WGS, WES or a gene panel? Which method should you choose?

A gene panel is a good choice when the disease or trait being studied is associated with a well-defined group of genes and high sequencing depth is required. WES allows a broader search for variants in coding regions, while WGS provides the most comprehensive genome analysis, including non-coding regions and a broader range of structural variants.

The choice of method should depend on the project objective, material type, expected variants, required sensitivity and the ability to interpret the data.

Comparison of methods

When should you choose WGS?

Choose Whole Genome Sequencing, if:

• you need the most comprehensive genome analysis possible,
• previous panel testing or WES did not explain the phenotype under investigation,
• you are interested in variants in non-coding, regulatory or deep intronic regions,
• you want to analyse SNV, InDel, CNV and structural variants using a single dataset,
• more uniform genome coverage without a hybridisation-based enrichment step is important,
• the project is intended to allow future reanalysis of the data,
• you are analysing an organism for which WES or a suitable panel is not available.

WGS does not require selective target capture. This reduces probe hybridisation bias and allows you to analyse regions not covered by WES or a standard gene panel.

However, a broader range does not mean that every variant type will be detected equally effectively. Analysing repetitive regions, some structural variants and repeat expansions may require dedicated algorithms, additional validation or long-read sequencing.

When should you choose WES?

Choose Whole Exome Sequencing, if:

• you suspect that the cause of the phenotype under investigation lies in a coding region,
• the list of candidate genes is long or has not yet been defined,
• you are studying a rare or genetically heterogeneous disease,
• the project uses a trio design: the proband and their parents,
• you want to identify new candidate genes,
• you need a broader range than a panel offers, at a lower cost and with lower amount of data than with WGS.

WES uses hybridisation probes to capture exons and selected adjacent regions important for splicing. The method focuses on approximately 1-2% of the human genome, but covers many variants that directly affect protein structure and function.

The scope of WES depends on the enrichment kit used. Not all exons are covered equally, and regions with high GC content, pseudogenes and homologous sequences may be difficult to analyse.

When should you choose a gene panel?

Choose a gene panel if:

• the disease or phenotype under investigation is associated with a known group of genes,
• you need a high depth of coverage in selected regions,
• the aim is to detect variants with a low allele frequency,
• you are analysing tumour material, FFPE samples or cfDNA,
• the project involves a large number of samples,
• you need a short analysis time and a limited number of variants to interpret,
• you want to use UMI tags to reduce the impact of technical errors.

A panel may include a few to several hundred genes. In addition to exons, it can include selected regulatory regions, splice sites, introns or mutational hotspots.

The main limitation of a panel is its fixed scope of analysis. A variant outside the regions specified in the design will not be detected, and expanding the study may require preparing a new library.

Sample Requirements

Requirements depend on the library preparation method, material type, expected sequencing depth and use of UMI tags.

Standard material for WGS and WES

The following are recommended:

• purified genomic DNA,
• at least 1 µg of DNA for the standard Illumina workflow,
• a concentration of at least 30 ng/µl,
• an appropriate volume that meets the requirements provided with the quotation,
• measuring concentration using a fluorometric method, e.g. Qubit,
• OD260/280 most commonly in the range of approximately 1.8-2.0,
• as little degradation as possible,
• no alcohol, salts, detergents or other inhibitors.

A gene panel

The required amount of DNA depends on both the panel type and the material type. Smaller amounts of DNA can be analysed, but FFPE samples, liquid biopsies and cfDNA require individual assessment of sample suitability.

For these projects, provide:

• the material type and fixation method,
• the amount and concentration of DNA,
• the degree of degradation,
• the tumour cell fraction,
• the expected variant allele frequency,
• the availability of a matched normal sample.

A small amount of DNA does not always make a sample unsuitable, but may limit the sensitivity of the analysis and increase the risk of artefacts.

Approximate data output and sequencing coverage

A gene panel

Data output depends on the panel size and required sensitivity. Typical mean sequencing coverage may be:

• approximately 100-250× for germline variant analysis,
• approximately 500-1,000× or more for somatic variant analysis,
• even higher raw sequencing coverage for cfDNA analysis, particularly when using UMI.

When using UMI, it is important to consider not only raw sequencing coverage but also the number of independent molecules and consensus coverage after deduplication.

WES

Indicative options include:

• approximately 5 Gb of data per sample - roughly corresponding to a mean sequencing coverage of approximately 50×,
• approximately 10 Gb per sample - often used to achieve higher sequencing coverage, e.g. approximately 100×,
• higher data output for somatic variant analysis or when a low allele frequency is expected.

When assessing WES quality, consider not only mean sequencing coverage but also the percentage of target regions achieving at least 20×, 30× or another value required for the project.

WGS

For the human genome, the most commonly used values are:

• approximately 90 Gb of clean sequencing data, theoretically equivalent to 30× for a 3 Gb genome,
• in practice, often approximately 90-120 Gb of data per sample, depending on the reporting method, read quality and the required effective sequencing coverage,
• approximately 30× for standard germline variant analysis,
• higher sequencing coverage for somatic or mosaic variant analyses, or analyses requiring the detection of variants with a low allele frequency.

The final data output should be calculated based on the scope of the study, the analysis objective, the quality of the material and the expected sensitivity.

Common mistakes when choosing a method

Project planning questions

Before choosing a method, establish the following:

Plan your project

Describe your project - we will recommend a method and amount of data.

Based on the study objective, material type, gene list and expected variant frequency, we will help you choose a gene panel, WES or WGS. We will also select the library preparation method, appropriate sequencing depth and scope of bioinformatics analysis.