Futurecasting: Why Now is Just the Beginning

Futurecasting: Why Now is Just the Beginning

Show Video

- Hello I'm Susan Tousi. Senior Vice President and Chief Product Officer at Illumina. It's been almost 20 years since the first human genome sequence was completed. Since then the time and cost required to analyze genomes have decreased by orders of magnitude. As the technology becomes more democratized the transformative power of genomics and healthcare becomes more widely anticipated. Our four guests have been deeply engaged in efforts to innovate, implement, and expand access to genomics technologies.

They join us today to discuss how far we've come and how far we expect to go in our common goal of precision healthcare for all. Joining us will be Charles Rotimi. NIH distinguished investigator and Director of the Trans NIH Center For Research on Genomics and Global Health, C-R-G-G-H. Charles also spearheaded formation of the H3 Africa initiative. We will also be joined by Michael Sherman, Executive Vice President and Chief Medical Officer of Harvard Pilgrim and Tufts Health Plan.

Also with us on the panel is professor dame Sue Hill. Chief Scientific Officer for the National Health Service or N-H-S- in England. And founder of the NHS Genomic Medicine Centers. And finally we have Christopher Mason.

Associate Professor of Genomics, Physiology and Biophysics at Weill Cornell. Medical Director of the WorldQuant Initiative for Quantitative Prediction. and Director of Emerging Genome Technology at Tempus.

Thank you all for joining us. Let's get started. I'd love for each of you to take a minute and tell us where you think we ae with genomic technology right now globally. - I think we are indeed making some very significant progress in terms of making genomics a global enterprise. But there are still you know gaps, you know in terms of who is leading and who is following and who is yet to come on board. So I say you know the Western countries you know are indeed doing quite well in terms of increasing access to genomics and using genomics to indeed inform you know health strategies.

You know again followed by the Asian countries and African countries and South America are indeed making strides. And I think those are areas where we indeed need to you know bring up to speed so to say. - I think we're at a point of tremendous technological innovation and that's all exciting. But if we're going to see this used broadly across United States and throughout the world, we're going to have to figure out how it fits into the care paradigm. Specifically it can't be seen as technology for the sake of technology.

But there's got to be clinical utility. Or so what, if we introduce this into a population here is what changes. And there has got to be a link to affordability. Otherwise those on the payer end will just see this as an additional expense and try to limit it perhaps more than this technology should be limited. So I think the next big challenge for us is to show how it fits together into the care continuum.

- Fantastic. Anything from Chris or dame Sue Hill? - So Susan if I may respond and I'm really delighted to be here and to be able to join this session. From my perspective I feel we're at a tipping point. A tipping point in terms of adoption into healthcare systems across the world. And this is because there's been an enormous amount of national initiatives in different countries across the world.

And I've personally heard around 65 different initiatives described to me in various meetings. Either through the Global Alliance for Genomics in Healthcare or the Global Genomic Medicine Collaborative. Most of those initiatives have started their life within research in a defined research project. But I would say the majority of them have an aim to ensure that the outcomes can be embedded into their healthcare system.

Certainly within the NHS in England we've taken the NHS is contribution to the hundred-thousand genomes project. Where over a hundred-thousand genomes were sequenced in patients with rare disease and their families, in cancer patients. And we use the evidence generated as part of that project to really embed whole genome sequencing into our healthcare system. And we're at the start of that journey. But where we are is there's so many different technological approaches now.

And as we move forward I think our challenge will be how do we take a multi modality genomic approach into our care systems? - Yeah and I might just say, it's really a pleasure to be here and I think we really are at that tipping point as was just described. Where now it's not just genome guided medicine but it's epigenome and metagenome guided medicine. Really genomics is informing almost all facets of healthcare and sort of disease maintenance. And this applies to everything from patients we see at the hospital to large scale clinical trials deployed on many sites.

