Collection Of Genomic Data Research Articles

Benefits of cross-border access to human genomes at scale for research and healthcare

Abstract Genomics data will soon be routinely generated and integrated into national healthcare systems. To maximise the potential of genomic medicine, data should be accessible for research where possible. This is the remit of ELIXIR, an intergovernmental organisation that brings together life science resources from across Europe. Innovative solutions are needed to ensure the validated research findings for disease or preventative medicine are then integrated into healthcare. To tackle this, the 1+MG initiative, a joint initiative of 25 EU countries, the UK, and Norway, aims to enable secure access to genomics and the corresponding clinical data across Europe for better research, personalised healthcare and health policy making. In the design and scale-up phase (B1MG project), recommendations and guidelines to advance towards the deployment of personalised medicine at a European scale have been produced, adopted by 1+MG and developed into a 1+MG framework. This includes guidance on data governance, standards, quality and infrastructure, recommendations on how to approach citizen engagement and a tool for countries to self-assess implementation into healthcare. The European Genomic Data Infrastructure (GDI) project supports the scale-up and sustainability phase of the 1+MG initiative, to deploy infrastructure across 24 countries to support the overall ambition. Recommendations are being used to promote governance and technical interoperability across European initiatives including the European Health Data Space (EHDS), and European Cancer Image Initiative (EUCAIM). The 1+MG will be established as a European Data Infrastructure Consortia in 2025 and will act as an Authorised Participant in the EHDS providing access to a permanent high-quality federated data collection of genomic and health data that will accelerate research, innovation and policymaking facilitating the deployment of genomic medicine across Europe.

A Review on Integrating Bioinformatics Tools in Modern Plant Breeding

Bioinformatics has become integral to modern plant breeding, facilitating the analysis of large-scale genomic data, identifying key genetic markers, and enabling precision breeding techniques such as genomic selection. Advances in sequencing technologies and computational tools have accelerated the pace of crop improvement, allowing for the rapid identification of genes associated with important agronomic traits, including yield, stress tolerance, and disease resistance. The integration of bioinformatics in plant breeding also presents significant challenges, particularly in terms of data management, multi-omics integration, and the interpretation and validation of complex datasets. The role of emerging trends such as pangenomics, metagenomics, and epigenomics in expanding the scope of plant breeding, as well as the increasing importance of artificial intelligence and machine learning in enhancing predictive accuracy and optimizing breeding strategies. Personalized plant breeding and precision agriculture are identified as promising approaches for tailoring crop varieties to specific environments and farming practices, driven by bioinformatics tools that enable detailed analysis of genomic and phenotypic data. Ethical considerations and data privacy issues are also addressed, emphasizing the need for transparent and equitable practices in the collection, sharing, and use of genomic data. The importance of collaborative efforts and global initiatives in advancing bioinformatics-driven plant breeding, with a focus on fostering international cooperation, building capacity in developing regions, and ensuring open access to bioinformatics resources. As the field continues to evolve, bioinformatics will play a Important role in developing sustainable agricultural systems capable of meeting the demands of a growing global population while mitigating the impacts of climate change. The current applications, challenges, and future prospects of bioinformatics in plant breeding, offering insights into the critical role of this field in shaping the future of agriculture.

P-340 Reconciling diversity in health and genomic data collection with the regulation of AI in clinical genomics

