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    "result": {"data":{"platform":{"topic":{"id":"6a55e2082ba1884376bcbe3a","slug":"precision-health","name":"Precision Health","path":"/topics/precision-health","__typename":"Platform_Topic","created":"2026-07-14T07:15:20.07","published":"2026-07-18T00:00:00.00","doiId":null,"outlineNumber":null,"trend":null,"description":{"text":"The enabling technology is genomic sequencing, which gives the complete DNA dataset of an individual. The first sequencing of a complete human genome, completed in 2003 after 13 years and an investment of approximately $3 billion, established the reference map against which individual genomes are read. [1](/citation/initial-sequencing-and-analysis-of-the-human-genome/) Advances in sequencing chemistry, instrumentation, and data-processing have since reduced both cost and turnaround time by many orders of magnitude, making it feasible to sequence the genome of a critically ill patient within hours and to contemplate population-scale programmes that would generate genomic profiles for millions of individuals.[^2] \n\nAlongside DNA sequencing, the parallel development of high-throughput measurement platforms for proteins (proteomics), metabolites (metabolomics), and gene expression (transcriptomics) is providing complementary molecular layers that together describe an individual’s biology with unprecedented resolution.\n\nRealising the clinical potential of this molecular information requires solving a set of interconnected problems that span technology, data science, medicine, and governance. The genetic variant, or mutation, responsible for a patient’s condition must be identified among millions of variants present in any human genome, then correctly interpreted and linked to a therapy. Population-scale datasets from diverse human populations are needed to train the interpretive models on which this depends. The resulting data must be shared across institutions and borders, while respecting individual privacy, community interests, and increasingly assertive national frameworks for data sovereignty. These challenges define the current frontier of precision health.\n\n**Key takeaways**\n\nPrecision health is undergoing a rapid transition from specialist research tool to clinical standard, with an individual’s genomic information beginning to inform diagnosis, prevention and treatment across medicine. It is data-driven health and in some cases can become personalised. **Rapid genomic diagnosis and clinical translation** is now evidence-based and technically ready for broad implementation, though challenges to infrastructure remain. **Gene discovery and mechanistic understanding of disease** is accelerating, with new sequencing technologies revealing a far richer landscape of pathogenic variation than previously accessible. **Multi-omics integration and population-scale precision medicine** is progressing rapidly through large biobank programmes that link genomic, proteomic, metabolomic, and clinical data, the main remaining challenge involving moving from association to understanding causation. **Genomic data governance and equitable implementation** represents an increasingly urgent programme of work: growing data-sovereignty legislation risks fragmenting the global knowledge-sharing infrastructure on which scientific progress depends, and genomic databases remain heavily biased toward populations of European ancestry."},"intro":{"text":"Precision health is built on the recognition that human beings differ at the molecular level in ways that are clinically consequential. The same disease, in two individuals, may have distinct genetic causes, proceed via different molecular mechanisms, and respond differently to the same treatment. Conversely, conditions that appear clinically dissimilar may share underlying biological pathways. Mapping this molecular individuality — and using the resulting information to guide prevention, diagnosis and therapy — is the ambition of precision-health research."},"anticipatoryImpact":{"text":""},"indicatorValues":[],"editions":[{"id":"6a55e4672ba1884376bcbf15","name":"2027","slug":"2027","numericValue":2027}],"anticipatoryImpactImage":null,"embeds":{"citations":[{"id":"6a99cb80ee39e0d3de63d785","slug":"initial-sequencing-and-analysis-of-the-human-genome","url":"https://doi.org/10.1038/35057062","name":"Initial sequencing and analysis of the human genome","authors":[{"id":"6a99cb45ee39e0d3de63d781","name":"International Human Genome Sequencing Consortium","slug":"international-human-genome-sequencing-consortium"}],"authorShowsEtAl":null,"edition":null,"publication":"Nature","accessDate":null,"startPage":877,"volume":409,"footnoteNumber":1,"year":2001}],"imageAssets":[]},"videos":[],"surveyObservations":{"text":""},"color":null,"banner":{"id":"6a55e44e2ba1884376bcbf11","name":"\"Myoblasts are artists 4\" by Wen Jin, University of Basel","image":{"id":"image_gesda-platform/banner/myoblasts-are-artists-4-by-wen-jin-university-of-basel_image__Screenshot_2026-07-23_at_09.03.28_yri27z","url":"https://res.cloudinary.com/shapeable/image/upload/v1784768628/gesda-platform/banner/myoblasts-are-artists-4-by-wen-jin-university-of-basel_image__Screenshot_2026-07-23_at_09.03.28_yri27z.png","thumbnails":{"mainBanner":{"url":"https://res.cloudinary.com/shapeable/image/upload/c_limit,w_1440/v1784768628/gesda-platform/banner/myoblasts-are-artists-4-by-wen-jin-university-of-basel_image__Screenshot_2026-07-23_at_09.03.28_yri27z.png","url2x":"https://res.cloudinary.com/shapeable/image/upload/c_limit,w_2880/v1784768628/gesda-platform/banner/myoblasts-are-artists-4-by-wen-jin-university-of-basel_image__Screenshot_2026-07-23_at_09.03.28_yri27z.png"}}}},"chartImage":null,"feedEntries":[],"citations":[{"__typename":"Platform_Citation","_schema":{"label":"Citation","pluralLabel":"Citations"},"typeLabel":"Journal","slug":"initial-sequencing-and-analysis-of-the-human-genome","url":"https://doi.org/10.1038/35057062","name":"Initial