 So the topic today is next generation sequencing in the clinic, gene panel testing for inherited conditions, and I know you've all been, you know, listening to many NGS talks already, so I don't have any technical background, but I do have a couple of background slides just to state some facts as a prelude to what I'm going to talk to you about. So this is a very, very widely publicized slide showing the decline of costs here for a genome caused by the advent of next generation sequencing, and just to refresh everybody's memory, it's been fairly recent that NGS, sorry, NGS, you know, started being available first of course in research, so that was about 2005, and very, very shortly after, it actually started being implemented in the clinic, so it was a couple of labs in 2009, my lab here implemented in 2011, and that was still considered early. So you can see that the cost declines more and more, and as a result, it is now increasingly implemented in many, many laboratories, the majority to the best of my knowledge focus on gene panels. However, implementation of exome genome sequencing is also quickly increasing, and I'll touch on that a little bit towards the end. And here is why NGS has been so successful and so interesting and attractive to many. So what you see here is an example of how our detection rate for our diagnostic gene panels evolved over time, so you see the percent of positive cases on the Y-axis and then going across different gene panels, our laboratory here was offering a different time point, starting very early in 2006 or 2007 with just five genes, and then moving up to 10, 19, 24, and NGS started happening over here at the beginning 46 genes, and the point I'm trying to make here is that over this time frame, we nearly quadrupled our detection rate, and this was for dilated cardiomyopathy, the scale is a little bit unfortunate because it doesn't look as much, but you go here from about 10% to nearly 40%. So that's why everybody got so excited. It seems like a great thing to increase detection rates, and now the obvious question is, is that a good thing for every disease? When should one do this type of testing, is it generally applicable? So I thought the best thing would be to use a real disease example, and of course I chose the one that I'm most familiar with, which is hypertrophic heart, which is inherited cardiomyopathy. So what you see here is just, you know, representation of the most common cardiomyopathy. We have hypertrophic, dilated, arrhythmic, red ventricular, and a couple other rare ones, and collectively, their incidence is about, it is greater than 1 in 500 individuals. They all are quite severe. They can lead to sudden cardiac death, and what they all have in common in addition is that they have a substantial genetic component, and all of this makes up for a really high incentive for predictive testing, for diagnostic and predictive testing, I should say. So why screen for mutations, and that should be obvious, but I think it's useful to review. So for diseases like, for all genetic testing, they're in principle two different incentives. One is clinical management, and that is sort of the lesser of the two reasons for cardiomyopathy, but there are some examples. For example, there's a cardiac variant of febrile disease that manifests only as hypertrophic cardiomyopathy, and now if you don't test for this, you know, using a gene panel, you can actually pinpoint the cause to the febrile gene, and then potentially give enzyme replacement therapy and cure this individual. But by and large, clinical management is not quite as available for cardiomyopathies as it is for other disorders. Cost is another argument, though. So current guidelines recommend clinical screening of first-degree relatives, of effective first-degree relatives, and here's something from a, you know, well, five-year-old paper from Carolyn Ho, but it's still relevant. So for the child of a patient with hypertrophic cardiomyopathy, this recommended clinical screening that comes to $6,000 for febrile and $20,000 over their lifetime, and if you contrast that with genetic testing, you can see how that starts saving money because once you have a pathogenic variant, you no longer have to follow up every first-degree relative. You can just reduce that to mutation-positive family members. And just to state the same thing with a more recent paper, this is from our laboratory as of this year, reporting our experience with nearly 3,000 pro-vans with hypertrophic cardiomyopathy. What we did here is to look at the impact of identifying positives and no longer having to screen negative effect as negative individuals, and so that came out to be $1.7 million savings over that cohort. So anyway, that's meant to sort of set the stage as to why genetic testing is believed to be useful for these disorders, and showing a little bit of history of how it has evolved. It's actually overall a very young discipline for cardiomyopathy. It started in 1990 only, where the first gene was discovered, and 13 years later, we had the first test, a very small test, as I showed you before. It was just a couple of genes, and then moving up quickly to our next-generation sequencing in 2011, and now it is routine to screen even more than 51 genes for patients. It's rapidly expanding, as you probably know. So there are challenges in what are those, and that gets us a little closer as to