Biobank Sequencing Strategy: From Array to WGS
A modern biobank sequencing strategy should start with the decision you must support, not the platform you already own. For many programs, the tipping point comes when array vs WGS trade-offs no longer favor arrays—especially as low-pass WGS imputation improves and large reference panels lift accuracy and portability. This practical guide lays out a defensible path from arrays to WES, 30× WGS, or low-pass WGS; shows how to operate at scale with cohort harmonization, joint genotyping, and version-frozen releases; and explains a cloud/data-layer that respects controlled-access norms. We close with a migration playbook, a snippet-ready FAQ, and concrete next steps with service links (RUO).
The decision point for biobanks in 2025
Whole-genome sequencing (WGS) has moved from special project to routine program. As large resources expand the number of high-coverage genomes and multi-ancestry samples, fine-mapping, rare-variant discovery, and cross-ancestry analyses become everyday work rather than one-off initiatives. This scale shifts the cost–power frontier and makes a fresh platform decision unavoidable.
Genome-wide distribution of common and rare variants in 1-Mb windows, stratified by CADD functional scores, highlighting the density of rare noncoding variation captured by high-coverage WGS. (Taliun D. et al. (2021) Nature)
At the same time, low-coverage WGS imputation has matured. Methods designed for very large haplotype references have demonstrated strong imputation accuracy at sub-1× depth, especially for common and low-frequency variants. In practice, this means some cohorts can capture more value per dollar than with many SNP arrays—if they validate rare-allele limits and calibrate across ancestries and phenotypes.
Using large WGS-based haplotype references, SHAPEIT5 reduces non-reference discordance versus Beagle across minor-allele-count bins, demonstrating gains that support more accurate imputation. (Hofmeister R.J. et al. (2023) Nature Genetics)
What is at stake: discovery power, PRS portability, and the long-term reusability of your cohort. The rest of this guide helps you decide when to switch—and how to do it without disrupting ongoing studies.
Where arrays, WES, 30× WGS, and low-pass WGS each win
Choose by goal and cost–power—not habit. The right platform depends on the biological question, expected effect sizes, sample diversity, and your tolerance for future reprocessing. Below is a compact, defensible profile for each option.
| Platform | Primary win (research goal) | Discovery coverage | Rare variants / SVs | PRS portability potential | Cost per sample (relative) | Operational load | Key risks & caveats |
| Arrays + imputation | Common-variant GWAS at very large N | Assayed SNPs + imputed sites; limited non-coding | Weak for rare; limited SV and repetitive regions | Good within matched ancestry; weaker across groups | $ (lowest) | Low; mature tooling | Panel mismatch, imputation bias, regulatory blind spots |
| Whole-Exome Sequencing (WES) | Coding burden tests; gene-centric discovery | Exons; minimal regulatory coverage | Good for coding rare; limited SV context | Narrow base for PRS; weaker than WGS | $$ (moderate) | Moderate; capture-bias management | Design updates; off-target reads; limited reuse for non-coding |
| 30× Whole-Genome Sequencing | Broad discovery; future-proof re-analysis | Coding + non-coding; uniform genome coverage | Strong for rare variants and SV detection | Strong base for multi-ancestry PRS | $$$ (highest) | High; storage/compute; harmonization needed | Budget impact; longer runtimes; strict QC required |
| Low-pass WGS (≈0.5–6×) + imputation | WGS flexibility near array economics | Genome-wide at low depth; relies on large references | Mixed: validate rare; limited SV | Often better than arrays with large, diverse references | $–$$ (lower to mid) | Moderate; imputation infra; reference updates | Ancestry mismatch; coverage non-uniformity; rare-allele bias |
Arrays + imputation
- Best for: common-variant GWAS at very large N with fast turnaround.
- Why it wins: low per-sample costs; mature analytics; straightforward logistics.
- Cautions: performance depends on reference-panel match and QC; blind spots in regulatory and repetitive regions; constrained rare-variant power.
Whole-Exome Sequencing (WES)
- Best for: protein-coding burden tests and gene-centric hypotheses.
- Why it wins: efficient coverage of exons; established interpretation ecosystem.
- Cautions: misses most regulatory signals; limited structural-variant (SV) context.