sequencing and analysis of the human genome","authors":[{"id":"6a99cb45ee39e0d3de63d781","name":"International Human Genome Sequencing Consortium","slug":"international-human-genome-sequencing-consortium"}],"authorShowsEtAl":null,"edition":null,"publication":"Nature","accessDate":null,"startPage":877,"volume":409,"footnoteNumber":1,"year":2001}],"subTopics":[{"id":"6a55e2042ba1884376bcbe35","name":"Rapid genomic diagnosis and clinical translation","path":"/sub-topics/rapid-genomic-diagnosis-and-clinical-translation","outlineNumber":null,"slug":"rapid-genomic-diagnosis-and-clinical-translation","__typename":"Platform_SubTopic","color":null,"topic":{"id":"6a55e2082ba1884376bcbe3a","slug":"precision-health","path":"/topics/precision-health"},"intro":{"text":"The translation of genomic sequencing into clinical diagnostics has advanced rapidly, particularly in the setting of severe illness in children. Rapid whole-genome sequencing (rWGS) can now return actionable results in neonatal and paediatric intensive-care units within 24 to 72 hours. This has enabled diagnoses and targeted therapies that were previously unattainable, and revealed genetic conditions in patients whose presentations had previously been attributed to non-specific or complex clinical causes."},"description":{"text":"The reliability of variant interpretation remains a challenge. A third of all genetic tests performed for symptomatic patients return a variant of uncertain significance (VUS) — a genetic change that cannot be confidently classified as pathogenic or benign with currently available evidence.[^3] Resolving a VUS requires access to evidence from other individuals carrying the same variant, “functional assay” — or experimental — data characterising the molecular consequences of the change, and computational tools that can integrate these inputs at scale.\n\nA further challenge is the multitude of non-coding variants that are associated with disease risk and the applicability of polygenic risk scores (PRS) for risk prediction. The analogue and combinatorial nature of PRS means that they can be interpreted only by population genetics: as technologies and associations improve, use of PRSs will probably play an increasing role in general health risk assessment, but for this to happen, population specific PRS frameworks will need to be shared globally.\n\nProgress therefore depends critically on two developments: global data-sharing infrastructure for linked genomic and phenotypic data, and programmes that systematically generate clinically relevant evidence for variants in disease-relevant genes before they are encountered in patients.[^4] , [^5] Even if these two developments are fulfilled, all the technologies mentioned depend on statistical associations, which creates a further challenge. While this can be achieved with very large numbers, it requires scaled resources for genotyping (identifying the genetic variant) and phenotyping (identifying the physical outcome of the variant, which is the most expensive process), and lacks mechanistic validation. \n\nThus, a third development is necessary: the integration of experimental physiological, cellular, and genetic information through targeted experimentation. This integration of genetic, genomic, clinical, and in vitro models with molecular sciences is being pioneered in the development of cardiac digital twins.[^6] Such holistic data integration moves the utility of genetic databases from single pathogenic assignment to a simulated model of each individual patient taking into account both confounding and contributory factors. The technical challenge is the computational integration of digital genomic data with analogue physiology models. Here, we anticipate that advanced AI approaches will be essential.\n\nThe pipeline of genetic therapies is expanding in parallel with diagnostic capabilities. Several hundred genetic therapies have received regulatory approval or are in advanced clinical development. Current trends, if sustained, suggest that the number of available therapies could reach several thousand within a decade, since identifying a pathogenic variant increasingly means identifying a treatment pathway. As this progress occurs, there is intensifying pressure to extend genomic medicine beyond specialist centres into mainstream clinical systems — and beyond high-income countries to the full global disease burden."},"anticipationScores":{"text":""},"anticipationScoresImage":null,"horizons":[{"__typename":"Platform_Horizon","id":"6a55e1cd2ba1884376bcbe30","name":"5-year horizon","slug":"5-year-horizon-28","intro":{"text":"Diagnostic genomics enters mainstream clinical care"},"description":{"text":"Rapid whole-genome sequencing is expected to achieve significant penetration across intensive-care units in high-income countries in the near term, with turnaround time stabilising at approximately five days in standard clinical settings. Standardised variant-interpretation frameworks will reduce the proportion of unresolved “variant of uncertain significance” (VUS) results, and several hundred approved genetic therapies are expected to become available, transforming diagnosis into actionable treatment for an increasing proportion of patients. In parallel, the mining of electronic health records could allow clinical data to be integrated, though this will require institutional commitments to render standard medical processes into a potential investigative engine.