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The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.
Background: The systematic analysis of a large number of comparable plant trait data can support investigations into phylogenetics and ecological adaptation, with broad applications in evolutionary biology, agriculture, conservation, and the functioning of ecosystems. Floras, i.e., books collecting the information on all known plant species found within a region, are a potentially rich source of such plant trait data. Floras describe plant traits with a focus on morphology and other traits relevant for species identification in addition to other characteristics of plant species, such as ecological affinities, distribution, economic value, health applications, traditional uses, and so on. However, a key limitation in systematically analyzing information in Floras is the lack of a standardized vocabulary for the described traits as well as the difficulties in extracting structured information from free text.
Results: We have developed the Flora Phenotype Ontology (FLOPO), an ontology for describing traits of plant species found in Floras. We used the Plant Ontology (PO) and the Phenotype And Trait Ontology (PATO) to extract entity-quality relationships from digitized taxon descriptions in Floras, and used a formal ontological approach based on phenotype description patterns and automated reasoning to generate the FLOPO. The resulting ontology consists of 25,407 classes and is based on the PO and PATO. The classified ontology closely follows the structure of Plant Ontology in that the primary axis of classification is the observed plant anatomical structure, and more specific traits are then classified based on parthood and subclass relations between anatomical structures as well as subclass relations between phenotypic qualities.
Conclusions: The FLOPO is primarily intended as a framework based on which plant traits can be integrated computationally across all species and higher taxa of flowering plants. Importantly, it is not intended to replace established vocabularies or ontologies, but rather serve as an overarching framework based on which different application- and domain-specific ontologies, thesauri and vocabularies of phenotypes observed in flowering plants can be integrated.
Background and purpose: Impaired kidney function is associated with an increased risk of vascular events in acute stroke patients, when assessed by single measurements of estimated glomerular filtration rate (eGFR). It is unknown whether repeated measurements provide additional information for risk prediction.
Methods: The MonDAFIS (Systematic Monitoring for Detection of Atrial Fibrillation in Patients with Acute Ischemic Stroke) study randomly assigned 3465 acute ischemic stroke patients to either standard procedures or an additive Holter electrocardiogram. Baseline eGFR (CKD-EPI formula) were dichotomized into values of < versus ≥60 ml/min/1.73 m2. eGFR dynamics were classified based on two in-hospital values as “stable normal” (≥60 ml/min/1.73 m2), “increasing” (by at least 15% from baseline, second value ≥ 60 ml/min/1.73 m2), “decreasing” (by at least 15% from baseline of ≥60 ml/min/1.73 m2), and “stable decreased” (<60 ml/min/1.73 m2). The composite endpoint (stroke, major bleeding, myocardial infarction, all-cause death) was assessed after 24 months. We estimated hazard ratios in confounder-adjusted models.
Results: Estimated glomerular filtration rate at baseline was available in 2947 and a second value in 1623 patients. After adjusting for age, stroke severity, cardiovascular risk factors, and randomization, eGFR < 60 ml/min/1.73 m2 at baseline (hazard ratio [HR] = 2.2, 95% confidence interval [CI] = 1.40–3.54) as well as decreasing (HR = 1.79, 95% CI = 1.07–2.99) and stable decreased eGFR (HR = 1.64, 95% CI = 1.20–2.24) were independently associated with the composite endpoint. In addition, eGFR < 60 ml/min/1.732 at baseline (HR = 3.02, 95% CI = 1.51–6.10) and decreasing eGFR were associated with all-cause death (HR = 3.12, 95% CI = 1.63–5.98).
Conclusions: In addition to patients with low eGFR levels at baseline, also those with decreasing eGFR have increased risk for vascular events and death; hence, repeated estimates of eGFR might add relevant information to risk prediction.
The change in allele frequencies within a population over time represents a fundamental process of evolution. By monitoring allele frequencies, we can analyze the effects of natural selection and genetic drift on populations. To efficiently track time-resolved genetic change, large experimental or wild populations can be sequenced as pools of individuals sampled over time using high-throughput genome sequencing (called the Evolve & Resequence approach, E&R). Here, we present a set of experiments using hundreds of natural genotypes of the model plant Arabidopsis thaliana to showcase the power of this approach to study rapid evolution at large scale. First, we validate that sequencing DNA directly extracted from pools of flowers from multiple plants -- organs that are relatively consistent in size and easy to sample -- produces comparable results to other, more expensive state-of-the-art approaches such as sampling and sequencing of individual leaves. Sequencing pools of flowers from 25-50 individuals at ∼40X coverage recovers genome-wide frequencies in diverse populations with accuracy r > 0.95. Secondly, to enable analyses of evolutionary adaptation using E&R approaches of plants in highly replicated environments, we provide open source tools that streamline sequencing data curation and calculate various population genetic statistics two orders of magnitude faster than current software. To directly demonstrate the usefulness of our method, we conducted a two-year outdoor evolution experiment with A. thaliana to show signals of rapid evolution in multiple genomic regions. We demonstrate how these laboratory and computational Pool-seq-based methods can be scaled to study hundreds of populations across many climates.