When to Use Whole-Genome vs. Whole-Exome Sequencing

Whole-genome sequencing and whole-exome sequencing remain two of the leading techniques for uncovering the genetic basis of diseases and understanding genetic predispositions. They each offer unique benefits and drawbacks, and the choice between them is often determined by the specific goals and constraints of a study. For researchers, understanding the distinctions between these two techniques is key to determining the best approach for their research goals.

Basic concepts

Whole-exome sequencing

Whole-exome sequencing (WES) is a method that involves sequencing only the exons from an organism of interest. The exons are regions within the genome that are transcribed into RNA and represent about 1–2% of the total DNA.¹ In many WES workflows, the primary focus is on the protein-coding regions of the exome, leaving out the majority of non-protein-coding exons.² WES library preparation involves the use of panels or probes to selectively enrich and focus on the exonic regions for the sequencing process. Analysis of this data involves aligning these regions to known references to identify potential variants.

Whole-genome sequencing

Whole-genome sequencing (WGS) is a comprehensive method that covers the entire DNA sequence of an organism's genome. Although library preparation might differ based on the sequencing instrument, the end goal is to process and sequence the entirety of the DNA for a full genome profile. Once sequenced, the data is mapped to a reference, offering a thorough overview of an individual's genome and the ability to detect genome-wide variations.

Comparing WES and WGS

WES

Both WES and WGS are valuable methods, yet they come with distinct advantages and limitations. WES is an effective choice when the study is focused on exonic regions. While the exome is only a small portion of the genome, more than 80% of disease-causing mutations are found in these regions.^1,3 Concentrating on the exome also reduces the amount of sequencing that is required and therefore produces significantly smaller datasets than WGS. This brings down the sequencing and data storage costs as well as facilitates an easier analysis process. Overall, WES can be a quicker and more cost-effective approach for studying or identifying a particular disorder.

A notable limitation of WES is its inability to cover a majority of the genome and portions of the exome. As a result, important variations might be concealed. Edd Lee, Director of Human Genomics Segment Marketing at Pacific Biosciences, cautioned that the apparent savings from WES's partial genome coverage might be offset by additional processing expenses. Furthermore, with the continuous discovery of significant variations in non-coding regions, Lee explained that WES restricts scientists' ability for retrospective data reanalysis.

WGS

“WGS is a more powerful and comprehensive method because it will provide information in both the coding and non-coding regions,” stated Lee. “Variants in non-coding regions can affect the expression or splicing of genes that would otherwise be missed in WES.” He also mentioned the advantages of long-read sequencers in WGS, highlighting their ability to offer additional regulatory information, like 5mC, which is absent in WES.

Cora Vacher, Ph.D., Global Market Segment Manager at Oxford Nanopore Technologies, underscored the advantages of WGS in identifying variants beyond the exonic regions. “WES is often assumed to be effective in targeting exonic regions; however, we now know how much more limited in scope it is in a post-telomere-to-telomere world. Scientists have shown what has been hidden in ‘dark regions’ of the exome as well as uncovered the significance of variants present in regions beyond exons,” Vacher said. She stressed that WGS also offers more consistent and accurate coverage of exonic regions than WES, which sometimes struggles with uneven probe coverage. Moreover, Vacher pointed out that WGS is quicker in the wet lab workflow, requires fewer steps, and this streamlined process could make it more suitable for validation in future clinical settings.

Despite these numerous advantages, WGS requires more sequencing, driving up costs compared to WES. It also produces a substantially larger volume of data, requiring specialized bioinformatics tools for processing. As a result, the analysis process is much longer and requires more extensive storage than WES.