[^7] Simultaneously, integration of cellular and physiologic models with genomic-variance data could provide biological explanations for the genetic associations. For example, cellular genetic screens using differentiated induced pluripotent stem cells will help resolve many of the rare VUSes identified in genetic screening.[^8] "},"color":null,"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89a","name":"5-year horizon","slug":"5-year-horizon","years":5,"title":"5-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"6a55e2842ba1884376bcbebe","name":"10-year horizon","slug":"10-year-horizon-29","intro":{"text":"Population-scale genomic screening becomes feasible"},"description":{"text":"Within a decade, whole-genome profiles are likely to be generated and pre-computed at birth in numerous healthcare systems (following the example of an ongoing UK National Health Service trial[^9]), removing turnaround time as a constraint in acute care. AI-assisted variant interpretation will substantially reduce the fraction of clinically unresolved cases. Genome-informed preventive medicine programmes — matching individuals to disease risks and screening interventions on the basis of their genomic profile across the lifespan — should become operational. Concomitant efforts in basic genetic sciences will provide the necessary foundational information to make biological sense of genetic variants, especially in genes either not commonly associated with a disorder, or acting as significant modifiers of a primary genetic mutation. Here efforts such as the Cancer Dependency Map (DepMap),[^10] Knockout Mouse Phenotyping Project (KOMP2),[^11] Allen Brain Cell Atlas,[^12] and the CellChatDB[^13] will be vital."},"color":null,"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89b","name":"10-year horizon","slug":"10-year-horizon","years":10,"title":"10-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"6a55e2a72ba1884376bcbec3","name":"25-year horizon","slug":"25-year-horizon-28","intro":{"text":"Universal genomic profiling is a clinical standard"},"description":{"text":"Genomic information will eventually be integrated into routine clinical care across health systems globally, and automated interpretation of genetic variants will become standard practice. Over this time scale, the focus of genomic medicine is likely to shift from diagnosis to prevention and early intervention — contingent on achieving equitable global access to the underlying infrastructure."},"color":null,"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89c","name":"25-year horizon","slug":"25-year-horizon","years":25,"title":"25-year","subtitle":"horizon"},"embeds":{"citations":[]}}],"indicatorValues":[],"embeds":{"citations":[],"imageAssets":[]}},{"id":"6a55e3352ba1884376bcbedc","name":"Gene discovery and mechanistic understanding of disease","path":"/sub-topics/gene-discovery-and-mechanistic-understanding-of-disease","outlineNumber":null,"slug":"gene-discovery-and-mechanistic-understanding-of-disease","__typename":"Platform_SubTopic","color":null,"topic":{"id":"6a55e2082ba1884376bcbe3a","slug":"precision-health","path":"/topics/precision-health"},"intro":{"text":"The identification of disease-causing variants in genes has accelerated substantially. "},"description":{"text":"Long-read sequencing technologies are revealing a rich and complex landscape of pathogenic DNA variation, such as large chromosomal inversions and translocations, splice-site mutations, methylation defects, structural variants, and non-protein-coding regulatory changes that alter gene expression (the translation of a genetic sequence into a protein, which then carries out a specific biological function) without modifying the protein-coding sequence itself.[^14] Challenges remain, for example the development of frameworks for interpreting variants in the 98 per cent of the human genome that lies outside protein-coding regions, much of which controls when and where genes are switched on and off in the body (also of functional importance).[^15]\n\nThe field requires a systematic programme to generate ground-truth functional data at scale. One pathway is to use saturation mutagenesis — the systematic creation and functional measurement of every possible variant in a defined set of key disease genes. This will produce reference catalogues that allow any subsequently observed variant to be interpreted against experimentally validated benchmarks.[^16] Such catalogues would enable large language models trained on genomic and clinical data to reinterpret existing patient datasets, dramatically reducing the proportion of unresolved cases. A further challenge is to characterise non-protein coding variants and understand why the same pathogenic variant produces different severities of disease in different individuals. This requires adding data concerning genetic background effects (such as the rest of an individual’s genetic sequence), environmental exposures, and immune and metabolic states that modify the consequences of a primary genetic variant.