Abstract Study question How far the existing and emerging EU regulations address concerns about lack of diversity in genomic databases which used to train AI in genomics? Summary answer The main regulations in the European Union provide no clear answers for reconciling the need for more diverse genetic data with regulatory standards. What is known already AI researchers need high quality genomic data that reflect the underlying diversity of human populations to develop and validate safe, effective, and equitable tools for genomic medicine. The problem is that most of the genomic data that are available to develop such tools are of insufficient quality, due to inappropriate use of population descriptors, and over representative of individuals of European ancestry. We used the example of developing algorithms to calculate polygenic risk scores ( PRS) and concerns about their accuracy for various sub-populations to illustrate our discussion. Study design, size, duration We have analysed the relevant EU regulations including, the upcoming AI Act, Medical Devices Regulation ( MDR), In Vitro Diagnostic Medical Devices ( IVDR) and the GDPR to assess how the concerns related to lack of diversity in genetic/genomic databases have been addressed. Participants/materials, setting, methods NA. Main results and the role of chance The main regulations for clinical AI in the European Union, namely those regulating medical devices, provide no clear answers for reconciling the need for more diverse genetic data with regulatory standards. On the other hand, some of the data protection regulation requirements arising from the EU General Data Protection Regulation (GDPR) may further challenge collection of sensitive attributes such as race/ancestry which might be needed for brining diversity to databases. On the other hand, several interesting impending new regulations addressing AI and algorithms such as the EU’s AI Act may help in some ways, but still fail to ultimately grapple with the fundamental obstacles in producing the diverse genomic data needed to achieve regulatory objectives. In particular, the proposed requirements related to data quality and transparency need to be accompanied by further guidance on how to define representativeness and how to address long-term impact on fairness concerns in the context of medical AI, when the developed tools are not of optimal use for populations not descending from European ancestry. Limitations, reasons for caution NA. Wider implications of the findings The legal analysis provided in this paper will provide insights for broader types of AI tools which may be implemented in the context of Assisted Reproductive Technologies and beyond. Trial registration number NA

Molecular and genomic testing in Native American patients with metastatic lung and colorectal cancers: A retrospective chart review.

e23136 Background: The Native American (NA) population has the poorest cancer survival of all racial groups in the U.S. A long history of research misconduct has caused many tribal communities to hold unfavorable views toward genomic data collection. Routine molecular and genomic testing is standard of care for most metastatic lung and colorectal cancers but the frequency of such testing in NA patients is unknown. Many NA patients have limited financial resources and must rely on coverage for health care from an Indian Health Service, Tribal Health Service, or Urban Indian Health (ITU) program. However, ITU programs do not operate their own cancer treatment services and must refer their patients elsewhere for such care. Methods: A Native American Navigation program (NANP) was established at the Stephenson Cancer Center (SCC) in Oklahoma to promote cancer health equity and provide high-quality cancer care by facilitating access to care for ITU patients from across the state. The NANP interfaces with ITU programs to obtain approvals for care as well as to coordinate clinical and supportive services, such as transportation or food assistance. We conducted a retrospective chart review of patients referred from an ITU program to the SCC NANP in Oklahoma from 1/2014 to 6/2023. Among patients with metastatic colorectal cancer (CRC), we investigated the frequency of testing for KRAS, NRAS, BRAF as well as MSS/MSI. In patients with metastatic non-small cell lung cancer (NSCLC), we identified the frequency of EGFR, ALK, ROS1, and PD-L1 testing. Results: A total of 305 NA patients were referred to SCC for colorectal and lung cancers. Of these, 68 patients had metastatic disease and qualified for molecular and genomic testing per National Comprehensive Cancer Network guidelines. The mean age of our cohort was 63 with a male-to-female ratio of 1.6:1. Most (85.3%) patients eligible for testing had testing for at least one biomarker. In patients with metastatic CRC, 100% received MSS/MSI as well as KRAS testing and the frequency of NRAS and BRAF testing was 69.6% and 84.8% respectively. In patients with metastatic NSCLC, the frequency of EGFR, ALK, and ROS1 testing was 95.8%, 87.5%, and 79.1%, with PD-L1 testing frequency being 83.3%. Conclusions: The overall frequency of molecular and genomic testing in these navigated NA patients (85.3%) was comparable to the range reported in the general population (66-90%). By facilitating access to cancer care, programs such as the NANP may help NA patients receive appropriate testing to guide treatment. Larger studies are needed to further assess the role of navigation programs in reducing cancer disparities by facilitating access to appropriate molecular and genomic testing for NA patients with metastatic cancers.

