SNP Arrays vs Low-Pass and Deep WGS in Population Genomics

SNP arrays, low-pass WGS, and deep WGS each offer different trade-offs for population genomics. This article compares these platforms in terms of cost, data quality, GWAS and PRS performance, and rare variant detection across human, plant, and animal studies. Use it as a practical guide to design a cost-effective population sequencing strategy and to decide when to combine arrays, low-pass WGS, and deep WGS in one research project.

Introduction: The Cost Puzzle in Population Genomics

Choosing between SNP arrays, low-pass WGS, and deep WGS is one of the most important decisions in any population genomics project. This single choice shapes your total budget, sample size, statistical power, and even which biological questions you can answer.

For many teams, the situation looks like this:

• Funding only covers part of the "ideal" design.
• The target sample size keeps growing as collaborators join.
• Stakeholders want one project to deliver GWAS, polygenic scores (PRS), population structure, and rare variant insights.

At the same time, sequencing options are expanding. Studies in human and livestock cohorts have shown that low-pass whole-genome sequencing (often 0.1–2× depth) plus imputation can rival or outperform many SNP arrays for common variant GWAS in diverse populations. Deep WGS remains the reference standard for rare and structural variants, but is harder to scale to tens of thousands of samples.

This article walks through how SNP arrays, low-pass WGS, and deep WGS compare in population genomics. It focuses on practical design choices, real-world trade-offs, and how to connect platform selection with downstream GWAS analysis, PRS evaluation, and population genetics analysis. The goal is not to promote one technology in isolation, but to help you design a cost-effective population sequencing strategy that fits your cohort, species, and budget.

Core Methods: What SNP Arrays and WGS Options Really Are

Understanding what each platform actually does is the first step towards a rational study design. This section provides short, clear definitions that can stand alone as direct answers to "What are SNP arrays?" or "What is low-pass WGS?" in a population genomics setting.

SNP Arrays

SNP arrays are fixed panels of pre-selected genetic markers that provide genotypes at known positions across the genome.

In population genomics, SNP genotyping array services are widely used because they combine relatively low per-sample cost with mature, well-understood workflows. Arrays work especially well for:

• Common variant GWAS in large human cohorts.
• Genomic selection in plant and animal breeding programmes.
• Follow-up genotyping of known risk loci or markers of interest.

However, arrays only capture variants present on the chip. They offer limited coverage of novel variants, rare alleles, and structural variants. Chip content may also be biased toward certain ancestries or breeds, which can affect performance in diverse cohorts.

Comparison of derived allele frequency spectra from whole-genome sequencing and various SNP array-derived marker sets, illustrating the underrepresentation of rare variants introduced by array design decisions (Geibel J. et al. (2021) PLOS ONE).

Low-Pass WGS

Low-pass whole-genome sequencing (low-pass WGS) is shallow whole-genome sequencing, typically around 0.1–4× coverage per sample, combined with genotype imputation.

Instead of reading every base many times, low-pass WGS lightly samples the entire genome and then uses a reference panel to infer dense genotypes. When the reference panel is appropriate for the target population, low-pass WGS can reach imputation accuracy similar to, or better than, many SNP arrays for common variants.

Low-pass WGS is especially attractive when:

• Your cohort includes diverse or underrepresented ancestries.
• No commercial SNP array exists for your species.
• You want data that support new analyses in the future without re-genotyping.

Because it captures variation across the whole genome, low-pass WGS fits naturally with population WGS sequencing services, GWAS analysis services, and PRS analysis in large-scale population studies.

Deep WGS

Deep WGS is high-coverage whole-genome sequencing, usually 20–30× or higher, designed to capture nearly all variants in a genome with high confidence.

Deep WGS supports:

• Rare variant burden tests and fine-mapping.
• Structural variant and copy-number variant discovery.
• Construction of high-quality haplotype reference panels.
• Comprehensive population genetics analysis, including recombination and demographic history.

Because deep WGS is still more expensive per sample and generates large data volumes, it is often reserved for smaller cohorts or for subsets of larger cohorts. Many successful population genomics projects combine deep WGS on a subset with lower-cost genotyping of the remaining samples.