\n\nUnique population structures are proving scientifically valuable. Consanguineous (closely related) populations, for example, in which a higher proportion of individuals express rare recessive variants, provide natural means of monitoring human gene function. Individuals who carry copies of a loss-of-function variant in a gene and are nevertheless healthy provide data that increases the precision of disease models and accelerates the interpretation of variants observed clinically.[^17] Research programmes anchored in these populations, particularly across the Arabian Peninsula, are yielding gene discoveries and therapeutic insights of global relevance.[^18]\n\nAt the mechanistic level, investigation of rare developmental conditions is exposing the complex relationship between genetic mutation and disease. In oncogenic (cancer-causing) pathways such as the RAS-MAPK cascade, for example, somatic mutations — those arising in a subset of cells after fertilisation rather than being inherited — are being identified in non-disease-causing proliferative conditions including complex vascular malformations. This demonstrates that the phenotypic outcome of a mutation depends critically on where and when in the body’s development it occurs.[^19] Similarly, the emerging biology of clonal haematopoiesis, in which somatic mutations accumulate in blood stem cells during ageing, is expanding understanding of how genetic change in adult tissues contributes to cardiovascular disease and neurodegeneration, besides the better-understood link to cancers."},"anticipationScores":{"text":""},"anticipationScoresImage":null,"horizons":[{"__typename":"Platform_Horizon","id":"6a55e2f82ba1884376bcbecd","name":"5-year horizon","slug":"5-year-horizon-29","intro":{"text":"Ground-truth catalogues are established for key disease genes"},"description":{"text":"Comprehensive, cross-referenced genome and phenotype databases covering multiple geographical regions should be available in the next five years, complete with appropriate protections for individual privacy and data sovereignty. Saturation mutagenesis programmes for an initial set of approximately 500 priority disease genes are likely to be under way on this timescale, establishing agreed assays and pilot data. Large language models will begin to reinterpret existing clinical and population datasets, with unusual genotype-phenotype combinations flagging gaps in biological understanding. Catalogues of gene tolerance for loss-of-function mutations could also be built for well-characterised genes within this period."},"color":null,"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89a","name":"5-year horizon","slug":"5-year-horizon","years":5,"title":"5-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"6a55e3102ba1884376bcbed2","name":"10-year horizon","slug":"10-year-horizon-30","intro":{"text":"Systematic functional interpretation of variants advances"},"description":{"text":"Further research achieves a comprehensive catalogue of outcomes of mutant variants for the initial set of investigated genes. For well-characterised genes, the interpretation of loss-of-function variants will be almost completely automated within this time horizon. Accumulated functional data will iteratively improve AI-model training, and models will even be able to start filling in gaps thanks to data from unexpected genotype-phenotype combinations (known as edge cases). A combination of experimental and computational approaches should begin to make it possible to interpret the effects of non-protein-coding variants."},"color":null,"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89b","name":"10-year horizon","slug":"10-year-horizon","years":10,"title":"10-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"6a55e32c2ba1884376bcbed7","name":"25-year horizon","slug":"25-year-horizon-29","intro":{"text":"Variant interpretation scales to the full disease genome"},"description":{"text":"Over the next quarter-century, saturation mutagenesis may extend to the full disease-relevant genome. Automated interpretation of genetic variants could become routine clinical practice in this time frame. Edge cases will gain particular scientific value as the primary means of identifying remaining biological unknowns. The integration of genetic, environmental, and modifier data will enable personalised risk prediction on this timescale. In many cases this should facilitate pre-emptive therapeutic intervention."},"color":null,"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89c","name":"25-year horizon","slug":"25-year-horizon","years":25,"title":"25-year","subtitle":"horizon"},"embeds":{"citations":[]}}],"indicatorValues":[],"embeds":{"citations":[],"imageAssets":[]}},{"id":"6a55e3a42ba1884376bcbef3","name":"Multi-omics integration and population-scale precision medicine","path":"/sub-topics/multi-omics-integration-and-population-scale-precision-medicine","outlineNumber":null,"slug":"multi-omics-integration-and-population-scale-precision-medicine","__typename":"Platform_SubTopic","color":null,"topic":{"id":"6a55e2082ba1884376bcbe3a","slug":"precision-health","path":"/topics/precision-health"},"intro":{"text":"Population-scale precision medicine seeks to go beyond the genome to describe human biology in its full complexity. "},"description":{"text":"This includes describing how gene activity varies across tissues and cell types (transcriptomics), how proteins are expressed (proteomics), how metabolites reflect the chemical state of the body (metabolomics), and how these molecular layers interact with each other and with environmental exposures over a lifetime. Large biobank programmes that link genomic data to deep molecular profiling in hundreds of thousands of individuals are already yielding insights that would have been unattainable a decade ago. Examples include the identification of genetic variants that influence the abundance of thousands of circulating proteins, the discovery of shared biological mechanisms underlying apparently distinct diseases, and the demarcation of disease subtypes by molecular rather than clinical criteria.