalance/low MAC
Workflow Overview
VCF/PLINK files
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v
[1. QC Filtering] ------> Sample and variant QC
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v
[2. Phase + Impute] ----> Align to panel, phase, impute to dosages, filter by R2 (-> phasing-imputation)
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v
[3. LD Pruning] --------> Independent variants for PCA
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v
[4. Population Structure] --> PCA for covariates
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[5. Association Testing] --> Logistic/linear regression on dosages
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[6. Results] -----------> Manhattan plot, QQ plot
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Significant associations
Step 1: Data Import and QC
Convert VCF to PLINK
# VCF to PLINK binary format
plink2 --vcf input.vcf.gz \
--pheno phenotypes.txt \
--make-bed \
--out study # load --pheno at conversion so PHENO1 is in the .fam for the controls-only HWE gate below
QC order is critical (-> population-genetics/plink-basics): variant missingness runs FIRST, in its own invocation, so a sample is not dropped for missingness driven by variants slated for removal.
Variant call-rate (first)
plink2 --bfile study --missing --out study_stats
plink2 --bfile study --geno 0.05 --make-bed --out study_var_qc # variant missingness BEFORE sample missingness
Sample QC
# Sample missingness AFTER variant missingness.
plink2 --bfile study_var_qc --mind 0.05 --make-bed --out study_sample_qc
# Sex check: split the pseudoautosomal region first or male PAR het reads as a sex error (see plink-basics).
plink2 --bfile study_sample_qc --split-par hg38 --check-sex --out study_sex_check
# KING-robust relatedness - structure-robust and IBD-free (this is the point of KING; no --genome needed).
plink2 --bfile study_sample_qc --king-cutoff 0.0884 --make-bed --out study_unrelated
Variant QC: MAF and controls-only HWE
# Controls-only HWE with mid-p. plink2 is NOT controls-only by default, so gate to controls explicitly:# a true risk variant depletes heterozygotes in cases and would fail a case-inclusive HWE test (see plink-basics).
plink2 --bfile study_unrelated --keep-if "PHENO1 == control" --hwe 1e-6 midp --write-snplist --out hwe_pass
plink2 --bfile study_unrelated --maf 0.01 --extract hwe_pass.snplist --make-bed --out study_qc
plink2 --bfile study_qc --freq --out study_qc
QC Checkpoint:
Variant call rate >95% (applied before sample missingness)
Sample call rate >95%
MAF >1%
HWE applied to controls only with mid-p (p>1e-6)
Step 2: Phasing and Imputation
Most GWAS impute the QC'd array genotypes up to a dense reference panel before association, to increase variant density and harmonize across platforms and studies. This stage is owned by the phasing-imputation skills; the workflow only orchestrates the handoff. The decisions that matter here, in order:
Select and prepare the panel (-> phasing-imputation/reference-panels). Match the panel ancestry to the cohort (TOPMed for diverse/admixed, HRC or 1000G for European, HGDP+1kGP for diverse-and-downloadable), reconcile genome build, and run the strand/allele harmonization check; a flipped palindromic SNP or build mismatch corrupts results without erroring.
Phase, then impute (-> phasing-imputation/haplotype-phasing, phasing-imputation/genotype-imputation). Pre-phase the QC'd VCF (Eagle2/SHAPEIT5) and impute against the panel per chromosome (Beagle/Minimac4/IMPUTE5), or upload to the Michigan/TOPMed server for controlled-access panels. Impute cases and controls TOGETHER; separate imputation manufactures false associations. The output carries dosages (DS), not hard calls.
Filter by quality (-> phasing-imputation/imputation-qc). Drop variants below an INFO/R2/DR2 cutoff paired with a MAF floor, MAF-stratified, because a flat cutoff is a hidden rare-variant filter. Carry dosages forward.
Goal: Increase variant density and harmonize across platforms by imputing the QC'd genotypes against a dense ancestry-matched panel, carrying dosages (not hard calls) into association.
Approach: Export the QC'd genotypes to VCF, hand off to the phasing-imputation skills for strand/build harmonization, phasing, and per-chromosome imputation (cases and controls together), then filter on the engine's quality field plus a MAF floor.