Side-by-Side Comparison: Cost, Data, and Flexibility

All three approaches deliver genotype information, but they do so with different cost structures and scientific strengths. A simple comparison helps you see when each option is the better tool for your study.

A conceptual summary looks like this:

This table is not a rigid rulebook; it is a starting point for discussion before detailed costing and power calculations.

Budget and Sample Size Trade-Offs

For a fixed total budget, arrays typically support the largest sample sizes, followed by low-pass WGS, then deep WGS. In many human projects, a mid-density SNP array may allow you to genotype two to three times more samples than deep WGS at current prices, though exact ratios depend on local costs and sequencing platform.

Low-pass WGS sits in the middle. Per-sample cost is usually higher than a mid-density chip, but substantially lower than 30× WGS. Studies in human cohorts, cattle, pigs, and aquaculture species have shown that low-pass WGS offers a favourable balance between cost and genomic coverage for population genotyping.

When you design a cost-effective population sequencing strategy, it is often more useful to think in terms of "cost per effective genotype" and "cost per unit of power" rather than only "cost per sample".

Common, Polygenic, and Rare Signals

From an analysis perspective:

• Common variant GWAS and standard PRS: arrays and low-pass WGS often perform similarly when imputation is strong. Work in human biobanks suggests that coverage around 0.5–2× can deliver GWAS and PRS performance comparable to many arrays, provided suitable reference panels are used.

Fraction of non-monomorphic variants captured by ultra low-coverage WGS compared with an imputed SNP array across minor allele frequency bins, using 30× WGS as a reference in a melanoma cohort (Chat V. et al. (2021) Frontiers in Genetics).

• Cross-ancestry PRS and fine-scale structure: low-pass and deep WGS can reduce dependence on chip content, which may miss ancestry-specific variants and haplotypes. This can improve PRS portability and population structure analysis in diverse cohorts.
• Rare variant and structural variant analysis: deep WGS remains the most reliable option. Low-pass WGS can assist with some rare variant tests, but calling very rare alleles and complex structural variants is still challenging at low depth.

These patterns matter as much as list prices when selecting SNP arrays vs low-pass or deep WGS.

Goal-Driven Designs: Match Method to Study Question

Rather than starting from a favourite technology, it is usually more effective to start from the scientific questions you want to answer. This section frames designs by goal, then maps those goals to practical combinations of SNP arrays, low-pass WGS, and deep WGS.

GWAS and Polygenic Scores in Large Cohorts

If your primary goal is common variant GWAS and PRS in a large human cohort, SNP arrays and low-pass WGS are both realistic choices.

A pragmatic approach is:

Use SNP arrays when:

• A high-quality, well-validated chip exists for your ancestry or population.
• Your existing data, collaborators, and analysis workflows are all array-based.
• Your main priority is maximising sample size per dollar for common variant GWAS.

Use low-pass WGS when:

• Your cohort has mixed or underrepresented ancestries where standard chips may perform poorly.
• You want to reuse the data for new analyses in the future, beyond the content of any current chip.
• You are willing to invest in appropriate reference panels and imputation workflows.

For teams running multi-centre or international studies, low-pass WGS can reduce dependency on a specific chip and avoid problems when different sites use slightly different array products. This makes low-pass WGS an attractive backbone for population genomics sequencing services and PRS analysis services in global cohorts.

Rare Variant and Structural Variant Studies

If your main interest is rare coding variants, loss-of-function alleles, or complex structural variants, deep WGS usually offers the best value, even if you can only sequence a smaller number of samples.

A common hybrid strategy is:

• Deep WGS on a subset of samples to build a high-quality reference panel and call rare variants.
• Low-pass WGS or arrays on the remaining cohort to support GWAS and PRS at scale.
• Joint analysis where rare variants from the deep WGS subset inform gene-based burden tests or fine-mapping in the larger cohort.

This kind of design can align well with integrated services that combine whole-genome sequencing, rare variant detection, and GWAS analysis in a single project plan.

