Over the past decade, there has been significant growth in the use of genetic information for medical care, particularly in the diagnosis and treatment of Mendelian disorders, due to the revolutionary impact of Next Generation Sequencing (NGS) technology (Smedley et al.). The two primary methods for clinical NGS utility have been gene panels and whole exome sequencing (WES), both of which only cover a small portion of the genome. However, there has been considerable discussion around the optimal application of whole genome sequencing (WGS) to augment WES, which has been the standard choice for rare disease and newborn screening diagnosis in the past decade (Smedley et al., Sun et al., Kingsmore et al.). Large-scale projects, such as the UK Biobank, the NIH’s Whole Genome Sequencing Project combined with the recent reduction in sequencing costs, have shown the potential for WGS to be used for routine care (Kingsmore et al., Stranneheim et al.). Here we explore the advantages of WGS and WES data to improve biomarker development, stratify patient populations, and understand how patients will respond to treatments based on their genetic makeup.
Patient Characterization is Advanced by Deep Genomic Data
WGS covers approximately 85% of the whole genome, providing a complete picture of both coding and non-coding regions (Spreafico et al.). Whole exome sequencing (WES), which comprises 1.5% of the entire human genome and is focused solely on the protein-coding regions, has helped identify most genetic disorders and drug targets to date (Spreafico et al.). In recent years, non-coding regions of the genome have been discovered to play an important role in human health, containing regulatory elements that control gene expression and epigenetic modifications that can affect disease susceptibility. WGS can identify almost all forms of genetic variation, including single-nucleotide variants (SNVs), in both the protein-coding and noncoding regions (such as introns and promoters) of the genome, small insertions/deletions (indels), and copy-number variants (CNVs) (See Table 1).
WGS can also be technically superior to WES as a diagnostic test due to reduced bias, leading to more accurate variant calling and fewer false positives. WES preparation is more reagent-intensive, has more steps where the sample can be impacted, and has some limitations in detecting certain types of genetic variations, such as GC-rich regions or repetitive sequences (Barbitoff et al.). However, newer NGS technologies, such as PCR-free library preparation options and developments in throughput, have offset the need for additional reagents used in WES, and virtual gene panels can be selected in silico from WGS data files if desired. Furthermore, these technologies allow the flexibility to generate different types of genomic data from a single experiment, such as genomic, haplotyping, and epigenetic modifications, ultimately helping researchers and clinicians gain a more comprehensive understanding of an individual’s genetic makeup and its relationship to health and disease (Morrison et al.).
WGS and WES can both be used to perform deep analysis on biological samples, however, depending on the volume of sample collected in clinical care, there may not be enough to be used for both tests. If only enough sample volume is available for one test, it is possible that WES can be identified informatically from the WGS results. Although WGS has historically been more expensive than WES, continued refinement of sequencing technologies has reduced costs each year (Schwarze et al.).
Looking to the Future
Genomic and multiomic data provides a comprehensive view of a patient’s genetic makeup, allowing for personalized treatment and the identification of new variations for future trials. Technological advancements have made these technologies more cost-effective and accurate, making them promising methods for routine care and subsequent use of data in drug development.
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