Platform pages for deeper planning:
30× Whole-Genome Sequencing
- Best for: broad discovery across coding and non-coding regions, robust SVs, clean re-analysis.
- Why it wins: maximal discovery surface; consistent calling; fewer future constraints.
- Cautions: higher upfront cost; requires disciplined harmonization and capacity planning.
Low-pass WGS (≈0.5–6×) + imputation
- Best for: large cohorts seeking WGS flexibility at near-array economics.
- Why it wins: with a large, ancestry-appropriate reference, common/low-frequency performance can rival or exceed arrays; costs scale gently.
- Cautions: pilot carefully at rare alleles, complex regions, and cross-ancestry calibration; watch coverage non-uniformity. Evidence from recent methods highlights both efficiency and rare-variant gains tied to very large references.
Takeaway: if common-variant discovery at extreme N is the primary goal, arrays or low-pass WGS are credible choices; if your program depends on rare variants, structural variation, or future-proof re-analysis, 30× WGS is the simplest path to robust results.
What the evidence says (accuracy, harmonization, scale)
Low-pass vs array accuracy. Comparisons in pharmacogenetics and complex-trait mapping show low-pass WGS can meet or beat arrays on imputation quality once coverage passes a modest threshold. At some PGx-relevant loci, concordance is high enough to enable reliable association testing. The conclusion is not that arrays are obsolete, but that low-pass is a viable alternative when paired with the right reference and quality controls.
Computation r² across minor-allele-frequency bins shows low-pass WGS at 0.4×–1× matches or exceeds array performance for many bins, supporting budget-aware WGS + imputation designs. (Wasik K. et al. (2021) BMC Genomics)
Harmonization standards reduce cross-center noise. The Functional Equivalence (FE) framework demonstrated that standardized pipelines can lower inter-center discordance for SNVs, indels, and SVs to below replicate variability, enabling multi-site aggregation without reprocessing everything from scratch. FE principles are also helpful when you mix legacy arrays/WES with new WGS and need analyses to remain comparable.
Scalable cohort calling is solved engineering. Open-source stacks that combine DeepVariant with GLnexus deliver accurate, joint-called cohort VCFs at scale, optimizing precision/recall against benchmarks and trio consistency across a range of depths and cohort sizes. This reduces operational risk from "pipeline drift" and supports reproducible, version-frozen releases.
Architecture & data-layer choice
For controlled human genomics, the modern default is compute-to-data: run analysis inside secure platforms rather than exporting large datasets. This pattern mirrors practices used in federal repositories and controlled-access environments.
Security baselines. Map safeguards to the HIPAA Security Rule (administrative, physical, technical). Even if your biobank is not a covered entity, these guardrails are a practical baseline for handling sensitive human data. In practice, that means multi-factor authentication, encryption at rest and in transit, auditable access controls, least-privilege roles, and routine access reviews.
Controlled access and approvals. NIH-style controlled-access workflows emphasize prospective approvals, well-defined data use limitations, and analysis performed within the platform. When you mirror these patterns in your private cloud, you reduce egress, centralize audit trails, and simplify third-party reviews.
Design checklist (add to your cloud runbook):
- Residency & isolation: keep primary buckets in the U.S.; segregate PHI-adjacent metadata; avoid unnecessary cross-region egress.
- Access controls: role-based tiers (read/compute/admin), short-lived tokens, enforced MFA, and just-in-time privileges.
- Data integrity: artifact signing for pipeline releases; immutable manifests; reproducible container images.
- Observability: comprehensive audit logs, access dashboards, and tamper-evident trails; quarterly attestations and key-rotation schedules.
- Cost controls: tiered storage (hot/warm/cold), checkpointed workflows for restart safety, and opportunistic compute where appropriate.
This section provides general guidance and does not constitute legal advice. Coordinate with your counsel and IRB requirements.
Migration playbook: Arrays → WGS without disruption
A staged plan preserves ongoing studies and protects historical comparability. You do not need a "big bang."
1) Pilot representatively
Select a subset spanning sites, library types, ancestry groups, and phenotype quality. If budgets require, compare 30× WGS and low-pass WGS side-by-side. Evaluate:
- Imputation quality by minor-allele frequency and region type.
- Concordance to truth sets and benchmark panels.
- Rare-variant sensitivity for key phenotypes.
- PRS portability on held-out subgroups with matched LD references.