The genomic database of fruits: A comprehensive fruit information database for comparative and functional genomic studies

Fruit has an important role in human nutrition and health; therefore, the systematic study of fruit genomic data is essential. The Genomic Database of Fruits (TGDF, http://tgdf.bio2db.com/), established through whole-genome analyses of 44 fruit species, is a comprehensive, user-friendly fruit database. TGDF contains a wealth of functional genes, including 11,350 flowering genes, 3161 auxin signaling genes, 2164 anthocyanin synthesis genes, 1464 abscisic acid (ABA) synthesis genes, 10,931 ​cell division and expansion genes, 1786 starch synthesis genes, 294 fruit size genes, and 6311 sugar transporter genes. Additionally, TGDF contains 1,433,368 CRISPR guide sequences from various fruit genomes, along with information on homologous genes and duplication types for the 44 fruit species. TGDF contains 6,417,060 gene annotations sourced from TrEMBL, SwissProt, Nr, and Gene Ontology databases, along with tools such as Sequence Fetch, BLAST, Synteny, and JBrowse for bioinformatics analyses. Transcriptomic data were also collected and collated from fruits, including details on instruments, tissues, or growth stages. This comprehensive, user-friendly resource is the first collection of fruit genomic data. Users can easily download genomic sequences, gene annotations, and bioinformatics analysis results from TGDF, which will be updated continually. We anticipate that TGDF will become a primary resource for fruit comparative and functional genomic studies.

Uncovering the boundaries of Campylobacter species through large-scale phylogenetic and nucleotide identity analyses.

Campylobacter species are typically helical shaped, Gram-negative, and non-spore-forming bacteria. Species in this genus include established foodborne and animal pathogens as well as emerging pathogens. The accumulation of genomic data from the Campylobacter genus has increased exponentially in recent years, accompanied by the discovery of putative new species. At present, the lack of a standardized species boundary complicates distinguishing established and novel species. We defined the Campylobacter genus core genome (500 loci) using publicly available Campylobacter complete genomes (n = 498) and constructed a core genome phylogeny using 2,193 publicly available Campylobacter genomes to examine inter-species diversity and species boundaries. Utilizing 8,440 Campylobacter genomes representing 33 species and 8 subspecies, we found species delineation based on an average nucleotide identity (ANI) cutoff of 94.2% is consistent with the core genome phylogeny. We identified 60 ANI genomic species that delineated Campylobacter species in concordance with previous comparative genetic studies. All pairwise ANI genomic species pairs had in silico DNA-DNA hybridization scores of less than 70%, supporting their delineation as separate species. We provide the tool Campylobacter Genomic Species typer (CampyGStyper) that assigns ANI genomic species to query genomes based on ANI similarities to medoid genomes from each ANI genomic species with an accuracy of 99.96%. The ANI genomic species definitions proposed here allow consistent species definition in the Campylobacter genus and will facilitate the detection of novel species in the future.IMPORTANCEIn recent years, Campylobacter has gained recognition as the leading cause of bacterial gastroenteritis worldwide, leading to a substantial rise in the collection of genomic data of the Campylobacter genus in public databases. Currently, a standardized Campylobacter species boundary at the genomic level is absent, leading to challenges in detecting emerging pathogens and defining putative novel species within this genus. We used a comprehensive representation of genomes of the Campylobacter genus to construct a core genome phylogenetic tree. Furthermore, we found an average nucleotide identity (ANI) of 94.2% as the optimal cutoff to define the Campylobacter species. Using this cutoff, we identified 60 ANI genomic species which provided a standardized species definition and nomenclature. Importantly, we have developed Campylobacter Genomic Species typer (CampyGStyper), which can robustly and accurately assign these ANI genomic species to Campylobacter genomes, thereby aiding pathogen surveillance and facilitating evolutionary and epidemiological studies of existing and emerging pathogens in the genus Campylobacter.

A systematic literature review of the 'commercialisation effect' on public attitudes towards biobank and genomic data repositories.

Initiatives that collect and share genomic data to advance health research are widespread and accelerating. Commercial interests in these efforts, while vital, may erode public trust and willingness to provide personal genomic data, upon which these initiatives depend. Understanding public attitudes towards providing genomic data for health research in the context of commercial involvement is critical. A PRISMA-guided search of six online academic databases identified 113 quantitative and qualitative studies using primary data pertaining to public attitudes towards commercial actors in the management, collection, access, and use of biobank and genomic data. The presence of commercial interests yields interrelated public concerns around consent, privacy and data security, trust in science and scientists, benefit sharing, and the ownership and control of health data. Carefully considered regulatory and data governance and access policies are therefore required to maintain public trust and support for genomic health initiatives.

