What's the most cost-effective way to run GWAS at scale using a sequence data pipeline?

The most cost-effective way to run GWAS at scale with sequence data is usually a two-stage, cloud/HPC-friendly pipeline that avoids full-coverage joint calling on every sample unless you truly need it.

Best-cost strategy

1) Start from aligned reads, not raw FASTQs, if possible

If sequencing has already been done, storing and reprocessing BAM/CRAM is typically cheaper than re-aligning everything from FASTQ repeatedly.

2) Use low-pass WGS + genotype imputation when feasible

For large cohorts, this is often the cheapest approach that still gives strong GWAS power:

• Sequence each sample at ~0.5× to 4×
• Call variants per sample or in batches
• Impute against a dense reference panel
• Run GWAS on imputed genotypes

This is usually far cheaper than deep WGS and often comparable for common-variant GWAS.

3) If you need true sequence-level discovery, use a joint-called VCF/BCF workflow

For higher-quality rare-variant analyses:

• Per-sample or per-batch calling to gVCF/BCF
• Joint genotyping only once across the full cohort
• Store in BCF/CRAM rather than bloated VCF/FASTQ

Common tools:

• BWA-MEM2 / minimap2 for alignment
• GATK HaplotypeCaller or DeepVariant for calling
• GLnexus for joint calling at scale
• bcftools for filtering/merging
• PLINK 2 or SAIGE / REGENIE for GWAS

Cost-saving pipeline design

A. Compute-efficient architecture

• Use preemptible/spot instances in cloud
• Run jobs in scatter-gather fashion by chromosome or genomic intervals
• Prefer containerized workflows with Nextflow or Snakemake
• Keep intermediate files in compressed, indexed formats
• Avoid repeated I/O by colocating compute with storage

B. Storage-efficient choices

• Use CRAM instead of BAM to reduce storage
• Delete intermediate BAMs/gVCFs after QC and final joint calls
• Keep only:
  • aligned CRAM
  • final BCF/VCF
  • sample QC metrics
  • phenotype/covariate tables

C. GWAS-analysis-efficient choices

For large cohorts and related individuals:

• Use REGENIE or SAIGE
  • much more scalable than naive linear/logistic regression
  • handles case-control imbalance and sample structure better
• Use LD-pruned common variants for initial mixed-model fitting
• Run rare-variant tests separately if needed

Practical “most cost-effective” recommendation by study type

If your goal is common-variant GWAS on thousands to hundreds of thousands of samples:

Low-pass WGS + imputation + REGENIE/SAIGE
This is usually the cheapest high-value option.

If your goal is rare-variant discovery or pangenome/sequence-based analysis:

Joint-called WGS with gVCF/GLnexus + BCF + REGENIE/SAIGE or burden tests
More expensive, but necessary for sequencing-based rare-variant work.

If you already have short-read data and want minimal spend:

CRAM-based reanalysis + selective variant calling on regions of interest
Useful if you don’t need full discovery across the entire genome.

What usually makes pipelines expensive

• Deep WGS on every sample
• Keeping FASTQ/BAM copies around
• Re-aligning and re-calling variants repeatedly
• Running GWAS with non-scalable regression tools
• Doing joint calling without batching or interval splitting
• Ignoring storage/network costs in cloud workflows

Bottom line

If you want the best cost-to-power ratio for GWAS at scale, the usual winner is:

Low-pass sequencing + imputation + scalable association testing (REGENIE/SAIGE)

If you need sequence-native variant discovery, use:

CRAM + per-sample calling + joint genotyping with GLnexus + compressed BCF + scalable GWAS

If you want, I can give you:

1. a reference architecture for AWS/GCP/HPC,
2. a tool-by-tool pipeline, or
3. a cost comparison table for low-pass WGS vs deep WGS vs array + imputation.