 Thank you very much, and thank you everyone for being here. The goal of this is really to capture some thoughts as we go forward with the day. So the overview, so obviously GWAS and clinical sequencing are changing how we practice and we'll practice medicine and research, I think that's clear. But what's the levels of evidence that it's required as we move down the road? So we can use traditional QTL to gene and animal models from QTL to gene using GWAS, from GWAS to gene variants, and obviously testing variants of uncertain significance. So we have a lot of expertise around this, and I just want to walk you through one example that's happened in our particular case. So we had a patient that we were actually studying from the Chronic Kidney Disease Consortium. And this individual patient had a mutation in Shroom 3, and this particular variant was a variant of unknown significance. It's highly conserved down through a whole bunch of different beasties. And so the question I want you to think as we go through this is what data would you require to say this variant causes Chronic Kidney Disease to put in a medical record, okay? So that's really the threshold I want you to think about, not to write a paper, but to put in a medical record. There's not going to be, in most of these cases, randomized case control studies to get these variants out there. So that's what I want you to think about as I go through this talk. So Shroom 3. So what do we know about Shroom 3? Well, it's a very commonly discovered GWAS hit. It's one of the most reproducible. Its function is unknown. There's over 11 GWASs that have been hit. And it's been associated with glomerular filtration in Albionaria, and it's associated with all populations. So it seems like a reasonable candidate. However, the evidence in the biology at the time when we got involved in this was it was known nothing about what it was in the kidney. There was basically no information, and in fact the homozygous null mouse was lethal. We didn't have the ability to go walk into a mouse and ask a simple question around that. So this is where we found ourselves. Now the reason we got involved in this is really because of rat data we'd been doing. We'd done QTL mapping over many, many years. In fact, this was a project that I had started with Lander when I was a postdoc with him a long, long time ago. And so Shroom 3 happens to sit in a QTL on chromosome 14. So there's evidence around that. And we went in and took a look at this from the faun hooded rat, which was the disease model, and the brown Norway rat, which is the control. And this is the characteristics. There are glomerular hypertension, protonaria, focal glomerular sclerosis, and potocyte effacement, really characteristic of what you'd find in a clinical environment. And we had taken it down and actually found this particular gene here, Shroom 3. And all of the red variants are variations for which you would say are potentially dysfunctional. So there's multiple variants in this gene that you would predict will cause this protein to break. Okay? So the interesting part about that was how would you attack that? Well, what we did is we then used zebrafish. And we went in and then modeled this. And so what you have to understand is that for renal function the green dye should stay. So when the kidney is working normally, you just keep the green dye. And when you have a dysfunctional potocyte, what you will happen is you'll lose the green dye. Okay? So that's the main element. So if you put a morpholino in for Shroom 3, so you knock out the zebrafish, you actually see that it goes away. If you now put in a normal rat variant, you will now see that you can actually recover that phenotype completely. So you take out the zebrafish and you can replace it with the rat. You now then can show that if you now take these three different regions and we then took these different regions and substituted them into the zebrafish genome for these genes. And what we were able to do then was narrow which part of the rat actually was functional. And what we found using the zebrafish as an assay was that these potential variants were now causal. So we started with rescuing and being able to narrow it down. And we actually got it down to the absolute variant that was causing this disease, if you will, in the rat. So with this information, it was GWAS-nominated. There's QTL data in the rat. Interesting about this is this one QTL in the original mapping. This gene actually is, there's two genes within that same QTL. So in the paper there's the nuances around that. But the point is that it's functionally we know it's causing it. And gene editing in the zebrafish we use to have the causal mutation. So how many people in this room would be willing to write in the medical record now, obviously some of you aren't MDs, but you can pretend you are, would put in the medical record that this is now enough evidence to say that this gene causes renal failure. Okay? One. All right? We'll move to the next. So how about more zebrafish data? Can you take this to more function? Because really I just told you that this variant has a broad effect, right? So what we'd like to be able to do is take this into a deeper depth. And so what we did then is we used the zebrafish, and now what I want to talk about is being able to go in and look at podocyte effacement. That's one of the key characteristics. So zebrafish, although it's a pro nephron, does have glomerular filtration membrane and does have podocytes, which you can see listed on the slide here. So we then went in and did a series of studies that I just showed you before looking at the green fluorescence protein labeled and seeing what was happening. Sorry, this is dextran. And so here we went in. We did the same study. We're able to show that we can cause skipping. We're able to go in and show that there's shroom 3. We can knock it out, and we can show that it has function. So then what we did is we did a really interesting strategy. That's just to tell you how this works. Hypothesis that it regulates the filtration barrier. So this is the strategy. So we then went in and we now used a Gal 4 mutation. So we got this variant from Ian Drummond, and we're now able to go in and take a podocyte specific and be able to say, okay, we're attaching it to podosin, and now we're only going to express and be able to knock this out specifically. So now we're showing, if you express this gene and it's broken, it should cause podocyte effacement. And sure enough, it causes podocyte effacement, and we can show also in the protein area. So now we've been able to say, okay, it's not just this particular variation, but we can take it all the way down to showing that there's podocyte effacement, which is actually what's thought around this. So now we've added more information. So now that we can show that in zebrafish that there's podocyte effacement, how many of you now would say that this is what causes the gene in humans? Okay, could cause. Okay, we're right in the medical record. Let me