what disorders really benefit from next-generation sequencing. The cardiomyopathies all have locus heterogeneity, and what we mean by that is there's one disease, but the mutation can reside in any one of several to many genes. We also have allelic heterogeneity, meaning that there are usually many different disease-causing variants in a given gene, to the extent that the majority can be private. We also don't quite understand the pathogenic variation spectrum yet, because it's a young discipline, and because there's so many new mutations that arise. So typically, if you sequence this order for a very long time, many, many thousands of probants, you will eventually understand the spectrum of prevalent pathogenic variants. We're not quite there yet for cardiomyopathies. And then finally, there is a fair deal of specific clinical overlap, which can complicate the testing process. And what I'll do in the subsequent slides is move through examples for each one of these samples. There's a bunch of... Is there any possible some use for this sample? I get a lot of background noise. Would it be possible some use? Thank you. Okay. So locus heterogeneity was the first problem, and that's illustrated here for dilated cardiomyopathy on the left and hypertrophic on the right. And as you can appreciate, it's not terrible. For HCM, it is actually quite manageable, but still. I mean, you have two main genes, and then a whole slew of other genes that can contribute. For DCM, it's already looking a little worse, you know. So there's no single gene that contributes the most. We have one that is pretty strong, but then there is a lot of different other genes. So you really want to test them all. So you need an assay that can actually interrogate that many in a single test in a given patient. What about allelic heterogeneity? So as I said, HCM, we have about 11 genes, and we've been testing them in our laboratory for a decade now. And what you see here is an analysis of the result, and it's asking how many variants have been seen in just one probe and two, three, four, and so on and so forth here along the X-axis. And what's shown here is that two-thirds of all variants have only been seen once. And there's a few outliers up here, those are prevalent, but, you know, the vast majority is sort of in the ones twice, three times. So that means one needs to really sequence the entire coding sequence of all these genes from maximum clinical sensitivity. And that's not just true for cardiomyopathy. If you look at all diagnostic testing performed in our laboratory, and this is shared with me by Heidi Rehn this slide, we did about 15,000 probands, and the diseases we're offering are listed down here. It ranges from cardiomyopathy, hearing loss, RAS offices, and so on and so forth. It's actually quite the same, you know, two-thirds or more are seen once and then it trails off. And then this is really a very tricky point, clinical heterogeneity, that has been largely underappreciated in the early days of genetic testing, and I'll show you a little bit of why that was and what the outcome is. So traditional genetic testing is usually configured this way, one gene panel for each diagnosis. So you have HCM, you order an HCM test, you have dilated cardiomyopathy, you go order a dilated and so on and so forth. So here's a case, a real case example that we received in our laboratory. This is a proband here with the arrow denoting an individual with a clinical diagnosis in a family history of dilated cardiomyopathy, strong family history. The physician ordered what was customary at the time, a DCM gene panel, and we did detect also what was quite frequent, a variant of uncertain significance. Now the variant ended up not segregating, so we tested all the affected individuals that were available and it was not present in a few, so unfortunately this variant was not the cause of disease, which is quite common. Now the interesting thing was that about a year later this patient was seen again by the physician, and at that point he revised, and that was Dr. Lachdawalla at the Brigham by the way, he actually revised the diagnosis to arrhythmic right ventricular cardiomyopathy. That is not uncommon either because ARVC has extensive clinical overlap with dilated cardiomyopathy, it's very difficult to diagnose, it's really the only true way to diagnose it with certainty is up on biopsy, which is usually not performed of course. And then he ordered a second panel, on top of the DCM panel that had already been performed now for this disorder, and this one identified a likely pathogenic variant, which did segregate. So what I wanted to, the point I wanted to make is that this traditional disease-centric testing does not make sense for disorders with this type of clinical and genetic overlap. This is causing a lot of, I mean it's costly, it's very time consuming because between the first time the test, the first test was ordered and this result I'm showing here were over a year, so that was not good for the family and you know, and it's costly. And here is a summary of this concept again. The reason why these older tests were configured for one disorder only usually is that these orders were typically defined quite narrowly based on morphological criteria and also these represented the most severe cases, which is typically how this order gets recognized. It's first, you know, recognized by severe