genetic counselors in population screening, the potential role of telehealth and AI. That might be an interesting discussion. Importance of keeping approaches and guidelines simple and understandable. The risks of and dealing with false positives, something we all think about regularly, and research needed to make health care system ready to handle population screening. How close are we? Or are we long way off on that one? There are a few things that we're not going to focus on. We're not going to address newborn screening. It has its own standing committee and expert bodies. And other than something as a model and using lessons learned from a newborn screening, we're really not going to talk too much about newborn screening. We're not addressing the screening of children or parent guardian consent. We really want to focus on adults in the meeting in the next two days. We're not debating appropriateness of nor evidence behind potential interventions once a screening for condition is found. So we're going to assume that there are professional guidelines established by others that will address those. And we're not proposing funding opportunities or mechanisms. Let's focus on where the gaps in knowledge are and how we might go about filling them through research studies. So a brief note on the structure of the meeting and its aftermath. So we've built in ample time for questions and discussions. And many of the sessions have panels. We want to allow for at least one or two clarifying questions from each of our presenters. But we want to save most of the discussion for a discussion period that's been built in. The moderators will redirect from topics. The meeting will redirect from topics to the meeting that we'll not address here. We've asked the moderators to save a few minutes at the end of each session to list a few key points that have been made during that session. And in-person attendees should raise hands to speak. Online attendees can put questions in the Q&A, but we can't guarantee we'll see them or address them during the meeting. But we will try to follow up afterwards if we don't make it to them in the meeting. A meeting summary aimed at the goal is to post a meeting summary in four to six weeks. That's traditional after genomic medicine meetings, and generally adhered to. And if a warranted, presenters and moderators will jointly prepare a white paper for journal submission. And some of those papers have been fairly influential in the past as well. So with that, if there's no burning questions, I will turn it back to Terry. You do a wonderful Gale impression. So thank you very much. So I really appreciate your stepping in. So I just wanted to recognize and maybe give a little bit of background on these meetings. Rex alluded to they're really critical in terms of the Division of Genomic Medicine and NHGRI planning, future studies in this field. We have an advisory group, the Genomic Medicine Working Group, which is a subgroup of our advisory council. These are the members, and they are almost all here unless they are filled by illness. So and then several of us at NHGRI who helped them out. The charge to the group is to assist in advising us on research needed to evaluate, move genomics into routine medical practice, reviewing current progress. Much of the goals of this meeting, as you said, now focused on genomic screening, identifying publicized key advances. That's our notable accomplishments website, which we can tell you about at some point. We don't need to talk about it here. Planning genomic medicine meetings on timely themes. And we identified population screening as a timely theme, not necessarily that it's something that would should begin tomorrow, but the research needed to determine what should be done and when probably is needed tomorrow. And then we also facilitate collaborations, coordination, et cetera. This is sort of a collage of the first 12 that we held, it's all that would fit on this slide and you can see that they're in a variety of topics. Again, as things come up and are nominated to the group and you feel free to nominate future topics for us. We had actually, we've had two others in addition to the 12 that were shown there. The most recent one was August of this past year. So they're roughly annually. And then this one is on population screening and genomics. So just to give you a feel for some of the products that have come out of these meetings, from the first we had after an initial sort of summit, we had a separate meeting that we called ClinAction from which the clinical genome resource grew. So that was specifically designed to address variant curation and how that could be done in a more group sourced way. From that has come the GenCC Consortium, which is an international effort as is ClinGen and they've both been quite successful. We added pharmacogenomics to emerge shortly after that first meeting. The Ignite Consortium, our network came out of that looking at implementation of genomic medicine, which has grown into another phase, which I'll show in a sec. I'll skip over some of these because we can't fit them all on one slide. The fourth meeting was on training and it led to the Inter-Society Coordinating Committee for Practitioner Health and Genomics, which is a very active group. The sixth led to the Global Genomic Medicine Consortium and from that the International 100,000 Cohorts Consortium, both of which are active groups and engaging groups all around the world. I've gone with sort of the major even numbers from the eighth meeting, which was sort of an overview of where our programs currently stood. We recognized we needed to develop some training efforts, particularly modules to help to train clinicians in this work. From the ninth, we had a program announcement on variant functions and disease, which also brought together the basic science and clinical communities. The 10th on pharmacogenetics led to ADOPT-PGX, which is part of the second phase of the Ignite Consortium, testing pharmacogenetic interventions for three treatment scenarios. In our 11th on implementation, we followed that up with a group of employers and exploring the role of employers in the possibility of genomic screening of their participants or their employees. The 12th meeting on risk prediction led to the next iteration of the Emerge Network, which is focusing on genomic risk assessment, as well as the population risk methods in diverse populations, sorry, polygenic risk in diverse populations, that consortium, the primed consortium, which is actively pursuing that kind of research and methods. Our informatics meeting led to a solicitation on patient-centered informatics, which we've received a few applications, not a lot, so keep that one in mind. Our 14th meeting led to a solicitation that was actually the applications were due yesterday. Yes, for genomic learning health system, so sorry if you missed that deadline. Yes, I know, I know, what can I say? And then we brought a concept, which is sort of the first stage in bringing forward a program. We don't know that we're necessarily bringing forward a program, but it has been approved by our council to at least be considered for moving forward, and that's on electronic consults or sort of distant consults provider to provider effort. So a lot coming out of these meetings, and we expect we'll have to squeeze in the 15th meeting after we have had this one. So with that, I will stop, and I'm gonna pull up the next slide and turn it over to the moderator. So I think can moderate from here to advance your slides. And Eric and Mark, take it away. I am. Well, thank you, Terry. Good morning, everyone. Thank you for coming here today and tomorrow. For those of you, I mean, looking around the table, I've seen a lot of you recently, like downtown DC last week. Thank you for enduring two weeks in a row of DC meetings. You can blame us for this one. Obviously, ASHG chose their location. On the other hand, seeing the double header nature of the ASHG meeting and this genomic medicine 15 meeting, I did arrange for really good weather both weeks. So, and if you believe I have any influence on the weather around here, I'm really impressed. In any case, I'm delighted to see all of you and I am glad there's good weather because the falls around here can be quite nice, as you've seen. Thanks to the organizers to put this together as a tremendous amount of work. But the feedback from these meetings are always extremely positive. The productivity, as you saw with Terry, is speaks for itself. Things really do come out of these genomic medicine meetings and we always learn a lot and it's really very valuable to NHGRI. I also want to put in a special thanks to my office communications crew who are over there with headsets on and doing various things. The other aspect of these meetings from the beginning, I think, was I guess we used to video capture it and put it up. I mean, now with Zoom, we do it live and we video capture it. The outreach of these meetings in part because of my terrific communications crew that captures this in a very high quality way and we'll get it up. And so it lives on. Besides live, we will have really good video of this that a lot of people around the world really do watch. We are all very fortunate to be here in this room, but not everybody's as fortunate. First of all, we can't have huge meetings, but more importantly, there's some people just practically can't travel here. So I really appreciate and when we really take very seriously the ability to get the information out of these meetings through our communications group. So I thank all of them for their hard work meeting up to the meeting now and post-production. So Mark and I are picking this off as the moderators, we have two speakers. We're not gonna give any bio because the bios are in the books. And so if you wanna read more, first of all, everybody in the room probably knows Les and Mike, but if you wanna read more, I know Les's bio is on page nine of the electronic PDF. I would just say in introducing Les a couple of things. First of all, he and I have grown up together professionally in some ways. We both came into the intramural program at NHGRI essentially from the beginning. He arrived 30 years ago because we're celebrating our 30th anniversary of NHGRI's intramural research program. I arrived one year later, even though I committed to come the year before and we were tenure track investigators together and then tenured and then have taken on various leadership roles. And Les is currently the director and the founding director of our intramural center for precision health research. And the other thing that I could say about Les, which says something about the lies, ideas of the organizing committee is that if you wanna get discussion going, you get someone like Les could give an early talk because he doesn't pull punches. He's gonna tell us what he really thinks and that will always stimulate conversation and rigorous discussion. So with Les, with that, let's take it over. Is that the bowl in the China shop introduction? In an incredibly loving way, yeah. Oh, thank you, thank you. Thanks, okay. All right, great to be here. I've attended a couple of these meetings and they were awesome. So I'm eager to participate with you here today. So I wanna talk a little bit about the Reverend here. And I think that we have to think about the Reverend because what we have are genomes. We're genome people mostly. And what we need to do is convert a genome into health. That's all we have to do. And the challenge here is that little blue arrow includes, pun intended, a myriad of considerations and nuances and complexities. But that's what we have to do. And we have to make that arrow work efficiently and effectively to improve health because that is our product. Now I'm gonna revive a bit of my rant from my ASHG presidential address and tell you there is nothing new about this. Genomics, genetics is just a technology. It's just a test and it obeys, it has to obey all the laws that apply to every other test we use in the medical center. And so it has sensitivity. It has specificity. It has PPV and it has NPV. And that's the way it works. And it is no different than a hemoglobin or a sodium in that respect. And so the PPV of the test, the positive predictability, which is of course that thing that we are going to operate on is a patient with a risk of a disease. We want the genome to tell us who those people are. And that result depends primarily on the testing scenario which the topic of the seminar is about changing, the topic of this meeting, sorry. So as geneticists, I have to say we're a little bit feeble in this regard because we have been spoiled by decades of practice where people with typically an enormously high prior probability of disease. These are loaded families, people who actually in many cases, patients who understand the disease as well or better than we do because they've lived with it for generations and decades. And they have this context and they have an enormous high prior probability of disease. And the irony is when we do genomic testing or genetic testing in that context, we actually don't change the probability of them having that disease very much by a positive result. So we're not moving them very much from a prior probability of disease to a posterior probability of disease. But if we then all of a sudden change and shift the game to a population-based game, then the prior probability plummets and it's a completely different business. So this is old, this is old, this is probably a woodcut of the Reverend Thomas Bates. It's probably a woodcut because the historians can't agree if it was a post following his death, made up a fantasy of what he looked like, but they think it's him. And this in a more modern context is what the Reverend said. And it's an equation, I'm very sorry. It's math, lots of people don't like math. And in fact, I think in college, there's a bit of a bifurcation. People who like math and science go into physics and chemistry and those kinds of engineering. And people who like science but don't like math went into biology. But here we are, we're biologists, we're geneticists and it's completely mathematical. So this equation governs not only how we interpret and classify and think about genetic diagnosis, but of course, again, it's all diagnoses. So what's the Reverend's equation saying? There's two forms of terms in there. This notation P parentheses A is the probability of some event A. Then the second notation, the probability of A bar B is the probability that A is true if B is true. That's a conditional probability. So what the Reverend is saying here is that something A, you wanna know if it's true. The probability that it's true, given some piece of evidence is equal to the probability of the evidence if A is true, if the thing is true. Times the probability of A, that is how likely that thing is in the first place divided by the probability B, because the more common the evidence is, the less likely it is that your assertion is correct. And so some people refer to this Bayes rule as the law of inverse probability because of this term, because it depends enormously on the probability of B being true if A is true. And so how do we then think about screening? Screening has to obey Bayes rule. It's probabilistic. The analytic validity of the genomic test result is a probability. The clinical validity is a probability. This thing that we now call the pathogenicity of variant. That's a probabilistic measure of the validity of the relationship between the variant and some condition. This term we coined called the clinical molecular diagnosis, which is if you take the variant and the patient's presentation into consideration, how likely is it that they have that underlying condition, which is different from how likely