Rare Diseases

In the early days of next-generation sequencing, read length was a critical factor. Compared to the previous standard of sequencing technology — Sanger sequencing reads, which were typically >700 bp long — NGS reads were quite short. This was especially true for the sequencing-by-synthesis technology developed by Solexa, which was acquired by Illumina in 2007 and became the basis for their market-dominating technology. The first genomes sequenced with that technology had 32-bp or 35-bp reads. They required development of a new generation of bioinformatics tools for sequence analysis. Fifteen years ago, I was part of the informatics team at WashU that analyzed NGS data, and remembered thinking that if we could figure out how to get Illumina read lengths to 50 base pairs, we’d be golden.

Now, of course, most Illumina instruments generate 150-bp paired-end reads and are capable of going out to 250 bp or 300 bp. At that length, with the many informatics tools now at our disposal, we can properly interrogate most of the genome and transcriptome. Yet these are still relatively short reads and they have limitations. Fortunately, long read single molecule sequencing platforms have also been evolving and improving over time. At the recent American Society of Human Genetics meetings, I heard more talks about long-read sequencing than any other time in memory. Another novelty: Most of them were describing work with the Pacific Biosciences (PacBio) platform; I heard far less about Oxford Nanopore.

One of the many ways we use PacBio long-read sequencing in house is to map and resolve complex structural variants (SVs). Often, previous testing with standard NGS (or microarray, or cytogenetics) will reveal the presence of a structural variant but its precise structure remains unclear. As the cost of PacBio sequencing continues to drop, it becomes more and more feasible to perform whole-genome long read sequencing of such patients. We describe some of our work in this area in Long-read genome sequencing resolves complex genomic rearrangements in rare genetic syndromes, which was published last week in Nature’s npj Genomic Medicine.

Patient #2 in that study harbored one of the most complex germline rearrangements I’ve encountered. She was a female with a neurodevelopmental disorder, 2 years old at the time of enrollment into our research study. Microarray testing had reported three deletions at chr18q11.2-q12.2 (deletion 1), chr18q12.3 (deletion 2), and chr18q21.1 (deletion 3).

When Multiple CNVs Indicate A Structural Variant

Multiple structural variants of the same type in relatively close proximity often suggest the presence of an underlying structural variant, and knowing that we enrolled the family trio for whole-genome long read sequencing. PacBio HiFi achieved an average coverage depth of 20–27×, with mean read lengths between 12,335 bp and 13,613 bp, and maximum read lengths ranging from 32,282 bp to 50,563 bp. The three reported deletions could even be appreciated on the CCS coverage map of chr18 from PacBio, which is not something we often use to estimate copy number (plot at right). The deletions were not present in either parent, which told us that whatever this was, it had arisen de novo.

When we visualized the PacBio long reads aligned to the reference sequence, there were obvious breakpoint-spanning long reads at each expected deletion breakpoint, such as these:

Those rainbow-colored portions of the reads are soft-clipped because they don’t match the reference sequence (due to spanning the structural breakpoint). These reads are long enough that both halves can be reliably mapped to their location in the human genome, thereby providing the precise breakpoints of the underlying variant. That’s where things got complicated, because some of these revealed the presence of yet a fourth CNV which was too small to be picked up by array testing, but was involved in the rearrangement. That meant eight breakpoints and they suggested the underlying rearrangement was a complex one laid out here:

In addition to the three deleted segments we knew about, one segment (D) was moved to a new location, and another one (C) was inverted. Talk about a complicated rearrangement! It would have been nearly impossible to decipher this with short reads alone. Anecdotally, we seem to be identifying these complex structural variants rather often, especially in patients with syndromic presentations. Sometimes Illumina WGS with paired-end reads can inform the nature of these, but we usually feel more comfortable resolving them with long reads that can map ~7-8kbp in each direction at a breakpoint.