Breeding and Non-Model Species

Plant and animal breeding projects often start from legacy SNP arrays or reduced-representation methods such as GBS. In these settings, low-pass WGS provides a way to upgrade genomic coverage while reusing earlier data.

A practical, phased pattern looks like this:

• Phase 1 – Maintain continuity: continue using the existing SNP array for routine genomic selection cycles while gathering performance data.
• Phase 2 – Build a reference panel: deep WGS a smaller panel of breeding lines or founder animals to create a species-specific reference panel.
• Phase 3 – Introduce low-pass WGS: sequence new selection candidates with low-pass WGS, impute genotypes using the panel, and gradually transition away from array-only designs.

This staged approach helps breeding teams pilot low-pass WGS while controlling risk and maintaining continuity with historical data. It fits naturally with agricultural genomics services, population sequencing services for livestock and crops, and population genetics services for non-model species.

Practical Checks: QC, Reference Panels, and Data Handling

Platform choice is important, but practical details often decide whether a project works smoothly in real life. Many issues arise not from arrays or WGS themselves, but from sample quality, reference panel mismatch, or underestimating compute and storage needs.

Sample and Variant Quality

Across all platforms, robust quality control is more important than squeezing out a little more coverage or marker density. Successful population studies generally:

• Define clear DNA input requirements, including concentration, purity, and acceptable degradation.
• Set sample-level QC thresholds, such as minimum call rates for arrays or minimum effective coverage for low-pass WGS.
• Apply variant-level filters before analysis, for example removing SNPs with low call rate, extreme deviation from Hardy–Weinberg equilibrium, or poor imputation quality.

Ignoring these basics can reduce GWAS power, inflate false positives, or bias PRS models. In our experience, projects that invest time in a clear QC plan at the start spend much less time troubleshooting later.

Reference Panels and Ancestry Fit

Low-pass WGS and array-based imputation both depend heavily on reference panels. When the panel reflects the target population, imputation can reach high accuracy even at relatively low coverage. When it does not, accuracy drops, especially for rare and population-specific variants.

Before committing to a design, it is worth asking:

• Is there a public or commercial reference panel that matches our ancestry or breed?
• If not, how many deeply sequenced samples would we need to build our own panel?
• Are we planning analyses that require very accurate phasing or rare variant calls?

In some projects, the cost of deep WGS for a few hundred reference samples is justified by the gains in imputation accuracy and analytic power in a much larger low-pass WGS or array cohort. This is especially relevant for underrepresented human ancestries and for non-model species.

Imputation R² values for ultra low-coverage WGS across allele frequency bins, showing higher imputation certainty than an imputed SNP array for both SNPs and indels at common and low-frequency variants (Chat V. et al. (2021) Frontiers in Genetics).

Storage, Compute, and Pipelines

Arrays, low-pass WGS, and deep WGS differ in data volume and computational requirements:

• Arrays produce relatively small files, and many teams can handle the data on local servers.
• Low-pass WGS generates BAM/CRAM and VCF files that are larger, but can be managed efficiently with modern compression and streaming tools.
• Deep WGS requires the most storage and compute, especially for joint calling, structural variant analysis, and complex population genetics workflows.

For population-scale work, it is important to check that you have access to:

• Reliable storage for raw data, intermediate files, and final analysis results.
• Scalable pipelines for GWAS, PRS, and population genetics analysis.
• Bioinformatics support, either in-house or via population genomics bioinformatics analysis services, to maintain and document these workflows over time.

Underestimating these needs can delay downstream results even when sequencing itself goes smoothly.

How CD Genomics Supports Your Population Genomics Projects

CD Genomics can help you turn high-level study ideas into a concrete, cost-effective population sequencing strategy. Rather than choosing between SNP arrays, low-pass WGS, and deep WGS in isolation, you can work with a team that sees the full pipeline from study design to variant interpretation.

Study Design and Platform Selection Support

Many clients approach us with a rough plan, such as "about 10,000 samples from a mixed-ancestry human cohort" or "a multi-year breeding programme with several thousand animals per year." Our first step is to translate those plans into comparable scenarios:

• Arrays only vs low-pass WGS only vs hybrid designs.
• Different coverage levels for low-pass WGS, based on reference panel availability.
• Deep WGS subset sizes that make sense for reference panel construction or rare variant targets.