And even to -- you know tracking astronauts when they get back from space and seeing how has their clonal metapolesis changed or other mutations they might've picked up during their mission. Or even finding new strange bacteria that have seemingly survived in this space station and then sequencing them and then really discovering strange adaptations of life you can pick up anywhere. So it's really, genomics is in everything everywhere and almost in every craft including spacecraft. - Yeah one additional issue is that the state of knowledge is changing so rapidly.

And there are so many studies showing that in many cases standards of care are not being followed even for things that we understand. Treating high blood pressure and diabetes et cetera. So there's also concern, how do the physicians out there in the trenches keep up with this? And I'm speaking to you from Boston.

And I have to remind my colleagues every so often, there are these things outside of Boston, they're called community hospitals and practices. And people actually don't do research and lecture and teach all day, they actually see patients, which is not a bad thing. In some cases they're seeing 20 or 30 patients a day.

And they're busy and trying to ensure that they actually do the right things, order the right tests and use the information appropriately is non trivial. So we also need to think about how we're creating this ecosystem including clinical decision support, AI, other types of innovations to ensure that the technology is used appropriately and creating value. - If I could just build upon that. Of course for any given patient there will be a whole multi professional team that has been involved in their care right, from referral through to the eventual intervention.

Whether that's with a medicine or whether it's because we can't do anything at all at that moment in time. And part of what we've got to do in embedding genomics into mainstream healthcare is really understand what each member of that multi professional team needs to know. Needs to be able to do. But to build it into what is part of their routine day-to-day practice rather than something that feels as an add on or something special. Because it's genomics and it involves DNA for example.

So part of what we shouldn't forget is the amount of work that we've all got to do in healthcare systems across the world in translating research into practice. And really to cut that down as rapidly as possible in terms of that time interval. So we get rapid adoption from research insights. And then within clinical practice that we've got a systematic approach to embedding and developing our workforce. Because that's where we'll get the value added for the system.

But the better outcome for the patients and our populations. - Yeah that's fantastic. - I wanted to just add you know that as we deploy genomics globally we need to be conscious of the different stages of the healthcare infrastructure of different parts of the world. In some instances the healthcare infrastructure is going to be extremely challenging to bring in new stuff, because the old stuff is really not well even implemented.

And we need to be extremely cautious of the fact that a lot of things that creates ill health around the world may not indeed be driven by genomics. And so we need to deploy it with the recognition that we need to know how people live their life. Our current healthcare structure is directed at curing diseases. We need the healthcare system that looks at prevention.

And how do we deploy genomic findings to help inform public health strategies? In the way instead of just telling somebody to take the next pill, to say maybe you need to look at your diet, you need to look at your physical activity, because you have this genetic background, or this level of risk. I think we need to recognize all of that. You know to fully realize the promise of our genomics. And they going to influence people's health, not just disease. - Yeah in many cases as you noted precision medicine, it's not just about diagnostics. It's about understanding risk, who to screen, how to ensure that we're screening different populations with different risk factors appropriately.

And about treatment. We're seeing transformative therapies that can cost millions of dollars that are based on our understanding and our ability to intervene with the genome. That said, you know the upfront expense it can be a challenge. Particularly in many healthcare systems across the world where the financing system hasn't quite caught up on the innovation side. And it means that we need to think a bit out of the box to ensure that healthcare systems that were not designed for these kind of transformative approaches actually do not serve as barriers. So I think there's an opportunity for us to innovate on that delivery and financing side as well.

- Certainly. In the NHS in England, our whole strategy for genomics is really rooted in the four P's of personalized or precision medicine. What one that there is a focus on using it for preventative approaches. And that may see us bringing in and understanding the place of polygenic risk scores. Of understanding more targeted sequencing for presymptomatic sort of cancers. Through to a much more precise diagnosis that enables us to then really personalize the treatment.

- First of all the state of technology has come a long way. But there are still a lot of challenges. And I think that NHS is such a wonderful example of to be ambitious about you know really applying whole genome sequencing into clinical practice. And I would love to hear more. What do you see as the main barriers? And what are some of the most, I guess, you know ambitious models that are being explored into really bringing precision medicine to life? - So I'll sort of make a start on that Susan if I may. I mean I still think there remains the challenges that we've already alluded to.