[^20]\n\nA central scientific challenge is the transition from association to causation. Genomic variants are useful here: they are uniquely well-suited to causal inference because germline genetic variation (that inherited from parents) is fixed in the DNA at conception and cannot be confounded by disease status.[^21] The other molecular layers, on the other hand — protein levels, metabolite concentrations, and gene expression — are dynamic and can be altered by disease as much as they cause it. This means that extracting true causal relationships from such data requires approaches that anchor multi-omic associations in genetic evidence. It also requires new models of interventional study design: small, precisely instrumented trials that perturb specific exposures and measure downstream molecular consequences in a standardised way.\n\nThe emerging field of exposomics is increasingly recognised as a necessary complement to genomics.[^22] Exposomics is the systematic measurement of the totality of environmental exposures experienced by an individual, from chemical pollutants and pharmaceutical agents to diet, microbiome composition and psychosocial stressors. Wearable sensors are generating streams of physiological data at population scale for this purpose. Together, these environmental and physiological data streams constitute what might be termed the “wearable omic”: a layer of longitudinal biological information distinct from, but potentially as informative as, static molecular measurements. Integrating such data with genomic and proteomic profiles, and doing so in a way that can establish causal relationships, will be a defining methodological challenge for the coming decade.\n\nResearchers hope to be able to convert multi-omics knowledge into actionable clinical guidance for individuals who are not yet ill. Risk-stratification tools based on polygenic scores alone are insufficient to motivate preventive intervention: when communicated as a 20 or 30 per cent lifetime risk of a common disease, they rarely change behaviour.[^23] Predictive tools that can identify individuals who face a greater than 50 percent probability of developing a specific disease will be required to shift the centre of gravity of medicine toward prevention, because this is the threshold at which clinical action becomes clearly justified and preventive interventions economically defensible. This will mean developing ways to integrate genomic, proteomic, and longitudinal physiological data into reliable, dynamic, and continuously updated risk estimates."},"anticipationScores":{"text":""},"anticipationScoresImage":null,"horizons":[{"__typename":"Platform_Horizon","id":"6a55e3652ba1884376bcbee4","name":"5-year horizon","slug":"5-year-horizon-30","intro":{"text":"Proteomic profiling transitions from research to clinical platforms"},"description":{"text":"Large-scale biobanks should demonstrate the value of multi-omics integration for disease subtyping, therapeutic target identification, and risk stratification on this timescale. Proteomics will move from broad research platforms to scalable clinical assays, and causal inference from multi-omic data will improve through integration with genome-wide genetic studies. Researchers are likely to develop and pilot frameworks for incorporating exposomic and wearable data into molecular epidemiology studies in this time frame."},"color":null,"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89a","name":"5-year horizon","slug":"5-year-horizon","years":5,"title":"5-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"6a55e3862ba1884376bcbee9","name":"10-year horizon","slug":"10-year-horizon-31","intro":{"text":"Dynamic multi-omic risk models enter clinical practice"},"description":{"text":"In the next decade, continuously updated risk models integrating genomic, proteomic, and physiological data will begin to identify individuals above clinically actionable disease-risk thresholds. Single-cell multi-omics at population scale should become technically and economically feasible, and new interventional study designs are likely to establish the causal architecture of gene-environment interactions for common complex diseases. Longitudinal molecular profiling — tracking how an individual’s molecular biology changes over years — should become operationally practical."},"color":null,"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89b","name":"10-year horizon","slug":"10-year-horizon","years":10,"title":"10-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"6a55e3a22ba1884376bcbeee","name":"25-year horizon","slug":"25-year-horizon-30","intro":{"text":"Precision health shifts to lifelong disease prevention"},"description":{"text":"Continuous, integrated health monitoring across genomic, molecular, physiological, and environmental dimensions seems technically achievable within a quarter-century. In this same timeframe, precision drug discovery — the rapid identification of targeted therapies for any individual patient, regardless of whether their condition is rare or common — could become a realistic clinical paradigm. The economic and clinical focus of medicine will move from treatment of established disease toward its anticipation and prevention."