the majority of cases, variations noted are of unknown impact. These even have their own name “VUS” (variants of unknown sig- nificance) and create a number of headaches in clinical practice, not just interpretationally but also with regard to ethical issues about even disclosing them. Particularly if disclosed to non-specialists they’re prone to cause misunderstanding (for a more in-depth discussion, see e.g. [2]). By some estimates, each of us is walking around with roughly half a million VUS in our respective genomes. So, while the WGS data captures all of this, we’re left in many cases unsure of how to interpret what we have.

Surprise #2: look to the exons if you want to know what happened outside them Paradoxically, the best approach to find evidence of meaningful non-exonic variation is prob- ably through WES. That’s right, we should look at the exons to find out what happened elsewhere. The key here is to remember that a WES is generated from cDNA and includes not just individual sequences but also relative observational frequen- cies of gene products and even particular splice variants of a single gene. If (and that’s a critical caveat) the cDNA library used for WES comes from the cell population of interest, this provides a snap- shot not of the actual non-exonic

sequences but of their significant effects. For example, in the case of mutations impacting net gene expression level, the impacted gene will represent a lower or higher level compared to expected when referenced to other housekeeping genes in the sample. Where the mutation impacts something more nuanced such as splice site bias in a particular gene, relative levels of gene isoforms will deviate in the sample from equivalent isoform ratios in control samples. While this doesn’t give us any informa- tion on what the actual root cause mutation(s) is (are), it ignores the impact of truly insignificant varia- tions which we’d otherwise classify as VUS and be left none the wiser.

So, what’s better, WGS or WES? The answer to that depends on what it is you’re looking for, and the resources available in terms of time, cost, and bioinformatics tools. WES rose to popularity early on and it remains a cost-effective focused strategy for looking at what is likely to be the most informa- tionally dense set of genomic data from a sample. Bear in mind the comment above though that cDNA populations and their derived WES data sets are tissue specific to some degree. In addition to this they have demonstrated biases against representing some sequence types and can lack the completeness of a WGS. In comparison, PCR-free

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WGS requires more cost and effort but is more complete in its cover- age and is generalizable across the whole organism (we’ll pretend this space wasn’t just recently devoted to somatic microchimerism as the exception to this). If at some point in the future we have vastly more data such that VUS are a thing of the past, then WGS will prob- ably be the ‘better’ choice. Before that occurs however, and as costs of NGS technology continues to drop and ease of use increases, we may reach a situation where the most complete and interpretable genomic picture is obtained by capturing both a WGS and a paired tissue-relevant WES. Each provides a slightly different insight to the genome and in reality the two forms of data are complementary.


1. Meienberg J, Bruggmann R, Oexle K, Matyas G. Clinical sequencing: is WGS the better WES? Hum Genet. 2016;135(3):359-62.

2. Hoffman-Andrews L. The known unknown: the challenges of genetic variants of uncertain significance in clinical practice. J Law Biosci. 2018;4(3):648-65.

John Brunstein, PhD, serves as an Editorial Advisory Board member for MLO. John is also President and CEO for British Columbia-based PathoID, Inc., which provides consulting for development and validation of molecular assays.



Instrumentation Laboratory-Acute Care ..................37

Instrumentation Laboratory-Hemostasis ................IBC

Kamiya Biomedical ..................................................23 Nova Randox Laboratories .........................................................11

Siemens Healthcare - Lab Diagnostics This index is provided as a service. The publisher does not assume liability for errors or omissions. 40 APRIL 2019 MLO-ONLINE.COM

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