it is that they have a manifestation of that condition. I'm gonna talk a little bit more about that. That is penetrance. And that is a separate probability from the likelihood, the probability of the clinical molecular diagnosis. And then of course, expressivity is a probability. And that's a qualitative distribution of the nature, not the quantity, but the nature of the phenotype. And so what we need is a probabilistic model genetic diagnosis in a screening context that takes all of those things into considerations. And we just have to admit that most people don't like probability, right? I always often say when I travel, there's only two kinds of people who sit next to me. The people who are sure they're gonna get to their destination safe and sound. And then the white knuckles, the people who are sure they're going to die. People don't like intermediate probabilities. And there's a great story on how many of you have read The Signal in the Noise by Nate Cohen, a wonderful book. I'm sorry, Nate Silver. And he talks about the National Weather Service and they used to issue precipitation warning, precipitation probabilities. And they found that the customers of the website were the least satisfied when the probability of precipitation was 50%. Because they didn't know what to do. Should they go to the scene of the movie or should they go on a picnic? And so what was their solution? They would randomly change it to 40 or 60%. And people were happier. I'm not advocating for that, but I think it's a very telling comment about people don't like probability. Okay, so what I wanna frame here is a notion that all genetic disease is a susceptibility to an abnormal phenotype. We tend to think of only things like cancer susceptibility syndromes as being disease, susceptibility, genetic diseases, and let's say birth defects as deterministic. But I think we have to consider all of them probabilistic and realize that the penetrance function just goes very, very high, essentially 1.0 for some variants. And a key distinction here is that we have to recognize that having the disease but no manifestations of the disease is non-penetrance and that not having the disease is not the same as having the disease and being non-penetrant. It's a critical distinction. And I think a lot of people miss that. And of course, we know in variant classification nearly all variants are not certain to cause disease. They have a probability of pathogenicity that is less than 100%. So if they are not certain to be causative, then you have a Bayesian problem, which is that it depends, the diagnosis of the patient depends on their prior probability of disease because there's a possibility that the variant is in fact not pathogenic. There's some chance of that. So harboring the variant means, I'm sorry, for a few variants, they essentially do have a probability of pathogenicity of 100%. There's a couple variants, probably Delta F508, a few of the HBOC variants where the evidence is so overwhelming for all practical purposes, we can say the pathogenicity of that variant is 100%. In that exceptional case, then Bayes law does not apply. If you have the genotype, you have the disease, period. Whether you have a manifestation, remember is a second consideration, that is a probabilistic function, but they have that susceptibility. And that's why we have to think of diseases as susceptibilities. So I think what we need to start doing is thinking about a stepwise approach, which is to first, what we're doing in ClinGen is assessing the probability of the pathogenicity of the variant. And what the reverend would say we're doing is measuring the probability of pathogenicity, given the evidence about a variant. And that's the predictors about the variant, historical data on the variant, et cetera, ideally not the phenotype of the patient. And then we do a laboratory and a clinical interpretation of the person who harbors that variant, i.e. the reverend would say the probability that they have the disorder, given the phenotype they have, because they have the variant. And that's what we're calling a clinical molecular diagnosis. And again, harboring a variant, the pathogenicity is less than 100% is not the same as having the disorder. It's probabilistic. And for those who have the disorder, that is they have a clinical molecular diagnosis, but they do not have an apparent phenotype, then you have to assess penetrance. And the penetrance function is the probability of a phenotype, given that they have the disorder. It makes no sense to talk about penetrance if you don't even know that the person has the disorder. So graphically, again, people don't like equations. Here's sort of what the math looks like. And I published this paper a couple of years back in genome medicine. And I use a hypothetical example of diagnostic testing versus population screening from our fans syndrome with the genes on the ACMG list. And when it's in a diagnostic setting, there's a high prior probability of disease. I estimate a clinician will often order a test for Marfan syndrome if they think there's like a 75% chance. Yeah, I think this guy has Marfan syndrome. So that would be this orange and beige colored circle. And when you have that high prior probability of disease, the math says the false positive rate is this little sliver over here. Because the false positive rate applies just to the people who don't have the disease. And then the true positives apply to the people who have the disease.  Over here is when you're doing screening. That's when the prior probability of disease plummets. Then the false positive rate applies to the people who don't have the disease and it starts getting really big and you're positive. And then the true positive rate is small because the number of people with the disease is small. And so your positive predictive value collapses. And that's the challenge that we face in genomic screening. So we're applying this principle to population screening in secondary findings. And this is a real pedigree of a recent family we've ascertained. And it was a secondary finding because secondary findings, the math of the screening is the same as population screening. Now there's a world of differences between population screening and opportunistic screening secondary findings. I'm not discussing, I'm just talking about the math. The math is the same. So this man was sequenced for a neurologic disorder, this 37 year old. And it turns out his mother has a likely pathogenic BRCA2 variant. And she does not have disease. My little annotations here are the age and years followed by whether or not they have the variant plus, minus or question mark.  So you can do the math here. And using this model, the Bayes model, if you use just the test result on the mom, ignore the phenotype of her or her family. She has a 65% hereditary breast and ovarian cancer. If given that she is phenotype negative, i.e. she's 69 years old and she does not have cancer, that affects her probability of disease. And in fact, it drops from 65 to 47% if you consider that. Then you include the fact that she has a 65 year old sister who is affected and does have the variant. That pushes it way back up. And then if you include all the relatives, people with and without the phenotype and people who either are unknown to have the variant or are known to have the variant, then it goes back up to 83%. So you can see how these probabilities change. Now you might argue there's not a huge difference between 65 and 83%. But if you look across 58 cases that we've done, here's the distribution of the probabilities of disease in 48 pedigrees. And it's really interesting. It ranges from, I have this little red line here because we determined that the three common variants in the Ashkenazi Jewish population for HBOC are of 100% pathogenicity. So by definition, anyone who harbors that we say has HBOC. So they're to the right with a probability of 100. But here's the ones who we aren't certain of. And the posterior probabilities range from 22% to 99.98%. And that's an interesting range. And I would argue that the people on the left side of this curve need to be managed differently than the people on the right side of this curve. That's not the same clinical scenario. So I think we need to adopt this probabilistic model. And we need to, of course, do a number of things to make this happen. We need robust classifications of variants to determine what ClinGen is doing, the probability of the pathogenesis given the evidence and the actual numerical value of those probabilities are important in these calculations. Then we need practical methods. And I underline emphasize exclamation mark practical methods to determine the probability of the diagnosis given the phenotype. And I think that's going to have to be patient decision making support coupled with clinical decision support and defined care pathways in the EHR that allow clinicians broadly to be able to do this in a way that supports this probabilistic model and get rid of this foolish and naive concept of determinism, which is this simplistic thinking.  That's just wrong. So a couple of closing thoughts. When I teach this example, when I teach this stuff to the genetic counseling training program students, I teach them three rules. I tell them, you are absolutely obligated, no excuses if you don't, you have to assess patient risk precisely. Tell them number two rule is that you actually can't calculate it accurately. We never have all the information. Our information's always incomplete and some of the information we have is incorrect. It doesn't in any way mitigate your obligation to do it precisely based on the information you do have. And the perfect is the enemy of the good. And so we'll settle for precise and understand that it's not perfectly accurate. And then we have to think about what it means. And what it means for the patient is my third rule, which is that that number that we derive is probably not the primary determinant of care management decisions downstream. That's a much more interesting and nuanced question as to risk perception by patients and that will drive how they manage their care. And I think so the larger challenge is we have to change our mindset from one of non-directiveness to management. In screening, we are not presenting patients with necessarily the same kinds of questions that we presented them when we did our old fashioned diagnostic testing. We are not going to be primarily consoling and counseling and helping people adapt to diagnosis. What we're gonna have to do is start to motivate people who don't have manifestations of disease because we're gonna find them by genomic screening. They're not coming to us with these loaded families. We're gonna have to motivate them to engage in these health behaviors in order to mitigate their risk of morbidity and mortality. And we have to do that in a way that doesn't require hours and hours of geneticists and genetic counseling care. It has to be built into the healthcare system more generally for so all practitioners can do it. I'll stop there. I think you don't wanna take Q and A now, right? Correct. We're gonna wait until Mike talks and then we'll take questions together. Thank you, Les. Thanks. I'm Mark Williams. I'm a professor at Geisinger. And I'm pleased to introduce my colleague, Mike Murray, who is an internist and medical geneticist. He's currently the clinical director of the Institute for Genomic Health at the ICANN School of Medicine in Mount Sinai. But more relevant to this particular talk, he is co-chairing the American College of Medical Genomics and Genomics working group on population screening. As you heard from Les, the ACMG has had publications for many years on secondary findings, which we say we should not be using this for making decisions about population screening, which of course led in the absence to any guidance on population screening from the college. And so, Mike is one of the co-leaders to take that on and we'll be talking a bit about that. So, Mike. Thanks, Mark. So knowing that I went after Les, I tried really hard to come up with a reverend or a religious leader to put in my title, but I failed. So I'll start with a disclosure and that is regarding genomic screening. So I've published on where I think we're headed with all this and this was last year in the publication site at there. But I think eventually we're getting to the point where every individual has comprehensive genetic data set that's generated for their health and meant to be used throughout their lives. So it's going to be linked to an electronic health record and there'll be two types of evidence-based indications to access it. One is for reiterative screening based on age and other triggers and the other is for diagnostic assessment. So that's where my head has been for the last 10 years since we started doing genomic screening at Geisinger. Who's ready to do this? Nobody's ready, but there is a group and I'll mention it at the end that I think we'll be ready soon to enter into this. How long will it take to get there? I don't think anybody knows that answer either. So as Mark mentioned, the American College of Medical Genetics and Genomics has a population screening work group that I have the privilege of co-leading with Sonia Erasmussen from Hopkins and it includes several people in the audience here. And we're taking on the idea of primary findings. So if you create a dataset for genomic screening, what screening results should you look for and generate? And so that stands in contrast to secondary findings as Mark mentioned, that is something that the ACMG has been thinking about and publishing on for over 10 years, starting in 2013 with work that Les led together with Robert Green to think about secondary findings from clinical datasets. So the observation was made 10 years plus ago that as we started generating these datasets for diagnostic testing, that you could go back into that data and find things that were potentially useful for that patient and their close relatives. If only they know about it and perhaps they had no other chance to get that information. So looking into those diagnostic datasets and using it for the secondary use of screening. That was extended to research datasets, including the work that we started at Geissinger in 2015. So using a large research dataset that was created for research, but consenting patients to potentially get back that data and use it in the healthcare setting. So this idea of primary findings, what do you do if you create a dataset just for this purpose is something that the ACMG work group is tasked with? We've been asked to create two work products. The first is to come up with a rubric or a way of deciding what goes on the screening list. And the second task is to come up with a version 1.0 of primary findings. So a list just like is generated for secondary findings. And we anticipate that that will be versioned over time just as the secondary finding list out of ACMG is versioned. So whenever you're talking about a programmatic screening for disease, everybody refers back to Wilson and Younger. This publication is now 55 years old. This is the original cover. This can be found on the internet. It's definitely worth a look. One of the things that they point out in their principles is that they are screening for disease. And so I want to make a point with the next slide about screening for disease versus screening for risk. And it's a little bit of a different take on some of the stuff that Les just talked about. So pictured here on the left is this bright shiny truck in the 1950s that was a mobile unit put out by the California Department of Health. And so you go in the back door there. You give some health information as an intake. You get a chest x-ray. You get a result. And they were trying to diagnose tuberculosis. We