},"color":null,"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89c","name":"25-year horizon","slug":"25-year-horizon","years":25,"title":"25-year","subtitle":"horizon"},"embeds":{"citations":[]}}],"indicatorValues":[],"embeds":{"citations":[],"imageAssets":[]}},{"id":"6a55e4222ba1884376bcbf0a","name":"Genomic data governance and equitable implementation","path":"/sub-topics/genomic-data-governance-and-equitable-implementation","outlineNumber":null,"slug":"genomic-data-governance-and-equitable-implementation","__typename":"Platform_SubTopic","color":null,"topic":{"id":"6a55e2082ba1884376bcbe3a","slug":"precision-health","path":"/topics/precision-health"},"intro":{"text":"The scientific progress described in the preceding sections depends, at every stage, on the ability to share data. "},"description":{"text":"The interpretation of a patient’s genetic variant, for instance, requires evidence from thousands of other individuals carrying similar variants. Similarly, the training of AI models that can generalise across populations requires diverse, globally representative datasets, and the study of rare diseases requires connecting the small number of patients worldwide who share a condition. Yet the infrastructure for doing this — legal, technical, institutional, and cultural — is fragile and under increasing strain.\n\nUnfortunately, the tools historically used to protect individual privacy and institutional interests — consent requirements, institutional review processes, data-use agreements — were designed for a world of small-scale, single-institution studies and are poorly adapted to the scale and connectivity of modern precision-health research.[^24]<sup>,</sup>[^25] Governance frameworks that treat privacy tools as a bottleneck to be managed around, rather than as instruments for responsible science, are a significant systemic problem. It needs to be appreciated that the risks of not sharing data — failed diagnoses, missed discoveries, AI systems that perform poorly in under-represented populations — are as real and as serious as the risks of sharing it. \n\nLegislation concerning data sovereignty represents a rapidly escalating structural challenge. Frameworks emerging across Asia, the Middle East, and elsewhere are treating genomic data as a strategic national asset and restricting its flow across borders.[^26] This has led to a shift toward federated research models, in which computational analyses are brought to the data rather than the data being centralised. While federated approaches preserve sovereignty and are technically sound, they do not resolve the underlying governance challenge. Even where data physically stays within borders, the rules governing its analysis must be interoperable for collaborative international science to function. The next phase of governance development requires not merely technical interoperability but policy interoperability: harmonised ethical and legal frameworks that allow federated systems to operate as a coherent global scientific resource.\n\nThe representational composition of genomic databases is not just an ethical problem: it also skews the science. Databases and AI models trained primarily on individuals of European ancestry produce diagnostic and predictive tools that perform less reliably in other populations, compounding rather than resolving health disparities.[^27]<sup>,</sup>[^28] It will be vital to anchor population genomics programmes in the Middle East, Africa, and South and East Asia. These can leverage population structures, disease patterns, and genetic architectures distinct from those found in European cohorts, providing both a scientific opportunity and a means by which non-European countries can develop precision-health capabilities on their own terms. Furthermore, the concentration of analytical and AI capacity in a small number of countries and institutions risks reproducing, at a new technological level, the inequities that have characterised earlier phases of biomedical research.\n\nFinally, equitable implementation requires not only the willingness to share data, but also solutions to the cost of informational access. As sequencing becomes a commodity, the data integration, processing, and access will be the critical bottleneck for broad implementation — and the cost of data access, integration, and maintenance is significant and growing. The inclusion of AI in the interrogation of such data further augments the cost. While common databases like OMIM[^29] are freely available, more advanced AI-based medical tools such as OpenEvidence[^30] are proprietary. The health solutions that can be guided by genomic approaches (such as gene therapy) are also often costly and will not be available to most people in most countries. A path forward here will require focussed discussions between economic and financial experts that explore ways to rationally finance these powerful innovations.[^31]"},"anticipationScores":{"text":""},"anticipationScoresImage":null,"horizons":[{"__typename":"Platform_Horizon","id":"6a55e3dd2ba1884376bcbefb","name":"5-year horizon","slug":"5-year-horizon-31","intro":{"text":"Federated research becomes the norm; governance matures"},"description":{"text":"Federated data architectures are likely to become the standard approach for cross-border genomic studies. Governance maturity, the demonstrable capacity to manage data responsibly at scale, could soon become a precondition for major research funding. Principle-based international frameworks, developed through bodies such as the Global Alliance for Genomics and Health (GA4GH), will address institutional-level interpretation barriers that currently prevent data-sharing within institutions as much as between them."