Which is having a workforce that is ready to adopt the technologies. But partly this is about the right test at the right time for the right patient. And you may be aware that we've introduced a national genomic test directory, which mandates which tests are done for which conditions at which time in their patient pathway. And linked to that, recognizing how technology is advancing.

We will do at least an annual review of that test directory on the basis of the emerging evidence. As we move forward though I think part of what we need to do is understand how we might use multi modality DNA technologies. Short and long read technologies. Optical mapping, all in the same patient for example. If we're not getting a diagnostic yield with one. But where we can see you know structural variance that might be important in certain cancers that we're not picking up with one technology.

I think we will be moving to using a multi modality technology approach. In a similar way we do for imaging in our healthcare systems. We need to think across the functional genomics pathway and not just at DNA and RNA, but also at proteomics. The importance the proteomics are going to play. And up stream at metabolomics and bio markers that will help us sort of monitor disease out in communities.

So this is a really exciting time. And I think it's incumbent on us all to see how we bring all this technology together. - I agree with you, this is a really exciting time. Because all the omics that you mentioned. You know the data types are different.

You know running different systems, kind of bringing that data together into a conclusion. What are some of the technologies and the advancements that we need to see to really make that a reality? - Being able to have clear data standards, the interoperability, the ability to handle large data sets, to interpret data in a standardized way, sort of genomic data. And to be able to get that data interpreted so that it's available in a clinically meaningful timeframe.

So we have to also focus on the data challenges if we're really going to get the embedding as well as the workforce as well as the technology. And we've got to develop the people who are either within our laboratory systems or in our clinical care systems. And we need right in the middle to work with patients and with our research and industry collaborators. Because that's how we'll make this work.

This eco system work for the benefit of everyone. - Yeah I couldn't agree more. I think that the computational algorithms are being deployed you know in cloud based systems and can really scale quickly. But also the processing of data as it happens it's being made in real time.

Gives it much greater utility, 'cause you can use it right away. And I think there are standards that are being developed that are help with the implementation, the FDA and this, at least in the US are deploying these standards continuously with the Genome in a Bottle consortiums and microbiome standards. And so I think you know that's one of the really inhibitors to broader adoptions being solved in terms of really good rigorous computational and physical standards. - Do we see a sharing of these large data sets? How do we see kinda algorithms getting to the data where it resides and sovereignty or kind of making sure that there is responsible use of patient data and clinical data? - I think we all have an interest in putting the pieces together.

If we can't figure out how to do that from a governance and from a HIPPA and from a data safety perspective, none of this is going to work. So we're all motivated. The technology arguably isn't the hard part, we're going to figure that out.

It's going to have that level of trust and understanding and agreement over how this is used and how it isn't used that we're going to have to overcome for this to be more widespread. - And then also just reimbursement is unfortunately still one of the biggest roadblocks. But I think that's also really ramped up recently, is payers are starting to really appreciate the fact that the long-term benefit for patients really is quite clear.

And so I think we're seeing more and more of it which I'm glad to see. - Yeah I think we will get to a point over the next few years where it'll be less of a discussion about why personalized or precision medicine is different. And I think it will be embedded and though of as business as usual.

But then we're going to be a couple of things we need to solve to get there. Not just the technology piece, which I'm actually more confident about. But about how the other pieces fit together. What are the enabling factors that need to happen? Well first we need to demonstrate that clinical utility. And that is that if we introduce a test, not just that it has a potential to provide new information, but that it will be used by the physician and the patient to do something differently.

And again for myself, I deal with different stakeholders with government for Medicaid and Medicare and with employers for commercial business. And more and more they're asking, why are you spending money? We're in front of employers who are looking at the top spends and the increases and asking us to justify why their dollars are being spent on that. And so we need to be able to understand and to explain that. I would also add there is a question of value. How do we balance access and affordability? You introduce the new test that can diagnose cancer earlier, that may be money well spent.