were only about 10 years since the discovery and availability of good antibiotic treatments to cure tuberculosis. So the effort here was to diagnose disease. When we're talking about detecting disease risk, we're talking about something different. And this is of course where DNA and genetics sits, but this is not unique. We do the same thing for blood pressure and cholesterol. So we don't look for high blood pressure or high cholesterol because of the specific need to lower those numbers. We do it because it's risk for major disease, heart attack, stroke, dementia, kidney disease. So when we're detecting disease, the goal is treatment. When we're detecting risk, the goal is prevention and early diagnosis. So in 2021, two years ago, we had a ACMG group that came up with some points to consider around DNA-based screening. That's in your booklet that you received. On the left, we put the 10 principles from Wilson and Younger, 55 years old, but still durable and important. And we targeted them for the genomic screening use. And I'm not going to go through all of them or even read out these four that I've highlighted for you, but these four will be mentioned in the slides ahead. So the first thing that I'd like to mention is this idea that people need to be reevaluated in some instances. On the left panel at the top there, this is the diagnostic pathway. And for all three of these panels, I'd tell you to watch the yellow ball as it moves across or where it is in space with regards to other things. So in the diagnostic scenario, somebody comes to the health system with a complaint. They have signs, symptoms, family history, something else that drives an evaluation. The evaluation includes genetic testing. When there's a genotype-phenotype correlation, they make a genomic diagnosis or a genetic diagnosis and treatment goes on from there. When you're talking about screening, the initiating event is the genetic finding. So it's at the start of the line. And then the evaluation goes forth after that. In some instances, that initial evaluation will lead to a diagnosis. There'll be a genotype-phenotype correlation. In others, that patient or that individual should be recommended for reevaluation. And how often and how to reevaluate them will be different for each disease or condition or genetic finding. This is a different take on some of the stuff Les said, too. I think about genotype-phenotype as this Venn diagram. So on the left, you have genotype only. On the right, you have phenotype only. And in the middle, you have this overlap between the two. This is where the genotype and phenotype meet. And this is where, for this context, we're talking about disease or condition that's being screened for. So there will always be, in every situation, some that have genotype only without disease, including the sickle cell variant and F508. Cystic fibrosis, they're smaller in some instances, larger in others, but we have to take this into account when we're thinking about doing programmatic screening of populations. And so this Venn diagram will change, but I think it will always be incompletely overlapping, no matter what the gene disease variant or population. And so some people have heard this in talks that I've given over the last 10 years and said, gosh, if you're going to tell people about risk and then they're never going to get to disease, maybe you shouldn't be doing this work. And so I'd remind people, just like Les said, there's nothing new in genomics that isn't elsewhere in medicine. So non-penetrant risk is not limited to DNA.  So she was undoubtedly told by many healthcare professions, probably most of which she outlived, that that was going to kill her, that she was going to get heart disease or cancer, but she proved them wrong. I'd also point out that non-penetrant risk does not necessarily run in families. You all kind of know this intuitively. So identifying a risk in one individual and then identifying it in their mother, father, brother, sister, children, there will be differences in who has disease and who doesn't. And that gives me a chance to mention that cascade testing and the evidence development around that is really important for screening projects at this stage of the game. One of the principles that we talk about is DNA-based screening as a replacement for something that already exists. And I'm going to suggest that it has to be an improvement over the standing way of identifying risk and managing risk. So the U.S. Preventive Service Task Force came forward with recommendations for screening women for BRCA one and two risk. Here are the three publications over this period of time from 2005 to 2019. And it comes down to this table from the 2019 document. Sorry if you can't read that small print, but it says that the U.S. Preventive Service Task Force recommends that primary care clinicians discuss with women their personal and family history related to the cancers associated with BRCA one and two and their ancestry. And to decide essentially to divide all women into two groups, those on the top row there with enough risk to go forward with discussions and potentially testing and those on the bottom row where those should not be pursued. Now, when this came out in 2005, I remember having lots of conversations about a lot of people were excited that we had a strategy that didn't cost anything because it cost nothing to get this family history. But if you talk to primary care physicians, they might tell you something different. 10 to 20 minutes of somebody's time to sit down and talk to somebody about this history to record it and then to act on it is something that just isn't operational in any primary care practice that I've ever heard of. I raised this in a number of meetings. If anyone knows an exception to that, I'd love to hear about it because I think we need to know how they're pulling that off. So if people aren't being screened in this way, perhaps DNA is the alternative. We did work at Geisinger published now five years ago in this manuscript where we took the first 50,000 plus people from the Geisinger cohort, and we looked for BRCA one and two within that cohort. We found 267 cases, which comes out to be about one in 190 people with these variants and this risk. When we went and we said, well, how many of these people or what's going on with these people, we were able to find out that about 20% to be exact, it was 18%, already knew about that risk or we're already having it managed within their healthcare. What about the other 80%? So that broke up pretty evenly into two other groups. In green there, you see those individuals who had never been tested, but also don't meet those criteria, the criteria from the US Preventive Service Task Force and other groups that were actually designed to increase the pre-test probability that if you had a BRCA one or two tests done, that it would be positive, but these individuals, that strategy is not sensitive enough to pick up. In red there are the individuals who did meet the criteria or would have met the criteria, but no one had ever interviewed them about it or offered them the testing. So when you ask the question, when you take this strategy to a population, how many people with these variants are unaware but for genomic screening? In this instance, it was eight out of 10. So 80%. When I first started giving talk about this data, I remember somebody raised their hand and they said, well, you're missing 80%, maybe you're just not doing a good job, which I thought was probably wrong, but I didn't have any evidence till the Healthy Nevada Project out of Reno, Nevada, published their work in 2020, which looked at about 25,000 people. They looked not only at BRCA one and two, but they also looked at Lynch syndrome and familial hypercholesteroemia risk. And they found that about one in 75 people have a risk for one of these three genomic conditions. And in their data set, about 10% knew about this risk, 90% didn't. So I think we're probably based on these two studies looking at 80 to 90% of people finding out about these common genetic risks for the first time through a screening project. The question is how are we going to manage that over time to bring real benefit to those people? So these three conditions are called the CDC tier one, commonly and in many publications. And that's how I refer to them. So here, if you extrapolate from the data in 2023, if we did a screen for the nine genes that are associated with these three genetic syndromes, you'd identify north of four million people that have a risk for these, that have these genetic risks. And in the table there across the bottom are those genetic syndromes, but other than the people in this room, not many people care about syndromic names for these things. What they care about is risk for heart disease and stroke, risk for breast, ovarian prostate and pancreatic cancer, risk for colon and uterine cancer. So most people that are thinking about strategies will include these nine genes in their strategies. And then what to add after that is certainly a puzzle that lots of us are trying to figure out. I would mention that one of the Wilson and Younger refined criteria that we put out was that clinical implementation strategies should be in place and available to anyone that has the risk identified so that the healthcare system can manage those risks. And when you think about heart disease, breast cancer and colon cancer in particular, we have workflows and systems in place across the U.S. healthcare system to really manage these risks so much so that I think of you identified those 4.3 million people in the next to them without missing a beat. So we need to think about other risks that we add and how they'll be managed within the current healthcare system. I won't blow the punchlines on David Vintres and other people that are in the audience's work about their March publication from this year, but cost effectiveness and economic analysis is essential to do good work coming out of that. When you think about how should something get on the list, what does it really come down to? I think what it really comes down to is clinical utility. If you put something on the list and you give back a test result, a screening result, that should prompt an intervention that results in improved health outcome. Anything short of that when we start doing this programmatically on large scale or population-wide, I think has to be questioned. Now, over the last 10 years or so, we've got caught up in this term of medical actionability. It's a good term, at least for the initial thinking about what to put on the list. That's the idea that something would have a potential next step if they got a result back. But that's necessary but not sufficient, I think, when you start talking about really operationalizing this. And these two concepts, obviously, are linked together. To paraphrase, gross and quarry, a screening test alone does not have utility. The clinical utility comes in when there's effective access to appropriate interventions. One of the things that I think we should all think about is that we're in this era of genomic screening projects and pilots. And it is really the time for people that are running these projects to think about the engaged population that they have and the engaged expertise that they have and how that can drive a different list for your project or program than what other people are using. So I use the example of the pathogenic founder variance in the APOL1 gene and the TTR genes that arose in Africa and are very important for populations regarding kidney disease risk and cardiac amyloidosis if there are members of that population that have African ancestry. So not only people that identify themselves, obviously, as African-Americans, but also those who are Afro-Caribbean and have significant ancestry from other parts of the world. The other thing is that monogenic risk for things like major psychiatric illness, there's now pretty good data that those are identifiable through a screening strategy. Most people that are running screening projects wouldn't have the default to take that on, but there are individuals that are thinking about taking the single utility by returning those results is a really important task. So again, going back to primary findings from data sets created from four genomic screening is one of the ways that the ACMG group is thinking about it. And in my last slide here, I'll just cut to the chase and going, I didn't know the rules before when I created these slides. So thank you, Rex, for pointing them out, but I'll just cut to the chase down here and I'll say maybe a white paper that I've been thinking about is that a group really needs to put together a timeline and the milestones for completion of the kind of implementation that we're talking about population-wide and then maybe two future conferences since I have the floor that we could think about is, one, we are now in a moment when the projects that are driving populations screening, the big projects, are mostly privately funded. So when I was referring in the beginning about who's ready to do this, there is money from private businesses and other sources that are ready to start doing this. And we have to think about what's going to happen over the next 10 or 15 years. I have no specific complaint about anybody that's done that or that I know is doing it. But when we have a patchwork of the U.S. population, each subgroup of which has a different group of people holding their data, de-identified, albeit, but what happens as far as the data environment and the potential uses of that data. I think we have a giant LC problem that's coming. I don't think we should wait until the first real disaster happens. I think we need to start thinking now about the rules, regulations, penalties, potential criminal penalties for the misuse of data and how we're going to manage that. And then the last thing I'll just throw out there is, I think we need to start thinking about the human genome project part two. And in my mind, what that is, is recognizing people having their own genomic data set as a public good for their own health and sequencing the whole population. Sounds a bit crazy, but so did Genome Project One when it was first proposed. And with that, I'll stop with my comments and I think Les and I are supposed to set up here. Actually, Mike, we made a change. Come back. We're just going to stay here. And so what we're going to do, thank you first of all. So thanks to both Les and Mike for getting us started. Very stimulating ideas. We have a lot of discussion. We have 45 minutes for discussion, which is great. And thanks for staying on time. We're going to alternate. So I can see this side of the table, but I can't see this side, but Mark can see this side of the table. So we're going to alternate. And what would be helpful is, yeah, if you do these sorts of things when you have a question, then we will keep our eye out and we will call on people. That includes people who are not at the, who are sitting in the second row. Please write, you know, he should be able to see that as well. So I can't see the name, but go ahead. I'm Kate Nicholson from the University of Pennsylvania. So I had a comment about Les's sort of thinking about this. I personally, unless you didn't mention, I'm struggling much more with the issue of somatic testing on tumors and then identifying variants that have and reflex testing, particularly, and I don't know how many people follow us at all with the new Esmo guidelines, which are extremely broad in terms of reflex testing. And one of the things I'm personally struggling with and too bad reasons in here is NF1 reflex testing in the context of people without a clinical diagnosis of NF1. So I just want to bring that up as another form of population screening that we should be