},"color":null,"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89a","name":"5-year horizon","slug":"5-year-horizon","years":5,"title":"5-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"6a55e3f52ba1884376bcbf00","name":"10-year horizon","slug":"10-year-horizon-32","intro":{"text":"Policy interoperability replaces technical interoperability as the central challenge"},"description":{"text":"Within a decade it could be possible that harmonised governance frameworks allow federated data systems to function as integrated research platforms that span different national regulatory environments. Reciprocity — the principle that countries and institutions contributing data should receive proportionate benefit from research conducted with it — will become a foundational element of international genomic collaboration. Genomic databases could achieve substantially improved representation of global human diversity within this time frame."},"color":null,"type":{"__typename":"Platform_HorizonType","id":"65c55ce79e947c438698a89b","name":"10-year horizon","slug":"10-year-horizon","years":10,"title":"10-year","subtitle":"horizon"},"embeds":{"citations":[]}},{"__typename":"Platform_Horizon","id":"6a55e40f2ba1884376bcbf05","name":"25-year horizon","slug":"25-year-horizon-31","intro":{"text":"Genomic data operates as a global scientific commons"},"description":{"text":"Given the right guidance, governance frameworks should eventually mature to treat genomic data as a global public good, balancing individual rights, community interests, national sovereignty, and collective benefit. 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We want to ensure that you get the information, content, and experiences that matter most to you. GESDA is committed to protecting the privacy of its stakeholders, communities, and other contacts.\n\n## Scope\n\nThis privacy policy applies to all personal data processed by full-time and part-time employees, volunteers when acting on behalf of GESDA, contractors and partners doing business on behalf of GESDA, as well as all legal entities, all operating locations in all countries, and all business processes conducted by GESDA.\n\n## Information Collected\n\n#### What information do we collect?\n\nGESDA collects the following personal data in line with the use purposes explained in a subsequent section:\n\n  * Your name and contact details\n  * Online profile data/usage\n  * Contact information\n  * Social media profile information\n  * Education and professional information\n  * Registration and participation in GESDA events and activities - Information about service usage\n  * Cookies\n  * Authentication data\n  * Location information\n  * Author and peer review information\n  * Other information you upload or provide to us\n\n#### How do we use your information?\n\nGESDA uses (and, where specified, shares) your personal information for the following purposes:\n\n  * To provide support or other services. GESDA may use your personal information to provide you with support or other services that you have ordered or requested. GESDA may also use your personal information to respond directly to your requests for information, including registrations for webinars, or other specific requests, or pass your contact information to the appropriate GESDA supplier or reseller for further follow-up related to your interests.\n  * To provide information based on your needs and respond to your requests. GESDA may use your personal information to provide you with notices of new product releases and service developments.\n  * To administer products. GESDA may contact you if you make use of (digital) products we offer, to confirm certain information (for example, that you did not experience problems in a download process). We may also use this information to confirm compliance with licensing and other terms of use and may share it with your company/institution.\n  * To assist in your participation in GESDA activities. GESDA will communicate with you, if you are participating in certain GESDA activities such as the GESDA Summit, authoring or reviewing a GESDA article, or GESDA humanitarian activities. GESDA may send you information such as update messages related to those activities (such as but not limited to the event's content, and event logistics)\n  * To update you on relevant GESDA events and opportunities. GESDA may communicate with you regarding relevant GESDA events and opportunities.\n  * To protect GESDA content and services. We may use your information to prevent potentially illegal activities and to enforce our terms and conditions.\n  * To get feedback or input from you. In order to deliver products and services of most interest to our stakeholders, from time to time, we may ask you to provide us input and feedback (for example through surveys).\n\n#### How can you control your information?\n\nYou can control the information we have about you and how we use as follows:\n\n  * If you are a registered guest for the GESDA Annual Summit 2021, any request for review, revise or correction of your personal data can be sent to summit@global.gesda specifying your request.