This month our clinical laboratory began offering genome sequencing as an orderable in-house test. It’s a milestone achievement made possible by a talented multidisciplinary team and 3+ years of pre-clinical work under a translational research study. Yes, clinical genome sequencing was already available to our clinical geneticists — as a sendout test to commercial laboratories — but there are distinct advantages to providing this state-of-the-art test in-house. Especially the rapid genome sequencing (rGS) test, for which results are called out just a few days. We have years of data showing that genomic testing results can inform patient care in acute cases. Not to over-hype it, but sometimes it saves lives.
Still, that is not my story to tell, so this post is more about the transition from exome to genome sequencing in a (pediatric) hospital setting. It seems likely that many institutions (not just ours) will make the leap this year. There are several factors driving this change, but one of them is simply the ever-increasing speed/ throughout of next-generation sequencing instruments. For a long period, approximately 2014-2020, exome sequencing was a more practical choice as the mainstay comprehensive genetic test.
The Exome Advantage
Often patients who qualified for genetic testing would first get cytogenetic and microarray testing for chromosomal abnormalities and CNVs, respectively. Depending on the patient’s clinical features, the next step would often be a gene panel, followed by exome sequencing. As a clinical test, exome sequencing was attractive as a comprehensive test because:
- Exome capture kits had matured significantly, achieving consistent coverage and enabling fairly reliable deletion/duplication calling.
- In terms of laboratory costs, generating ~40-50 Gbp (gigabase-pairs) of data per sample was far less than the ~120 Gpb required for genome.
- Turnaround times were pretty good.
- The variant interpretation was likely to be gene- and exon-centric anyway.
Simply put, exome sequencing interrogated virtually all genes with a reasonable turnaround time and cost, so it made sense as the comprehensive test. If it was working so well, the natural question might be:
Why move to genome sequencing?
Speed, for one thing. The hybridization process (where probes capture target regions) adds about 1-1.5 days to the laboratory prep time between library creation and when things get loaded on the sequencer. The instruments are now so fast that this increases the lab time by about 50% compared to going straight to genome. The throughput is also so high that exome libraries need to be increasingly multiplexed (i.e. run lots of things at once) to be sequenced. Believe it or not, that can also introduce a delay because one has to wait until enough samples have accumulated to pool and sequence them.
“We don’t have enough samples to sequence” is a phrase I never thought I would hear. Man, how a decade can change things.
Reagent costs are a factor, too, since exome kits cost money. As the per-base cost of sequencing goes down, the savings you get from exome capture instead of genome decrease as well. The capture also requires more input DNA, which can be an issue when dealing with precious clinical samples. So genome sequencing is faster, requires less DNA, and ends up costing about the same for reagents. That’s on top of the obvious advantages GS offers in terms of variant detection.
Does genome sequencing have a higher diagnostic yield than exome sequencing?
In most cases, it should. That’s the theoretical answer. GS interrogates both coding and noncoding regions, and it’s better suited to detecting copy number variants (CNVs) and structural variants (SVs) because the breakpoints of such variants often lie in noncoding regions. Plus, exome capture introduces some hybridization biases which, while somewhat addressable during analysis, make it harder to detect changes in sequence depth that signal the presence of a copy number variant.
However, in my opinion, a major diagnostic advantage of genome sequencing comes from its ability to cover genes and exons that don’t play nicely with exome capture. Immune system genes, for example, are notorious for their poor coverage by exome sequencing. We have numerous examples of diagnostic variants uncovered by genome sequencing which were missed by exome testing due to coverage. From the clinician’s point of view, genetic test results from genome sequencing (even when nondiagnostic) come with more confidence that all of the relevant exons and genes have been interrogated.
A second advantage of genome sequencing is the ability to find deep intronic “second hits” in patients who have a single pathogenic variant in a recessive disease gene. Under exome sequencing, you generally have to do another test. With genome data, labs can at least screen nearby noncoding regions (introns, etc) to see if a second variant is present. Computational tools to predict splicing effects of variants have improved substantially in the past few years to the point where SpliceAI scores have been incorporated into ACMG/AMP guidelines. With clinical GS, upon the identification of a single variant in a promising recessive gene, labs can thus screen the data for rare variants in trans that are predicted to disrupt splicing. We have done this in a translational research setting and I think it will be a major source of improved diagnostic rates.
When should a clinical genome be ordered for an exome-negative patient?
This is an important question as clinical GS becomes more widely available. We know that 50-70% of exome tests are nondiagnostic, and it’s reasonable to assume that most patients who have undergone comprehensive testing in the last decade had an exome, not a genome. As I wrote in my recent post on post-exome strategies for Mendelian disorders, a negative exome result means that genome should be considered as the next step. If the clinical test already was genome, this changes the calculus.
I think it will be difficult to establish a perfect set of rules because every patient is different. However, I’d suggest that clinical GS should be considered when:
- The WES testing was done more than two years ago. This seems to be the sweet spot for exome reanalysis anyway, because enough new genes and disease-causing variants have been discovered to significantly boost diagnostic rates.
- New and relevant phenotypic information has emerged. Clinical exome testing is almost always guided/driven by the phenotypic data provided to the laboratory. If that changes, so too could the result. In particular, new phenotypes of features with significant genetic associations (dysmorphism, seizures, metabolic changes, neurological/neuromuscular changes, etc.) can significantly impact how variants are considered.
- There is medical urgency. A patient who continues to decline, or whose care is limited due to the lack of diagnosis, stands to benefit.
- Previous testing or new knowledge hints at a possible diagnosis. So-called “Section 2” variants and newly identified genes/pathways relevant for a patient’s phenotype may justify a harder look at certain loci.
What are the limitations of genome sequencing as a first-tier test?
No test is perfect, and despite the many advantages of genome as a first-line test, it comes with some limitations. GS may have similar experimental costs, but it comes with higher analysis costs (especially computational processing and data storage) because it’s 3-4x more data per sample. Processing that is an occasional cost, but storing data is like the Netflix subscription that never ends. The human staffing costs of interpretation are also higher because there are more variants (and detected variant classes) to evaluate. Balancing workload among staff also becomes more challenging, especially for rapid turnaround tests. And on the technical side, there is sequence depth to consider: Typical depths for exome sequencing (150-200x) have more power to detect somatic/mosaic variation. Patients undergoing testing for conditions associated with somatic mutations — the obvious example being tumor sequencing — are likely to benefit more from exome or panel testing.