Whole genome sequencing (WGS) and whole exome sequencing (WES) are two powerful next-generation sequencing technologies. Their use in research and clinical settings have changed our understanding of human genetics and its implications for health.
However, these two methods vary significantly in scope, coverage, and typical use cases. In this article, we explore the differences between WGS and WES. We also discuss examples of typical uses in research and clinical settings.
Sequencing Coverage
Whole genome sequencing covers 3 billion base pairs — the entire human genome. This includes exons (coding regions) and introns (non-coding regions). WGS will include variations throughout the genome, including less studied areas.
In comparison, whole exome sequencing covers 60 million base pairs (about 2% of the genome). Although this is a small fraction of a person’s genetic material, it contains DNA regions responsible for protein coding. Most disease-causing genetic variations can be found in the exome.
Data Size and Complexity
Because WGS sequences more of the genome, it generates much more data than whole exome sequencing. This can present a challenge when it comes to analyzing and storing genomic data.
WES data sets are typically much smaller, creating less of a computational burden for analysis and storage. For example, a standard WGS run at Psomagen generates about 90 Gb of data per sample. A standard WES run generates about 10 Gb of data per sample.
Cost
Historically, WGS has been much more expensive than WES. For years, geneticists were working toward sequencing a human genome for under $1,000. Today, sequencing a high-quality human genome can be accomplished for much less than that.
WGS is still a more expensive option than WES. Some researchers use this difference in price to sequence at a higher coverage on WES than WGS. For example, the same standard WGS run that generates 90 Gb of data at Psomagen is conducted at 30x coverage, while the standard ~10 Gb WES run is conducted at 100x coverage.
Use Cases
With these differences in mind, it’s possible to sort NGS projects into whether they would benefit from WGS or WES.
WGS is most often applied in research settings. By identifying novel variants, structural variants, and changes in non-coding regions, it helps genetic researchers make discoveries. It is also helpful in studying complex diseases with potential genetic contributions from various regions of the genome, even regions not responsible for protein coding.
For example, WGS was used in an NHLBI study to analyze red blood cell types in over 60,000 ethnically diverse program participants. Their analysis uncovered 14 single variant RBC trait associations that had not been reported previously. Several variants were identified as contributing to a carrier state that causes inherited RBC disorders.
WES is valuable in clinical settings, especially when clinicians suspect a known genetic disorder. By identifying variants in protein-coding regions, WES users can explore regions that are more likely to contribute to disease pathogenesis. This is also helpful in research contexts when scientists suspect a certain region is relevant to their query.
This use of WES can be seen in a 2020 publication that explored the causes of ocular coloboma. Researchers conducted exome sequencing on a family in which two siblings had coloboma of the iris, retina, and choroid. Analysis identified variants in the CDON cell-surface receptor.
Variants in CDON are already associated with abnormalities in brain development. By exploring these known coding regions, this team of researchers identified the first compound heterozygous CDON variants as a cause of isolated coloboma.
WGS and WES methods have made valuable contributions to our understanding of human health and expanded the possibilities afforded by personalized medicine.
WGS obtains a comprehensive view of the entire genome, with information about coding and non-coding regions. It is a powerful tool for genomic exploration and genetic disease research. In contrast, WES provides a cost-effective way to identify disease-causing variants in a clinical setting.
As genomic technologies continue to evolve, WGS and WES will be necessary based on a project’s objectives and available resources. These and other next generation sequencing approaches will be at the forefront of many scientific discoveries.