If you have a one time transformative therapy that prevents a need for later hospitalization and other types of care, again that may be a wise choice economically. You know we tend to focus disproportionately on those up front costs, and we need to collect the data and demonstrate to all of the stakeholders how it fits together and that it makes sense. So balancing access and affordability is critical. And then finally, and this is something which is really at the forefront thinking about health equity. We've seen with the pandemic and even prior to that that different racial and ethnic groups have different outcomes and different challenges with respect to access to care. And we need to do better.

So as we move forward with increasing the amount of technology and precision medicine, we need to make sure that no one is left behind. And that we address all groups equally. - Yeah I just want to beat up on that excellent point. I think you know, we known this for a long time but I think COVID, the pandemic has really put in front of us that yes technology is wonderful.

Technology is going to deliver some very important health improvements. But we also know that the way we deploy that technology and acceptance by the public is critical if we are indeed going to get the benefit that we believe this technology should deliver. So we know for example that, while we are very excited about he RNA technology that drove you know vaccinations and all that. But we know that there are many many major parts of the world where I don't know, people are busy saying okay yes you developed these things but how come we are not getting it? I think there is that issue of whatever we do to always consider that we have a global community. And if we are going to deploy these in the way that people will be happy with whatever technology we're developing that to create assets immediately.

And I think also that we need more counselors, like genetic counselors, who are going to help us to communicate some of these findings to the community. We don't want people to be afraid of the technology that we develop. You know we want it to be acceptable. And how do we do that you know in a way that people are indeed comfortable? We know now that some people are saying, oh how come you develop this vaccine so rapidly? And so we don't trust it. So what does that really mean? I think it's a lack of communication that the RNA technology that drove the vaccine is not new.

It's been in development for a decade. And therefore if we have communicated this right you know from the very beginning, maybe we would create less hesitation and misunderstanding of people thinking that this is something we just quickly put together. I really think we need to be conscious of the global implications of what we are doing.

And I cannot overemphasis you know, what is really important here. And that is the ability to recognize that we are one you know global population. But by studying different groups that we can bring different understanding to biology. And that different understanding can indeed benefit all of us.

- Let's talk about how we turn these barriers into opportunities. What does that look like? - So yes there are things we need to figure out as a society of these type of opportunities and technology are to be introduced into routine clinical practice. So let's start with a question of what really happens in the real world. So I work for a payer. And you know we looked at our approach toward approving NIPT, non-invasive pregnancy testing to try to diagnose Trisomy's which the most common is trisomy 21 or Down Syndrome. And the policies and the recommendations are changing, but historically most payers limit access to that type of testing.

And payers haven't rushed to introduce this more globally because they worry about that up front cost. So we actually did a very unusual real world study. And for a period of time we relaxed all those restrictions and we said we're going to make this broadly applicable and available to all women who from the physician orders it. And what we found is there was only a minimal increase in the cost and meanwhile we saw this better technology eliminate the need for other less precise testing which could have false positives, false negatives, require additional amniocentesis et cetera.

It made sense and it allowed for better care at essentially the same cost. And we as an organization of course maintained that more open policy. And we're seeing others follow. So you know we're really excited that we launched as of January first an initiative where we actually are expanding access to all genome sequencing.

And we're going to work to collect data in conjunction with a group at Harvard Medical School to understand and hopefully demonstrate what we all believe. That doing this actually improves care and does not increase the total cost. - Yeah Michael I'm really looking forward to the results of that. - I was just going to come in here with a few comments. And just say when we launched the NHS genomic medicine service in 2018, a key element of the service was to ensure that there was equitable access. That was equitable access both to the technology but also eventually to access to clinical trials and also to medicine.

So it was end to end. And obviously within the NHS we're able to do that because we can actually collect data to understand where we have difficult to reach communities. And then to start to think about those interventions. But I wanted to come back on Michael's point to say that I totally agree with him about really world evidence. Essentially I'm representing the payer here. And both policy and strategy for the healthcare system.