ASAP: a platform for gene functional analysis in Angelica sinensis

BackgroundAngelica sinensis (Danggui), a renowned medicinal orchid, has gained significant recognition for its therapeutic effects in treating a wide range of ailments. Genome information serves as a valuable resource, enabling researchers to gain a deeper understanding of gene function. In recent times, the availability of chromosome-level genomes for A. sinensis has opened up vast opportunities for exploring gene functionality. Integrating multiomics data can allow researchers to unravel the intricate mechanisms underlying gene function in A. sinensis and further enhance our knowledge of its medicinal properties.ResultsIn this study, we utilized genomic and transcriptomic data to construct a coexpression network for A. sinensis. To annotate genes, we aligned them with sequences from various databases, such as the NR, TAIR, trEMBL, UniProt, and SwissProt databases. For GO and KEGG annotations, we employed InterProScan and GhostKOALA software. Additionally, gene families were predicted using iTAK, HMMER, OrholoFinder, and KEGG annotation. To facilitate gene functional analysis in A. sinensis, we developed a comprehensive platform that integrates genomic and transcriptomic data with processed functional annotations. The platform includes several tools, such as BLAST, GSEA, Heatmap, JBrowse, and Sequence Extraction. This integrated resource and approach will enable researchers to explore the functional aspects of genes in A. sinensis more effectively.ConclusionWe developed a platform, named ASAP, to facilitate gene functional analysis in A. sinensis. ASAP (www.gzybioinformatics.cn/ASAP) offers a comprehensive collection of genome data, transcriptome resources, and analysis tools. This platform serves as a valuable resource for researchers conducting gene functional research in their projects, providing them with the necessary data and tools to enhance their studies.

Optimal Protocols and Management of Clinical and Genomic Data Collection to Assist in the Early Diagnosis and Treatment of Multiple Congenital Anomalies.

Standardized protocols have been designed and developed specifically for clinical information collection and obtaining trio genomic information from infants affected with congenital anomalies (CA) and their parents, as well as securing human biological resources. The protocols include clinical and genomic information collection on multiple CA that were difficult to diagnose using pre-existing screening methods. We obtained human-derived resources and genomic information from 138 cases, including 45 families of infants with CA and their parent trios. For the clinical information collection protocol, criteria for target patient selection and a consent system for collecting and utilizing research resources are crucial. Whole genome sequencing data were generated for all participants, and standardized protocols were developed for resource collection and manufacturing. We recorded the phenotype information according to the Human Phenotype Ontology term, and epidemiological information was collected through an environmental factor questionnaire. Updating and recording of clinical symptoms and genetic information that have been newly added or changed over time are significant. The protocols enabled long-term tracking by including the growth and development status that reflect the important characteristics of newborns. Using these clinical and genetic information collection protocols for CA, an essential platform for early genetic diagnosis and diagnostic research can be established, and new genetic diagnostic guidelines can be presented in the near future.

Multiancestry sex-stratified genomic associations with HIV viral load and controller status from the ICGH.

Biological sex and host genetics influence HIV pathogenesis. Females have a higher likelihood of spontaneous viral control and lower set point viral load (spVL). No prior studies have assessed sex-specific genetics of HIV. To address this, we performed a sex-stratified genome-wide association study using data from the ICGH. Although it is the largest collection of genomic data in HIV, this multiethnic sample of 9,705 people is 81.3% male. We sought to identify sex-specific genetic variants and genes associated with HIV spVL and control. We confirmed associations in the HLA and CCR5 regions in males and HLA in females. Gene-based analyses detected associations between HIV spVL and PET100, PCP2, XAB2, and STXBP2 only in males. We detected variants with a significant sex-differential effect on spVL in SDC3 and PUM1 (rs10914268) and PSORS1C2 (rs1265159) and on HIV control in SUB1 (rs687659), AL158151.3, PTPA, and IER5L (rs4387067). Those variants have epigenetic and genetic interactions with relevant genes with both cis and trans effects. In summary, we identified sex-shared associations at the single-variant level, sex-specific associations at the gene-based level, and genetic variants with significant differential effects between the sexes.

