Coronavirus disease 2019 (COVID-19) and influenza are respiratory illnesses caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses, respectively. Both diseases share symptoms and clinical risk factors(1), but the extent to which these conditions have a common genetic etiology is unknown. This is partly because host genetic risk factors are well characterized for COVID-19 but not for influenza, with the largest published genome-wide association studies for these conditions including >2 million individuals(2) and about 1,000 individuals(3-6), respectively. Shared genetic risk factors could point to targets to prevent or treat both infections. Through a genetic study of 18,334 cases with a positive test for influenza and 276,295 controls, we show that published COVID-19 risk variants are not associated with influenza. Furthermore, we discovered and replicated an association between influenza infection and noncoding variants in B3GALT5 and ST6GAL1, neither of which was associated with COVID-19. In vitro small interfering RNA knockdown of ST6GAL1-an enzyme that adds sialic acid to the cell surface, which is used for viral entry-reduced influenza infectivity by 57%. These results mirror the observation that variants that downregulate ACE2, the SARS-CoV-2 receptor, protect against COVID-19 (ref. 7). Collectively, these findings highlight downregulation of key cell surface receptors used for viral entry as treatment opportunities to prevent COVID-19 and influenza.

Previous studies have shown that 47% of patients with rhinitis do not seek care from healthcare professionals. As a result, their symptoms, medication preferences, and impact on quality of life are not well-understood. This survey-based study sought to better understand rhinitis symptoms and their management in a population of internet users who may or may not seek rhinitis care in a healthcare setting. The study involved administration of an anonymized survey. Subjects were recruited via social media, online advertisements, and through a link on an industry-sponsored website. The survey included questions about pattern and duration of symptoms, disability associated with symptoms, and medications used for therapy. 83,913 voluntary subjects completed the survey between August 2022 and August 2023. 85.7% and 90.3% of respondents reported that during a flare, their rhinitis symptoms to be “Somewhat troubling” or “Very troubling” when sleeping and participating in recreational activities, respectively. 39.2% of respondents reported use of 3 or more medications to manage their symptoms. Oral antihistamines were most commonly used (87.7%), followed by nasal corticosteroid spray (46.5%), saline sprays/rinses (29.7%), and decongestants (28.1%). Notably, only 4.5% of respondents had used any form of allergen immunotherapy. Analysis of this survey suggest that many rhinitis sufferers use 3 or more medications to treat symptoms but still often remain uncontrolled. Many in this population fail to use guideline-based therapies despite commonly having symptoms that interfere with quality of life and sleep. Allergen immunotherapy clearly remains underutilized.

The Encyclopedia of DNA elements (ENCODE) project is a collaborative effort to create a comprehensive catalog of functional elements in the human genome. The current database comprises more than 19000 functional genomics experiments across more than 1000 cell lines and tissues using a wide array of experimental techniques to study the chromatin structure, regulatory and transcriptional landscape of the Homo sapiens and Mus musculus genomes. All experimental data, metadata, and associated computational analyses created by the ENCODE consortium are submitted to the Data Coordination Center (DCC) for validation, tracking, storage, and distribution to community resources and the scientific community. The ENCODE project has engineered and distributed uniform processing pipelines in order to promote data provenance and reproducibility as well as allow interoperability between genomic resources and other consortia. All data files, reference genome versions, software versions, and parameters used by the pipelines are captured and available via the ENCODE Portal. The pipeline code, developed using Docker and Workflow Description Language (WDL; https://openwdl.org/) is publicly available in GitHub, with images available on Dockerhub (https://hub.docker.com), enabling access to a diverse range of biomedical researchers. ENCODE pipelines maintained and used by the DCC can be installed to run on personal computers, local HPC clusters, or in cloud computing environments via Cromwell. Access to the pipelines and data via the cloud allows small labs the ability to use the data or software without access to institutional compute clusters. Standardization of the computational methodologies for analysis and quality control leads to comparable results from different ENCODE collections - a prerequisite for successful integrative analyses.