aware of and considering when you're talking, when you think about this sort of secondary findings, this is another whole set of secondary findings that I think have a big impact. And I'll just make Mike a quick comment that we've done obviously the same study, although not published, looking at our rate of your say one in two mutations and how many know and panits that's much higher. Actually, it's about 50%. So. Yeah, I guess on two responses that of first is that of course, if they're having somatic testing, there's a reason why someone did that. So that elevates their prior probability of disease. So that's really not population screening. That's working up a patient with disease. Given the list of genes now that they're doing, I mean, it's probably. Oh, so for genes other than related to their disease. Oh yeah. I get you. Okay. It's essentially secondary findings. So the list is probably 30 genes, maybe longer now. So that is, I understand what you're saying, but no, this is like essentially like you do someone who has, I'm going to say renal cancer and you essentially find a beer, say one mutation. It's not an associated cancer. You incidentally find it and it is probably from the screen. I'm just saying like that's a whole another thing we're struggling with where you're, how do you manage that? Sorry. Yeah. Thank you for the clarification. So there's two issues there. The number one is that really, we come up there is lowering the probability of the analytic validity of the fight, right? Because that finding that somatically is not as good as finding it constitutionally. So that's number one. And then number two is an interesting debate going on that some, I'm not a cancer epidemiologist, but my colleagues who are tell me there are some questions. Regarding whether or not all genes that are cancer susceptibility genes are pan cancer susceptibility genes with just different relative risks and that our power numbers aren't adequate to demonstrate that, but they think there's a very long tail with a risk. And that's a different issue. I'm happy to have a big debate about that. This is in the context. And then people are getting reflections through the reflex testing. So there's a really broad set of genes now that are suggested where you should have reflex testing. So you're, you are identifying people who have unrelated diseases incidentally through reflex testing. Sorry, just to clarify how that's, that's, that's happening. And so to me, that is another form of population based screening, to be honest, that's, that's somewhere in between a population screening and opportunistic screening. Yeah. Great. I'm going to take moderator's prerogative and ask a question also to Les. I love the concept of the practical, probabilistic model of population screening. And it struck me that we could potentially get to something to look like the Richards criteria for very ancient interpretation where you say, here are the different pieces of information that you need. Here's how you assess them in a rigorous way. So the people could move away from doing them as one office and just kind of say, here's the information we need to find. And here's the information we need to generate to be able to do that. Is that the direction you think we need to proceed? Absolutely. And we're collaborating with, I don't know how many of you know this group at Harvard, Giovanni Parmigiani, who has this website called ask to me, which is called all syndromes known to man, pardon the gender specificity of that. And it's basically epidemiologic meta analyses of cancer risk for all these syndromes by age. And then they back calculate the likelihoods of disease, likelihoods of finding a variant in a person who has disease. So all the data are there. And then again, for the reverend, you just flip that into the inverse probability. You can ask the question if you have disease, what's probably a phenotype, what's probably have the variant and such. So we can do this. And he has a very simple plug-in tool. And we're just going to have him flip that and then provide, oh, I have this person with this variant and they have cancer when they were 63. What's the likelihood or their aunt has cancer. And we can, you know, derive because, you know, the cascade testing that Mike brought up is, is as essential as it is difficult to do practically. But we can begin to derive risks from even family members who aren't genotypes using Mendelian principles. And so you can do simple plug-in computer-based algorithms that will spit out the risk of disease based on that input data. And you shouldn't have to be a Bayesian expert or geneticist to be able to do that. I think I saw Carol say that up. I think Rex was first and then Heidi. Well, he knows. So he took his pick and then I, and that's why. So it was my turn. And then we immediately found a flaw in our system because I'm covering this and somebody in the middle. So I'm going to start with Carol, but I will use Rex next on my next pair Carol. Very good. So both talks brought up a very similar question. In my mind that might follow on a little bit from, from Mark's comments and questions and that's evidence. So the probability of pathogenicity given evidence and the amount of evidence we need to move something into a tier one. And so what are our gaps there? What, what are the areas that we can improve the evidence or the process to determine pathogenicity and what are the types of evidence we need to move something into something like a tier one screen? This is, I love that question. And I think it's one of the problems I have with the tier one. And I think grouping together, for example, HBOC with FH, I think was crazy. And I, the numbers are one thing, but I think what we have to do is, is we do have to understand the numbers and we have to understand them precisely what our predictive power is and how we likely we are to change the healthcare outcome. But we also have to have a really sober discussion of our error modes. What happens when we have a false positive? What happens when we have a false negative? And I would argue that it's a completely different situation. If you have a false positive for HBOC it's not going to ruin anyone's life. I would think very differently about HBOC. And so we've got to understand the numbers and we have to have our community has to get together and make really clear headed decisions about we're going to do this. Here's what happens if we go down this pathway. Here's what happens if we go down that pathway. And I think it's one of the things that we have to do that happens if we go down this pathway. Here's what happens if we go down that pathway. And so it's not only the numbers, but it's a values discussion about what errors we can really tolerate. So I mentioned that one of the evidence gaps, one of the evidence gaps that we're going to face is right now we have to extrapolate outcomes from data that's derived by a different methodology. So we say that BRCA variants are 80% to 90% associated with disease over lifetime. That's based on different data than we're generating through screening. We don't know that number for screening, but we need to get to it. And places like Geissner now have almost 10 years of experience. We probably need 20 or 30 to really get those numbers, but we got to get to them and it's going to be lower. And it's going to be different. Great. Caitlin. Thank you. Maybe we should name sides of the room, right? So team, the team have teams on the middle. I'm Caitlin Allen, and I'm from the Medical University of South Carolina in Charleston. So Les, you mentioned motivation. And I think Mike, you mentioned utility, buy-in. And so I'm thinking about this from more of an implementation perspective. And maybe adding into that equation, we've talked a lot about the clinical utility, but thinking about research utility. And so for our population screening program at MUSC, that's been a really a huge motivating factor for us is that we have now a large population of genomes that we can then go back and do research. And that's been huge to get patient buy-in for participating and also our provider buy-in and leadership buy-in. So just maybe adding that to the equation or curious to hear any thoughts about sort of the research component of motivation and buy-in. So I think in every setting we should be getting participants to be offered the chance for their data to be used as research. Yes. So asking every participant that gets screened to be involved in research is essential. I think if we're going to do this right over time, we need as many people participating as possible. So that motivation, which most people will agree to, is really important. So I think what you're doing there makes a lot of sense. Yeah, which is that I think it overlaps with Genome Medicine 14, which is this silly notion of this huge wall that divides research and clinical practice. We just have to get over that. And every research participant should be seeing the clinical benefit of their participation and every patient data should contribute to every other person's health care through the research mechanism some way. We just have to make that happen and think more openly about that. I can just clarify that Genome Medicine 14 was on Genomic Learning Health Systems, which is doing exactly that, that basically every patient is a research participant. Every research participant is a patient. I just want to make a quick clarifying comment, Mike. There obviously have been several popularly very well done case control studies looking at risk in BERSA1 and 2 as well as other cancer susceptibility genes, which I think is what you would want to be looking at both carriers and bridges, which were published in New England Journal in 2021 and a lot of sort of resulting studies and the penetrance for BERSA1 and 2 done those well done epidemiologically matched case control studies was about 50%. So there really, those data are there and I think that they were done in studies that were really well known like nurses health and things like that. And I'm sorry that Pete's not here also to comment on that. Thanks. I would also just note that Mike's point is still relevant because the case control studies still involve somebody that has phenotype and in population screening, we're talking about ascertainment, irrespective of phenotypes. So there's still a gap there. Heidi Rehm. Thanks. So as we get better evidence and we better understand probabilities, we can better feed into, you know, recommendations for clinical screening and follow-up and we have a great framework and flingent for actionability and all the variables. But I think one aspect of this that I anticipate being a challenge is that patients' perception of what is important to them or not differs. And I think a great example of that is amniocentesis and the risk of losing the pregnancy versus the risk of a child with a chromosomal abnormality and where those lines intersected became the age a woman should get an amnio and before that they shouldn't. As if the risk of loss of a pregnancy was equivalent to the risk of a child being born with a chromosomal abnormality and obviously for each individual woman and couple, there was maybe very different concerns. A couple that took 10 years to get pregnant versus one that took one day to get pregnant. So as we think about getting good at the front end of this, do we just leave it to the physician to have to have long discussions about patient preferences or how are we going to build in sort of scalable approaches to patient preference is my question. That's a great question. That's probably for genetic counselors in the room to respond to. A session. Good. And I think what we need is to understand the basis, the fundamental basis of what these counseling decisions are and figure out a way to make what is currently a 45 or 60 minute patient encounter, which is totally unaffordable, totally not scalable. And if it's required is the end of genomic medicine. Don't want to overstate that. But if we can understand what, how this process works, I would suggest in a major large majority of cases, we could build online computer based online interactive tools that could help people understand where their values and their key decisions that they want to or outcomes that they want to maximize or minimize and say, look, a person who thinks about this the way you do generally goes down this pathway for these reasons. And we, I think we could supplant the, a large majority of this decision making from this super expensive one-on-one patient encounters to tools like to do exactly what you're hoping. I think I saw Jonathan's hand up. Okay. I'm sorry. Oh, wait, that's that side. So I maybe I missed, I maybe I missed Jeff. So Jeff and then Jonathan. Go ahead, Jeff. Thanks. We have a lot of referees around here. Tough crowd. Thank you. Yes. This is Jeff. Actually, it works well because Mike's a good follow up to Heidi. So putting, I'm a public health person at HRSA. And you, you mentioned about newborn screening might fit in, you know, in certain as learning lessons. I was really struck, Mike, by the picture of the woman with a cigarette at 100 years of age and how hard it is right now to get healthy and how hard it is to change. And particularly your comments about clinical utility. So thinking about newborn screening, sickle cells, we have great ascertainment. We've known the gene for decades. We're getting better at some of the genotype, phenotype correlations. And yet still fewer than half of children get hydroxyurea transcranial Doppler is even lower in adults. And there's such a huge health systems issue right now with things that we are very clear about. It's one of the biggest challenges that we face. And I'm not sure that, but just bear all of us to remember that you may have systems in place. Man, they're not really getting to everyone. I just follow up on that. It's a really important comment. And again, that's part of our changing our mindset from dealing with families who come to us highly motivated already with dealing with the John population, which is mostly unmotivated or maybe I don't know, was it negatively motivated? What would you call that? I can tell you in our secondary findings, there's no study of families were ascertaining includes FH exactly zero of the children in those families have been screened for hyperglossomal. And that's an AAP recommendation routine care and zero. We met covered a single one. It has no penetration. So you have to make this as automated and simple as we possibly can. And I just add that, you know, this is one of the reasons why action ability and clinical utility don't equal each other. And we've got to understand the reasons for that and come up with better systems for getting people from an actionable result to a clinical outcome. Yeah, optimistic note though is that and at least the studies that have been publishing their data to this point, more than half of individuals that have been received a result have actually made a health behavior change of some type in terms of at least getting a test ordered or something of that nature. So it is encouraging that it seems like this may be having some effect more so than what we do typically with diet and exercise and those sorts of things. Ned. Thanks. I really enjoyed the two talks, especially being right next to each other trying to navigate the information between the two is fascinating for me. So Michael, you tell us there's a lot of undiagnosed variants out there unless you tell us not all variants are created equally or uncreate. I don't know how to say our have the same risk. Some are more risky than others. And I think about applying that in the setting of population based screening where you're going to generate a lot more tests. That's what Jeff was talking about, the impact on the system