cases, and then over time the true spectrum of associated phenotypes is more appreciated, so this is the tip of the iceberg phenomena. Perfectly understandable why we all configured our tests this way, but today we do know this overlap with DCM and ARVC, there's also overlap between HCM and ACM, so really what we're doing now is offering, and it's not just our laboratory, I think the community is moving to multi-disease gene channel testing because of this overlap, and you know, we know now that what I showed you in this isolated case example is true for about 3% of patients with dilated cardiomyopathy. They come in with a diagnosis of DCM, we end up finding a pathogenic variant in a gene known to be associated with ARVC. All right, and one final example of this from a different type of disorder, which is now the resopathies, which are noon and spectrum disorders. If you look at gene reviews, this is what was written in 2012, I actually don't know if it's been updated, now it wasn't updated as of a year ago, but anyway, at that time, it says that 80 to 90% of patients with Costello syndrome, one of the resopathies, parymutation in the H-RAS gene, so that sounds great, so as a physician reading this, you would go ahead and order a H-RAS first, and then maybe if negative, you know, do something else, but here's what we saw, so this is the broad referral population that we received in our laboratory, and you can see that the minority of patients actually had a variant in H-RAS. The majority had mutations elsewhere in related pathway genes. So adhering to this traditional paradigm would have caused a negative report, and it is unclear how many physicians would have reflexed to an additional test, because again, that is costly. So to summarize this, these multi-disease gene panels do often improve a clinical diagnosis, and the reasons are just to summarize this phenomenon of phenotypic expansion. As I told you, the original clinical definition was naturally based on the more severe cases, but then as a consequence, and ended up being too narrow as the full range of clinical variability emerged over time, phenotypic overlap, you know, that is not uncommon, so here the disorders present the same, and that can lead to a diagnostic quote-unquote error, not really an error, but how is the physician going to be able to be accurate here? It happens more often, though, as genetic testing is moving out of specialty clinics in general, genetics care, which almost invariably leads to a decrease in detection rate. We've seen this more and more because the cases aren't precisely diagnosed. And this is now widely recognized by the clinical and diagnostic community. I've had several conversations with physicians here at Partners Health Care who are saying, yep, this makes so much sense. We're actually now beginning to change our workflow, and it's shown here. So this is what next-gen sequencing has caused, which is now beginning to be called this sequence first, then diagnosed workflow. And, you know, I'm not to go through all the details. This shows the full clinical workflow from the patient over diagnosis, and then ordering a genetic test and receiving a report. What's happening is that the upfront work, establishing a clinical diagnosis, and then using that to order a test is replaced by sequencing first and then putting the diagnosis at the end, taking the clinical as well as molecular data into account. And that's quite exciting, actually. It's very rewarding to be part of this, and we do solve cases more than we did before. And as a last background slide, there is definitely a trend towards genome-wide testing, but it doesn't stop at just sequencing more and more genes. And this is a little off-topic, but I thought I'd throw it in because it is just breathtaking what's happening right now. So we've already seen this trend towards genome-wide testing in other disciplines. You know, in cytogenetics, it was very early already that that field transitioned to genome-wide copy number arrays, whatever it's in the old days. There were single gene or single analyte tests, like a fish, a southern, and so on. The same thing happened for genome-tapping tests where in the old days you would look at amutation, maybe some, and, you know, if you were lucky, a lot, but not so many, and it quickly moved to genome-wide snippets and arrays. And now we have the same thing happening for NGS, next-gen sequencing. And the reason I'm throwing this all in one slide is that NGS actually has the potential of replacing all these tests because it is beginning to be, for sure, possible to genotype because, you know, you don't have to interrogate every position in the sequencing test you could choose to just look at your SNPs, but it's also beginning to be feasible to copy number alterations, which is a really great argument for using an NGS test. And so I would say it's fully expected by most of us in the community that this technology will eventually consolidate most genetic testing when appropriate. Of course, this isn't going to be the case for every disorder. So I want to move on to talking and talk a little bit about which genes should be on the panel, and that gets us to assessing the clinical validity of variants and genes who are connected. We really can't assess the clinical validity of a gene without looking at the variants that have been published in that gene. And so I want to mention