Big-Cohort Compute: Hail, plink2 & bigsnpr Basics
TL;DR — For cohorts from 10k to 1M+ samples, use Hail for distributed ETL and GWAS at scale, plink2 (with PGEN/PVAR/PSAM) for fast single-node QC, PCA, and conversions, and bigsnpr for FBM (file-backed) memory-mapped modeling like LDpred2. Keep compute and storage in the same US/EU region to avoid inter-region egress pricing. Start with a "10k–50k plink2 → bigsnpr PRS," "100k–300k plink2 pre-QC → Hail GWAS," or "300k–1M+ Hail-first with targeted exports" pattern.
When your study scales beyond tens of thousands of samples, "quick scripts" hit I/O, memory, and cost walls. The combination of Hail, plink2, and bigsnpr offers a pragmatic way to keep time-to-insight short without runaway budgets. This guide shows exactly where each tool shines, the minimal architectures that actually scale, and how to make region choices in US/EU that protect both performance and cost.
Choosing the Right Engine for the Job: Hail vs plink2 vs bigsnpr
Hail (distributed ETL + large-scale statistics).
Hail's MatrixTable keeps variants (rows), samples (columns), and entry fields aligned for high-throughput genetics. On Spark, you can run variant/sample QC, annotate covariates, and execute linear_regression_rows or logistic_regression_rows across hundreds of thousands of samples. If you expect repeated regressions, wide joins, or rapid cohort growth, Hail is the sustainable default for GWAS at scale.
plink2 (single-node speed with modern I/O).
plink2 excels at format conversion, QC, PCA, and many association tasks on one large machine. Prefer PGEN/PVAR/PSAM over legacy BED when plink2 is your main consumer—plink2 PGEN vs BED tradeoffs favor PGEN for speed and features.
bigsnpr (R modeling on disk-backed matrices).
bigsnpr (with bigstatsr) lets you work beyond RAM using FBM (file-backed big matrix). This is ideal for LDpred2 and other PRS methods. If your analytics team lives in R and needs out-of-core modeling without cluster ops, bigsnpr is a smart choice.
A practical split of responsibilities
- Distributed ingestion + heavy stats → Hail
- Fast single-node QC, PCA, conversions → plink2 (PGEN)
- Out-of-core modeling/PRS in R → bigsnpr (FBM + LDpred2)
Related reading: What Tools Analyze Population Structure
Minimum Viable Architectures That Scale
Hail on Dataproc (GCP): the quickest reliable on-ramp
Launch managed Spark clusters next to your data and submit Hail jobs without heavy DevOps. Co-locate the cluster with your object storage (e.g., us-central1, europe-west4, europe-west1) to avoid inter-region egress. Checkpoint between expensive transforms so retries skip hours of recomputation.
# Start a Hail-ready Dataproc cluster in a US region (Iowa) hailctl dataproc start my-hail \ --region=us-central1 \ --num-workers=20 \ --worker-machine-type=n2-standard-8 # Submit your Hail analysis and then stop the cluster hailctl dataproc submit my-hail my_gwas.py hailctl dataproc stop my-hail
Why teams like it: managed Spark lifecycle, reproducible configs, and near-zero cold-start friction for large collaborative projects.
Overview of GWAS phases from DNA sampling to post-association analyses; modules include pre-association QC, association testing (including mixed models), and post-GWAS tasks such as fine-mapping and PRS. (Brandenburg J-T. et al. (2022) BMC Bioinformatics)
plink2 on a big-memory node: single-node rocket
Adopt PGEN/PVAR/PSAM as your canonical store. Convert once (--make-pgen) and reuse across QC, PCA, and association to avoid repeated text parsing. For 10k–50k cohorts, a 32–64 vCPU instance with ample RAM and NVMe can complete end-to-end QC and association in hours.
# Relatedness pruning up to second degree (KING scale) plink2 --pfile cohort \ --king-cutoff 0.0884 \ --make-just-fam \ --out unrelated
Why teams like it: no cluster overhead, predictable runtime, and first-class support for modern association and relatedness tasks.
Nextflow-based GWAS Workflow C showing modular conversion to PLINK, QC to produce quality-controlled PLINK files, and downstream association/structure analyses. (Baichoo S. et al. (2018) BMC Bioinformatics)
bigsnpr workstation or VM: memory-mapped modeling
Load your cleaned PLINK set into an FBM and proceed directly to LDpred2 or other penalized models. Memory mapping makes disk bandwidth—not RAM—the main constraint, enabling PRS on commodity instances.
Why teams like it: familiar R ecosystem, deterministic FBM artifacts, and straightforward parallelization.