overall. I think about clinical utility and what I didn't hear again was the harms. Because if you're going to generate more uncertainty, you're going to generate more positives with variation and penetrance and we're going to leave it up to an unprepared medical workforce make decisions. We are going to buy more mistakes, more errors and more harm. So as we think about clinical utility, always think about not just the good things, but all of the harms that we're going to accrue because of population based screening. And the whole, right, that we all hope, is that when we balance it all out, we're doing more good harm. But I just ask the group never to forget the kind of downside to the results. Great point. Great point, Simba. Related to that, you know, when we talk about false positives, I was thinking as this is being discussed, we need to make sure that we're distinguishing that from non-penetrance. So some people just blend those two together. And so biologic evidence that a variant can cause disease is pathogenic, is different than giving back a variant that the data was misinterpreted and is actually not pathogenic. And that will hopefully help to clarify some of those issues. Yeah, I would just add to that. There's a wonderful quote I came by, came across when preparing the talk. Isaac Asimov said, the uncertainty that comes from knowledge is not the same as the uncertainty that comes from ignorance. And it's really important to remember that. And, you know, we in medicine are extremely good at washing our hands of awful things that happen because of our ignorance. And our only choice here is to make decisions based on no genomic information or based on our decisions based on genomic knowledge. And we can make better decisions with more knowledge. I don't think we should ever confuse that. Not to say that we won't make mistakes. And your question earlier, we have to do error mode analysis and say how many people are going to end up in this error mode and how many people are going to end up in that error mode. And we make rational decisions. We grossly, newborn screening is a great example, right? It has a very high false positive rate. And the whole system is built to work with that high false positive rate because we insist on the sensitivity being high because avoiding a kid having a devastating disorder like PKU is the outcome we really want to avoid. And so we way overdiagnose it and then we get rid of that in the follow-up testing. Not to say we should do that in genomics, but we have to say, what do we want for the false positive rate? What do we want for the false negative rate? And where's the right trade-off? And I would say that those trade-off numbers are completely different for FH than they are for HPOC. And we have to have that discussion and say, okay, where should we set those? Because we don't get perfection. Perfect is the enemy of the good here, but what's the optimal good we can get out of this? One more just basic orientation piece. I think that these types of questions have a very different context if this meeting was about, are we going to implement population screening or not? This meeting is about what are the research questions that need to be answered if we're going to proceed to population screening. So these types of issues where we may have fundamental differences, mostly those differences are being driven by, we don't have the information, which means they're really a high priority for research. And so for those of us that have been tasked to capture information, which gives me another opportunity to be criticized about how I'm doing here, we will be contextualizing these types of debate points into a research agenda. Jonathan. Jonathan Burke from UNC Chapel Hill. The first two talks are why I came here. This is so good. I'm also giving a talk, but this is thanks for the setup. One sort of commentary thinking about the two talks is given the evidentiary requirements for doing this and getting anywhere close to right. It's going to be perfect. I think it really drives us to the conclusion of starting with a very, very small and very well understood set of conditions. Just so for the ACMG committee to think about, let's not go to 70 right away. Let's try to focus it in. And I guess the other sort of point about that is if we're not really close to that sort of whole genome analysis and healthy people goal, as you kind of pointed out, what would you think about at least as a starting point for that considering whole genome analysis in consenting adults before we go all the way to whole genome analysis in all new boards. That seems to me like we could get a long way towards the goals of identifying all those risk factors and getting our feet fully wet before we start into new boards. So we have this interesting issue that will come up. We do a whole genome, and then we limit our analysis to the CDC tier one. Won't somebody take that data and say, let's go for secondary findings here and look for all other kinds of things. So then do you recommend doing just a limited data production to only look at those things? Well, then you're talking about wasted opportunities from a budgetary standpoint. It's a conundrum. One thing that I didn't mention at the front, which is worth mentioning is the ACMJ has been very clear over the years that the secondary findings list was not generated as a population screen list, even though everyone has assumed that from the beginning. But when you think about it, the secondary findings are from clinical data is generated within a patient provider context, where that provider, by ordering that test takes on the responsibility to see through the outcomes related to that test. So it's going to be very different when we're doing screening of populations and no one's really in charge of kind of seeing through all those variants. And we'd rather address that too. George. Mark, thanks. Actually, since I put up my card, my questions have evolved. And I'm not sure I remember the original question I wanted to ask, but I love that quotation about uncertainty and knowledge and ignorance. And I'll be honest with you, increasingly the challenge is distinguishing knowledge from ignorance. A lot of people tell us they've done their research and you dive into it. It gets a little difficult. So let me make it brief and thank Mark for reminding us of the core purpose for what this session is about. And maybe make a friendly amendment that as we address the research directions for when to screen, who to screen, how to screen, maybe also think of the engaging the people we screen and the research questions about implementation. I think they agree that very relevant if we could extend the discussions to that. And then finally, there's something about the automation and use and artificial intelligence to extend how much we can do. All for it. It's really terrific. The challenge we're also beginning to see is that there's a segment of the population that have no access to that. And so as we do more and more of that, we tend to exaggerate the disparities that already exist. What are some thoughts around how we will become that before everyone has full access to automation and AI and machine learning? Yeah, I would say I'm really a proponent of both opportunistic and population screening because I think as it's currently structured, I can't imagine a healthcare resource that is more disparate in its availability to patients than genomics and genetics. But the obstacles that people have to go through to get to see a person like me or Mark are ridiculous and only the most resourced, educated, advantaged people can navigate that. It's ridiculous, it's terrible, and that's disparity. And by making it as broadly available as we can, we have an opportunity. Not to say that it will eliminate disparities, it won't, not to say it will make irrelevant all of the other disparities in our healthcare system, it won't, but it will begin to level that playing field of access to genetic and genomic healthcare, and I think we have to push really hard for that. We have a lot to go through, but we're doing well. Christine. Sure, thank you. So as we want to continue to improve our understanding of, and our communication of risk and penetrance and probability, it seems that the screening is not going to be just a one and done type of deal with the screenings. We're going to have to continue to engage them so that we understand their outcomes over time. And I was interested in what Mark was saying about how, I think you said 50% of screenings are taking at least one action right after the results. But how do we, or what are the thoughts about ensuring that we continue to engage so that we have a fuller understanding of outcomes? I think the easiest way to answer that is to stay tuned because we have sessions throughout the meeting that are going to be really focused on that. So if you're willing to kind of put your question in the parking lot, hopefully we'll get some answers and some interesting research opportunities for that. Is that, that's not an answer, but it's practical, I think. I'm going to go to Terry because there's an online question from April Adams.  Can you hear me? I actually just had a comment. I think it's similar to some of comment that was put in the Q&A. And just as we're kind of going through this process and thinking about implementation and how this is going to be widespread and equitable is just, yeah, remembering when we make comments like who is motivated and who is not, remembering that those barriers for motivation may really be set in the healthcare systems and the structures that we've created, right? It may not be the onus on that individual. It may be that our healthcare system has prioritized people who have resources, people who have high health literacy, you know, and not prioritize people who are from marginalized populations. And so I think starting in a room like this with lots of stakeholders and having language that is not putting all of that onus on that individual is a really big place to start with that trickle down of why does equity matter? Why does diversity matter? How are we going to actually engage people in a way that doesn't make them feel more marginalized? And we'll probably talk more about this as we get through the meeting. Yeah, I would just add, we do have, I think, an outstanding session this afternoon that will address at least some of those issues. So stay tuned. I know there was two down here. So here, props, yeah, let you go. Thanks, I'm Kelly. He's from Hudson Alpha. This kind of circles back to, I think, Heidi's comments earlier about patients and bringing their kind of preferences into, ultimately, what turns into outcomes that we can't necessarily script that, that there's all these other variables at play. And I guess just wanted to bring up the additional variable of patients believing the results that they get back and when it does or doesn't corroborate with their story, their narrative, their history. And we bumped into people that get a negative result and they flat out say, well, I don't believe it. I think that's wrong. Or vice versa, they get a positive and they say, well, I don't believe it. And that's going to play into those actions that are taken in those conversations. Sometimes we can change their minds. Sometimes I'm convinced we don't change their minds on whether they believe it at the end of the day. But as we scale things up, just making sure that we're kind of looking for ways to improve that belief and have those narratives discussed and approach that with populations in more scalable ways. I don't know the answer to that, but that's going to play into the ultimate impact and benefit of patients. Thanks for that comment. I have two probably irrelevant questions. One was, Mike, you mentioned at the end this idea of monogenic psychiatric disease. Maybe you could amplify on that because I'm not sure I know what monogenic psychiatric disease is. There's a whole polygenic psychiatric disease problem that people are using polygenic resource to find people with schizophrenia or depression. And that's going to be an interesting implementation issue. That was sort of an information question for me, and maybe I'm the only one in the room that doesn't know what you're talking about. And then the other question was for Les, and it may be that we'll deal with this later. But if you're... I love the Bayes theorem sort of approach to this. It makes perfect sense. If you have a patient who has a phenotype and you're then going to... And the phenotype could be cancer susceptibility. That could be a phenotype. Or the phenotype could be something that you actually put your hand on, like cardiomyopathy or something like that. Maybe this is a question for actually Jonathan Berg too. How far into the long, long, long list of potentially causative or associated genes does Bayes tell us to go? If I have somebody who had cardiomyopathy, there's four genes that I might want to look at that have 10 genes or something like that. But then there's another 150 that somebody wants me to look at. And how do I sort of balance that? How do I approach that? And I'll tell you why at the break why I'm asking this particular question. But I'd be interested in your thoughts. So it's like a monogenic psychiatric disease. Yeah, so I will send that reference around. I've been approached by people, Yale and now at Sinai that are interested in looking into that. But it's a good point. What do you mean by that? And I think we're also, you know, focused on single and nucleotide variant here. But the reality, and this gets into the polygenic, if you will, is that we can do cognitive screening and there's publications out of Geisinger that have shown that we are not doing diagnostic testing. A lot of people carry known pathogenic copy number variants like 22Q11.2. And the vast majority of them is not 100% of them have neuropsychiatric complications related to that. And so if you want to constitute that as a monogenic screen disease, I think there is some evidence of that. Okay. Your second question is awesome. And in fact, we're incorporating that concept is in an upcoming paper and it will be in the version four of the ACMG amp recommendations, which is that negative evidence, if you have a phenotype that has locus heterogeneity, negative evidence at one locus comprises positive evidence at other loci. That was not in version three of Richards at all. But that's what you're saying, right? Because when you start once, was it, who was, was it Sherlock Holmes? Once you've eliminated the impossible. Whatever remains forever improbable. Right. That's it. That's basic. Yeah. And so I, you know, that's why I'm a genome fan because I want all the variants. And then I want to do a probabilistic assessment starting with the most likely culprits. And once I've excluded them, I'm walking down that curve of contribution until I get to the most likely answer. Does that make sense? Sort of. It doesn't tell you when to stop. Because once you have the bird in the hand, the question is, do you keep looking in other bushes? That's an interesting question. Bays won't tell you whether or not to do that. I'll just tell you what the likelihood is that you might find something else if you keep looking. Do I start with the 10 genes or do I start with the 200 genes? I think you should always have all 200, but you should start with the 10. I like that. Jillian Hooker, our concert genetics. I want to go back to the conversation that Heidi started less as response and some colleagues comments about genetic counseling and patient preferences and even the scalability of genetic counseling. And first point out that from a numbers perspective, genetic counseling is one of the most rapidly growing professions and the numbers are a lot bigger today than they were five or six