How to Decide Between 100X Whole Exome Sequencing (WES) and 30X Whole Genome Sequencing (WGS)?

Whole Exome Sequencing (WES)

Whole Exome Sequencing (WES) is a cutting-edge technology that zeroes in on the exonic sections of the human genome, comprising just 1.5% of the entire genome. These exons are vital as they harbor a majority of known disease-causing mutations. WES employs probes or amplicon-sequencing techniques, to selectively target and sequence DNA within these exonic regions. This precision enables the identification of genetic mutations that play a pivotal role in protein function. To ensure robust results, experts recommend a sequencing depth of at least 100X, equivalent to around 12G of data.

WES distinguishes itself by overcoming the cost and depth limitations associated with whole-genome resequencing. It provides a high sequencing depth, cost-effectiveness, and the ability to directly examine protein-coding sequences for variants affecting protein structure. The deep sequencing capacity facilitates the identification of common, low-frequency, and rare variants. Additionally, WES exclusively targets exonic regions, significantly reducing costs, turnaround times, and workloads. However, it's crucial to note that WES may have limitations in detecting copy number variations (CNVs) and structural variants (SVs) due to its preference during the capture process and limited coverage of non-coding regions.

Advantages of WES

  • Disease-Causing Mutations: Approximately 85% of known disease-causing mutations in human diseases are located within the exon region, making WES a powerful tool for the exploration of new disease-related genes in humans.
  • Diagnostic Yield: WES yields a higher diagnostic success rate for patients who are closely related, exhibit severe symptoms, or manifest multiple symptoms.
  • Rich Annotations: WES covers over 85% of disease-causing mutations, including those in non-coding regions. It also benefits from extensive database and literature annotations, aiding clinicians in interpretation.
  • Coding Region Variants: WES is proficient in detecting Variants of Uncertain Significance (VUS) within the coding region, which is crucial for exome research.

Shortcomings of WES

  • Misses Non-Coding Mutations: About 10% of known disease-causing mutations associated with human diseases are situated in non-coding regions, potentially eluding detection by WES.
  • Challenges with Unique Genomic Regions: WES may face challenges when capturing genomic regions with high GC content, repetitive sequences, or pseudogenes.
  • Complex Mutations: Recognizing specific mutation types, such as complex copy number variants and structural abnormalities, can be challenging using WES.

Whole Genome Sequencing (WGS)

Whole Genome Sequencing (WGS) stands as a cutting-edge genetic analysis technique that entails the meticulous sequencing of an individual's complete genome. Subsequently, these sequenced DNA fragments are compared to a reference genome, allowing for the discernment of genetic variations. One of the most distinguishing aspects of WGS is its unparalleled capacity to unveil information from the non-coding regions of the genome, a pivotal factor in deciphering the genetic underpinnings of intricate diseases. If your research delves into non-coding and structural genetic variations or if you possess a limited grasp of disease pathology, then WGS emerges as the preferred technology.

Typically, WGS endeavors to achieve a sequencing depth of at least 30X or higher, yielding a substantial volume of data, often exceeding 90 gigabytes. Nevertheless, it's crucial to acknowledge that WGS does come with a set of constraints, encompassing its elevated cost, data intricacy, and the intricacies of interpreting low-abundance variants attributable to the relatively shallow sequencing depth.

One salient advantage of WGS is its all-encompassing scope; it obviates the need for a prior capture step and encompasses the entire genome. This methodology not only encompasses coding regions but also extends its reach into non-coding and regulatory domains. Moreover, WGS excels in the detection of structural variants, including balanced translocations and inversions.

However, there are several shortcomings associated with WGS

  • Challenges in Highly Repetitive or Homologous Regions: Accurately discerning and analyzing regions of the genome that are densely repetitive or homologous can prove challenging, as distinguishing between these regions can be a formidable task.
  • Uncertain Functional Significance of Newly Unearthed Variants: When novel genetic variants are uncovered, their functional significance may remain shrouded in uncertainty, necessitating further validation. This poses a notable challenge for clinicians tasked with interpreting these findings.
  • Limitations of the Reference Genome: The human genome reference sequence is constructed based on a limited number of individuals, which may lead to potential errors and gaps in the reference sequence. This, in turn, can result in both false-positive and false-negative identifications of genetic variants.
  • Incomplete Coverage of Regions with High GC Content: WGS may furnish incomplete coverage of genomic regions characterized by high GC content, potentially affecting data accuracy.
  • Low Sequencing Depth and Impaired Mutation Analysis Precision: The relatively modest average sequencing depth of 30X in WGS can diminish the accuracy of mutation analysis, possibly yielding a substantial number of false-positive mutations. These false positives mandate additional validation steps, thereby complicating the process of interpretation and confirmation.

Comparing WES and WGS Data with 75X Sequencing Depth

When we assess WES and WGS data with an identical 75X coverage to determine the proportion of known variants within the sample that remain undetected (false-negative variants), the results reveal a marked contrast. Specifically, the false-negative rate for WES stands at 2.17%, whereas WGS exhibits a remarkably lower false-negative rate of just 0.022%. To put it differently, WES, at 75X coverage, may overlook 2 out of every 100 variants, whereas WGS, with the same coverage, may only miss 2 out of every 10,000 variants. This underscores the substantial superiority of WGS in terms of both sensitivity and positive predictive value.

True Positive (TP) 30,552 2,814,222
True Negative (TN) 47,423,697 2,293,827,518
False Positive (FP) 6,014 8,973
False Negative (FN) 676 618
Sensitivity 97.84% 99.98%
Specificity 99.99% 99.99997%
Positive Predictive Value 83.55% 98.7%

In summary, the distinction between WES, which necessitates a higher sequencing depth (100X) in contrast to 30X for WGS, may not appear significant at first glance. Nonetheless, it's crucial to recognize that WGS excels in the comprehensive detection of various variant types, particularly those in non-coding regions. Consequently, choosing between WES and WGS should be driven by the specific demands of the research or diagnosis, in addition to considerations related to budget and data processing.

A study conducted in 2018 compared the merits of whole-exome sequencing and whole-genome sequencing. This research involved simultaneous WES using the ION proton platform on 70 participants, achieving an average sequencing depth exceeding 100X. The findings demonstrated that 35 of these participants received a definitive molecular diagnosis, whereas WES failed to identify disease-causing mutations in nine patients, which were successfully detected by WGS. These overlooked loci encompassed single nucleotide variants (SNVs) located in deep introns, small copy number variations (CNVs), SNVs within non-coding regions, mitochondrial variants, and SNVs with low exon coverage. It's important to note that alternative capture methods may not encounter the same limitations.

Therefore, the selection between WES and WGS should be predicated on the specific requirements of the study or diagnosis, as well as budgetary and data processing considerations.


  1. Lionel, Anath C., et al. "Improved diagnostic yield compared with targeted gene sequencing panels suggests a role for whole-genome sequencing as a first-tier genetic test." Genetics in Medicine 20.4 (2018): 435-443.
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