OBJECTIVES:The enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analysing population-scale datasets in real time to monitor and better understand the evolving pandemic. The objectives of this study were to examine the relationship of risk factors to COVID-19 susceptibility and severity and to develop risk models to accurately predict COVID-19 outcomes using rapidly obtained self-reported data.DESIGN:A cross-sectional study.SETTING:AncestryDNA customers in the USA who consented to research.PARTICIPANTS:The AncestryDNA COVID-19 Study collected self-reported survey data on symptoms, outcomes, risk factors and exposures for over 563 000 adult individuals in the USA in just under 4 months, including over 4700 COVID-19 cases as measured by a self-reported positive test.RESULTS:We replicated previously reported associations between several risk factors and COVID-19 susceptibility and severity outcomes, and additionally found that differences in known exposures accounted for many of the susceptibility associations. A notable exception was elevated susceptibility for men even after adjusting for known exposures and age (adjusted OR=1.36, 95% CI=1.19 to 1.55). We also demonstrated that self-reported data can be used to build accurate risk models to predict individualised COVID-19 susceptibility (area under the curve (AUC)=0.84) and severity outcomes including hospitalisation and critical illness (AUC=0.87 and 0.90, respectively). The risk models achieved robust discriminative performance across different age, sex and genetic ancestry groups within the study.CONCLUSIONS:The results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic.

Multiple COVID-19 genome-wide association studies (GWASs) have identified reproducible genetic associations indicating that there is a genetic component to susceptibility and severity risk. To complement these studies, we collected deep coronavirus disease 2019 (COVID-19) phenotype data from a survey of 736,723 AncestryDNA research participants. With these data, we defined eight phenotypes related to COVID-19 outcomes: four phenotypes that align with previously studied COVID-19 definitions and four ‘expanded’ phenotypes that focus on susceptibility given exposure, mild clinical manifestations and an aggregate score of symptom severity. We performed a replication analysis of 12 previously reported COVID-19 genetic associations with all eight phenotypes in a trans-ancestry meta-analysis of AncestryDNA research participants. In this analysis, we show distinct patterns of association at the 12 loci with the eight outcomes that we assessed. We also performed a genome-wide discovery analysis of all eight phenotypes, which did not yield new genome-wide significant loci but did suggest that three of the four ‘expanded’ COVID-19 phenotypes have enhanced power to capture protective genetic associations relative to the previously studied phenotypes. Thus, we conclude that continued large-scale ascertainment of deep COVID-19 phenotype data would likely represent a boon for COVID-19 therapeutic target identification.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters human host cells via angiotensin-converting enzyme 2 (ACE2) and causes coronavirus disease 2019 (COVID-19). Here, through a genome-wide association study, we identify a variant (rs190509934, minor allele frequency 0.2–2%) that downregulates ACE2 expression by 37% ( P = 2.7 × 10 − 8 ) and reduces the risk of SARS-CoV-2 infection by 40% (odds ratio = 0.60, P = 4.5 × 10 − 13 ), providing human genetic evidence that ACE2 expression levels influence COVID-19 risk. We also replicate the associations of six previously reported risk variants, of which four were further associated with worse outcomes in individuals infected with the virus (in/near LZTFL1 , MHC, DPP9 and IFNAR2 ). Lastly, we show that common variants define a risk score that is strongly associated with severe disease among cases and modestly improves the prediction of disease severity relative to demographic and clinical factors alone.

Background We present ARCHes, a fast and accurate haplotype-based approach for inferring an individual’s ancestry composition. Our approach works by modeling haplotype diversity from a large, admixed cohort of hundreds of thousands, then annotating those models with population information from reference panels of known ancestry. Results The running time of ARCHes does not depend on the size of a reference panel because training and testing are separate processes, and the inferred population-annotated haplotype models can be written to disk and reused to label large test sets in parallel (in our experiments, it averages less than one minute to assign ancestry from 32 populations using 10 CPU). We test ARCHes on public data from the 1000 Genomes Project and the Human Genome Diversity Project (HGDP) as well as simulated examples of known admixture. Conclusions Our results demonstrate that ARCHes outperforms RFMix at correctly assigning both global and local ancestry at finer population scales regardless of the amount of population admixture.