years ago when we were talking about a shortage. And I think many folks have sort of acknowledged or sort of accepted a shortage as a truth without actually like digging into what's behind that. And in fact, I think many program directors right now are worried that perhaps they're training too many people given the contraction in the lab market that they're increasingly worried about the job market for genetic counseling. And so what we have then is not so much a shortage, but a distribution problem of where genetic counselors are, where positions are, which is related very much to the finances of it, which is sort of covering these positions, which are also on their way to getting a little bit better with a new CPT code slated to come out in 2025 that I think will really, really help in that situation. And then to the like we can't scale to individual level to our going on to engage in screening. I think already we're doing that in MRI and echocardiogram colonoscopy. These are individual provider patient interactions, most of which cost far more than a genetic counseling appointment. And so I think there are economic models that might be more sustainable for follow up to population level screening where genetic counselors could actually save costs in a way that would make them more scalable. And so I think we're going to be able to do that in the future. Unless I'm missing something, which I guess is my question, this is their piece I'm not seeing there. Labor will always be the most expensive part of healthcare. And anything that we can do in healthcare that supplants an hour of any provider's time with some technique, some technology, some non-human interaction that can do that, it can do that. And it does, will save money. And you're right that genetic counselors per hour are less than geneticists, but they're still real money. And what do you think an hour of genetic counseling actually costs the healthcare system in totals? Probably $200 or $300? We'll have the answer from the rock. We haven't done that yet. There's a study going out right now. That's actually being calculated as we speak. I think that's a great question. And it will most certainly be less than the cost of an MRI, an echocardiogram, or a colonoscopy. I think that no one would debate that at all. The other editorial comment I just make is that numbers are one thing, distribution is another. And so the distribution of the resource is also something that's important to consider. But I think this will be a great research question, which is how do we actually efficiently deliver this if in fact we move it forward? I'm going to turn it back online to Carol Horowitz. Hi, everyone. I'm so sorry I can't be there in person. Following up on this conversation. I think it's not just how many genetic counselors and the distribution. I think more research is needed into when we need genetic counselors. I was struck when we have now tested many thousands of folks for April one variance. that almost none asked for and had any genetic counsel, even though it was offered to all of them for free. And I think we need to think about when this is, as Terry Monoglio, you mentioned many years ago to me, is able and like a pre-atni test, do we need a genetic counselor to go through that? And how do we start looking at all the different tests we do and what the resources are needed to give good quality equitable care that's accessible to many people? We have Bruce Korf online who has a question. That's gonna be the last question. Yeah, I'm one of those that had an unexpected encounter with an unwelcome small genomes. I'm sorry, I'm probably about 300 feet away from you all, but that's probably what you want right now. Actually, I was gonna make a comment, which is a bit delayed. It was, I think Kate made the point about finding NF1 variants in people who have had cancer testing, usually breast cancer testing. And we've seen a fair number of people like that who most of the time don't have any signs or symptoms. Occasionally they do and they just didn't understand what they were or some of them may be mosaics, but some actually really don't have anything that you can find and most of them are older people. I think it may turn out to be the case. We haven't proved this yet, but that some of them may have clonal hematopoiesis. And we're also doing an analysis of all of us data now and we find a pretty substantial number of known pathogenic NF1 variants where there's no trace in the record of any clinical diagnosis. And I think I'm not sure they all have that because no doubt some of those records are incomplete, but I suspect some too may have clonal hematopoiesis. And although maybe a small point is something just to keep in mind as we look at older populations, if that's what we end up doing with screening. One other point, which is a little bit, which is based on comment just made a moment ago about genetic counseling. You know, I take the point about the distribution, but I would also make the suggestion that we will never ever have enough genetic counselors or medical geneticists to see the people who are involved in screening and that we need to think in different terms in looking at ways to communicate information. I personally think that the genetic counselor of the future will include artificial intelligence-based sort of chats that people will be able to converse with and get information as these systems are improved, which no doubt they will be. Thank you. Any comments from the speakers? Last comments you want to make? Less is good, Michael. Okay, well, first of all, let me thank both speakers for stimulating our thinking in this opening session. Lots of great questions from participants. We did leave some 10 cards up. We recognize there's more interest than we have time for, but this is just the first session and I'm sure you'll work your questions into some future sessions. So we will take a break now and we will reconvene at 11.05 for session number two. Thank you. If folks could take a seat, please, we're gonna get started. Thank you. Well, welcome everyone to session two, Genomic Screening Technologies. My name is Erin Ramos. I'm the Deputy Director of the Division of Genomic Medicine at NHGRI and I'm co-moderating the session with Jeff Roscoe. Jeff introduced himself briefly, but he's a pediatrician and Director of the Division of Services for Children with Special Needs at HRSA. So I will introduce our fantastic speakers, try to keep us on time and then Jeff will mostly moderate the discussion. I moved over here so I can see folks on the opposite side of the room. We'll see how folks. Aang is our first speaker. Christine is joining us from the Department of Molecular and Human Genetics at Baylor College of Medicine. She's also the Chief Medical Officer and Chief Quality Officer at Baylor Genetics. So Christine is perfectly positioned to talk to us about technical approaches and logistical considerations for population-based genomic screening. Thanks, Christine. Thank you, Erin and thank you very much for inviting me to share some perspective on the current state of genomic sequencing from a technical and logistical framework. And I should say that the running title of this talk originally was Nuts and Bolts of Genetic Screening, which may be a more descriptive idea of what I'm gonna be talking about today. So I just wanted to start with a summary of things that we've learned, lessons learned from optimizing high-throughput genomic screening. First of all, the input is very important. So there should be a consistent DNA source and quality. We should also optimize and simplify the workflow with automation as much as possible. And this is especially important for library preparation, which has been somewhat refractory to automation, but there's been a lot of very good advances in that for now. Of course, it has to be cost efficient for the laboratories. And then also there should be a focus on continuous improvement. So there should be continual upgrade process, not only with the testing for laboratory protocols, but also for the analysis. And this can present some challenges for the laboratory because with every new major change or not even so major validation, testing and validation is necessary. So you wanna be very prudent in terms of deciding when and which advances you want to introduce to your workflow and that they must be durable. You also have to define your metrics. These are pretty well understood for next generation sequencing, but metrics are very important to monitor throughout the process. And also you want to monitor them long-term. And I'll be speaking about ways of doing this by looking at the results and the data that are being generated. Analysis, I think we touched upon this a little bit earlier. I'm sure we'll be talking about it more. It must be automated, but there's always going to be a manual component to this as well. And in order to keep your database updated is of the utmost importance, but also to communicate how often that database is updated. And then finally, we talked about the input, but what about the output? There must be clear communication of reporting practices so that providers and patients and screenies understand exactly the result that they're getting. So I just wanted, I am Cleo Lab Director for both Baylor Genetics and the Human Genome Center Clinical Laboratory at Baylor College of Medicine. So I wanted to talk a bit about requirements to launch a clinical test. And I'm gonna focus mainly or mainly exclusively on LDTs, but as we all know, there is more discussion about oversight of laboratory develop tests. First of all, it needs to be performed in a clear environment, a clear certified laboratory. Cap accreditation of course is preferred as well. The indication for testing needs to be clearly defined as well as elements on the requisition such as consent for additional testing or research that we touched upon earlier today. Specimen requirements, the specimens that are acceptable for the test and have been validated for the test. SOPs have to be generated and there has to be a process for validating your reagents, your instruments and your vendors on a regular basis. In terms of the test design and the validation, the technical limitations of the test must be determined and disclosed with the rest of the description of the test. Asset interpretation also needs to be fully disclosed. Your rationale for validation, and this can take many different forms, but especially with the different variant types that you're going to be reporting on. So SMDs, copy number variants if you intend to report on those. Your clinical reporting criteria, again, the limitations of testing and reporting and then importantly, the post launch evaluation. So these tests, once they're validated and launched, they need to be continually monitored for performance as well as for the results that are given. So if you are expecting a certain population frequency, a variant or a gene, you must continually monitor your results to make sure that there are no surprises and that will address some of the false negative, false positive reporting that we've been touching on. So clinical test validation, and this was discussed in the first talk, there is analytical validation, which is of course your accuracy, your precision, your sensitivity, your specificity, reproducibility, limits of detection and the choice of your validation samples. So for a large panel, you must have positive controls and validation samples that are going to be a good representation of the genes that you're going to be assessing, especially the more common ones. And then of course the clinical validation. So what is the purpose of your test? Is it newborn screening, is it family planning, is it wellness, the evidence for including those genes and the action ability that is associated? So in general, these are the stages of validation of a clinical NGS test. The test design, the development and the optimization, and we talked about, I touched on some of this earlier, the test scope is very important. So what is the endpoint of your test? What is the information that you're hoping to provide to the patients? The samples types and the turnaround time. So this should be very clearly communicated to participants so that they are not waiting for their results and they can expect their results within a certain timeframe. The wet lab workflow and any automation or if you can automate the whole process, the QA metrics and performance need to be determined. And then of course your pipeline needs to be established. You may need to develop additional modules for some challenging regions, both on the analytical side and on the technical side. And I'll talk about this a bit later. Your variant confirmation approach. So are you going to buy an orthogonal method or are you going to rely mainly on the metrics of your NGS analysis? The test validations I talked about needs to be very thorough. And we should also remember this in your pipeline, both wet lab and analytical is going to be exposed with high throughput. So you have to make sure that these processes have been tested and stress tested as much as possible with volume, with difficult regions and level of detection. And then the test performance needs to be monitored as I've mentioned with QA metrics established and tracked. And also importantly, sample identity, contamination to ensure that you're not having sample mix up. And I'll talk about this a bit later as well. So this is a very, it may sound simple but a very important logistical concern. So the sample collection, what type of sample is going to be accepted? These are some of the considerations in deciding which type of sample is going to be used for the test. Participant convenience is of course very important, at most importance. The laboratory should provide kidding. So whatever materials are needed to gather the test sample should be provided in a kit that's going to be provided to patients, as well as detailed instructions for self-collection. And I'm talking mainly about where the patient is going to be collecting their own sample, not where the sample is collected, let's say in the laboratory setting of a medical center. Very importantly, there should be bulletproof labeling as well as downstream matching to the patient's contact information. This sounds fairly simplistic but this is one of the major sources of error in a clinical laboratory that labeling is not performed accurately, especially if you're testing partners, multiple family members at the same time. Samples come unlabeled, samples come with labels of the other person who is being tested at the same time. The cost obviously is very important, the ability to automate whatever sample you choose for DNA extraction, but most of these considerations have already been solved. The sample stability during shipment and the time to processing. So they should be able to withstand mailing through the postal service and some delay from the time that the sample is obtained to the time the sample is processed. The failure rate of the chosen method should be assumed as well. Of course, whole blood is the gold standard with I think still the lowest failure rate. Saliva is a little bit higher, maybe about five to 6% or though this can vary based on the quantity of saliva obtained. And then also the ability to store the DNA long-term. So it's stability long-term if a bio repository is to be part of the testing process. So these are sample collection options from non-invasive on the left to invasive on the right. I just put some visuals in terms of the types of instructions that are provided to patients. I think the dried blood spots is one of the ones where you have the card and you write your name on the card. So I think there's less possibility of sample mix up there and misidentification, but I was