Long-read Sequencing as a First Tier Test

As long-read sequencing gets cheaper, the notion of implementing it as a first-tier test becomes much more realistic. If I’m not mistaken, Children’s Mercy in Kansas City has already done so with a clinically validated long-read WGS assay for rare disease patients. PacBio should, in theory, capture SNVs/indels with similar performance to Illumina, and these can be analyzed with already-extant pipelines. Long reads also enable more comprehensive detection of structural variants. PacBio’s native SV caller, PBSV, does a reasonable job of calling these and providing an initial categorization (variant type, size, and certain classes of repeat-associated elements like ALUs). Yet this new detection power comes with a cost: the analytical challenge of sifting through thousands of SVs in addition to the 4-6 million SNVs and indels present in any individual’s genome.

In our paper, we describe a strategy for prioritizing those SVs using SvAnna, a phenotype-aware annotation and prioritization pipeline. Reviewing the top hits from SvAnna is a reasonable strategy for evaluating variants that could explain a patient’s phenotype. However, structural variants of unclassified types — called BND (breakend) by the PBSV pipeline — still required some manual intervention. Many of them turn out to be ordinary SVs that couldn’t be correctly classified based on the evidence, or whose breakpoints are too distant (on the same chromosome, or another chromosome) to be believed.

Detection of copy number variants with PacBio HiFi reads is theoretically possible — one obtains read depths from CCS reads and these are correlated with genomic copy number. At the edge of the deletions shown in the IGV screenshots earlier on in this post, you can visually appreciate the depth change accompanying copy number changes. This implies that CNVs are callable as changes in CCS depth. This has not been an area of significant bioinformatics development, at least to my knowledge, but I plan some testing with VarScan 2 to see how easily it can be adapted to this data type.

The Future of Long Read Sequencing

It’s sometimes hard to imagine anything replacing Illumina paired-end sequencing as the main workhorse genomic assay. However, long reads do have some distinct advantages. As the bioinformatics state-of-the-art for PacBio catches up to the ever-improving platform technology, it will only get better. One might expect an increase in diagnostic yield, which would immediately be transformative for care of rare disease patients. The other exciting aspect of more widespread long-read sequencing is what it will reveal about the frequency and distribution about hard-to-detect structural variations in the human population.

In my last post on clinical interpretation of noncoding variants, I focused mainly on which noncoding variants were appropriate for clinical interpretation and the guidelines for modifying evidence codes for variants with regulatory rather than coding effects. However, the Ellingford et al study in Genome Medicine which proposed those modifications also included some fascinating analyses into what we know about noncoding variants and their role in disease.

Noncoding Variants Are Under-Represented in ClinVar

The first insight is not terribly surprising: variants in noncoding regions known to regulate established disease genes are dramatically under-reported to ClinVar, the central repository for disease-causing variation. If one considers the “genomic footprint” encompassed by the all MANE transcripts broken down by the number of bases in each gene region (promoter, exon, splice site, intron, and UTR), here are some useful reminders:

• Essential splice site regions (i.e. the first two and last two bases of each intron) occupy the smallest slice of the footprint (<<1%) but have an outsized representation among (likely) pathogenic variants (>5%).
• Untranslated regions (UTRs) and upstream promoters account for only 0.34% of high-confidence pathogenic variants but despite occupying approximately the same amount of genomic space as protein-coding exons and having well-established regulatory roles.
• All three of the above are dwarfed by introns, which account for >90% of the genomic footprint of MANE transcripts.

Admittedly, high confidence pathogenic variants in ClinVar include far more intronic variants than UTR or promoter variants, but more than half of these are in splice sites, which only represent 4 bases of each intron. It should also be noted that the proportion of “variants of uncertain significance” or VUS is much lower among the intronic variant group (around 22%, versus 44% for coding and 63% for UTR variants). This suggests that most of the intronic variants in introns are submitted because they’re known to cause disease by disrupting splice sites. The non-splice-relevant majority of most introns is not really represented here.

Intronic Second Hits in Undiagnosed Probands

The authors of the Ellingford et al study performed another analysis that I thought to be useful but didn’t have room to discuss in my last post. They were discussing guidance on applying the PM3 rule, which is used for recessive disorders when a variant is known to be in trans with an established pathogenic variant. In the original 2015 guidelines, the logical argument behind the rule is that when a variant in a recessive disease gene which is demonstrably in trans (on the opposite allele) from a known disease-causing variant, this can be taken as evidence of pathogenicity. Essentially, it helps identify and classify novel pathogenic variants when they occur as compound-heterozygotes with a known pathogenic variant.