A multi-center natural history study of precision-based genomics in prostate cancer (PC).

TPS5121 Background: With genomic-based therapies approved for advanced PC, there has been a substantial rise in the clinical use of genomic sequencing. In addition to identifying actionable targets, there is a long list of less common ‘tail’ alterations in metastatic PC with unclear prognostic or predictive significance. We hypothesize that integrative clinico-genomic studies of uncommon subgroups of PC, defined based on a molecular biomarker of low-prevalence or based on an exceptional clinical outcome to therapy, can lead to the identification of new predictive and resistance biomarkers and will accelerate the development of precision medicine strategies for patients with PC. Methods: This is a multi-institutional prospective observational study for longitudinal collection of clinical and genomic data from participants (prts) with distinct molecular alterations of interest and/or exceptional phenotypes. The primary objectives are: 1) to define the natural history of men with select molecular alterations and/or exceptional clinical responses, 2) to establish a biospecimen repository for extensive molecular analyses to study the biology of PC and mediators of therapeutic response and resistance. Exploratory objectives include: 1) understanding the impact of co-occurring genomics alterations and other molecular alterations on the natural history of PC in these defined subsets, 2) identifying biologic mechanisms of exceptional response, primary and secondary resistance, and tracking evolutionary patterns that occur during acquired resistance. Eligible prts have a diagnosis of PC with known germline and/or somatic variants in PIK3A, PIK3CB, and/or AKT, PALB2, BRIP1, RAD50, RAD51, RAD54, RB1, SPOP, Wnt/B-catenin pathway, MMR genes ( MLH1, MSH2, MSH6, PMS2), EPCAM and/or TMB-high (as defined by TMB ≥ 10 mut/Mb). Any platform for genomic testing is acceptable and data is collected centrally. Prts deemed exceptional responders (with or without mutational aberrations) are defined as those with a complete response or a confirmed partial response of any duration of time to systemic therapies or any response of exceptional duration (particularly with therapies targeting PARP, the androgen receptor, and/or immunotherapies). Data including demographic, disease characteristics, treatment outcomes including response to therapies, and survival are collected along with other pathologic, radiographic, and molecular/genomic data. Correlative sample collection includes archival primary and metastatic biopsy specimens and prospective collection of blood for future analyses. This study will provide insights into the significance of individual and co-occurring alterations and will help establish a path forward for the expansion of drug indications for PC patients with rare variants. 37 prts have been accrued with an accrual ceiling of 2000 prts. Clinical trial information: NCT04706663 .

IRIDA-ARIES Genomics, a key player in the One Health surveillance of diseases caused by infectious agents in Italy.

Pathogen genomics is transforming surveillance of infectious diseases, deepening our understanding of evolution and diffusion of etiological agents, host-pathogen interactions and antimicrobial resistance. This discipline is playing an important role in the development of One Health Surveillance with public health experts of various disciplines integrating methods applied to pathogen research, monitoring, management and prevention of outbreaks. Especially with the notion that foodborne diseases may not be transmitted by food only, the ARIES Genomics project aimed to deliver an Information System for the collection of genomic and epidemiological data to enable genomics-based surveillance of infectious epidemics, foodborne outbreaks and diseases at the animal-human interface. Keeping in mind that the users of the system comprised persons with expertise in a wide variety of domains, the system was expected to be used with a low learning curve directly by the persons target of the analyses' results, keeping the information exchange chains as short as possible. As a result, the IRIDA-ARIES platform (https://irida.iss.it/) provides an intuitive web-based interface for multisectoral data collection and bioinformatic analyses. In practice, the user creates a sample and uploads the Next-generation sequencing reads, then an analysis pipeline is launched automatically performing a series of typing and clustering operations fueling the information flow. Instances of IRIDA-ARIES host the Italian national surveillance system for infections by Listeria monocytogenes (Lm) and the surveillance system for infections by Shigatoxin-producing Escherichia coli (STEC). As of today, the platform does not provide tools to manage epidemiological investigations but serves as an instrument of aggregation for risk monitoring, capable of triggering alarms on possible critical situations that might go unnoticed otherwise.