Need help matching tools to your study design? Explore our Population Structure Analysis Services for fit-for-purpose pipeline design and reporting.
Landscape of 11 PRS pipelines built from sex-specific and sex-agnostic GWAS summary statistics using PRScs, LDpred2, and PRScsx; model selection and testing steps are highlighted. (Zhang C. et al. (2022) Frontiers in Genetics)
Comparative prediction performance across multiple traits and cohorts within a reference-standardized framework, highlighting scenarios where LDpred2 and related shrinkage methods lead. (Pain O. et al. (2021) PLOS Genetics)
Data Layout & I/O Strategy You Won't Regret Later
Hail MatrixTable for long-lived states
MatrixTable's row/column/entry/global schema eliminates error-prone denormalization. Use write() to persist stable checkpoints and repartition when needed to keep shuffles in check. Partitioning by genomic order improves predicate pushdown for region-based analyses.
# Export a Hail MatrixTable to PLINK for downstream clumping hl.export_plink(mt, 'gs://bucket/out/my_gwas', varid='variant_qc.variant_id')
plink2 PGEN vs BED for modern pipelines
Legacy BED is ubiquitous, but PGEN unlocks higher throughput and newer features in plink2. If downstream tools are Hail or bigsnpr, convert at the edges—PGEN ↔ MatrixTable ↔ FBM—and keep conversions explicit in your pipeline (Makefile/Snakemake/Nextflow).
bigsnpr FBM for out-of-core modeling
FBM supports random access at disk speeds and works well with windowed, LD-aware methods such as LDpred2. Place FBMs on fast SSD storage; archive FBM paths and checksums alongside model parameters for reproducibility.
For report-ready PCs, outlier handling, and stratification plots, see PCA QC for GWAS: Outlier & Stratification Detection Guide.
Scaling Recipes: 10k → 100k → 1M Samples
Pattern A (10k–50k): plink2 QC → bigsnpr PRS
- QC and relatedness in plink2 with call rate, HWE, MAF, and --king-cutoff to your tolerance.
- Modeling in bigsnpr: read PLINK into FBM and run LDpred2 (or other PRS methods).
- Deliverables: QC summary, PRS coefficients, and evaluation metrics.
Why it wins: plink2 keeps I/O minimal; bigsnpr's FBM delivers out-of-core modeling without cluster overhead.
Pattern B (100k–300k): plink2 pre-QC → Hail ETL + GWAS
- Pre-QC in plink2 to remove obvious artifacts; maintain PGEN as the working set.
- Hail ETL + statistics: ingest to MatrixTable, compute variant/sample QC, PCs, and linear/logistic regression with covariates on a cluster.
- Export summary stats or targeted genotype slices back to PLINK for clumping or to bigsnpr for PRS.
Why it wins: heavy joins and regressions parallelize in Hail while plink2 keeps early steps fast.
Pattern C (300k–1M+): Hail-first ingestion → targeted exports
- Ingest and normalize directly into Hail in the same region as storage.
- Checkpoint after import, QC, and annotation; treat checkpoints as versioned deliverables.
- Export narrowly (regions, hit lists) to plink2/bigsnpr rather than copying full matrices.
Operational tip: codify MatrixTable schemas and cluster specs in code; provenance is as critical as p-values at this scale.
Planning a million-sample push? Our GWAS Analysis Services can stage a budget-capped pilot (1–2 phenotypes) before you commit.
Region & Cost Strategy for US/EU (Avoid Egress Surprises)
Keep compute and storage in the same region and move derivatives, not raw matrices, across borders. Here's a quick navigator:
| Scenario | Recommended Home Region | Why This Region | Risk If Not Aligned |
| EU consortium with residency needs | europe-west4 (Netherlands) or europe-west1 (Belgium) | EU residency; mature infra; strong intra-EU peering | Cross-region/extra-EU transfers add cost and legal review |
| North America data hub | us-central1 (Iowa) or us-east1 (S. Carolina) | Cost-effective compute; central peering | Shipping data to EU collaborators becomes expensive |
| Mixed-cloud analytics | Choose one "analysis home" per study | Minimizes cross-cloud transfer and duplicate storage | Paying twice: egress + re-ingest + duplicated artifacts |
Tactics that pay off immediately
- Co-locate Dataproc/VMs with buckets; avoid silent cross-region writes.
- Share summary statistics and plots across regions; send genotype slices only when necessary.
- Use preemptible/spot instances for per-chromosome tasks; add retry logic and keep checkpoints durable.
For architecture reviews and budget modeling, visit Genomics & Bioinformatics Services.