say that. Stay with the same question. How many, besides Terry, anybody else would add that we got one more person, okay? A few more people, okay? All right. But this patient already has kidney disease. Putting it in their medical record is not such a big deal. It's how you're going to interpret it for the future. Keep that in mind. That is our, that is a position. Okay, so keep that in mind. How about if we just test the patient's variant? Wouldn't that be a really good idea? So that's, guess what? We did the exact same strategy and we now took this patient's exact variant. And we showed what I showed you before. And now what we did is we went through and we actually went in and we took a common variant from a human and showed that it rescued. So we rescued the phenotype and we made the exact single base permutation from this patient and showed that it caused the disease in the zebrafish, okay? So, same argument. Would you write this in the medical record? Okay, so we've been through this exercise. Now let me tell you the interesting part about this. I presented this at the American Society of Nephrology in November, okay? And I asked this exact question. So of 500 clinical scientists in the room, how many of them do you think agreed that they would write this in the medical record for this patient with this data? Zero. Zero. In fact, what I got were four people that stood up that told me that a zebrafish is not a human, a rat is not a human, and there's now data that's also come out that I'll show you next that a mouse is not a human. And so it doesn't matter how many times you do this without a randomized case control study, we are not going to write this in the medical record, okay? So the reason I'm telling you this story is that this is a group of people who were trying to figure out how to do this. Now there's a lot of education and the point of this is really to illustrate what people think. This is a paper that just came out. I don't think showing that a mouse would either convince people. So the point that I wanna make, the simple point in setting the frame of this meeting is, I think that in this room, we think about sequence first, ask questions later is gonna drive a lot of basic research. I think basic research at the speed of the clinic is gonna be absolutely essential. But how do we define proof? Now obviously there's risk benefit, the sicker the individual, the more we're willing to do this. But as we walk into this meeting, I just want you to think about that, at least from one perspective, if we're going to try to affect care, the data upon which we're trying to drive needs to demonstrate enough evidence, whatever that is. And so that was the only point that I wanted to make. There's a lot of people that were involved in this. My former laboratory up at the Medical College of Wisconsin, Flo, actually this was her thesis work. She's now a postdoc with George Church, a large number of people that I've worked with across the team, and then also the chronic kidney disease. So I'm happy to take questions. I'm not sure there's a lot of questions around this, but I'm really more about setting the stage of where we're going for the rest of the two days. Thank you. Thank you, Howard. So Rex first, Mark next, Nancy next, less next. You'll be happy there are no, any questions, right? So I think this is a really great way to start off this meeting because it really does highlight what it is that we're trying to think about. In the minds of those 500 people in the audience, is how do we ever get over this idea that you need some kind of a randomized control study in order to do that? Because that's going to be a barrier that we're never going to achieve. So, thoughts? I have to, I have to be completely honest. So I was shocked. Okay, I didn't expect, I didn't expect a huge number of people to actually stand up and agree with us. But I was shocked that nobody was willing to raise their hand that they would write it in their automatic record. As Nancy said, hey, this is a patient. We already know there's a mutation that's there. You've gone through all of this elaborate biology. I expected some agreement around that. So I honestly don't know. And that's why I think this meeting is incredibly important. Now, I'm a PhD. So I have a different mindset on this. And I think that's why this is really important to have the physicians in the room and the basic scientists around the room. Because if we can't convince the physicians here that I'm going to argue are more willing to think about it, then I think we can't take it out to the general population. But that's just an opinion. So my question was related to what I consider to be a huge pile of missing data that as a clinician, I would need to understand to be able to interpret the functional data. And that is, what's the allele frequency? Has this ever been seen in any of the exome collections? Is there any evidence of segregation data? You know, are there other individuals that have been reported with different mutations in Shroom 3? Those are the types of things. And it may be a null set. But I think, you know, with absent any of that other data, the functional data while convincing would certainly not be enough for me to be definitive about causation. I might suggest, you know, that we could put something in there to say, well, there's a variant in this that's of uncertain significance. It needs additional study. But if you think about the implications of making that assignment, since presumably this is now something that you're proposing as an auzomal dominant, now you're thinking about cascade screening and all the other impacts of this. And so, those are the types of things that I need to be able to contextualize it. I can't make a decision based solely on functional data. So, short answer, Howard, and then we're going to stop. So, for the sake of clarity, which I didn't get into this. So, chronic kidney disease is a polygenic, multifactorial disease. And so, in this particular case, there is evidence in the population of Shroom 3. That was the GWAS data. So, the challenge on this is a little bit harder than what we're talking about for the Mendelian disease and that it is, in fact, only one of the elements. It is no way the only element. And it will be entirely possible, and I'll even predict, that in the cohort, there are going to be people that have a mutation in this gene that do not get to the disease because there's complementary. So, the complexity of this is a little bit more because it's a common, complex disease, common in the sense of there's more than 200,000 people that have this. But even with that information. So, I think those are fair points, and thank you. I think that at least stimulates, I hope, the conversation over the next few days. I think we could probably sit here and talk about that presentation for the rest of the day. But we won't. So, Marty Westerfield will give the basic science perspective on the need for integration. And if you want to know anything more about him, read it in the biography.