We then discuss trade-offs in sample size, cost per sample, and analytic power for GWAS, PRS, and rare variant analyses. Where possible, we refer to published benchmarks and our own aggregated project experience, while keeping individual project data confidential.

This consultation stage connects directly to our population sequencing services, whole genome sequencing service, and SNP array genotyping service offerings.

End-to-End Sequencing and Bioinformatics

Once a design is agreed, CD Genomics can manage the laboratory and analytical workflow under a single project structure:

• Sample receipt, DNA QC, and library preparation for arrays, low-pass WGS, or deep WGS.
• Sequencing on appropriate platforms, with agreed coverage and quality targets.
• Primary data processing, variant calling, and imputation where relevant.
• Downstream analyses such as GWAS, PRS, population structure, selection scans, and basic demographic inference for research-use-only projects.

All steps are documented, and QC reports are shared so you can track coverage, call rates, and imputation performance against predefined thresholds. This allows your internal team to focus more on biological interpretation and decision-making.

Flexible Engagement and Case Experience

Different teams need different levels of support. Some clients ask CD Genomics to handle only the lab work and deliver aligned reads or joint-called variants. Others request complete sequencing and bioinformatics analysis service packages, including structured result summaries for internal stakeholders.

Typical collaboration models include:

• Large human or animal cohorts where we manage continuous batches of samples over several years.
• Plant breeding programmes where low-pass WGS is gradually introduced alongside existing SNP arrays.
• Non-model species projects where initial deep WGS and reference panel construction are followed by larger low-pass WGS or array-based expansions.

In each case, platform choice is treated as part of an evolving strategy that adapts to your scientific questions and resources.

A Simple Framework to Move Forward

To help you decide whether to involve CD Genomics, it can be useful to walk through a short checklist:

1. Define your main analyses – GWAS, PRS, rare variants, structural variants, or population genetics.
2. Estimate sample size, budget, and population structure, including ancestry mix or breed diversity.
3. Shortlist one or two platform strategies, for example SNP arrays only, low-pass WGS only, or a hybrid approach with deep WGS in a subset.
4. Share your draft plan with the CD Genomics team for a feasibility review, QC planning, and a detailed, itemised quote.

This process turns a complex "SNP arrays vs low-pass and deep WGS" debate into a structured, data-informed decision.

To discuss your project or request a tailored population sequencing strategy, you can contact CD Genomics and share a brief description of your cohort, goals, and constraints.

FAQ: SNP Arrays vs Low-Pass and Deep WGS

References

1. Flannick, J., Korn, J.M., Fontanillas, P. et al. Efficiency and Power as a Function of Sequence Coverage, SNP Array Density, and Imputation. PLOS Computational Biology 8, e1002604 (2012).
2. Chat, V., Ferguson, R., Morales, L. et al. Ultra Low-Coverage Whole-Genome Sequencing as an Alternative to Genotyping Arrays in Genome-Wide Association Studies. Frontiers in Genetics 12, 790445 (2022).
3. Geibel, J., Reimer, C., Weigend, S. et al. How Array Design Creates SNP Ascertainment Bias. PLOS ONE 16, e0245178 (2021).
4. Lloret-Villas, A., Pausch, H., Leonard, A.S. The Size and Composition of Haplotype Reference Panels Impact the Accuracy of Imputation from Low-Pass Sequencing in Cattle. Genetics Selection Evolution 55, 33 (2023).
5. Li, S., Yan, B., Li, T.K.T. et al. Ultra-Low-Coverage Genome-Wide Association Study—Insights into Gestational Age Using 17,844 Embryo Samples with Preimplantation Genetic Testing. Genome Medicine 15, 10 (2023).
6. Bai, W.-Y., Zhu, X.-W., Cong, P.-K. et al. Genotype Imputation and Reference Panel: A Systematic Evaluation on Haplotype Size and Diversity. Briefings in Bioinformatics 21, 1806–1817 (2020).