Quality, Reliability & Reproducibility
Cross-tool consistency checks
- Counts & IDs: verify sample and allele counts match across plink2 and Hail after QC; keep variant IDs canonical (e.g., chr:pos:ref:alt).
- PCA parity: document parameters and confirm concordance on a test subset whether PCs come from plink2 or Hail.
- Association sanity: replicate one phenotype across tools; confirm effect directions and lead hits.
Checkpoints over heroics
Long chains of transformations are fragile. Checkpoint after VCF import, QC, and annotation; tag them semantically (e.g., cohort.v1.qc.mt). Retries should resume in minutes, not hours.
Reproducible modeling in bigsnpr
Fix random seeds, record LD window sizes and shrinkage parameters, and archive FBM paths with checksums. Store PRS coefficients and thresholds alongside run logs.
Reporting that reviewers accept
Ship a standard pack: QC summary, PCA plots with outlier flags, Manhattan/Q–Q for top phenotypes, and methods (tool versions, key flags, region placement). This accelerates manuscript and stakeholder review.
Need standardized PCA and stratification plots? See our PCA Analysis Service for report-ready outputs.
Examples of publication-grade Manhattan, multi-study overlay, regional, and LocusZoom-like plots generated by the topr R package for rapid visual QC and annotation of GWAS results. (Juliusdottir T. (2023) BMC Bioinformatics)
FAQs
If the plan is single-node QC, PCA, and a handful of associations, plink2 with PGEN is typically faster and cheaper. If you'll run distributed ETL, repeated regressions, or wide joins, start in Hail to avoid refactoring mid-project. Many teams do plink2 pre-QC → Hail GWAS to balance speed and scale.
Keep variant IDs canonical from the start. Use import_plink to bring PLINK binaries into Hail and export_plink to hand results back for clumping or other downstream steps. When sending to bigsnpr, export only what the model needs (e.g., a region slice or hit list) and load to FBM.
Yes. bigsnpr's FBM is memory-mapped, so disk throughput—not RAM—is the practical limit. It's a good fit for LDpred2 and other windowed, LD-aware methods. Put FBMs on fast SSD and record paths/checksums for reproducibility.
A common practice is to remove pairs with KING kinship ≥ ~0.0884 (up to second-degree). Tighten to ~0.177 for first-degree or ~0.354 for duplicates/MZ twins if the study design requires it. Align thresholds with phenotype characteristics and downstream models.
Choose a single analysis home region, keep compute and storage there, and exchange summary stats instead of full genotype matrices. You'll avoid most inter-region egress while keeping collaboration fluid.
Start Your Pilot
- Pick your analysis home (US/EU) and co-locate compute with storage.
- Choose a pattern that matches your scale:
- 10k–50k: plink2 QC → bigsnpr PRS (FBM + LDpred2).
- 100k–300k: plink2 pre-QC → Hail GWAS (linear/logistic regression).
- 300k–1M+: Hail-first ingestion → checkpointed stages → targeted exports.
- Codify provenance: version toolchains, MatrixTable schemas, and PGEN/FBM artifacts.
- Run a scoped pilot: 1–2 phenotypes, explicit budget cap, and success criteria on wall-clock and cost.
Scale with confidence—from population structure analysis and PCA QC to full GWAS analysis with cloud cost control.
Get started:
- Genomics & Bioinformatics Services
- Population Structure Analysis Services
- PCA Analysis Service
- GWAS Analysis Services
Related reading:
References
- Brandenburg, J.-T., Clark, L., Botha, G. et al. H3AGWAS: a portable workflow for genome wide association studies. BMC Bioinformatics 23, 498 (2022).
- Baichoo, S., Souilmi, Y., Panji, S. et al. Developing reproducible bioinformatics analysis workflows for heterogeneous computing environments to support African genomics. BMC Bioinformatics 19, 457 (2018).
- Zhang, C., Ye, Y., Zhao, H. Comparison of Methods Utilizing Sex-Specific PRSs Derived From GWAS Summary Statistics. Frontiers in Genetics 13, 892950 (2022).
- Pain, O., Glanville, K.P., Hagenaars, S.P. et al. Evaluation of polygenic prediction methodology within a reference-standardized framework. PLOS Genetics 17(5), e1009021 (2021).
- Juliusdottir, T. topr: an R package for viewing and annotating genetic association results. BMC Bioinformatics 24, 268 (2023).
- Privé, F., Aschard, H., Ziyatdinov, A., Blum, M.G.B. Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr. Bioinformatics 34(16), 2781–2787 (2018).
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