The Federated European Genome–Phenome Archive as a global network for sharing human genomics data
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Secure and federated genome-wide association studies for biobank-scale datasets
Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variation linked to health and disease1,2. However, existing data-sharing regulations limit the scope of such collaborations3. Although cryptographic tools for secure computation promise to enable collaborative analysis with formal privacy guarantees, existing approaches either are computationally impractical or do not implement current state-of-the-art methods4,5,6. We introduce secure federated genome-wide association studies (SF-GWAS), a combination of secure computation frameworks and distributed algorithms that empowers efficient and accurate GWAS on private data held by multiple entities while ensuring data confidentiality. SF-GWAS supports widely used GWAS pipelines based on principal-component analysis or linear mixed models. We demonstrate the accuracy and practical runtimes of SF-GWAS on five datasets, including a UK Biobank cohort of 410,000 individuals, showcasing an order-of-magnitude improvement in runtime compared to previous methods. Our work enables secure collaborative genomic studies at unprecedented scale.
Comparative analysis of the Mexico City Prospective Study and the UK Biobank identifies ancestry-specific effects on clonal hematopoiesis
The impact of genetic ancestry on the development of clonal hematopoiesis (CH) remains largely unexplored. Here, we compared CH in 136,401 participants from the Mexico City Prospective Study (MCPS) to 416,118 individuals from the UK Biobank (UKB) and observed CH to be significantly less common in MCPS compared to UKB (adjusted odds ratio = 0.59, 95% confidence interval (CI) = [0.57, 0.61], P = 7.31 × 10−185). Among MCPS participants, CH frequency was positively correlated with the percentage of European ancestry (adjusted beta = 0.84, 95% CI = [0.66, 1.03], P = 7.35 × 10−19). Genome-wide and exome-wide association analyses in MCPS identified ancestry-specific variants in the TCL1B locus with opposing effects on DNMT3A-CH versus non-DNMT3A-CH. Meta-analysis of MCPS and UKB identified five novel loci associated with CH, including polymorphisms at PARP11/CCND2, MEIS1 and MYCN. Our CH study, the largest in a non-European population to date, demonstrates the power of cross-ancestry comparisons to derive novel insights into CH pathogenesis.
Data linkage multiplies research insights across diverse healthcare sectors
In all fields of study, as well as government and commerce, high-quality data enables informed decision-making. Linking data from disparate sources multiplies the opportunities for novel insights and evidence-based decision-making for an increasingly large range of administrative, clinical, research, and population health use cases. In recent years, novel methods, including privacy-preserving record linkage methods, have emerged. However, regardless of the method, successful data linkage is highly dependent on data quality and completeness and has to be balanced by the increased risk of re-identification of the subsequently linked data. Opportunities for the future include sharing tools for responsible linkage across silos, enhancing data to improve quality and completeness, and ensuring linkage leverages inclusive and representative datasets to ensure a balance between individual privacy and representation in research and novel discoveries. Here we provide a brief overview of the history and current state of data linkage, highlight the opportunities created by linked population data across critical research sectors, and describe the technology and policies that govern its usage.
Evolution, genetic diversity, and health
Human genetic diversity in today’s world has been shaped by evolutionary history, demographic shifts and environmental exposures, influencing complex traits, disease susceptibility and drug responses. Capturing this diversity is essential for advancing precision medicine and promoting equitable healthcare. Despite the great progress achieved with initiatives such as the human Pangenome and large biobanks that aim for a better representation of human diversity, important challenges remain. In this Perspective, we discuss the importance of diversity in clinical genomics through an evolutionary lens. We highlight progress and challenges and outline key clinical applications of diverse genetic data. We argue that diversifying both datasets and methodologies—integrating ancestral and environmental factors—is crucial for fully understanding the genetic basis of human health and disease.
Optimising the mainstreaming of renal genomics: Complementing empirical and theoretical strategies for implementation
To identify and develop complementary implementation strategies that support nephrologists in mainstreaming renal genomic testing. Interviews were conducted with individuals nominated as ‘genomics champions’ and ‘embedded genomics experts’ as part of a mainstreaming project to identify initial barriers and investigate empirical strategies for delivering the project at initial stage. Data were mapped onto implementation science framework to identify complementary theoretical strategies. Interviews with 14 genomics champions and embedded genomics experts (genetic counsellors, nephrologists, renal nurses), identified 34 barriers to incorporating genomic testing into routine care, e.g., lack of long-term multidisciplinary team support and role clarity. In total, 25 empirical implementation strategies were identified such as creating new clinical teams. Using the Consolidated Framework for Implementation Research, 10 complementary theoretical implementation strategies were identified. Our study presents a novel approach complementing empirical strategies with theoretical strategies to support nephrologists in incorporating genomic testing into routine practice. Complementary strategies can potentially address barriers and inform future studies when mainstreaming renal genomics. This process underscored the need for integrating collaborative efforts among health professionals, patients, implementation scientists and the health system to overcome identified challenges to mainstream genomic testing. Future research should explore the applicability of these strategies to support mainstreaming genomic testing in different clinical settings.
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