 Awesome. Thank you, Jonathan. Lastly, Alyssa, we'll circle back to policy and we'll talk about how to pursue genomic data sharing in greenfield policy spaces. Thank you so much. This has been an excellent panel. Thank you for putting it together. My name is Alyssa Pritchett. I work at the World Economic Forum on the platform for shaping the future of health and healthcare. And one of my main areas of focus has been what we call leapfrogging with precision medicine, which looks at how do we shape the trajectory of precision medicine so that it advances in a way that's equitable and beneficial to everyone around the world. So much of my work focuses on emerging economies, low-income countries, and in talking to the communities, this issue around genomic data really rose up. And the situation is emerging economies represent new healthcare markets. Their populations are growing but also have the fastest growth and disease burden. Their populations have an incredible biodiversity, genetic diversity, but they've often been left out of research. And that's starting to change. What we're seeing is that certain countries, certain companies want to come into these communities and do genetic research. However, there's often a lack of genomic data policy to govern how that research is conducted and to make sure that the populations are safeguarded. And what this means is there's an openness or a potential for outside organizations then to come in, extract genetic information, and use it to their own benefit, or run afoul of cultural, social, religious norms, do something that embarrasses the population. There are a number of things that can go wrong, and should they go wrong, we expect the repercussions will be swift and highly restrictive, which causes two negative things primarily. One is global collaboration on genomic-based healthcare will be impeded. And the second is that for those populations that already have been traditionally underrepresented, who are probably out of healthcare disparity, developing a deeper understanding of their own unique genetic profile that can lead to more appropriate diagnostics and treatments will also be, again, held back. So we wanted to start to help address this policy gap. And with that, let's go forward. There we go. So with that, we developed a suite of tools. I'm going to talk today mostly about our genomic data policy and ethics white paper. So the team and I wanted, again, to address this need for newer modified policies to govern the collection and use of human genomic information, and wanted to lay out something that policymakers in collaboration with researchers, with providers, with patients, with civil society, with businesses could come together and develop in a way that fosters the continued advancement of biomedical research and discovery in a way that, again, is safe, appropriate, and fits within the context of their countries. So how we got here, it was about a year of research, desk research, thought leader interviews, workshops in San Francisco and Kigali in Dubai, virtually. We had, for the white paper, about 23 reviewers from all different backgrounds, disciplines, continents, except Antarctica, that read through and gave us feedback. And just to point out, it's clear to me, but in the middle of that row, the bearded gentleman is Cameron Fox, who was incredibly instrumental in the ethics side of this work. So let me jump right into it. The white paper essentially has two parts, and the first part is the policy framework. And what we did is, through all of our research, crystallized 21 policy principles that we felt were pretty universal and very foundational for stakeholders to work from, to modify, to add to as they think about developing their own genomic data policy again for their own countries and populations. And we focused this on four key categories, four key pillars of genomic data. The first is consent. Without consent, there's no need to talk about anything else. People are not appropriately agreeing to provide their data. And within the consent category, comprehension is the very first principle, very first principle, very first category, comprehension. People truly need to understand to what it is they're providing their genomic data. And with this, the consent forms need to be clear. They need to be in the local language. They need to be read if somebody is not able to read themselves. And there need to be mechanisms for both feedback loops so people can play back what it is that they're interpreting and make sure it's accurate and also ask questions. So this is really the starting point for this entire policy framework. And within this category, the other policy principles we have are openness, respectfulness, fitness for purpose, and re-notification. People don't always want to be re-notified and they should have that option within their consent. Building on this, we then go into the data privacy pillar. And at this point, this starts to look at the organization that is receiving that genomic information. And the very first principle here is autonomy. And it was a bit of a controversial one, but we felt very strongly that people need to have the autonomy to determine the privacy of their own personal genetic information. And they should not be compelled to disclose that information. Now, to be clear, this is different than data ownership. That's a whole other conversation. But autonomy really, we felt needed to be the first step here. We then build on it with principles of confidentiality, non-maleficence, beneficence, and transparency. Moving to the next pillar. And again, 10 minutes. So you can talk about these more in the Q&A. But moving into the next pillar, which is data access, this is kind of the furthest away from the initial person who's provided their data. You're now looking at two or more institutions, organizations that have genomic data and may be seeking to provide access to each other. So this is the only bucket that has an extra principle. This gets us to 21. So we have six here. They're mostly governance-related principles. Really focus on creating systems, structures that are sound, that are trustworthy, particularly when, which is particularly critical when parties may not already have some kind of trust established between them. The first principle here is restraint. That an organization really must consider the risks and benefits both at an individual and societal level before they embark on any data access. The other principles in here are consideration, responsibility, reliability, accountability, and vigilance. Vigilance is very much about the role that an organization plays in maintaining adherence to that original consent and ensuring that they're not providing or granting access to other organizations that would not be able to similarly uphold that consent and keep that data secure. So vigilance is about being a protector of that data and saying no, saying no to access when it's appropriate. Then we move into the final pillar, which is benefit sharing. And this really looks at, as genomic data is collected, is used, moves on to potentially be commercialized through products, through services. How is the value at each of those steps being shared with the participants? And the first pillar, or sorry, policy principle here is justness. That benefits must be shared in a fair and equitable manner. Further cooperation, clarity, dignity, and inclusion are critical principles. So moving on, the other half of the white paper really focuses on the six ethical tensions that we identified. And these underpin those four policy categories. Oftentimes, policy is developed without an eye to the ethical norms of a society. And oftentimes, ethics won't be addressed until something goes wrong. We really wanted this to be thought through carefully and developed in concert with the policy. So I'll breeze through these given time, but the ethical tensions start with balancing individual privacy and societal benefits. So in other words, without appropriate privacy laws, people are at risk for harm. But if those privacy laws are too restrictive, you're not going to get the data access that's needed to drive societal benefits. Balancing open and restricted data access, balancing receiving benefits and altruistic donations. And you'll see differences in different communities. Some are especially the rare disease community are very eager to share information without expecting anything in return. And other communities find that benefits are much more appropriate as part of the use of their genomic information. Balancing community and researcher oversight, meeting the needs of communities, but respecting the scientific method and the wisdom of researchers in developing studies, balancing inclusion and exclusion, balancing confidentiality and duty to inform. And just to mention, these are all superseded by power dynamics. So even the best late intentions can fall apart if people are not aware of the power differentials that are at play in their relationships with other people and organizations. Pictured here, I do want to give credit to Noel Aguirre-Amanda, who is our fellow from the Ministry of Health and the Government of Rwanda, who is also very, very instrumental in developing this work. So as I'm about at time, I just want to say this is part of a suite of materials. So there is a resource guide that contains prevailing genomic data policies, laws, standards from around the world. There is the white paper. There is a companion ethical tensions guide, which has case studies, questions to answer. And we have a toolkit for those who want to put together their own workshops on this topic. Reach out to me if you would like to be involved in scaling this. And thank you to several of our partners who made it happen. That's my 10 minutes. Thank you. Thank you, Alyssa. And all of our panelists for your wonderful presentations. We covered a wide range of topics related to genomic data sharing today. So please continue to submit your questions to all of our panelists.