The natural question, when considering this rule for noncoding variants, is whether it is reasonable to apply the same argument since there could be many, many noncoding variants inherited from the other parent. So the authors went to the Genomics England (GEL) database and obtained 2,016 rare disease trios which:

1. Had whole-genome sequencing data for proband, mother, and father
2. Were currently nondiagnostic according to GEL (i.e. no Tier 1 or Tier 2 variants)
3. Harbored a single rare heterozygous LOF variant in a recessive disease gene

They restricted their search space to 794 genes in which biallelic variants cause disease (i.e. autosomal recessive). There were 2,714 such single variants in 2,016 unsolved probands. The number of probands without a starting-single-hit is unfortunately not provided. In any case, the authors next queried the WGS data for those trios to identify possible noncoding second hits, i.e. variants in the UTR, intron, or 2kb promoter of the same gene that were in trans (inherited from the other parent) and rare enough to cause a recessive disorder. In total, 37.8% of the single-hit trios (1,027 of 2,016) had at least one noncoding variant in trans. As you might expect from the analysis above, nearly all of these (94%) were in intronic sequences:

In a practical application, evaluating those second hits would require a detailed interpretation of possible regulatory elements and variant effects. As a proxy for these, however, the authors simply asked what proportion of intronic variants had a SpliceAI score of >0.20, indicating a good probability of splice disruption. This relatively low bar eliminates most variants. Only 55 of 2,418 (2.2%) in trans variants have a SpliceAI score of >0.20. Based on this relatively small proportion, the authors feel that the use of PM3 is justified as long as other common-sense requirements are met (e.g. rareness, gene-disease validity), and I agree with that conclusion.

Beware Second Hits in Large Genes

One note of caution: the probability of observing a possible second hit in the same gene (coding or noncoding) is proportional to the size of the gene and (to some extent) the total number of variants present in the individual due to ancestry. In this analysis, for example, the authors identified 22 variants in trans with one pLOF variant in the WWOX gene, (very large at >1.1 Mbp in size) in an individual who self-identified as Black or Black British: African. Because of these factors, it may eventually be necessary to use a probability-driven approach to decide whether or not observation of a second hit is significant enough to warrant additional evidence of pathogenicity.

Guidance for Unsolved Rare Disease Patients

Another compelling facet of this analysis is the insight into possible explanations for undiagnosed rare disease patients. GEL’s pilot publication, other large studies, and our own internal data report a fairly consistent diagnostic rate of ~25-30% for patients who get exome or genome sequencing. This means 70-75% of rare disease patients who undergo comprehensive testing fail to achieve a diagnosis. How many of those can be explained by a missing second hit in a noncoding regulatory region?

Well, 53 of the starting 2,016 undiagnosed trios had one pLOF variant which is compound-heterozygous with an intronic, possibly-splice-disrupting variant in an established autosomal recessive disease gene. That alone is 2.6% of the undiagnosed cohort who now have a promising lead. If one applied other types of functional annotation to the noncoding variants (beyond splice prediction), even more could have a reasonable candidate that merits clinical correlation.

Another Starting Point for Noncoding Variants

As illustrated above, when looking for innovative ways to solve undiagnosed probands, starting with ones who have a single hit in an autosomal recessive gene — especially one that seems to fit the clinical phenotype — is a great first strategy. What about the 2/3 of patients who don’t have a second noncoding variant in trans? There’s another logical set of noncoding variants to pursue in such cases: those that occur de novo in a proband whose parents are unaffected. There are several reasons this makes sense:

• The number of variants is manageable. Based on the baseline mutation rate and our own experience, every child has about 60-70 detectable high quality de novo variants genome-wide that pass manual review.
• De novo variants account for a large proportion of diagnosed cases. Although it can vary, often as many as half of genetic diagnoses from rare disease cohorts involve de novo mutations.
• Starting with strong evidence. The recent guidance from Ellingford et al notes that de novo evidence codes can be applied to noncoding variants similar to how they’re applied to coding variants. When parental relationships are confirmed, the stronger code for a de novo variant is “Strong” (PS2). That gets you a long way toward (likely) pathogenic.