CAS Array: design and assessment of a genotyping array for Chinese biobanking.

Chronic diseases are becoming a critical challenge to the aging Chinese population. Biobanks with extensive genomic and environmental data offer opportunities to elucidate the complex gene-environment interactions underlying their aetiology. Genome-wide genotyping array remains an efficient approach for large-scale genomic data collection. However, most commercial arrays have reduced performance for biobanking in the Chinese population. Deep whole-genome sequencing data from 2641 Chinese individuals were used as a reference to develop the CAS array, a custom-designed genotyping array for precision medicine. Evaluation of the array was performed by comparing data from 384 individuals assayed both by the array and whole-genome sequencing. Validation of its mitochondrial copy number estimating capacity was conducted by examining its association with established covariates among 10162 Chinese elderly. The CAS Array adopts the proven Axiom technology and is restricted to 652429 single-nucleotide polymorphism (SNP) markers. Its call rate of 99.79% and concordance rate of 99.89% are both higher than for commercial arrays. Its imputation-based genome coverage reached 98.3% for common SNPs and 63.0% for low-frequency SNPs, both comparable to commercial arrays with larger SNP capacity. After validating its mitochondrial copy number estimates, we developed a publicly available software tool to facilitate the array utility. Based on recent advances in genomic science, we designed and implemented a high-throughput and low-cost genotyping array. It is more cost-effective than commercial arrays for large-scale Chinese biobanking.

Future on a Flashdrive: Timely Considerations for the Imminent Adoption of Whole Genome Sequencing in Pediatric Healthcare

In just twenty years, humanity has progressed from the first sequenced human genome to the ability to sequence one in a matter of hours and for only hundreds of dollars. This rise in affordability and speed has enabled physicians to use whole genome sequencing (WGS) as a diagnostic tool, particularly in cases of rare disease in pediatric patients where it has already demonstrated immense potential. However, such a rapid development in technology powerful enough to unlock a person’s genetic information has also led to necessary questions regarding when and how it is applied. In this assessment, we discuss the implications of WGS adoption in pediatric healthcare, focusing specifically on ensuring ethical and equitable collection and communication of genomic data as well as the need for secure and accessible data storage methods. We identify several key areas where further policy is most pressing and provide value-driven recommendations centered on guaranteeing pediatric patient safety, equity, and empowerment during the broader introduction of WGS tools. In particular, we advocate for legal frameworks that limit present usage of WGS to only those patients with a clear and present need, guidelines that expand the labor force that can conduct WGS, increasing access and equity, improved standards for storage, access, and sharing of WGS data, and finally expanding Medicaid coverage to include WGS use in critical care settings.

Sharing genomic data for health research: institutional trust and trustworthiness, and informed consent.

[See related article at www.cmaj.ca/lookup/doi/10.1503/cmaj.212063][1] KEY POINTS Recent years have seen a dramatic increase in the collection, storage and curation of human genomic data for biomedical research. These data sets hold great promise for research into the genetic basis of disease, and

Genomic Epidemiology Unveil the Omicron Transmission Dynamics in Rome, Italy

Since 2020, the COVID-19 pandemic represented an important worldwide burden. Well-structured surveillance by reliable and timely genomic data collection is crucial. In this study, a genomic monitoring analysis of all SARS-CoV-2 positive samples retrieved at the Fondazione Policlinico Universitario Campus Bio-Medico, in Rome, Italy, between December 2021 and June 2022, was performed. Two hundred and seventy-four SARS-CoV-2-positive samples were submitted to viral genomic sequencing by Illumina MiSeqII. Consensus sequences were generated by de novo assembling using the iVar tool and deposited on the GISAID database. Lineage assignment was performed using the Pangolin lineage classification. Sequences were aligned using ViralMSA and maximum-likelihood phylogenetic analysis was performed by IQ-TREE2. TreeTime tool was used to obtain dated trees. Our genomic monitoring revealed that starting from December 2021, all Omicron sub-lineages (BA.1, BA.2, BA.3, BA.4, and BA.5) were circulating, although BA.1 was still the one with the highest prevalence thought time in this early period. Phylogeny revealed that Omicron isolates were scattered throughout the trees, suggesting multiple independent viral introductions following national and international human mobility. This data represents a sort of thermometer of what happened from July 2021 to June 2022 in Italy. Genomic monitoring of the circulating variants should be encouraged considering that SARS-CoV-2 variants or sub-variants emerged stochastically and unexpectedly.