struck by the differences between the non-invasive and the whole blood method. So if you look at the whole blood, there is a healthcare provider that needs to collect the blood and there are multiple types of medical devices. So there's the needle to pierce the vein, there are the tubes, but then most importantly also there's the bio hazards that are produced. So needle sharps and others that all have to take into consideration as resources needed for whole blood. This is taken from paper, recent paper by O'Brien and colleagues and it's from the Oregon project for population screening of inherited cancer and familial hypercholesterolemia. No, it's difficult to see this, but this is basically their workflow from their patient consenting to the patient asking for kits. About 25% of patients who ask for kits did not return them. Then the sample is processed in the laboratory and about 5% of those samples and this was mouthwash failed the DNA extraction step. And then taking the sample through NGS reporting in this particular example, they did choose to retest a positive. So, saliva kits were sent out to presumptive positive patients and another sample was obtained for orthogonal confirmation and this was done by Sanger. But just an example of a workflow that was in place for this project. Choice of platform for a high throughput testing. So just a couple of examples here, genotyping arrays I think were popular maybe a little bit in the past. UK Biobank project had an array of 800,000 markers and this is perhaps some better suited for biobanks, genome centers and other core laboratories. All of us is using a GDA array as part of their testing process. So they're reporting on ancestry from this array as well as doing concordance. Arrays are cost effective, high throughput, low failure rate, but of course there's less flexibility after the design. Targeted NGS panels can be used as well. Examples are universal carrier screening panels, hereditary cancer panels. There's less data produced which will allow you to have higher coverage and this can result in your ability to accurately call CNBs. But of course there's less flexibility after design and a lot of work and curation that has to go into designing these panels. WES has less data, less cost than WGS. Of course you have the ability to reanalyze but you may be missing some regions that are important for PGX and potentially also PRS. WGS hypothesis free ability to reanalyze the highest cost and the highest amount of data. And then there are some hybrid designs that we heard about last week at ASHG. This is a low pass WGS which can be combined with a relatively low pass or less than a clinical grade WES. Example of an NGS workflow. So the blue is a technical production again to automate this as much as possible. The green is the identification of DNA variants. This can be automated. And the yellow is your tertiary analysis and there are a lot of tools. So this is your annotation, filtering, prioritization and then for more diagnostic tests your variant prioritization. To some extent, this is becoming more automated but there is a manual element as well. Just wanted to- Just to interrupt one minute left. Okay, we do have ability to automate some of these processes and for population screening we rely heavily on the existing databases. Quality metrics that can be applied of course there are quality metrics that can be applied to the initial nucleic acid samples after library preparation. Primarily it's looking at insert size, post sequencing their pass fail metrics including the sample identity and then the post sequencing monitoring as I mentioned before including what I think is important the periodic review of your positivity rates to make sure that you're capturing both your positives as expected as well as not over reporting. Just wanted to give a brief example of our carrier screening panel that we do in our laboratory. This would be a tier four as designated by ACMG. So one point that I wanted to bring up here is that typically these panels are not uni-directional. So it's not just NGS that you're doing. There are difficult genes but ones that have high clinical utility such as Fragilex, FXN, CYP 21A2 for congenital adrenal hyperplasia. These cannot be accurately assessed just by a uni-directional NGS. You have to have as we do in our lab another workflow. So for CYP 21A2 we do a long range PCR and then we spike it into the NGS. So you have to have separate assays and then you need to join your assays together. You also have to make sure you're taking that one sample through all the different workflows and you're not having any sample swaps there. So sample identity becomes very important. And then we do orthogonal confirmations for specific genes, especially the more challenging ones such as SMN1. I am going to... So return of results, we're going to have a lot of discussion about this but of course critical to clearly communicate the result parameters to providers and participants. And then the re-contact, re-analysis and possible subscription has to be defined in advance as well. So just in summary, I shared an overview of the current state of clinical methods for population genomic screening. There are clear distinctions between the reporting for diagnostic versus screening genomic tests, typically pathogenic likely path for screening. Of course, the US is for diagnostic, but I wanted to make the distinction that the laboratory quality measures are not distinct. They must be exactly the same for diagnostic testing as for screening. And that can put some challenges on a laboratory because of the high throughput nature of this type of testing. Thank you. Thank you so much Christine. You were asked to touch on a lot in a short period of time. So thanks. So our next speaker, Bob Kerrier, spent more than 20 years as the chief statistician of the Genetic Disease Screening Program at the California Department of Public Health. Although the focus of this workshop we heard earlier is on adult individuals, the lessons learned from newborn screening I think can and should really be factored into our discussion. So we're grateful to have you here today, Bob. Thanks. Thanks for inviting me. And I have no financial interest to disclose the opinions presented on my own and some of them are pretty strongly held. I should have put up the disclosure of a paper that I published a little bit ago called whose title is Newborn Screening is on a Collision Course with Public Health Ethics, which appeared in the International Journal of Newborn Screening. Anyway, why focus on newborn screening? The main reason is it's already the by far the largest genetic screening program in the country. And in addition, the newborn period is perhaps a unique opportunity to intervene in genetic disease before symptoms develop. So one might say, well, why don't we just sequence everybody, all of the newborns and identify all the treatable disorders and get going? The goal of this, my part of the talk is to just say what a bad idea that would be. But for people who are less aware, newborn screening starts with the collection of the blood card about at one day of age. And usually the parents have little or no involvement in that. So newborn screening as a medical test is unique and not being consented. I'll avoid all of my rants about that in the talk. But it then goes to the laboratory where there's a lot of biochemical analysis and then the results are reported relatively quickly. They need to be reported quickly because the target disorders are serious and urgent. I will say on the way by that this first step of doing the biochemical analysis radically changes the prior probability of the, or the posterior probability of disease among the positives is much higher than the general population continuing the base that's theorem series. So this is a state program. And so in the context of public health screening, generally the state has the usual public health ethics considerations. I want to underline at least three of them that newborn screening really is universal. Essentially every baby in the country is screened, every baby, four million a year. And as a universal program, it also applies to everybody regardless of race, ethnicity, socioeconomic status, insurance coverage, anything. It always happens. And a part of that is that the state program needs to be a trusted partner in the process. And when we come to genomic information, this becomes its own kind of challenge. For newborn screening, the choice of disorders is really important. Because the parents haven't been involved, the disorders need to be certainly serious. The state justifies it by saying, this isn't the best interest of the child and we have to move forward. The baby could have a metabolic crisis at two or three days of age if the MCAT isn't diagnosed. Of course, the conditions screened have to be treatable. But along the way, because the state is screening everybody, the goal is detection of all of the disease. But the screening test needs to have a low false positive rate and consequently a high positive predictive value. On top of all of this, this is a hugely high throughput result. The state of California tests 1500 babies a day. And that all has to happen promptly. I don't think genomic testing is in that ballpark quite yet. But single gene or small numbers of gene sequencing is used in newborn screening a lot after an initial positive biochemical test. And it has two functions. One is to reduce the false positives from the biochemical test. Or the other is to aid in the subsequent differential diagnosis of the positive result. Well, let's take a look at a couple of examples. This is a schematic of cystic fibrosis screening in California. The first box there is the biochemical testing. And only about a percent and a half go on to further testing. So it's already weeded out a lot of people. The first tier is actually a mutation panel. At the time of this paper, it was only 40 genes in California. It's now up to a hundred. But on the way by, I wanna point out that in addition to the usual snips and small indels, cystic there are a couple of whole exon deletions that are sufficiently common to be important. And there's some deep intronic variants. And all of this comes from knowing what's going on with many, many patient samples from CF. In the, if two mutations are identified from the panel, it goes on to the diagnostic test for CF. If there's only one mutation that goes on to sequencing the whole gene. And in that, I wanna point to the, if two or more variants are found, including any BUS or anything, one of them is known pathogenic. So it goes for diagnostic testing. And I will point out that if you compare the panel results, the number of CF cases, that's the N equals 138. And in the results from the variant, the sequence saying that almost the same number of CF cases were identified at those two stages. Turning to Adreno local dystrophy. In this case, the screening test is considered positive because the baby, if it passes two tiers of biochemical assays, at that point it gets referred for diagnostic follow-up. But at the same time, the relevant gene, ABCD1, is sequenced to help among other things distinguish between ALD and other peroxisomal disorders. So, but now let's consider what happens if we started to do primary population screening. That is, just go directly to genetics sequencing. There are a lot of disorders that look like candidates for newborn screening, except there's no biochemical test. There's their relatively early onset. They have clinical follow-up, but there's no test. And the group at UNC has identified over 400 gene disease pairs that would be potentially suitable for genetic screening. Their definition of suitable in mind of early onset are a little different, but okay, fine. But, and this I think is, to me, one of the really important things to think about in genetic screening, that when there is a diagnostic test, you can afford to consider all kinds of variants, including VUSs, that's what the cystic fibrosis model did, that you have one known pathogenic variant plus something else, let's check it out. But when there is no diagnostic test, then you're really relying on the genetic result itself, not just to predict the genotype, but really the goal is to predict the eventual phenotype. And in that case, you have to just stick to what you know, which is probably, I keep thinking about autosomal recessive disease, because essentially all of newborn screening is that. So you really have to just refer, like homozygous cases of a single, of a known pathogenic variant, but given that what's known about the pathogenic variants, that starts to impact your equity. And so this is a really difficult thing. Every single gene is its own screening test. And so you have to know what you're looking for. Some diseases like crepe disease is commonly caused by a large deletion. So depending on the disease, you have to know, you have to know when you have to be able to find copy number variants. And enough about the US is, one of the things that goes with this, because the knowledge base isn't uniform across ethnicities, is that there can be racial, ethnic distinctions in screening. And we'll take a look at one of those. The thing that struck me about this paper was that the number of variants that were found did not seem to vary according to race or ethnicity, but when it came to interpretation and identification of pathogenic variants, the differences were significant. And I think this is probably not a surprise to anyone. I keep focusing on autosomal recessive disorders because it strikes me that the notion of pathogenicity doesn't belong to a single variant in this case. It's the combination. So here these are PAH variants, the causative gene for PKU. So in each group, we have two homozygous variants and the compound heterozygote between them, and then looking at the frequency of various levels of disease. And so for the two groups, the left group and the middle group, there's one bad variant and one, not moderately pretty okay variant. The resulting compound heterozygote is in one case not so bad. And in the other case, almost as bad as the worst variant. The group on the right, I think is the most, it's not troubling and it shouldn't be surprising, but in this case, the compound heterozygote is worse than either of the homozygots. And it just reminds us that enzyme structure and function isn't a linear combination of the variants that went into it. So it almost goes without saying, but compared to current newborn screening methods, genomic sequencing is much more expensive per patient. It's reduced sensitivity and specificity. And it's which is exacerbated by racial ethnic differences. This secondary sequencing is really valuable, but that's not really what we're here for. And there's this promise of newborn screening for all of these other disorders if we could use sequencing. So here's my list of things that really need to be worked on. Many of them are in progress. There are people here that know more about this than I do. I really would love to have screening tests in place that distinguish between soon onset and down the road. Bob, sorry to interrupt, about 30 seconds left. Holy moly, this is the last slide. So let's just say this. Diagnostic testing would allow the inclusion of more VUS. Sharing variant data, not in individual laboratory silos, but across the world really. The interpretation of compound heterozygots. Can we automate that? I don't know. And there needs to be guidance, real guidance on pre-symptomatic management of genetic disease. We don't, I think my impression is that we don't have a good sense of what to do with cases that are identified with a genotype that don't yet have a phenotype. So, and thanks. Thank you so much. So our last speaker of the session before the