upfront that this is a very hot topic in the community, and there are various bodies now that have geared up to develop standards for assessing clinical validity, and you're probably very familiar with some of that. So the ACMG and AMP have come out this year, sorry, last year with a new guideline for clinical grade variant assessments for Mendelian disorders, and then there is also the fairly young consortium called ClinGen, the clinical genome resource, which has really aimed at uniting medical geneticists and developing approaches to really curate and centralize and share all that data, and is focusing not just on variants but also genes, assessing which genes are clinically valid in terms of their published evidence. So let me dive a little deeper into that. Like I said, it is impossible to figure out the clinical validity of a gene without understanding the published variants, whether or not they are pathogenic. So a few words on that. When we assess variants clinically, it should actually be the same in research, but in a clinically very structured process, we ask basically whether the variant affects the protein or gene function first, and then we ask whether that causes disease, and those two aren't always linked. And then we classify these variants based on the available evidence into one of five categories. Five is these categories that you see here are the ones recommended by the College of Medical Genetics and AMP, and most laboratories begin to adhere to this. And at the end, we ask one more question, and we then take that variant and ask whether this variant also causes this patient's disease, because it may be pathogenic, but it may not be responsible for the patient that I have in front of me, because maybe it's an adult onset variant, but the patient I see is a child, so it's questionable whether this variant is really causing that disease. So there are these layers in clinical variant assessment. And then we string together what we see, so to summarize this, again, we go through the results, annotate and classify these variants into these five categories, and the end result is that a patient gets a report, and there are three flavors, positive, negative, and in-between inconclusive. So what is classified as likely pathogenic or pathogenic ends up being a positive report, meaning that we believe that we found the cause of disease definitively or likely. So I'm going to show you this slide again, and now focus on a different aspect that you might have already picked up on when I showed it the first time, and I just glossed over it. There's this nasty surge in inconclusive test reports that we saw. Yes, we have a quadruplication of positive cases, but an even deeper increase in inconclusive. Now, this is not good, but it's worth diving a little bit deeper and understanding what the causes are, and if all of it is really bad. So why more of these inconclusive? The two main reasons. One is that we can have a novel variant that has no published evidence, and the variant type on top of it is of unclear impact, so this is true for many novel mis-sense variants, even when the gene is very well established. But the second category is the one that I want to dive into a little deeper later. It could be a novel variant of any kind, really, in a gene whose role in disease is not definitively established. And this is really, this is very prevalent in our community still, but let me just quickly go over the first reason. Novel variant with no published evidence, and the variant is of unclear impact. So this is unavoidable. As soon as we start sequencing a gene that is even definitively very strongly established with the disease, and we sequence it in its entirety because there's so much allelic heterogeneity and so many private mutations, with the good improved diagnosis and through the come some inevitable bad. So how bad is the bad? And that depends on many factors, actually, and here's where the physician comes in. It is entirely influenced by the patient's ability to deal with uncertainty. It's also, as I showed you, important to see whether there's a family history because one can turn a variant of uncertainty and significance into a pathogenic variant by family studies. And also the world is moving closer together now and with many, many large databases being established, centralized and interconnected. We've seen an increased ability to solve cases by connecting patients around the globe. So it's not as easy to condemn these VUSs as you might think. And so my personal opinion on this is here for the tortoise orders with a high degree of allelic heterogeneity, there simply would never be any progress if one only tested what is already known. You'd be stuck with just five or six common pathogenic variants, and that's just it. And here's just an example, you know, underlining how you can, you know, use family testing, and I showed you that before to make a variant, to move this out of the uncertain significance category. Now, much more important is this, a novel variant in a gene whose role is not definitively established. And that is a relatively young discipline to go into the assessment of gene disease relationships. So what's happening is that if a gene, if a role of a gene is not well understood, you will never be able to interpret a variant if you don't understand the role of the gene for the most part. Traditionally, that's not been a problem