2. AncestryDNA, 1300 West Traverse Parkway, Lehi, UT 84043 10 11 Corresponding Author: Kristin A. Rand, AncestryDNA, 153 Townsend St, Suite 800, San 12 Francisco, CA 94107, United States of America (e-mail: kristinmuench@gmail.com) 13 14 Word Count: 3,200 15 16

AbstractHuman infection with SARS-CoV-2, the causative agent of COVID-19, leads to a remarkably diverse spectrum of outcomes, ranging from asymptomatic to fatal. Recent reports suggest that both clinical and genetic risk factors may contribute to COVID-19 susceptibility and severity. To investigate genetic risk factors, we collected over 500,000 COVID-19 survey responses between April and May 2020 with accompanying genetic data from the AncestryDNA database. We conducted sex-stratified and meta-analyzed genome-wide association studies (GWAS) for COVID-19 susceptibility (positive nasopharyngeal swab test,ncases=2,407) and severity (hospitalization,ncases=250). The severity GWAS replicated associations with severe COVID-19 nearABOandSLC6A20(P<0.05). Furthermore, we identified three novel loci withP<5×10−8. The strongest association was nearIVNS1ABP, a gene involved in influenza virus replication1, and was associated only in males. The other two novel loci harbor genes with established roles in viral replication or immunity:SRRM1and the immunoglobulin lambda locus. We thus present new evidence that host genetic variation likely contributes to COVID-19 outcomes and demonstrate the value of large-scale, self-reported data as a mechanism to rapidly address a health crisis.

ABSTRACT Background The enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analyzing population-scale datasets in real time to monitor and better understand the evolving pandemic. Methods The AncestryDNA COVID-19 Study collected self-reported survey data on symptoms, outcomes, risk factors, and exposures for over 563,000 adult individuals in the U.S. in just under four months, including over 4,700 COVID-19 cases as measured by a self-reported positive test. Results We replicated previously reported associations between several risk factors and COVID-19 susceptibility and severity outcomes, and additionally found that differences in known exposures accounted for many of the susceptibility associations. A notable exception was elevated susceptibility for males even after adjusting for known exposures and age (adjusted odds ratio [aOR]=1.36, 95% confidence interval [CI] = (1.19, 1.55)). We also demonstrated that self-reported data can be used to build accurate risk models to predict individualized COVID-19 susceptibility (area under the curve [AUC]=0.84) and severity outcomes including hospitalization and critical illness (AUC=0.87 and 0.90, respectively). The risk models achieved robust discriminative performance across different age, sex, and genetic ancestry groups within the study. Conclusion The results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic. THUMBNAIL What is already known on this subject The COVID-19 pandemic has exacted a historic toll on human lives, healthcare systems and global economies, with over 83 million cases and over 1.8 million deaths worldwide as of January 2021. COVID-19 risk factors for susceptibility and severity have been extensively investigated by clinical and public health researchers. Several groups have developed risk models to predict COVID-19 illness outcomes based on known risk factors. What this study adds We performed association analyses for COVID-19 susceptibility and severity in a large, at-home survey and replicated much of the previous clinical literature. Associations were further adjusted for known COVID-19 exposures, and we observed elevated positive test odds for males even after adjustment for these known exposures. We developed risk models and evaluated them across different age, sex, and genetic ancestry cohorts, and showed robust performance across all cohorts in a holdout dataset. Our results establish large-scale, self-reported surveys as a potential framework for investigating and monitoring rapidly evolving pandemics.

The AncestryDNA® science team has developed a fast, sophisticated, and accurate method for estimating the historical origins of customers’ DNA going back several hundred to over 1,000 years. Our newest approach improves upon our previous version in the number of possible regions that a customer might be assigned (from 61 to 70) as well as an increase in accuracy to both regions assigned and the percentage assigned to each region. We have added nine new regions as well as made improvements to the composition of our reference panel and inference algorithm, resulting in more accurate estimates overall. Given the cutting-edge nature of this type of science, we will continue to refine our approach and improve estimates.

Despite strides in characterizing human history from genetic polymorphism data, progress in identifying genetic signatures of recent demography has been limited. Here we identify very recent fine-scale population structure in North America from a network of over 500 million genetic (identity-by-descent, IBD) connections among 770,000 genotyped individuals of US origin. We detect densely connected clusters within the network and annotate these clusters using a database of over 20 million genealogical records. Recent population patterns captured by IBD clustering include immigrants such as Scandinavians and French Canadians; groups with continental admixture such as Puerto Ricans; settlers such as the Amish and Appalachians who experienced geographic or cultural isolation; and broad historical trends, including reduced north-south gene flow. Our results yield a detailed historical portrait of North America after European settlement and support substantial genetic heterogeneity in the United States beyond that uncovered by previous studies.