\n\n#### Personal data about minors and children\n\nGESDA does not knowingly collect data from or about children under 16 without the permission of parent(s)/guardian(s). If we learn that we have collected personal information from a child under 16, we will delete that information as quickly as possible. If you believe that we might have any information from or about a child under age 16, please contact us.\n\n#### How will you know if the Privacy Policy is changed?\n\nGESDA may update its Privacy Policy from time to time. If we make any material changes you will be notified by means of a notice on our website prior on the date the change becomes effective. We encourage you to periodically review this page for the latest information on our privacy practices.\n\n## Technical And Regulatory Information\n\n#### Logging practices\n\nGESDA automatically records the Internet Protocol (IP) addresses of visitors. The IP address is a unique number assigned to every computer on the internet. Generally, an IP address changes each time you connect to the internet (it is a \"dynamic\" address). Note, however, that if you have a broadband connection, depending on your individual circumstance, the IP address that we collect may contain information that could be deemed identifiable. This is because, with some broadband connections, your IP address doesn't change (it is \"static\") and could be associated with your personal computer.\n\nAs well as recording the IP addresses of users, GESDA may also keep track of sites that users visited immediately prior to visiting GESDA's website and the search terms they used to find it. We keep track of the pages visited on GESDA's website, the amount of time spent on those pages and the types of searches done on them. Your searches remain confidential and anonymous. GESDA uses this information only for statistical purposes to find out which pages users find most useful and to improve the website.\nGESDA also captures and stores information that you transmit. This may include:\n\n  * Browser/Device type/version\n  * Operating system used\n  * Media Access Control (MAC) address\n  * Date and time of the server request\n  * Volume of data transferred\n\n#### External links behaviour\n\nSome of the links on GESDA's websites link to other sites created and maintained by other public- and/or private-sector organizations. GESDA provides these links solely for your information and convenience. When you transfer to an outside website, you are leaving the GESDA domain, and GESDA's information management policies no longer apply. GESDA encourages you to read the privacy statement of each external website that you visit before you provide any personal data.\n\n#### Cookies and web beacons\n\nCookies and web beacons are electronic placeholders that are placed on your device by websites to track your individual movements on that website over time. GESDA uses both session-based cookies (which last only for the duration of the user's session) and persistent cookies (which remain on your device and provide information about the session you are in and waits for the next time you use that site again).\n\nThese cookies and web beacons provide useful information to GESDA, enabling us to recognize repeat users, facilitate the user's access to and use of our sites, allows us to track usage behavior, and to balance the usage of our websites on all GESDA web servers.\nTracking cookies, third-party cookies, and other technologies such as web beacons may be used to process additional information, enable non-core functionalities on the GESDA website and enable third-party functions (such as a social media \"share\" link). We may also include web beacons and other similar technology in promotional email messages to determine whether the messages have been opened.\n\n#### Do Not Track (DNT)\n\nThe online advertising industry has self-regulatory initiatives designed to provide consumers a choice in the types of ads they may see online and to conveniently opt-out from online behavioral ads served by some or all of the companies participating in these programs. Our websites do not respond to DNT consumer browser settings.\n\n#### Responses to legal requests\n\nGESDA reserves the right to share your information to respond to duly authorized information requests of governmental authorities or where required by law.\n\n#### Your data rights\n\nGESDA complies with all applicable data privacy laws and regulations including, but not limited to, the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Under these laws and regulations, you may have certain rights to your data. Should you wish to exercise any of these rights, please send an email request to info@gesda.global with \"Data Privacy Request'' in the subject line and in the email please identify the specific privacy right you request assistance with. Please note additional information may be requested prior to fulfilling a request and that GESDA reserves the right to charge a fee, where permitted, to cover the cost of certain requests.\n\n#### How do I contact you if there is an issue?\nIf you have any questions or concerns about this Privacy Policy or about the use of your personal information, please feel free to contact us by email at info@gesda.global\n\n#### Contact Information\nGeneva Science and Diplomacy Anticipator (GESDA) c/o Fondation Campus Biotech\nChemin des Mines 9\n1202 Geneva\n+41 58 201 02 61\ninfo@gesda.global\n"},"welcomeUrl":"https://radar.gesda.global/app/welcome","welcomeTitle":"Welcome to GESDA and thanks for joining us! 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