And I think just like Michael, some of our early cases as we're sort of into live clinical testing at the moment has really shown we can get a diagnosis when using whole genome sequencing. When people have gone around on health service for several years from one different specialty to another. And so when we start to look at the value chain from genomics, it's important that we look beyond just the pathway of care that an individual is on at a particular time. Because often it's involved multiple specialties before they've ended up that particular child or that adult has ended up where they are now. And we need to understand how we can bring the global academic and research community together with our industry collaborators to really understand whether those VUS's are important pathogenically.

And that would involve you know at times a lot more complex functional genomic studies that of course healthcare systems themselves cannot do. And they need that arrangement. And just from in a year working in that way from the data from the hundred-thousand genomes project, we've got solutions to over 200 patient cases.

Just by working in collaboration. So I think it's also about new ways of working in a more collaborative and partnership way with industry, with academia, and with clinicians. - Let's talk about collaboration. - Collaboration is so important. And even you know this micro chasm here where you've got individuals from different types of organizations sitting and talking is critical if we're going to see broad adoption.

I'm getting questions from colleagues about clinical innovation. Why do we need a clinical innovation department? Why is it important? And you know one of the examples I provided was what's some are calling liquid biopsies. The blood test that have the promise of early detection of cancer. You know and it's hard not to want to see that succeed.

I've been reading investment reports in the financial literature suggesting that in three years this will be a 25 billion dollar business for the top companies in the space. And that allows me to go back to my colleagues and say this is coming and I'd much rather be ready for it than not. Which means, let's be collaborating with physicians, with patient groups, with manufacturers, to understand how this all fits together. And understand where it makes sense, where it doesn't, and not being put in the position of saying no just based on a knee jerk reaction to what may appear to be an additional cost in the system that may actually be not just cost saving but life saving. - We have a lot of really important data sitting in silos that are not being connected today. It is clear that we're going to need to apply AI to make sense of this you know vast magnitude of information.

Chris what do you think is the opportunity for AI to really help us to derive insights from this data? - You know I think we'll see more AI embedded into almost every infrastructure that's touching genomics or biomedicine. It'll really become part of the test itself, a really smart test will become the new normal test. Where any time you sequence a mutation in a patient, you see a molecular signature, you track an epitope differential, you compare whatever you've measured relative to any other patients that ever has had that measurement. And so I think a lot of the machine learning of course requires large data to be efficacious. But we're starting to reach that stage of really really big data that you can actually have extremely accurate algorithms that are even better than a lot of pathologists in some cases.

So the old challenge of you send 10 slides to 10 pathologists you get 11 different diagnoses. That might become a thing of the past. And you know I think what we'll see is three big things. We'll see a lot more industry academic and government collaborations.

Where people are starting to break out of their silos and share data and learn from each other. So this requires new thinking of how do we have win win scenarios between previously disparate academic and industry collaborations. But we're seeing more of that. We're seeing people share their data quickly like with GISA, we're sharing viro sequence stuff, immediately sharing it. To track an epidemic in basically real time.

And then I think eventually we'll see more democratization of the sequencing. People sequencing in their home and doing testing in their home. Eventually you could have it embedded into your toilet or it could be in your air monitoring. So I think we'll see a really a profusion of information coming from almost all aspects of life.

But which will help us learn more about what the dynamics are around it. - Oh absolutely. - Yeah I think the promise of AI is tremendous. I really believe that it is what is going to help us to deploy what we see in terms of genomics into the clinic. And make it also available in the contest of public health.

The amount of data that we need to integrate, it's really beyond our statistical capability. And we need the AI technology to help drive this. And I think also again, I try to take us back to society's that may not have all the resources that is needed. I think AI really does have a promise for developing countries, to be able to read thousands of pages of medical records in seconds. And be able to make you know appropriate diagnosis and even prescribe the right kind of medicine for individuals. But we need to do it in a way that society will be comfortable with it.