Evidence suggests that low-pass can rival arrays on imputation and association power when paired with a large reference, but you must test for your phenotypes, ancestries, and QC thresholds.
2) Lock an FE-aligned pipeline
Agree on reference build, aligner, recalibration, joint calling, and filters. Adopt FE principles so you can ingest data from multiple centers without reprocessing headaches. Freeze a version-frozen release per cycle with a pipeline manifest and change log. FE studies show that once pipelines are functionally equivalent, cross-center discordance drops beneath replicate noise—exactly what you need for aggregation and meta-analysis.
Where our team can help (RUO): Variant calling service
3) Joint-call + QC at cohort scale
Use DeepVariant + GLnexus (or an equivalently validated stack) to create joint VCFs with consistent genotypes across batches. Track coverage, duplication, insert size, contamination, Ti/Tv, and Mendelian error rates; investigate outliers and document remedies. Optimize block sizes and I/O for your environment to keep costs predictable as sample counts grow.
4) Release predictably; freeze for reuse
Publish release notes, a citable identifier (e.g., DOI), and a QC dashboard for every dataset release. Keep prior releases online in warm/cold tiers with indexes intact so collaborators can reproduce analyses rapidly. Predictable cadence prevents reruns and keeps analysts focused on biology, not plumbing.
5) Budget holistically with a cost–power model
Model cost per discovery (e.g., expected loci discovered, credible-set shrinkage, or PRS calibration targets) rather than cost per sample. Run sensitivity analyses for array → low-pass → 30× WGS under your phenotyping and ancestry mix. This framing speeds approvals because it ties spend to scientific outcomes and time-to-stable-results.
FAQ
Often yes for common and some low-frequency variants—when paired with a large, ancestry-appropriate reference and strict QC. Always pilot rare-allele and cross-ancestry performance before scaling.
Adopt Functional Equivalence standards and joint calling; publish version-frozen releases with change logs and manifests so everyone can cite the same dataset.
Use a HIPAA Security Rule–style baseline (administrative, physical, technical safeguards) and mirror NIH-style controlled-access practices for approvals and in-platform analysis. Coordinate with local counsel and IRB.
WGS provides the broadest discovery and harmonization; low-pass + imputation can work well if trained and evaluated with diverse references. Always report subgroup calibration and confidence intervals.
Run a representative pilot, lock an FE-aligned pipeline, joint-call with QA dashboards, and ship a version-frozen release on a predictable schedule. Use a cost–power model to justify scaling.
Action: Next steps
- Model your cost–power scenarios (arrays vs low-pass WGS vs 30× WGS) using your phenotypes and ancestry mix; present cost per discovery and time to stable results.
- Schedule a pilot with acceptance thresholds for imputation quality, rare-variant sensitivity, SV detection, and PRS portability.
- Request a pipeline & harmonization review to finalize FE alignment, joint calling, QC gates, and version-frozen release conventions.
Helpful internal routes:
- Cohort design & population genomics solutions (RUO)
- Sequencing overview
- Whole-genome resequencing (RUO)
- Whole-exome sequencing (RUO)
- GWAS & association pipelines (RUO)
Related reading:
References
- Taliun, D., Harris, D.N., Kessler, M.D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).
- Wasik, K., Berisa, T., Pickrell, J.K. et al. Comparing low-pass sequencing and genotyping for trait mapping in pharmacogenetics. BMC Genomics 22, 197 (2021).
- Hofmeister, R.J., Ribeiro, D.M., Rubinacci, S. et al. Accurate rare variant phasing of whole-genome and whole-exome sequencing data in the UK Biobank. Nature Genetics 55 (2023).
- Regier, A.A., Farjoun, Y., Larson, D.E. et al. Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects. Nature Communications 9, 4038 (2018).
- Duncan, L., Shen, H., Gelaye, B. et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nature Communications 10, 3328 (2019).
- Rubinacci, S., Hofmeister, R.J., Sousa da Mota, B., Delaneau, O. Imputation of low-coverage sequencing data from 150,119 UK Biobank genomes. Nature Genetics 55, 1088–1090 (2023).
- Poplin, R., Chang, P.-C., Alexander, D. et al. A universal SNP and small-indel variant caller using deep neural networks. Nature Biotechnology 36, 983–987 (2018).
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