The human genome contains approximately 3 billion base pairs and only about 1% has been translated into functional proteins. Mutations, or variants, with direct phenotypic consequences can occur in any part of the human genome and determining changes in the order of nucleotides in an individual’s DNA is often crucial to properly diagnosing a genetic disease.
Next Generation Sequencing (NGS) based tests have proven highly effective in diagnosing many genetic diseases, particularly monogenic rare diseases.
To date, more than 7000 rare diseases are known and new ones are regularly described in scientific literature. NGS has truly helped to boost the diagnostic yield of genetic testing and increase the number of reported disease-causing genes.
Sequencing information obtained with NGS may capture the interaction between multiple genes causing overlapping phenotypes [1] and may be valuable when there is a genetic contribution in heterogeneous and complex diseases (cardiomyopathies, connective tissue disorders, autism etc.) where are large number of genes contribute to the phenotypic spectrum [2].
The advent of NGS technologies has had profound effects in advancing genomic medicine because it radically changed the scale and the cost at which genetic testing can be performed in clinical diagnostic laboratories. This high-throughput technology allows for the sequence of billions of DNA fragments with high precision in a single experiment and currently it is possible to analyze large gene panels (>100 genes), whole exomes (WES) and even whole genomes (WGS).
WGS enables geneticists to determine the complete sequence of a patient’s DNA including all coding, non-coding and intergenic regions as well as mitochondrial DNA [3].
While WGS covers the whole genome, WES is focused only on protein-coding regions and allows genes to be sequenced in their entirety: exons, introns that border them and, if necessary, regulatory regions upstream and downstream of the gene. Finally, gene panels capture only a predefined, manually-curated list of genes of interest usually associated with a specific set of diseases.
Despite the fact that the entire genome is the larger container of genetic information, when prescribing an NGS-based test, geneticists have to balance the pros and cons of these varying approaches and may end up choosing different solutions for different patients suspected of having a genetic disease.
So, what are the determining factors in choosing the right sequencing approach?
From a technical standpoint, WGS has different advantages compared to WES. It enables doctors to sequence the entire genome of a patient, thus also detecting pathogenic mutations that fall into deep intronic regions (not captured by exome sequencing) and guarantees a better uniformity of coverage. This makes the data generated with such an approach more suitable for detecting Structural Variants (SV) at single-base resolution (e.g. copy number variants, translocations and inversions) [4]. Several studies also demonstrate that WGS can detect hundreds of potentially damaging coding SNVs per sample, about 16% of which are homozygous, including some in genes known to be involved in Mendelian diseases that would have been missed by WES [5].
On the contrary, WES is extremely valuable in the diagnostic process because although it captures only 2% of the entire genome it is estimated that 85% of the variants that cause a genetic disease occur here.[6]. Many studies have reported only modest improvement in diagnostic rates when using WGS over WES [4]. Although WGS provides more comprehensive coverage, WGS data is an order of magnitude larger than WES data and may make interpretation more challenging. This can often lead to inconclusive results, as many variants detected may be problematic to classify.
WES is popular because it is the most cost-effective and focused strategy for interpreting what is likely to be the most informationally dense set of genomic data from a sample.
From an economic standpoint, WGS currently costs two to three times as much as WES and the investment needed to equip a laboratory with a high-throughput sequencing instrument able to process WGS samples is often not affordable. Moreover, WGS requires a high level of hardware and software resources that can process and store large WGS files, further adding to the high operational cost. [7,8].
Currently, it is possible to analyze WGS data quickly and there are several examples of timely uses of WGS to analyze newborns that show phenotypes attributable to rare diseases. However, even in these cases it is necessary to have the right technology to get results in a timely manner.
To conclude, WES and WGS remain two extremely powerful techniques for diagnosing genetic diseases. The choice of using one technique over the other is determined by key factors such as need, sensitivity, time and costs: WGS, for now, remains the last step for a scenario where even WES was not able to find an accurate diagnosis.
Despite their clinical utility, WES and WGS testing are still a niche technology, often available only after standard clinical tests fail. Using WES as routine practice, and WGS in a subset of cases would be a huge step towards fast and accurate diagnosis for many patients with rare diseases currently in the midst of a diagnostic odyssey.
Written by Matteo Galbiati
[1] Cheah SY, Lurie JK, Lawford BR, Young RM, Morris CP, Voisey J. Interaction of multiple gene variants and their effects on schizophrenia phenotypes. Compr Psychiatry. 2016 Nov;71:63-70. doi: 10.1016/j.comppsych.2016.08.015. Epub 2016 Sep 2. PMID: 27636509.
[2] Di Resta C, Galbiati S, Carrera P, Ferrari M. Next-generation sequencing approach for the diagnosis of human diseases: open challenges and new opportunities. EJIFCC. 2018;29(1):4-14. Published 2018 Apr 30.
[3] Rare Genomics Frequently Asked Questions, What is the difference between Exome Sequencing and Whole Genome Sequencing? (2021). Available at: https://www.raregenomics.org/faq
[4] Meienberg et al. (2016). Clinical sequencing: is WGS the better WES?. Human genetics, 135(3), 359–362.
[5] Aziz Belkadi et al. (2015). Comparison of WGS and WES to detect exome variants. Proceedings of the National Academy of Sciences, 112 (17) 5473-5478.
[6] Petersen et al (2017). Opportunities and challenges of whole-genome and -exome sequencing. BMC Genet 18, 14.
[7] Schwarze, K., Buchanan, J., Taylor, J.C. et al. Are whole-exome and whole-genome sequencing approaches cost-effective? A systematic review of the literature. Genet Med 20, 1122–1130 (2018).
[8] Soden SE, et al. (2014). Effectiveness of exome and genome sequencing guided by acuity of illness for diagnosis of neurodevelopmental disorders.