And we need to do it in such a way that we are bringing diverse perspective you know to that technology. - Absolutely. And Charles do you see an infrastructure challenge in terms of really allowing for a data exchange with some of these societies that don't have as much whether it's cloud infrastructure or data sharing infrastructure that we have here in western nations? - Yes absolutely there is. But I think it's solvable. And I think this is where global communities can indeed come together.

For example if we look at cloud computing if it's Microsoft, Amazon, or whoever. I think we can create those infrastructure in such a way that it's a global infrastructure that has very minimum requirements to be able to interact with it. And even do significant cost sharing that recognizes the resources of different parts of the world. I think that you know from my own understanding and working in developing countries, one of many barriers that I think in terms of data sharing is the long term herd view of if I share my data my community and my research probably is not the one who's gonna benefit from it. And that. You know we go into a bigger repository where people who have more better infrastructure can indeed access that data and use it to benefit your own community.

So I think that's where the global perspective I think is critical. That whatever platform strategies, computational tools that we are developing that we you know think about how we deploy globally. You know so that individuals and scientists and their communities can indeed feel a part of the global enterprise. And that to me I think is important in terms of really sharing data globally. And to also understand that, the infrastructure at different stages.

So if we are developing something to drive a tool, to drive the ability to combine multi or mixed data and to analyze it and make fair decisions from it, we need to also think about how we can do that within the healthcare system that is very limited. You know so one of the you know things I always think about is the idea where we can use genomics to reduce cost for developing countries. I think I can make a counter argument that developing countries may indeed need genomic technology more than even the developed countries. If indeed we can be able to use this to drive cost, you know and create access you know widely.

For example can we put all new bone screening onto a chip such that instead of costing five dollars it's costing a few cents. That would revolutionize what we do in terms of new bone screening. In developing countries. You know so that to me I think is the challenge that we face. And while we are thinking about how payers will reimburse, but we need to think about how do we make the technology friendly? How do we make the technology to reflect the different stages that global communities are? - Yeah I couldn't agree more.

I think the pandemic has really taught us how connected we are. And the importance of having I mean, a surveillance infrastructure globally. And you know certainly kind of extending that to an infrastructure allows for data sharing and new discoveries globally. So do you see this concept of a true data exchange, of all of these important data sets, genotypic phenotypic data sets that will be exchanged globally for new duration of new insights and understanding? - Absolutely.

Without understanding what these things mean we're not going to see a change in how patients are treated. And you know when we talk about the science, that's great. But ultimately it's about making a difference for each and every patient. And it really requires that kind of understanding. Ensuring that information, which is now emerging at a very rapid pace, it could be weekly, monthly, whatever, doesn't require five years to make it into the exam room.

And that's why this is so important. - Thinking about you know 10 years down the road. Ideally we're all together and we're looking back at what's been accomplished. What do you think is going to be accomplished in the next 10 years? And maybe I'll start with you Chris. I mean I'm fascinated by the fact that your lab talks about a 10 step plan for the 500 year kind of survival of our species on earth and every other planet and in space. So maybe you could bring it in just a little bit, like for like the next 10 years what do you think we're gonna accomplish in terms of personalized medicine and the survival of our species? - So I think it's really, next 10 years are gonna be even more extraordinary than what we've seen in the past 10 years.

You know it's every day there's more data that exists than any other prior day. So every day that I wake up is literally the best day ever to do my job. And it's the best day for any geneticist to have a capacity for discovery or just understanding of biology. So it doesn't mean you'll have the best every day.

Of course you can stub your toe or have experiments fail. But it is I'm really excited because in the next 10 years a lot of the lessons we've learned for designing cells or immunotherapies will be perfected and more broadly deployed. A lot of the ideas that we think of liquid biopsies for patients will start to deploy at city scale. So the liquid biopsies for cities, we're sequencing sewers on a daily basis for track COVID variants.