To me, noncoding variants are always interesting regardless of whether they provide a missing genetic diagnosis — more than enough reason to delve into these first when tackling noncoding regions.

 

As genetic testing continues to expand in both clinical and research settings, variants of uncertain significance (VUS) present a persistent challenge. For the uninitiated, VUS is one of five classifications assigned to genetic variants under ACMG guidelines which indicate the likelihood that a variant causes disease.

Generally, uncertain significance is the default classification for variants that cannot otherwise be classified as pathogenic/likely pathogenic (i.e. disease causing) or benign/likely benign (not disease causing).

If you’re familiar with genetic testing trends over the last decade, you probably know that VUS are increasingly prevalent on genetic testing reports. In part, that’s due to technological advances, e.g. high-throughput DNA sequencing, that make it possible to interrogate more of the patient’s genome in a timely and cost-effective manner. Gene panels for specific conditions now often encompass thousands of genes, and comprehensive testing — genome or exome sequencing — is increasingly available as a first-tier or second-tier test.

Increasing knowledge — specifically, the number of genes associated with disease — is another important contributor to the VUS explosion. The pace of gene discovery accelerated in the NGS era and continues to grow, as illustrated by the statistics provided by the Online Mendelian Inheritance in Man (OMIM) database:

Long story short, more variants detected in every patient (by comprehensive sequencing) combined with more genes that are possibly reportable (due to association with disease) means more variants on genetic testing reports. And, as I’m about to tell you, for reportable variants, VUS will often be the expected classification. 

ACMG Variant Interpretation 101

First, a very brief introduction to the types of evidence that are used when interpreting variants and how they are represented. ACMG evidence codes are letter/number combinations.

• The first letter indicates the type of evidence (Pathogenic or Benign).
• The second 1-2 letters indicate he strength of evidence (Very Strong, Strong, Moderate, or SuPporting).
• The number is a category we use to keep them all straight.

So for example, PS2 is the evidence code applied when a variant occurs de novo in a patient with confirmed maternity/paternity. This is the second (2) type of strong (S) evidence of pathogenicity (P), hence the code PS2. For another example, when a variant’s population allele frequency is greater than expected for the disorder, it gets the code BS1 (the first type of benign strong evidence). The weakest level of evidence, supporting, is given the strength-designation P. For example, BP1 applies when you have a missense variant in a gene in which almost all disease-causing variants are truncating/loss-of-function.

When a variant is assessed, each type of evidence is evaluated to see if it applies. The final set of evidence is combined into a formula to determine the final classification. The rules for combination to get a pathogenic or likely pathogenic variant are shown to the right. So for example, for a variant with very strong (VS) evidence of pathogenicity, only one additional strong evidence code is required to classify it as pathogenic. If that second piece of evidence is moderate strength, the variant would be classified likely pathogenic. There’s a similar formula for benign/likely benign evidence.

How We Get To VUS

What if we have some evidence that a variant is pathogenic, but not enough to meet this threshold? Or worse, what if we have a lot of benign evidence but one pathogenic code? The ACMG guidelines lay it out:

VUS Due to Conflicting Evidence

Under the ACMG framework, every variant is assessed both for pathogenic and benign evidence criteria. It is thus quite possible — and does happen on a regular basis — that a variant has both types of evidence, i.e. conflicting evidence. For example, a variant that does not segregate with disease in a family (BS4) and has no predicted effect on the encoded protein (BP4) might still be rare in the general population (PM2).

Another example we often encounter is a missense variant that is rare (PM2), segregates with disease (PP1), and is computationally predicted to be damaging (PP3), but in a gene in which most known disease-causing variants are null variants (BP1). As written above, under ACMG rules, any variant with both types of evidence, no matter the tipping of the scale, defaults to VUS.

VUS Due to Insufficient Evidence

This is the more common pathway to classifying a variant as VUS: there is not enough evidence of pathogenicity to meet the threshold of likely pathogenic, or there’s benign evidence but not enough for likely benign. For example, a novel missense variant (PM2) in a dominant disease gene which is computationally predicted to damage the encoded protein (PP3), without additional evidence, is a VUS (PM2, PP3). Missense variants in general struggle to garner enough evidence to reach pathogenicity due to the strength of evidence codes that can be applied to them; more on that in the next section.

