Whole Genome Sequencing vs Whole Exome Sequencing: Which Genetic Sequencing Method is Right for Your Research?

1. Introduction

1.1. Whole Genome Sequencing (WGS)

Whole genome sequencing (WGS) refers to the sequencing of an entire genome, including both coding (exonic) and non-coding regions. This comprehensive approach provides an in-depth look at an individual’s genetic makeup, identifying all types of genetic variations such as single nucleotide polymorphisms (SNPs), insertions and deletions (indels), structural variants, and copy number variations (CNVs). WGS is used in a variety of research fields, from personalized medicine to understanding the genetic basis of complex diseases.

• Common applications of Whole Genome Sequencing (WGS):
  • Personalized Medicine: WGS enables the identification of genetic factors contributing to diseases, leading to more personalized treatment strategies.
  • Population Genetics: WGS is used to explore genetic diversity and evolution across different populations.
  • Rare Disease Diagnosis: WGS is essential for diagnosing rare genetic disorders by uncovering variants not found by traditional methods.

1.2. Whole Exome Sequencing (WES)

• Common applications of Whole Exome Sequencing (WES):
  • Clinical diagnostics: WES is widely used in clinical diagnostics to identify genetic causes of disease in patients.
  • Cancer research: WES plays a significant role in identifying mutations within the coding regions of the genome that are involved in cancer development.
  • Genetic disorders: WES is highly effective in diagnosing inherited diseases, especially those caused by mutations in the exonic regions.

2. Pros and cons of Whole Genome Sequencing (WGS)

• Advantages of Whole Genome Sequencing:
  • Comprehensive genetic information: WGS offers a complete genetic analysis, including both coding and non-coding regions, making it ideal for uncovering a broader spectrum of genetic variants.
  • Broad application: WGS is critical for understanding complex diseases that involve both coding and non-coding regions, including neurological disorders and cancers.
  • Identification of rare variants: WGS can uncover rare and novel genetic variants that may not be detected by more targeted methods like WES.

Figure 1. Whole Genome Sequencing specifications for sequencing and data analysis

• Disadvantages of WGS:
  • Cost: WGS is significantly more expensive than WES due to a need of sequencing larger amount of data.
  • Data analysis complexity: The vast amount of data generated by WGS requires advanced bioinformatics tools and substantial computational resources, which can present considerable challenges during data processing and interpretation.
  • Detection of rare variants: Although WGS provides uniform coverage across the entire genome, the sequencing depth at clinically relevant regions such as disease-associated genes may not always be sufficient to accurately detect rare variants. Improving sensitivity often requires increasing the overall sequencing depth, which can substantially raise costs since the entire genome, not just the regions of interest, must be sequenced at higher depth.

To overcome this, supplementary methods such as targeted sequencing may be used to enhance sensitivity in detecting low-frequency variants.

3. Pros and cons of Whole Exome Sequencing (WES)

Figure 2. Whole Exome Sequencing specifications for sequencing and data analysis

• Advantages of WES:
  • Cost-effective: WES is far less expensive than WGS, as it only sequences the exome, generating less data. This makes it an attractive option for large-scale studies.
  • Focused on disease-causing variants: The exome contains most of the known disease-associated mutations, making WES ideal for clinical diagnostics.
  • Simpler data analysis: WES generates smaller datasets, which are easier and less resource-intensive to analyze.
• Disadvantages of WES:
  • Limited to coding regions: WES misses variants outside the exome, including those in regulatory or non-coding regions, which may be crucial in some diseases.
  • Uneven coverage: WES sometimes struggles to capture all exonic regions due to factors like hybridization efficiency, potentially leaving gaps in the data.
  • Limited detection of structural variants: WES is unable to identify larger structural changes such as copy number variations (CNVs), insertions, deletions, or translocations. 

Figure 3. Diagram of the losses of single nucleotide variants (SNVs) at various levels associated with the use of WES. 

(A) Exons fully covered are represented by boxes filled entirely in red; exons partly covered, by boxes filled with red stripes; and exons not covered at all, by white boxes. Exons from protein-coding genes include exons encoding exclusively or partially UTRs, as well as exons mapping entirely to coding regions. 

(B) Number of high-quality coding SNVs called by WES and WGS (white box), by WES exclusively (red box), or by WGS exclusively (TRUE, FALSE: validates SNVs detected by Sanger sequencing; FALSE. Credit: Belkadi A, et.al. (11)

4. Which method is right for your research?

The decision to use WGS or WES largely depends on your research goals and available resources. Here’s a summary:

• WES is best when you:
  • Focus on protein-coding regions and known disease-causing mutations.
  • Need a cost-effective method for sequencing a larger number of samples.
  • Are dealing with genetic disorders whose causes are known to lie in the exome..
• WGS is ideal when you:
  • Require a more comprehensive genetic analysis, including non-coding regions and structural variants.
  • Are exploring genetic variations that may not be captured by WES, especially in rare diseases.
  • Need information about both common and rare genetic variants across the whole genome.

For instance, Retter et al. (2016) demonstrated that WES could be highly effective in clinical diagnostics, with a diagnostic yield of 28.8% across 3040 clinical cases. Importantly, this yield increased when multiple family members were involved in the analysis, highlighting WES’s potential for advancing clinical decision-making.