# Convert QC'd PLINK back to VCF, align to the panel, phase + impute (see phasing-imputation skills for the full commands)
plink2 --bfile study_qc --export vcf bgz --out study_qc
# ... reference-panels: strand/build harmonization; haplotype-phasing: phase; genotype-imputation: impute to dosages ...# Post-imputation quality filter on the engine's field (DR2 Beagle / R2 Minimac), with a MAF floor
bcftools view -e 'INFO/DR2<0.3 || INFO/AF<0.01 || INFO/AF>0.99' imputed.vcf.gz -Oz -o imputed.qc.vcf.gz
QC Checkpoint: cases and controls imputed together; INFO/R2 filtered (MAF-stratified) with a MAF floor; dosages (DS), not hard calls, carried into association.
Step 3: LD Pruning for PCA
# Exclude long-range-LD regions and inversions FIRST (MHC, 8p23.1, 17q21.31, LCT) - their internal r2 is# high and real, so a window prune keeps them and a PC then tracks the inversion (-> population-structure).
plink2 --bfile study_qc --exclude range longrange_ld.txt --make-bed --out study_noLR
# Identify independent variants (r2 0.1 matches the linkage-disequilibrium / population-structure default).
plink2 --bfile study_noLR --indep-pairwise 50 5 0.1 --out pruned
plink2 --bfile study_noLR --extract pruned.prune.in --make-bed --out study_pruned
Step 4: Population Structure (PCA)
# Calculate principal components
plink2 --bfile study_pruned \
--pca 10 \
--out study_pca
# The eigenvec file contains PCs for use as covariates
Visualize PCA
library(ggplot2)# Load PCA results
pca <- read.table('study_pca.eigenvec', header =FALSE)
colnames(pca)<-c('FID','IID', paste0('PC',1:10))# Load phenotype for coloring
pheno <- read.table('phenotypes.txt', header =TRUE)
pca <- merge(pca, pheno, by =c('FID','IID'))# Plot
ggplot(pca, aes(x = PC1, y = PC2, color = as.factor(PHENO)))+
geom_point(alpha =0.5)+
labs(title ='PCA of Study Samples', color ='Phenotype')+
theme_minimal()
ggsave('pca_plot.pdf', width =8, height =6)
Step 5: Association Testing
Run the association on imputed DOSAGES, not hard calls, so the imputation uncertainty is propagated (PLINK2 reads dosages with --vcf imputed.qc.vcf.gz dosage=DS, or use a .pgen built from dosages). The examples below use the QC'd best-guess genotypes for brevity; substitute the dosage input for an imputed analysis.
The engine choice is set by sample structure, not convenience (-> population-genetics/association-testing). PC-covariate GLM (below) is valid only for unrelated samples whose confounding is continuous ancestry; any related, family-based, or fine-scale-structured cohort needs a linear mixed model with leave-one-chromosome-out, because PCs cannot remove a covariance structure.
Situation
Engine
Hand off to
Unrelated, continuous-ancestry confounding only
PLINK2 --glm (PC covariates)
population-genetics/association-testing
Relatedness / family / fine-scale structure
LMM (regenie / SAIGE / BOLT-LMM) with LOCO
population-genetics/association-testing
Biobank binary, case:control worse than ~1:10 or low MAC
SAIGE (SPA) or regenie (--firth/--spa)
population-genetics/association-testing
Rare-variant, aggregate by gene
burden/SKAT/SKAT-O via SAIGE-GENE+/regenie
population-genetics/rare-variant-association
Threshold
5e-8 is EUR-common-array folklore; ~1e-8-3e-8 (African LD), ~5e-9 (WGS/rare)
library(qqman)# Load results# comment.char='' is REQUIRED: PLINK2's header starts with '#CHROM', which the default# comment.char='#' would eat (dropping the header and the first variant).
results <- read.table('gwas_results.PHENO.glm.logistic.hybrid', header =TRUE, comment.char ='')
results <- results[!is.na(results$P),]# qqman's manhattan needs a NUMERIC chromosome: recode X/Y/MT (e.g. X->23) or filter to autosomes first.