And it looks like it might become more embedded into a standard infrastructure for the city. You think of looking at the weather or looking at sort of water quality, air quality, you'll think about genomic quality and genomic mapping of a city might become standard. And maybe one of my favorite things is you know all the sequencing of all the genomes, it gives us essentially an extraordinary panoply of adaptations to learn from. About how has biology adapted to every nook and cranny of this planet? And by 2032 if all goes well we'll get samples back from Mars when The Perseverance figures out its final captures and gets picked up. So we may even around 10 years from now start to get sequences of something that may have been on Mars if it's there, we'll see. - So Susan in the UK just before Christmas the government published its genome UK its strategy.

Which set out a 10 year ambitious plan for the UK to really make the UK a global or continue the UK being a global leader in genomics. And it's got three main pillars. One of those is around preventative sort of healthcare approaches.

The second is the alignment with research and development. And the third is personalized medicine. And. This is a strategy that covers both human genomics and pathogen genomics with a number of underpinning themes around data for example around workforce. So we have set out a clear plan, we're working on an implementation plan.

In the NHS we set out a sort of 10 year journey for the NHS. And that was around embedding genomics into healthcare. And if you think that we're going to be in a position to probably have somewhere between 60 and a 100 million whole genome sequences by that time. I think it's encumbered on us all to make sure we've got the right data sharing so that we can really learn more around diversity, around disease, around disease cohorts, and applications in our common disease. And I think in 10 years time we'll be well down that journey. And be linking genomics as a matter of routine to targeted and personalized medicines.

- Yeah when I look at my crystal ball I think if we look 10 years out, first of all we're going to see the pace of research accelerate. The type of work my colleagues here are doing. 'Cause we're just scratching the surface. But if you think about the practical application to the broader population I don't think we're going to be speaking about precision medicine as if it's something different. I think it's something where we will expect that when we see our physicians and they diagnose high blood pressure for example, the biggest category won't be idiopathic or unknown but there will be a greater understanding of what the underlying root cause is and a more precise treatment for example versus trial and error. And we could say the same about so many other diseases.

Whether depression or diabetes. Again and I agree with everything we've heard about the idea that we will detect more rare diseases. Where now we see them as groups of symptoms, and they're not really diagnosed, well understood, well characterized. Or therefore well treated. So a lot to look forward to. I also think we will be ahead with respect to the underlying interconnectivity that we need for this to work in most practices.

And that's something that is going to impact all of us and our loved ones. And it's so important if we're going to see better care emerge from all these exciting innovations. - I think that. For me. I feel like a kid in the candy store in terms of the future of genomics.

I think it's really, there's no bounds you know in a sense. I think we are going to learn more and more. But the challenge I think is really to assign functions to all these variants that we are discovering. That you know true associations you know can offer statistical analysis. How do they inform our biology? And we need to bring that to scale. Not taking one variant at a time.

How do we indeed develop strategies that allow us to integrate the functions of these variants at a genomic level? I think that's really a challenge that I see in you know 10 years or so. And also once we understand the functions of some of these variants, how do we do it in such a way that we are not reaffirming old prejudices that society has? For example how do we describe variations that we discover? If it is more prevalent for example in one group compared to others, how do we articulate that in such a way that we are not creating more societal problems? By indeed understanding what something causes in biology. So I think for me it's important for us to understand how to communicate.

And the kind of label that we put on genetic variations. Such that we are indeed seeing ourselves as one group. And that have indeed been able to adapt to different environmental settings. Therefore setting variants may call our different frequencies.

But that does not mean that those group are inherently different. You know so I think those are you know challenges that I think we need to wrap our mind around. And so that as we are using genomics to solve the world problems we are not creating new ones.

- I love the idea of being in a genomics candy store, it's basically where every day the store gets bigger and you find new candy basically. So I a hundred-percent agree, that's great. - That sounds great to me too. Well this has been incredibly inspiring. I think the idea of a kid in a candy store, every day is the best day yet of you know the opportunity to do our jobs even better. I just think the future could not be more bright.

And I am so looking forward to doing that together with all of you.

2021-05-16 21:32

Show Video

Other news