Genomic Privacy: Advocating for the Convergence of Legal and Technical Solutions

Genomic Privacy:Advocating for the Convergence of Legal and Technical Solutions Can Kockan (bio), Dov Greenbaum (bio), Danielle Lee (bio), and Mark Gerstein (bio) The economy of DNA: consumer genomics and its risks Genomic data collection and analysis is an incredibly valuable sector within the information economy, yet genomic information has never been more pervasive nor publicly accessible. Just this past spring, the National Institutes of Health's "All of Us" program announced that it would make nearly one hundred thousand unique and diverse whole genome sequences available for researchers, and several other public efforts have similarly set out to collect and disseminate enormous amounts of genomic data for research.1 Beyond such institutions looking to innocuously advance their research and even beyond amateur genomic sleuths combing through our genetic information to try and crack unsolved crimes,2 there also exists reason to be concerned about matters relating to genetic privacy on the corporate side. [End Page 246] Companies such as MyHeritage and Ancestry. com collect client DNA in various degrees of quantity and quality, at times even using this data for commercial purposes and selling clients' genomic information to drug companies for hundreds of millions of dollars.3 In addition to the growing size of both public and private DNA databases, these databases have also grown increasingly more revealing as research continues to uncover additional correlations between genes and disease and hereditary conditions. For example, identical twins share 100 percent of their DNA, parents and siblings share up to 61 percent of their DNA, and even distant third cousins share up to 2.2 percent of their DNA.4 Thus, if even one of these individuals chooses to disclose their genomic information, they are in effect disclosing not only theirs but also much of the genomes of their close relatives. One study showed how the genetic information of the majority of Americans of European ancestry can be identified through their distant relatives' DNA that already exists in various public and private current databases.5 The result of such data is an ever-expanding genomic panopticon, as predicted by the late Supreme Court Justice Antonin Scalia.6 Following the COVID-19 pandemic, the genomic information consumed and produced around the world has only further contributed to expanding the realm of genomic data available to us. Genetic tests have become especially de rigueur when traversing borders, and it was even reported recently that various heads of state refused PCR testing when meeting the Russian president Putin out of fear that Russian intelligence might abuse and misuse the resulting genetic information.7 Yet, this fear is not limited to politicians, celebrities, or sports personalities whose genetic information could contain exploitable knowledge.8 In fact, given all the sunk costs for the infrastructure of pandemic genetic testing, some countries will likely continue to find excuses to collect and examine the DNA of visitors for a host of potentially actionable genetic data, including controversial correlations like propensity to violence or depression.9 As such, now would be the best time to enhance genetic data protection and privacy laws before we become too complacent with giving away our genes at the border, or anywhere else. A brief history of the technical and legal solutions for genomic privacy U.S. legislators have long assumed the primary role of protecting our genetic data, from instituting federal regulations like the Federal Genetic Information Nondiscrimination Act of 2008 (GINA) to state laws including those that target the limited use of genetic information in areas of health insurance, employment, and even direct-to-consumer genetic testing companies like 23andMe.10 However, laws are slow to change, and regulations are slow to implement. The Health Insurance Portability and Accountability Act (HIPAA) is exemplary of slowness in responding to outdated regulations. The 1996 law outlines limitations on the use of protected health information (PHI). Under HIPAA's Privacy Rule, individually identifiable health information is protected. Deidentified information is exempt from this rule, as is other data deemed unidentifiable.11 This definition is anachronistic in our world of big data, and even heretofore perceived benign data has long been shown to be as revealing as PHI.12 Practically speaking, these regulations are also...

Diversifying the genomic data science research community.