Considerations in decision-making

In addition to the obvious factors of cost and time, several other key considerations differentiate WES from WGS. “While WES may work for known and suspected monogenic conditions, WGS is required when searching for unknown causal variants, especially genomic studies for rare and undiagnosed genetic diseases (RUGD),” stated Vacher. “Only WGS has the unbiased approach to RUGD, and also provides a richer breadth of data for investigating multifactorial or complex diseases in large cohort studies.” Building on that, Vacher emphasized the benefits of not being confined to just the coding regions. “We can delve into non-coding genetic factors and regulatory elements, which are crucial in understanding the underlying mechanisms of disease.”

The choice of technique can also be influenced by the specific disorder under investigation. “Diseases associated with repeat expansions are better suited to WGS assays,” explained Lee. “Additionally, WGS can detect copy number so any diseases where copy number has an effect is likely to be detected better with WGS.”

There are also several instances where WES remains a valuable approach. It's particularly useful in research targeting genetic disorders with mutations known to reside within coding regions. Clinically, WES has shown to be effective for the diagnosis of certain diseases, such as those related to the nervous system, dermatology, and seizures.⁴ Additionally, WES offers higher sequencing depth, which is beneficial to researchers needing extensive coverage of their target regions. Given these advantages and its cost-effectiveness, WES can be a practical choice depending on the scope of the investigation.

Emerging trends and innovations

Significant developments in sequencing technologies and bioinformatics have markedly influenced the usage of these two techniques. In discussing advancements, Lee singled out the new PacBio WGS pipeline for its enhanced comprehensiveness. “It delivers the ability to make calls from all variant classes (SNV, indel, SV, STRs, etc.),” he stated. This pipeline also seamlessly phases small variants with structural variants (SV) and offers valuable methylation information. Lee also believes that the time has come for WGS to be the default choice over WES. “While WES will likely always be cheaper to sequence, factoring in the cost of WES probes, reagents, and additional labor might get us closer to WGS costs,” he added.

Similarly, Vacher explained that “Recent advances in sequencing have transformed the landscape, increasing access to WGS and highlighting that what’s missing from WES matters for human genetics and disease." She enthusiastically spoke about Oxford Nanopore’s platform, which now offers real-time, high accuracy, any-length reads, and native methylation base calling. Combined with advancements in bioinformatics tools and workflows, she asserted that they have enhanced variant detection, annotation, and the overall interpretation of data, streamlining the WGS process from start to finish.

When asked about the future preferences between WGS and WES, Vacher was confident in her prediction. “As the cost of WGS continues to decrease and platforms become more accessible, I predict that WGS will become the gold standard and WES will soon be considered a process of the past.” Expanding on the topic, she emphasized the accelerating shift toward WGS. “Our comprehensive approach allows for a level of understanding that WES, despite its merits in the past, now struggles to match. Going forward, we predict that WGS is poised to be the mainstay in genomic sequencing, bringing us closer to unlocking the full intricacies of the genome.”

Ultimately, while WES has its benefits and specific applications, the continuous advancements in technologies, combined with the dropping costs, indicate that WGS will likely dominate the sequencing landscape. Both experts are in agreement that WGS is not only the present but also the promising future of genomics.

References

1. Choi M, Scholl UI, Ji W, et al. Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. Proceedings of the National Academy of Sciences. 2009;106:19096-19101. doi:https://doi.org/10.1073/pnas.0910672106

2. Aspden JL, Wallace EWJ, Whiffin N. Not all exons are protein-coding: Addressing a common misconception. Cell Genomics. 2023;3:100296. doi:https://doi.org/10.1016/j.xgen.2023.100296

3. Cooper D, Krawczak M, Antonorakis SE. The nature and mechanisms of human gene mutation. The Metabolic and Molecular Bases of Inherited Disease. Scriver CR, al Beaudet, Sly WS, and Valle D. New York, McGraw-Hill, Inc. I; 1995.

4. Zhang Q, Qin Z, Yi S, et al. Clinical application of whole-exome sequencing: A retrospective, single-center study. Experimental and Therapeutic Medicine. 2021;22(1):753. doi:https://doi.org/10.3892/etm.2021.10185