results$X.CHROM <- suppressWarnings(as.integer(sub('^chr','', results$X.CHROM)))
results <- results[!is.na(results$X.CHROM),]# Manhattan plot
png('manhattan.png', width =1200, height =600)
manhattan(results, chr ='X.CHROM', bp ='POS', snp ='ID', p ='P',
suggestiveline =-log10(1e-5), genomewideline =-log10(5e-8))
dev.off()# QQ plot
png('qq_plot.png', width =600, height =600)
qq(results$P)
dev.off()
Calculate Genomic Inflation
# Lambda (genomic inflation factor)
chisq <- qchisq(1- results$P,1)
lambda <- median(chisq)/ qchisq(0.5,1)
cat('Lambda:',round(lambda,3),'\n')# Lambda should be close to 1.0 (1.0-1.1 acceptable)
Extract Significant Hits
# Select the P column by header, not a fixed index (firth-fallback adds columns and shifts positions).
awk 'NR==1{for(i=1;i<=NF;i++) if($i=="P") p=i; print; next} $p<5e-8' \
gwas_results.PHENO.glm.logistic.hybrid > significant_hits.txt
awk 'NR==1{for(i=1;i<=NF;i++) if($i=="P") p=i; print; next} $p<1e-5' \
gwas_results.PHENO.glm.logistic.hybrid > suggestive_hits.txt
Parameter Recommendations
Step
Parameter
Value
Sample QC
--mind
0.05
Variant QC
--geno
0.05
Variant QC
--maf
0.01
Variant QC
--hwe
1e-6
LD pruning
--indep-pairwise
50 5 0.1 (after long-range-LD exclusion)
PCA
--pca
10
Significance
p-value
5e-8
Common Errors
Symptom
Cause
Fix
"0 variants matched" the panel
chr-naming (1 vs chr1) or build mismatch
bcftools annotate --rename-chrs; match study build to the panel before any check
Flipped BETA / cancelled or manufactured signal
Strand-flip / allele-swap at intermediate-frequency palindromes
Run the Rayner check + AF-concordance FreqPlot; drop MAF>0.4 palindromes
Case-control association that is imputation artifact
Cases and controls imputed SEPARATELY
Impute jointly; separate imputation makes error differ between arms
Disproportionate rare-variant loss
Flat INFO/R2 cutoff
MAF-stratified R2 thresholds + a MAF floor
A PC tracks an inversion, not ancestry
Long-range-LD region kept before pruning
Exclude MHC/8p23.1/17q21.31/LCT BEFORE --indep-pairwise
Elevated lambda
Polygenicity OR confounding (lambda alone cannot separate)
Read the LDSC intercept (~1 = polygenic, fine); do NOT reflexively genomic-control
Residual structure after PCs
Relatedness/fine-scale structure (a covariance PCs cannot remove)
LMM (regenie/SAIGE/BOLT) with LOCO
Inflation at low MAC / extreme case:control ratio
Score/Wald anti-conservative
SPA (SAIGE) or Firth
chrX mis-analyzed
chrX coded as an autosome
split-PAR, sex covariate, explicit X handling
References
McCarthy S, Das S, Kretzschmar W, et al (2016) A reference panel of 64,976 haplotypes for genotype imputation. Nature Genetics 48:1279-1283. DOI 10.1038/ng.3643. (HRC.)
Taliun D, Harris DN, Kessler MD, et al (2021) Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590:290-299. DOI 10.1038/s41586-021-03205-y. (TOPMed diversity.)
Sheng X, Xia L, Cahoon JL, et al (2023) Inverted genomic regions between reference genome builds. HGG Advances 4:100159. DOI 10.1016/j.xhgg.2022.100159. (liftover/BBIS strand danger.)
Mbatchou J, Barnard L, Backman J, et al (2021) Computationally efficient whole-genome regression for quantitative and binary traits. Nature Genetics 53:1097-1103. DOI 10.1038/s41588-021-00870-7. (regenie.)