The Encyclopedia of DNA elements (ENCODE) project is an ongoing collaborative effort to create a comprehensive catalog of functional elements initiated shortly after the completion of the Human Genome Project. The current database exceeds 6500 experiments across more than 450 cell lines and tissues using a wide array of experimental techniques to study the chromatin structure, regulatory and transcriptional landscape of the H. sapiens and M. musculus genomes. All ENCODE experimental data, metadata, and associated computational analyses are submitted to the ENCODE Data Coordination Center (DCC) for validation, tracking, storage, unified processing, and distribution to community resources and the scientific community. As the volume of data increases, the identification and organization of experimental details becomes increasingly intricate and demands careful curation. The ENCODE DCC has created a general purpose software system, known as SnoVault, that supports metadata and file submission, a database used for metadata storage, web pages for displaying the metadata and a robust API for querying the metadata. The software is fully open-source, code and installation instructions can be found at: http://github.com/ENCODE-DCC/snovault/ (for the generic database) and http://github.com/ENCODE-DCC/encoded/ to store genomic data in the manner of ENCODE. The core database engine, SnoVault (which is completely independent of ENCODE, genomic data, or bioinformatic data) has been released as a separate Python package.

Transcriptome-wide maps of RNA binding protein (RBP)-RNA interactions by immunoprecipitation (IP)-based methods such as RNA IP (RIP) and crosslinking and IP (CLIP) are key starting points for evaluating the molecular roles of the thousands of human RBPs. A significant bottleneck to the application of these methods in diverse cell-lines, tissues and developmental stages, is the availability of validated IP-quality antibodies. Using IP followed by immunoblot assays, we have developed a validated repository of 438 commercially available antibodies that interrogate 365 unique RBPs. In parallel, 362 short-hairpin RNA (shRNA) constructs against 276 unique RBPs were also used to confirm specificity of these antibodies. These antibodies can characterize subcellular RBP localization. With the burgeoning interest in the roles of RBPs in cancer, neurobiology and development, these resources are invaluable to the broad scientific community. Detailed information about these resources is publicly available at the ENCODE portal (https://www.encodeproject.org/).

The Encyclopedia of DNA Elements (ENCODE) Data Coordinating Center (DCC) is responsible for organizing, describing and providing access to the diverse data generated by the ENCODE project. The description of these data, known as metadata, includes the biological sample used as input, the protocols and assays performed on these samples, the data files generated from the results and the computational methods used to analyze the data. Here, we outline the principles and philosophy used to define the ENCODE metadata in order to create a metadata standard that can be applied to diverse assays and multiple genomic projects. In addition, we present how the data are validated and used by the ENCODE DCC in creating the ENCODE Portal (https://www.encodeproject.org/). Database URL: www.encodeproject.org.

The Encyclopedia of DNA Elements (ENCODE) Project is in its third phase of creating a comprehensive catalog of functional elements in the human genome. This phase of the project includes an expansion of assays that measure diverse RNA populations, identify proteins that interact with RNA and DNA, probe regions of DNA hypersensitivity, and measure levels of DNA methylation in a wide range of cell and tissue types to identify putative regulatory elements. To date, results for almost 5000 experiments have been released for use by the scientific community. These data are available for searching, visualization and download at the new ENCODE Portal (www.encodeproject.org). The revamped ENCODE Portal provides new ways to browse and search the ENCODE data based on the metadata that describe the assays as well as summaries of the assays that focus on data provenance. In addition, it is a flexible platform that allows integration of genomic data from multiple projects. The portal experience was designed to improve access to ENCODE data by relying on metadata that allow reusability and reproducibility of the experiments.

The Encyclopedia of DNA elements (ENCODE) project is an ongoing collaborative effort to create a catalog of genomic annotations. To date, the project has generated over 4000 experiments across more than 350 cell lines and tissues using a wide array of experimental techniques to study the chromatin structure, regulatory network and transcriptional landscape of the Homo sapiens and Mus musculus genomes. All ENCODE experimental data, metadata and associated computational analyses are submitted to the ENCODE Data Coordination Center (DCC) for validation, tracking, storage and distribution to community resources and the scientific community. As the volume of data increases, the organization of experimental details becomes increasingly complicated and demands careful curation to identify related experiments. Here, we describe the ENCODE DCC's use of ontologies to standardize experimental metadata. We discuss how ontologies, when used to annotate metadata, provide improved searching capabilities and facilitate the ability to find connections within a set of experiments. Additionally, we provide examples of how ontologies are used to annotate ENCODE metadata and how the annotations can be identified via ontology-driven searches at the ENCODE portal. As genomic datasets grow larger and more interconnected, standardization of metadata becomes increasingly vital to allow for exploration and comparison of data between different scientific projects.