because the old tests were limited to just a few genes, so naturally one would choose those that are really well established. There was no doubt. But that barrier is gone with NGS. So all of a sudden, there was this possibility of adding more and more genes, and naturally we all added as many as we could only to realize what I just described to you, that, hey, that isn't a great thing always because we should have actually read the publication a little more deeper and assessed its validity critically. Luckily, we've all caught on to that, and now there is this discipline called gene assessment. And the sad truth is that many published claims for a gene disease relationship just do not withstand the rigor of clinical grade curation. And it's not easy to point fingers because there's the publication pressure everybody has. Journalists don't like to take negative papers. Everybody's trying to hype up their findings. This is all very normal, but it does hurt you when you use these genes clinically. So now we actually do this. The clinical genome resource, and I didn't mention that this is a large NIH-funded consortium of many centers has established guidelines and is about to publish them. Establishing evidence levels ranging from definitive over strong, moderate, limited, and so on to none. And then has established a rule-based framework of what evidence is required to make a gene definitively or strongly associated with disease. So the pillars of evidence that are used are the number of clearly pathogenic variants are reported, and here's where you need to understand variant assessment, the number of studies available, the number of probands with the variant, statistical evidence for other type, case control boards, and then functional data. And all of these things are actually tricky because one needs to establish rules for what is a valid piece of functional data. And that's underway. And I wanted to show you an example that I personally lived through. So this gene, McZillan, is on most laboratory cardiomyopathy panels, and here is the original publications. We have one for dilated and one publication for hypertrophic. And you see the titles, you know. McZillan mutations lead to dilated cardiomyopathy. Mutations in this gene are associated with HCM, so that is the statement. But if you look more closely, so in black is what's in the paper, and in red is what you see, and this is a couple of years ago, when you really look. So there was good evidence as for the paper, but when we looked at the variant that they based their claim on, they found a particular variant, and we found these variants in 0.3% of the population. Sorry, in 0.7% of the population, this was the exome sequencing project. So no matter what the evidence is there, this is a red flag, and we're not saying it doesn't cause disease, but it's not really a slam dunk gene, so we approached it a little more cautiously today. And even worse, for the HCM paper, the two miscellaneous variants that were found by these authors, one of them we've already down classified is likely benign, based on the frequency, and this was pre, this was before the Broad Institute released their exact database. I actually don't know. They might have moved down to benign now, so this is what we do clinically. And at the end of the day, the goal is to develop guidance as to what type of evidence is right for what type of test, and what you see here is a pyramid with different levels of evidence, and naturally, most genes actually live in this bucket down here, limited or no evidence, and then moving up to moderate, strong, and definitive. And there is no expert, there's no clear consensus yet, but most of us include genes moderate, strong, and definitive in diagnostic panels. But when you go to predictive testing, you do want to be a little more selective and only use definitive, potentially strongly associated. But this is right now where a lot of activity happens in the community to actually form expert panels and adjudicate these genes and say, okay, of the 50 published hypertrophic cardiomyopathy genes, these are the ones that meet criteria to be included in a diagnostic panel. And that's precisely what is happening under the ClinGen umbrella, just one example. So I'm co-chairing a group for cardiovascular domain, cardiovascular disorders, and we do tackle both. We try to really nail down a framework for variant curation for this disease, and even more important, we are trying to establish a recommendation for cardiomyopathy panel testing, doing the gene curation. So now the question is, I would say I would like to start by saying in my mind, the utility of these multi-gene and multi-disease panels is quite recognized. I don't think anybody is debating that. Yes, there is a higher risk of detecting VUSs, and the only negative, that is a negative, but it can be minimized with rigorous gene selection, as I just explained. How on earth so are we going to keep up with the increasing rate of gene disease discovery? Disease gene discovery, sorry. As a laboratory director, it's quite hard to constantly redevelop, revalidate, and update these gene panels. It simply isn't sustainable, and in some disease orders, the knowledge is virtually exploding. So we need to find a way to keep up. How do we do that? So this is a big debate in our community right now. What's better, a gene panel or an exome? If more genes are better