Over the past 20 years, the explosion of genomic data collection and the cloud computing revolution have made computational and data science research accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support these programs in local education and research at underserved institutions (UIs). These include community colleges, historically Black colleges and universities, Hispanic-serving institutions, and tribal colleges and universities that support ethnically, racially, and socioeconomically underrepresented students in the United States. We have formed the Genomic Data Science Community Network to support students, faculty, and their networks to identify opportunities and broaden access to genomic data science. These opportunities include expanding access to infrastructure and data, providing UI faculty development opportunities, strengthening collaborations among faculty, recognizing UI teaching and research excellence, fostering student awareness, developing modular and open-source resources, expanding course-based undergraduate research experiences (CUREs), building curriculum, supporting student professional development and research, and removing financial barriers through funding programs and collaborator support.

Delimiting continuity: Comparison of target enrichment and double digest restriction‐site associated DNA sequencing for delineating admixing parapatric Melitaea butterflies

AbstractParapatrically distributed taxa pose a challenge for species delimitation due to the presence of gene flow and inherent arbitrariness of exactly defining the species boundaries in such systems. We tackled the problem of species delimitation in a parapatric species pair of Melitaea butterflies using two popular genomic methods—double digest restriction‐site associated DNA sequencing (ddRAD) and target enrichment. We compared newly generated target enrichment dataset with 1733 loci to the already available ddRAD data from a previous study on the same set of specimens using a suite of phylogenetic, population genetic, and species delimitation methods. We recovered consistent phylogenetic relationships across the datasets, both demonstrating the presence of a genetically distinct Balkan lineage and paraphyly of Melitaea athalia with respect to Melitaea celadussa. Population genetic STRUCTURE analyses supported the presence of two species when using ddRAD data, but three species when using target enrichment, while a Bayes factor delimitation analysis found both two and three species scenarios equally decisive in both datasets. As the results obtained from both methods were largely congruent, we discuss some practical considerations and benefits of target enrichment over RAD sequencing. We conclude that the choice of method of genomic data collection does not influence the results of phylogenetic analyses at alpha taxonomic level, given a sufficient number of loci. Finally, we recommend a solution for delineating species in parapatric scenarios by proposing that parapatric taxa be consistently classified as subspecies or complete species, but not both, to promote taxonomic stability.

Life-cycle health technology assessment for precision oncology.

e18704 Background: Rapid advances in precision oncology challenge timely and sustainable reimbursement decisions. Life-cycle health technology assessment (LC-HTA) can enable conditional patient access to promising precision oncology innovations alongside evidence development. Our objective was to create a life-cycle evaluative framework, called PRecision oncology Evidence Development in Cancer Treatment (PREDiCT). Methods: Through an iterative, health system and stakeholder-informed approach, we designed our LC-HTA framework. Elements supporting data and evidence generation were subsequently implemented within British Columbia, Canada’s provincial cancer control system. Our development, refinement, and pilot implementation process included a structured literature review, multi-disciplinary international expert consultation, a formal gap assessment, and a series of pan-Canadian inter-disciplinary stakeholder workshops to refine framework elements. Results: We engaged n = 15 pan-Canadian and international stakeholders to co-develop the LC-HTA framework. Defining framework components include: (a) managed access that defines the time horizon and pricing conditions of real-world healthcare system trialing; (b) collection of core data elements required to enable economic evaluation of precision oncology using real world data; (c) externally leveraged real world data and evidence generation to determine comparative effectiveness, cost-effectiveness, and the value of conducting additional research; and (d) data interpretation updating decisions, including investment, continued evaluation, or disinvestment from managed access. Key to the success of early framework implementation is the expansion of infrastructure to enable routine collection and linkage of genomic sequencing and cancer treatment data, patient quality of life and clinical outcomes, as well as health resource use spanning the diagnostic, treatment, and follow up trajectory. Conclusions: Sustainable integration of precision oncology requires the design and implementation of learning healthcare systems (LHS) that integrate genomic data with other health information. LC-HTA moves beyond static estimates of clinical and cost-effectiveness to continuously generate evidence that reduces evidentiary uncertainty and supports life-cycle decisions. We are embarking on a PREDiCT pilot to implement the framework in real-time to demonstrate the ability of real-world data to support life cycle evaluation.