discussion is Jonathan Berg. I know Jonathan quite well. We've worked together on ClinGen for the past 10 years. Jonathan wears many, many hats at UNC, one of which is directing the program for precision medicine and healthcare, which has implemented a clinical service offering screening of CDC tier one gene. So Jonathan brings lots of experience to discussion. Take it away. Well, thanks for inviting me. And I think I'm gonna pick up some threads from some of the other talks. I'll try to give another way of thinking about this. And the way that I've been kind of cogitating around this is really thinking about the number needed in genomic screening. So there are a couple of well-established sort of terms that are used in medicine, number needed to treat being individuals who already have identified risk factors. How many of those do you have to treat with something to prevent that poor health outcome versus number needed to screen, which would be the number of people you have to screen for those risk factors to find the one that you're going to essentially help by preventing that adverse event. So all of this as noted previously, we're talking about the risk for poor health outcomes, right? Monogenic disease, risk for poor health outcomes, polygenic risk for poor health outcomes, however we wanna formulate it, that's the concept. And can we use that similar logic to examine genomic screening for monogenic diseases that convey high risk? All right, so the first part of this is gonna talk about test performance and population prevalence. I probably don't have to go into too much detail about the way we classify variants, but just to put it a really fine point on it, this scale from benign to pathogenic is about probabilities. And there's a really sort of important zone there in that orange to red where we're talking about high level VUSs and likely pathogenic variants that are particularly useful in a clinical context when you have a diagnostic workup as less pointed out, but also our potential false positives, especially when you start thinking about that low prevalence population. So we're gonna, I'm gonna show you my version of math based on images here in the next couple of slides. So if we're gonna start with a population of blue people where there's a few orange people in there with a monogenic disease, the prevalence of that is fixed. It is what it is in our population. This example is approximately one in a hundred. That's gonna be at the best and in terms of the prevalence of the diseases we're talking about. So this is an optimal situation. And of course we have the test performance, which we've already talked about as being tunable, right? Which types of variants are we gonna include as positive screens? That's gonna yield us a population of patients, some of whom are the people with disease and some of whom don't have that disease but have been picked up because of the way that we set that tuning. And so then we can calculate things like the true positive rate, the false positive rate and sensitivity of the test. And I've given this one an 80% sensitivity. And we can also calculate the false positives and they're in the specificity, which in this one, I've given a 99% specificity. And you can kind of see how those numbers relate to each other. Of course, this is just determining whether an individual has or likely has that disease, right? With all of the caveats about their prior probability to have it, not whether they're going to develop the symptoms of the disease. So we're gonna get there in just a minute. All right, so let's start with those groups. Now we can do our math, right? So you've got your four true positives divided by all of the positives. That's your positive predictive value. So in this particular example, we've got 67% positive predictive value. That's gonna be very good performance compared to the prevalence as we'll look out for other conditions. And the negative predictive value is going to be very high, almost no matter what sensitivity we choose to use as our threshold, because there's so many people in the population and because these conditions are so rare. So what then is the number needed in this case? So I'm gonna use a number needed to diagnose, and I use the term diagnose as a molecular diagnosis, if you will, probability of a molecular diagnosis, that one true positive person. And that's gonna depend on the sensitivity. And so this is an example table, just basic calculations of the prevalence of the condition and then setting out some kind of basic clinical sensitivity and specificity values that relate to each other in terms of the fact that if you're going for 100% clinical sensitivity, your clinical specificity is gonna get pretty low. And if you go for the maximum clinical specificity, your sensitivity is gonna suffer, but that's the trade-offs that we're gonna have to make. And so you can see with a one in 250 condition like perhaps HBOC, something in that realm, if we're going to be going for a midline clinical sensitivity of 90% with a specificity of up to perhaps 99%, we're still going to end up with about three false positives for every true positive. There's just gonna be some leakage of those people into our population. And it gets much, much worse, the rarer the conditions get. You almost can't do screening for a one in a million condition with anything other than the absolutely well-known pathogenic variants or you'll just wind up with lots and lots of false positives. Okay, so what are some critical values? And this gets to the research questions. We have to get to the prevalence of these monogenic conditions. So we know where to start from in terms of those priors. Most estimates we have are pretty much hand-waving guesses. This is about a one in 50,000 condition or whatever, but there is nice population ascertainment now from biobanks where we can look for the path of likely path variants. We saw some examples of that earlier. We could use those numbers to get a baseline ballpark, but of course there's gonna be some questions about whether those people are actually truly having that disease or not based on that ascertainment. But that could give us at least a lower bound estimate. All right, here's another way to think about thresholding the clinical performance of genomic tests. So I'm thinking about this, not now as a single variant that's coming back, but as the population of variants that might come back from a given test. And if you had a distribution, something like this, where it's almost a frequency histogram of number of variants reported, if this were the characteristics of your test and you had a couple of sort of the higher level likely path and path variants that gave you a pretty high percentage of the cases overall, you could get a reasonable sensitivity at very high specificity. But then you'd have to ask yourself, is it worth gaining a little bit of additional sensitivity down into that orange realm at the cost of some reduced specificity?  Obviously the better we understand pathogenic and benign variants, the better catalogs we have. Shout out to ClinGen. If we can eventually get the MAV data to really help us classify these variants and separate them so that essentially we're pushing things to path or pushing things to benign, then we will do better with our predictive ability. Oh, and I would just point out, I think this is an area where we could ask our clinical labs to help us with this. So for people who are clearly have a high, high, high prior probability of a given monogenic disease, what kinds of distribution of these variants do you see in that population? Would be an interesting way to look at that. Okay, so the clinical performance is gonna be something we need to learn and understand to know, to calculate the number needed. If you have a variant that's responsible for all cases, then you can really go with that variant. That's gonna give you a lot of information. But obviously most diseases have a much more complex mixture of variants. And that's gonna cause us some problems in terms of really estimating what our sensitivity and specificity is. And I would also add that the complexity of the combinatorial diplotype of recessive conditions and which two variants an individual has even greatly more complicates that. Okay, so part two is about penetrance, actionability and preventing for health outcomes. This is why we're doing the screening. So under normal risk management for that whole general population that are true negative, they're getting their appropriate routine management, they're individualized by family history, et cetera. They're getting average population outcomes, that's great. The one individual that we missed on our test as a false negative is likely getting inappropriate routine management. We might not be able to help that and they do have the opportunity for clinical diagnosis. So there is still a safety net for that person in the sense that they're probably getting at least some amount of medical evaluation and follow up for things generally. Under high risk management, we're gonna have our two false positives who are getting inappropriate high risk management that gets to the harms that are likely going to exceed the benefits for these individuals and they'll have below average outcomes probably. If we look at our four individuals who are positive, now you have the advantage of cascade testing, I'm not gonna talk too much more about that but within those we have what we previously talked about of disease penetrance, right? This is a fixed value for the condition and some of the people will be non-penetrant. That's the one person that kind of comes up to the top there. I refer to this as over diagnosis. They truly do have that monogenic disease. We're just gonna be treating them more than they need to be. Whereas the three people in the bottom circle there are gonna potentially benefit from the high risk management. So these are gonna be the will be penetrant people and our goal is for them not to become penetrant. We want them to not develop that disease either because we've treated them, prevented it, picked it up early, et cetera. We wanna convert those into light blue dots. We're not gonna be successful all the time, right? So in this example we've sort of prevented that poor health outcome in about two thirds of this population likely gonna benefit those individuals more than we harm them and hopefully have above average outcomes. Now this non-penetrant overdiagnosed person goes in with the false positives. This is someone who we are not helping by all of that high risk management. They were never gonna develop disease anyway so all we can do is harm them by whatever it is that we do. So this is a way to calculate the number needed to treat. In this case there are six people being treated. Only two of them benefited. We get an NT of three, right? And so these numbers could actually be calculated through some of our population research to figure out what that actually looks like. Timeline is also important. If we're screening prior to symptom onset then we have a greater opportunity to mitigate those harms. We're gonna potentially have more people that we can prevent that disease in but if we're starting the screening coincident with the onset of disease, well some of those people have already had that disease. It's not gonna change their outcomes at all. And we saw that some of that example in the Biobank data. If we're starting the screening after symptom onset then the best thing we can do is identify people that should have been diagnosed anyway and potentially improve their family health through cascade testing. So what do we need to know? We have to know the penetrance of monogenic disease for each of the diseases we're screening for. We obviously have ascertainment bias from the affected populations that we've studied so far. We're gonna probably have a much lower penetrance for population screened patients. That's gonna increase the number needed to treat since the proportion of individuals that would benefit are gonna start to go down. I think we also need to better characterize the age-based natural history. When do symptoms start? When do people start to develop the poor health outcomes? And when can we time the intervention so that we pick up those people before that happens? Okay, so a couple of strategies. One is to follow up the tests, right? On all positives, like we have a gold standard test we do. Well, that's gonna cost money and we need to calculate that into our cost of screening. But we could potentially resolve some of those people as false positives. They are actually.  There's the issue of how to deal with this incomplete penetrance, right? Well, one way to do that is through what I've referred to as proximal surveillance, some low burden way of following people, maybe generating some additional data, looking at their family history more closely, finding out a little bit more about their risk and characterizing it so that some of those people are gonna go on and get low burden care for their lifespan and not get something definitive done, whereas others might trigger a definitive intervention because of a phenotype that develops, right? There's a slight widening of the aorta. Somebody's following that. It hits a point where they then need the surgery, that kind of thing. On the other hand, you could really work on the refined risk assessment and really try to triage people into those who really don't need a whole lot of additional management and those who do need whatever that definitive management is. And this might be the sort of thing that you would bring into the decision making about something like prophylactic surgery, right? How are we gonna decide which people to remove an organ from based on that risk? It's a medical workup and decision making and I think that's gonna be costly. And I'm not sure we're capturing that effectively in our current estimates of cost. So essentially we need these strategies and they need to be defined for each condition that we're gonna be doing screening for and we need to build this into the cost of a screening program so that we're not just thinking about how cheap it is to do the DNA sequencing but we're thinking about all of the costs of the management. Okay, so critical value to know. This is another way of saying clinical utility. I'm taking action ability and making it quantitative. What is the quantitative action ability of each monogenic disease? Donathan, one minute left. Got it, I have one slide. So how much reduction in morbidity and mortality will we expect to get, right? Can we really put our hands on that? How effective are those strategies to reduce the false positives and mitigate the overdiagnosis? And in the absence of any controlled trials or 20 year follow-ups, how are we gonna estimate what that number needed to treat is really to achieve those, to reduce the poor health outcomes? So going back to my toy example, again, this is sort of a best case scenario because it's a fairly high prevalence condition. I've given it pretty good test performance characteristics, estimating the number needed to screen. We've got about 125 people to find one diagnosis. The number needed to treat is about three. So we would need to screen 375 people to find one person who we're gonna help. And in doing that, we're going to identify people who are false positives and overdiagnosed and we're gonna do them harm. And so we have to make sure that we're balancing that as we think about these conditions. So the conclusions then, we need this key evidence. We need monogenic disease prevalence for the things that we're considering screening for. We really need to understand the clinical test performance and the spectrum of variants that we're getting out in each of these diseases. We really need to understand the natural hist