Whole genome sequencing (WGS) and whole exome sequencing (WES) are two of the most popular methods used in modern genetic research. While both methods provide a comprehensive view of an individual's genetic makeup, there are some key differences between the two techniques that researchers need to consider when choosing which method to use. In this article, we will explore the differences between WGS and WES and their relative advantages and disadvantages.
Advantages and Disadvantages of WES
One of the major advantages of WES is that it is a cost-effective way to sequence a large number of samples. Since only the exome is sequenced, the amount of data generated is significantly less than WGS, which can result in lower sequencing and analysis costs. Additionally, since the exome contains the majority of known disease-causing variants, WES is a powerful tool for identifying genetic causes of disease.
Fig 1. WES Specifications: Sequencing and Analysis
Advantages and Disadvantages of WGS
One of the major advantages of WGS is that it provides a more comprehensive view of an individual's genetic makeup. WGS can identify variants that are not present in the exome, including those in non-coding regions and structural variants. This can be particularly useful for identifying rare or novel variants that may be missed by WES. Additionally, WGS can provide information about ancestry and population genetics.
Fig 2. WGS Specifications: Sequencing and Analysis
Which Method is Right for Your Research?
The choice between WES and WGS depends on the research question and available resources. If the research question is focused on identifying disease-causing variants in protein-coding regions, then WES may be the most appropriate method. It costs a lot less than WGS as it is used to target less than 2% of the genome. This is useful if you are targeting a known gene or set of genes, or a disease associated with coding regions as it allows you to increase the sample number. This is also important for large population comparisons. In addition, WGS data is an order of magnitude larger than WES data which can make interpretation more challenging.
If the research question is broader and requires a more comprehensive view of an individual's genetic makeup, then WGS may be more appropriate. It allows you to examine a lot more of the genome than WES, and examine single-nucleotide variants, indels, structural variants, and CNVs in both the coding and non-coding parts of the genome. This can be especially important when examining diseases that are linked to non-coding regions of the genome. WGS also has more reliable sequence coverage and coverage uniformity. Differences in the hybridization efficiency of the capture probes used in WES can result in little or no coverage in certain areas of the genome.
To get more information about Novogene, please visit our website: https://www.novogene.com/us-en/resources/blog/wgs-vs-wes-which-genetic-sequencing-method-is-right-for-you
References
1. 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.
2. 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.
3. 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.
4. 1000 Genomes Project Consortium, 2015. A global reference for human genetic variation. _Nature_, _526_(7571), p.68.
5. 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.
6. 1000 Genomes Project Consortium, 2010. A map of human genome variation from population scale sequencing. _Nature_, _467_(7319), p.1061. 1000 Genomes Project Consortium, 2010. A map of human genome variation from population scale sequencing. _Nature_, _467_(7319), p.1061.
7. 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.
8. 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.
9. 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