5. Summary table

Feature	Whole Genome Sequencing (WGS)	Whole Exome Sequencing (WES)
Coverage	Entire genome (coding + non-coding regions)	Only exonic (protein-coding) regions
Cost	Higher due to larger data volume and comprehensive coverage	More affordable and cost-effective
Applications	Personalized medicine, rare diseases, population genetics, CNVs	Clinical diagnostics, cancer research, genetic disorders
Data complexity	High, requires significant computational resources for analysis	Lower, easier to analyze due to smaller dataset
Variants detection	All genetic variants (SNPs, CNVs, structural variants)	Primarily exonic variants (SNPs, small indels)
Data amount	Large, requires more storage and computational power	Smaller, more manageable data size

6. Case studies and recent research

• Clinical application of WES:  One of the key advantages of WES is its diagnostic yield. As shown by Retter et al. (2016), a large-scale study with 3040 clinical cases demonstrated that WES has an overall diagnostic yield of 28.8%. In cases where multiple family members were included in the analysis, the diagnostic yield increased to 31%. This emphasizes the power of WES in identifying genetic causes of disease, particularly in complex genetic conditions.
• Diagnosing inherited metabolic disorders: A study by Delanne et al. (2021) highlighted WES’s potential in diagnosing inherited metabolic disorders (IMDs). In a cohort of 547 patients with unspecified developmental disorders, WES identified 32% of cases with a positive diagnosis, including 12% diagnosed with 15 distinct IMDs. Remarkably, WES was also shown to diagnose IMDs in fetuses with undefined symptoms, reinforcing its diagnostic capabilities.
• WGS in population genetics: WGS has revolutionized research into population genetics. As of 2015, over 2504 genomes from 26 different populations had been sequenced, providing a comprehensive database for studying human genetic diversity. This data not only advances research into human evolution but also supports to identify genetic risk factors for common and rare diseases.

7. Conclusion

Both Whole Genome Sequencing (WGS) and Whole Exome Sequencing (WES) are powerful tools with distinct advantages and limitations. WGS offers a broader, more comprehensive view of the genome, ideal for complex genetic research and personalized medicine. However, its cost and data complexity make WES a more practical choice for studies focusing on exonic regions or when working with large sample sizes. Advancements in sequencing technologies continue to make both methods more accessible and impactful in advancing our understanding of human genetics, disease, and therapy.

References

1. Belkadi, Aziz et al. “Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants.” Proceedings of the National Academy of Sciences of the United States of America vol. 112,17 (2015): 5473-8. doi:10.1073/pnas.1418631112
2. Novogene. (n.d). WGS vs. WES: Which genetic sequencing method is right for you? [Blog post]. Retrieved from https://www.novogene.com/amea-en/resources/blog/wgs-vs-wes-which-genetic-sequencing-method-is-right-for-you/
3. Resta, C. D., Galbiati, S., Carrera, P., & Ferrari, M. (2018). Next-generation sequencing approach for the diagnosis of human diseases: Open challenges and new opportunities. EJIFCC, 29(1), 4-14.
4. Retterer, K., Juusola, J., Cho, M.T., Vitazka, P., Millan, F., Gibellini, F., Vertino-Bell, A., Smaoui, N., Neidich, J., Monaghan, K.G. and McKnight, D., 2016. Clinical application of whole-exome sequencing across clinical indications. Genetics in Medicine, 18(7), pp.696-704.
5. Delanne, J., Bruel, A.L., Huet, F., Moutton, S., Nambot, S., Grisval, M., Houcinat, N., Kuentz, P., Sorlin, A., Callier, P. and Jean-Marcais, N., 2021. The diagnostic rate of inherited metabolic disorders by exome sequencing in a cohort of 547 individuals with developmental disorders. Molecular Genetics and Metabolism Reports, 29, p.100812.
6. 1000 Genomes Project Consortium, 2015. A global reference for human genetic variation. Nature, 526(7571), p.68.
7. Walter, K., Min, J.L., Huang, J., Crooks, L., Memari, Y., McCarthy, S., Perry, J.R.B., Xu, C., Futema, M., Lawson, D. and Iotchkova, V., 2015. Management committee (2015). the uk10k project identifies rare variants in health and disease. Nature, 526(7571), pp.82-90.
8. 1000 Genomes Project Consortium, 2010. A map of human genome variation from population scale sequencing. Nature, 467(7319), p.1061.
9. Estivill, X. and Armengol, L., 2007. Copy number variants and common disorders: filling the gaps and exploring complexity in genome-wide association studies. PLoS genetics, 3(10), p.e190.
10. Redon, R., Ishikawa, S., Fitch, K.R., Feuk, L., Perry, G.H., Andrews, T.D., Fiegler, H., Shapero, M.H., Carson, A.R., Chen, W. and Cho, E.K., 2006. Global variation in copy number in the human genome. nature, 444(7118), pp.444-454.
11. Chaisson, M., Wilson, R. & Eichler, E. Genetic variation and the de novo assembly of human genomes. Nat Rev Genet 16, 627–640 (2015). https://doi.org/10.1038/nrg3933