Variants in new or emerging disease genes are especially prone to the “Insufficient Evidence” VUS classification because the etiology of disease is still being established. If only a handful of disease-causing variants have been reported, it’s often difficult to ascertain:

• Whether null variants or missense variants are the predominant type of causal variants
• The presence of mutation hotspots or critical functional domains in which variants almost always cause disease
• The maximum population frequency of established disease-causing variants

Also, relatively new disease genes rarely have robust functional studies that can be used to enhance variant classification. These problems are all exacerbated for missense variants.

Some Variants Have It Easy: Null Variants and De Novo Mutations

You will note that having Very Strong evidence gets you a long way toward classifying a variant as pathogenic. Unfortunately, there is only one type of evidence that carries the weight Very Strong: PVS1. This is reserved for null variants, i.e. the types of variants (e.g., nonsense, frameshift, canonical ±1 or 2 splice sites, initiation codon, single exon or multiexon deletion) that are “assumed to disrupt gene function by leading to a complete absence of the gene product by lack of transcription or nonsense-mediated decay of an altered transcript.” (Richards et al 2015).

As I mentioned earlier, null variants that qualify for PVS1 only need one more piece of moderate-strength evidence to reach likely pathogenic. That’s great for null variants, but such variants represent a tiny fraction of the variants encountered in most genes in most patients. Missense variants are far more prevalent but face an uphill battle toward pathogenicity.

It’s a similar story for de novo mutations: a variant in a dominant disease gene that occurred de novo is rewarded with the strong evidence code PS2. That’s a long way toward a pathogenic classification. However, applying PS2 requires that you test both parents *and* that the parental relationships, especially paternity, have been confirmed. Again, it’s great when the stars align and you can do this. It’s also one of the reasons most labs prefer having family trios (proband and both parents) whenever possible. Yet we live in the real world where:

• Children are sometimes adopted or in foster care
• Families cannot afford all available testing
• Parents may no longer be alive
• Parents may be incarcerated
• Parents may be unwilling to participate in genetic studies

In these situations, testing both parents is not an option and that usually prevents some of the strongest evidence from being applied.

Consequences of Null and De Novo Variant Bias

The biases that favor null variants and de novo mutations may have scientific underpinnings, but they also exert real-world consequences that often skew the perceptions of emerging disease genes. Let’s be honest: it is far easier to publish a cohort of patients who all have de novo loss-of-function mutations in the same gene. I have been a part of multiple GeneMatcher collaborations in which the study leaders either gave preference to patients with null / de novo variants or were forced to do so to get the work published.

This often means the first few papers linking a gene to a disease describe only de novo / null variants, and that becomes the expected etiology of disease. Even established disease genes can be affected by the null-variant bias: because missense variants are harder to classify as likely pathogenic, they often remain VUS. Anyone who glances at the landscape of disease-causing (P/LP) variants for a gene might (incorrectly) assume that only null variants cause disease. I strongly encourage researchers to push back when they are told that the first paper will only include the “easy button” LOF/de novo variants.

Effects of Changing Variant Interpretation Guidelines

The ACMG 2015 guidelines for interpretation of sequence variants were published 8 years ago this month. It was an important milestone in our field, the members of which increasingly recognized that many variants reported as disease-causing were (in retrospect) probably not. The methods for classifying variants were inconsistent, and there was no universal set of rules that someone could apply. The 2015 guidelines provided such a framework.

However, a lot can change in eight years, and although the ClinGen Sequence Variant Interpretation (SVI) working group has released subsequent recommendations on the use of computational evidence for missense variants and refining classification of splicing variants, these are interim guidance.

The long-awaited revised framework for variant interpretation, which implements a points system to improve accuracy/consistency, is not yet published. In theory, it will help us resolve some VUS. That remains to be seen. Just as some types of evidence can be assigned higher strength (e.g. computational predictions of variant impact), other types of evidence are be blunted (e.g. rareness in the population). We won’t know until the revised guidelines are published, which probably will not be in 2024.

What About Variants in Candidate Genes?