for some disorders, why not just do all of them? So this slide summarizes the current landscape. Gene panels are the predominant next-generation sequencing test, really focusing usually on tens to hundreds of genes, and they come with a lot of perks. We have high coverage. We can usually return a credible result for every single base in the test, and they're by and large used for clinically very well-defined cases. But there is a big push to go here, because exome is getting better, and it's been living in this niche of being used for complex phenotypes or diagnostic odyssey, but there really isn't less and less difference between an exome and a gene panel, and I'll show you why. So a lot of laboratories are trying to figure out if and when it is appropriate to even move over here. The incentives are very obvious. A large fraction of the gene panels are offering a negative. And the decision rate at best is often 50%, and that's just less than optimal. And I showed you before, there's a growing appreciation of phenotypic expansions. There's always been an argument for hypothesis-free testing. How sure are you that you have the right phenotypes? Well, you don't often. And additional tests simply can end up being more expensive in the end. If you count up all the tests that you end up ordering, if you do it sequentially, you're quickly more expensive than an exome. And of course, you'd always be up to date, and it's also operationally easier to maintain for labs. Barriers, yeah, there are barriers. Cost is one still, so the gap is quickly closing, so I'd almost disregard this. Incomplete coverage is another frequency-cited barrier, and that is true. Exomes are still not quite as good as the targeted panels. But a lot of it is a design flaw. The vendors that are offering these have not done a good job, and so the community is stepping up to help them with that. There's also an additional risk, and that is a little more difficult to deal with, which is, as we're rapidly expanding into more and more genes, we're losing our intimate or a priori knowledge on these tested genes. In the old days, and I've launched many genes over the years, you knew this gene inside out, down to like, oh, here's an exon that is repetitive or something. We totally lose that ability now that it's possible to overnight test so many genes. But these barriers are really fast disappearing, and as I said before, many small tests can quickly end up being more expensive, and we look into this a little bit here in our ecosystem. This is one example that is representative. It's a real example. The audit test was sharpening to a sequencing, and that blocked out the laboratory. The test consists of 23 genes, and included copy number analysis, the sensitivity, clinical sensitivity was 65%. Now we ask the question, if this physician had ordered an exome, you know, how would it have looked then? And so, in this case, one would have needed additional deldupe testing because the exome doesn't really do this well. But if we factor that in, we would have had 10 more genes at our disposal because since this laboratory developed their tests, 10 more credible genes came out, and we also looked at our exome quality. It was quite well covered, so it was legitimate. And the clinical sensitivity would have been 10% or actually 15% higher. And the exome turned out to be cheaper despite the fact that we would have added separate deldupe testing. So this is really how it often goes. The next barrier that is almost gone is the lack of completeness. And this is a slide that I used to show, I've shown many times, but I think I need to stop doing it because it's no longer a big issue, showing you what happens to our 51 cardiomyopathy genes that we had on our cardiomyopathy panel a few years back. If we look at our targeted capture assay, the gene panel by itself, it looked like this. In blue, you see everything that is fully and adequately covered. And it's a small slice that we would say does not reach the required coverage less than 1%. And what we do in our clinical lab in many labs do that is we use Sanger sequencing to then fill in this slice, providing 100% coverage. And that you can see on an exome. The same 51 genes on an exome look much worse. So only 85% are fully covered, 15% are not. And that is something that is not possible to fill in by Sanger sequencing. Now, you know, we got together in the community and helped the vendors develop a better test, which is shown here, an enhanced exome. And you can see the same kind of analysis, and now the exome-derived data looks almost the same as the targeted capture data. So that no longer really is an argument to not do the exome. The most severe barrier, I think, is the educational gap. Today we're still living up here in this quadrant. Exome sequencing is ordered by experts. If you have like a low, medium, high scale for testing labs and physicians, this is where we are right now. Highly educated genetic study physicians order and highly capable laboratories do the test. So we're doing this now. And it's just really difficult for laboratories to keep up with physicians. So we need to do a lot to educate, but that's a separate topic, really. So in my mind, we need to actually redefine the question we're asking. So assuming adequate coverage and assay costs, and I showed you that this is likely no longer going to be an issue in the near-term future, exome and genome sequencing can be... One has to remember that the way we're using exome genome sequencing can be different. So if everybody thinks sequencing everything means you have to analyze everything, and that's not true. So what we can do is we can genotype. We can run the exome, but we can only look at known pathogenic positions. We can sequence. We can do panel testing if we know the well-established genes. We can also do all genes when the clinical diagnosis is not clear, but the family history suggests a genetic ideology, and that's sort of the way exome genome sequencing is used currently. But price and coverage is really the only factor that's gating our ability to use it like a genotyping test or a smaller-scale sequencing test. So the critical question really is how specific is the patient's phenotype. That will dictate which set of genes we look at first and maybe stop there, and how deep the analysis needs to be. And this is now really almost what we're building right now, life. This is a test we're about to launch, and we're not the only ones in the community that are trying to marry these two worlds. Can we use an exome, but can we make it behave entirely like a targeted panel? And that's exactly what we're trying to do. So a traditional disease-focused panel looks like this. We, and I said this before, we have 100% coverage using sangrasequencing to fill in. Off just a small number of genes. We typically report deep, like pathogenic down to variants of uncertain significance or even likely benign variants. On the other end, we have exome genome sequencing where we also often start with an indication-driven gene list but the patient has something that looks like cardiomyopathy. Many laboratories will look specifically at cardiomyopathy genes, but here, typically, the coverage is variable and fill-in sequencing is often not done. And reporting is restricted often to pathogenic and likely pathogenic variants only simply because the scope of the exome is bigger, particularly when we go down to the next layer where you start looking at all the genes and ask, is this useful to factor in this potential phenotypic uncertainty? Is it something that I didn't expect? And then there's also the possibility of finding things incidentally. But what we would like to do is really this, have a targeted panel, not doing everything that we're doing here, so we're guaranteeing 100% coverage and we're reporting very deep, but we retain the ability and the nice things of an exome even when we need it. If this is negative, it's easy to then say, well, let's go look at the rest. Maybe the diagnosis wasn't accurate. Maybe it is worth just looking and related disorders. So that's sort of the new thing and it's meant to bridge the gap between exome and panel testing. So here's a couple of final words on the importance of standardizing a structured gene evaluation. This is also a real example from our laboratory. The goal was to define the contents of a new indication-driven gene panel and it happens to be inherited renal disorders. We did a survey of databases, so it was used ontology-driven database tools to create a draft list and it was 279 genes. And there are two things. We worked with a clinical expert and sort of ran this by this expert and say, which genes do you think one should do? And we then used the ClinGen matrix I showed you before, which is not rocket science, but forces us to do a very third clinical validity assessment and here's the result. Of the 279 genes, the expert-driven opinion yielded this. So, you know, 126 were deemed mission critical and 22 were nice to have and the rest were, you know, neither was unimportant, essentially. When we looked at this with the ClinGen matrix, we found that not all of them, but a third actually, a third more than a third, only met definitive evidence criteria for gene disease associations and also in the rest of the genes that were not deemed of importance, we found some that met these criteria. Also, some of these genes that were deemed to be important didn't meet evidence levels at all, so I just go to show you that it's very important to do this in a structured, rigorous way. And with that, I wanted to summarize. So, I hope that you can appreciate that multi-gene and multi-disease testing can be useful for disorders with clinical and genetic heterogeneity. A genome will soon be cheap enough to be the first-line test for all genetic disorders and how soon is soon, I don't know, but that's where we're going. And understanding the clinical scenario is key. The test really becomes an informatics exercise. You can do anything from analyzing just a few sites, and here I'm listing an example. You know, if provided the genome sequencing is incredibly cheap, you could totally run it and ask only, you know, what is present at the two positions that you need to analyze for achondroplasia. You can do a single gene, if need be. You know, there's an example, burr-top-de-bay syndrome, 90% of all variants are in filament C, so it would make sense to start there first. You can analyze a set of genes, and I took you through HCM or exome, and curating the validity of gene disease relationships is probably the most important thing we have to do over the next few years. And with that, I'm going to acknowledge so many people that have contributed to all these things, and thank you for your patience, and happy to take questions.