I should take this moment to remind you — as I sometimes have to remind myself — that ACMG variant interpretation should only be applied to sequence variants that affect established disease genes. It should not really be used for variants in unknown genes or candidate genes not yet associated with human disease. That’s because we can’t assess pathogenicity of a variant without a definitive link between the gene and disease.

For clarity, we try to avoid the use of VUS when discussing candidate genes. Occasionally I hear the term GUS — for Gene of Uncertain Significance — and I really like it, but it does not seem to have gained much momentum.

More VUS, More Problems

The increasing number of VUS on genetic testing reports — and our inability to definitively classify them — present significant challenges for clinicians, laboratories, and patient families.

• For the lab, a VUS is a non-diagnostic outcome. They can be reported, but generally in the dreaded “Section 2” of the test report.
• Patients with VUS thus may not qualify for gene therapy or clinical trials if those are available.
• Clinicians must decide whether or not to pursue further testing, either to clarify the VUS or to keep searching.

Which VUS Merit Further Scrutiny?

It’s important to emphasize here that not all VUS are created equal. Because of the conflicting-evidence-means-VUS rules described above, plenty of variants receive this classification but are extremely unlikely to be disease-causing. On the other hand, sometimes VUS offer a promising potential diagnosis in a patient who otherwise has no significant findings. Perhaps the most important question to be answered is the phenotypic overlap, i.e. whether the gene’s associated condition matches the patient clinical presentation. This is why good clinical phenotyping is critical for genetic testing, especially when interpreting uncertain results.

The number, strength, and types of ACMG evidence codes that accompany a VUS classification are also relevant considerations. Some of the proposed revisions of variant classification guidelines allow for tiering of VUS into subsets representing the amount of pathogenic evidence behind them. If these come to pass, they’ll offer a useful communication tool for laboratories to indicate. In the meantime, I tend to refer to them as weak or strong VUS, with the latter category possibly warranting follow-up. Examples of strong VUS include:

• A VUS that is compound-heterozygous with a pathogenic variant in a recessive disease gene.
• A VUS with multiple pathogenic criteria that segregates with disease in a gene that fits the phenotype. For example, VUS (PM2, PP3, PP5) would indicate a rare variant that’s predicted to be deleterious and has been reported as disease-causing by another laboratory.
• A VUS with a predicted effect that could be evaluated by additional testing, such as metabolic/biochemical testing or even RNA-seq for potential splice variants.

This leads to the last section of my post, the million-dollar question.

How Can We Resolve a VUS?

I get asked this question all the time. Honestly, if you’re reading this post and have some ideas, I’d love if you shared them in the comments section below. Note, resolution can go either way: building a case for pathogenicity for a suspicious VUS, or ruling out a VUS that might otherwise be a concern. Here are some strategies we and other groups have tried.

• Segregation testing. Determining the segregation pattern and disease status in family members informs, at the very least, the plausibility of a variant fit and there are ACMG evidence codes for both segregation (PP1) and non-segregation (BS4).
• Clinical evaluations. The clinician can review patient/family medical records, bring them in for another clinical visit, or refer them to a relevant specialty to determine the presence (or absence) of clinical features associated with the disorder.
• Checking the latest population allele frequency databases to determine the variant’s prevalence in presumed-healthy individuals.
• Reaching out to other laboratories who have reported the variant according to ClinVar or the literature can sometimes yield useful information.
• Identifying additional patients with the variant can provide or strengthen certain categories of pathogenicity evidence. This is something we use in my ClinGen Variant Curation Expert Panel to resolve VUS in the RPE65 gene.
• Additional patient testing, such as biochemical/metabolic testing, methylation profiling, etc. that would support or exclude the diagnosis
• Variant functional studies in cells, organoids, or animal models. Obviously we’d love this to clarify any variant, but it can be expensive and time-consuming.

Sometimes strategies like the ones above can push a variant to a more definitive classification, and sometimes not. The hard truth is that some VUS cannot be resolved at the present time. Formal classifications aside, the clinicians can make their own judgements about uncertain findings, and counsel and treat the patients accordingly.

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:

1. 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.
2. 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.
3. There is medical urgency. A patient who continues to decline, or whose care is limited due to the lack of diagnosis, stands to benefit.
4. 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.