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Motivation Summary statistics from genome-wide association studies enable many valuable downstream analyses that are more efficient than individual-level data analysis while also reducing privacy concerns. As growing sample sizes enable better-powered analysis of gene–environment interactions, there is a need for gene–environment interaction-specific methods that manipulate and use summary statistics. Results We introduce two tools to facilitate such analysis, with a focus on statistical models containing multiple gene–exposure and/or gene–covariate interaction terms. REGEM (RE-analysis of GEM summary statistics) uses summary statistics from a single, multi-exposure genome-wide interaction study to derive analogous sets of summary statistics with arbitrary sets of exposures and interaction covariate adjustments. METAGEM (META-analysis of GEM summary statistics) extends current fixed-effects meta-analysis models to incorporate multiple exposures from multiple studies. We demonstrate the value and efficiency of these tools by exploring alternative methods of accounting for ancestry-related population stratification in genome-wide interaction study in the UK Biobank as well as by conducting a multi-exposure genome-wide interaction study meta-analysis in cohorts from the diabetes-focused ProDiGY consortium. These programs help to maximize the value of summary statistics from diverse and complex gene–environment interaction studies. Availability and implementation REGEM and METAGEM are open-source projects freely available at https://github.com/large-scale-gxe-methods/REGEM and https://github.com/large-scale-gxe-methods/METAGEM. [ABSTRACT FROM AUTHOR]

Motivation Gene–environment interaction (GEI) studies are a general framework that can be used to identify genetic variants that modify the effects of environmental, physiological, lifestyle or treatment effects on complex traits. Moreover, accounting for GEIs can enhance our understanding of the genetic architecture of complex diseases and traits. However, commonly used statistical software programs for GEI studies are either not applicable to testing certain types of GEI hypotheses or have not been optimized for use in large samples. Results Here, we develop a new software program, GEM (Gene–Environment interaction analysis in Millions of samples), which supports the inclusion of multiple GEI terms, adjustment for GEI covariates and robust inference, while allowing multi-threading to reduce computation time. GEM can conduct GEI tests as well as joint tests of genetic main and interaction effects for both continuous and binary phenotypes. Through simulations, we demonstrate that GEM scales to millions of samples while addressing limitations of existing software programs. We additionally conduct a gene-sex interaction analysis on waist-hip ratio in 352 768 unrelated individuals from the UK Biobank, identifying 24 novel loci in the joint test that have not previously been reported in combined or sex-specific analyses. Our results demonstrate that GEM can facilitate the next generation of large-scale GEI studies and help advance our understanding of the genetic architecture of complex diseases and traits. Availability and implementation GEM is freely available as an open source project at https://github.com/large-scale-gxe-methods/GEM. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

New SARS-CoV-2 variant of concern imported from the United Kingdom to Vietnam, December 2020 Here, we describe clinical, epidemiological characteristics, and sequencing of the B.1.1.7 variant from a patient with pneumonia imported from the United Kingdom to Vietnam. On December 14, 2020, the United Kingdom reported a newly identified variant of SARS-CoV-2 (known as VOC 202012/01 or B.1.1.7 lineage).1,2 This carries several mutations, including the spike protein N501Y substitution and 69-70del, resulting in substantially increased transmission rates of 50-74% greater than other variants3; the variant rapidly circulates worldwide.4 In such a context, a full characterization of this variant is essential for a further development of response strategies to COVID-19. [Extracted from the article]

Study Design. Cohort study. Objective. We aimed to evaluate the associations of genetic and nongenetic factors with degenerative cervical myelopathy (DCM). Summary of Background Data. There is mounting evidence for an inherited predisposition for DCM, but uncertainty remains regarding specific genetic markers involved. Similarly, nongenetic factors are thought to play a role. Materials and Methods. Using diagnosis codes from hospital records linked to the UK Biobank cohort, patients with cervical spondylosis were identified followed by the identification of a subset with DCM. Nongenetic variables evaluated included age, sex, race, Townsend deprivation index, body mass index, occupational demands, osteoporosis, and smoking. Genome-wide association analyses were conducted using logistic regression adjusted for age, sex, population principal components, and follow-up. Results. A total of 851 DCM cases out of 2787 cervical spondylosis patients were identified. Several nongenetic factors were independently associated with DCM including age [odds ratio (OR)=1.11, 95% CI=1.01-1.21, P=0.024], male sex (OR=1.63, 95% CI=1.37-1.93, P<0.001), and relative socioeconomic deprivation (OR=1.03, 95% CI=1.00-1.06, P=0.030). Asian race was associated with lower DCM risk (OR=0.44, 95% CI=0.22-0.85, P=0.014). We did not identify genome-wide significant (=5×10-8) single-nucleotide polymorphisms (SNPs) associated with DCM. The strongest genome-wide signals were at SNP rs67256809 in the intergenic region of the genes LINC02582 and FBXO15 on chromosome 18 (P=1.12×10-7) and rs577081672 in the GTPBP1 gene on chromosome 22 (P=2.9×10-7). No SNPs reported in prior DCM studies were significant after adjusting for replication attempts. Conclusions. Increasing age, male sex, and relative socioeconomic deprivation were identified as independent risk factors for DCM, whereas Asian race was inversely associated. SNPs of potential interest were identified in GTPBP1 and an intergenic region on chromosome 18, but these associations did not reach genome-wide significance. Identification of genetic and nongenetic DCM susceptibility markers may guide understanding of DCM disease processes, inform risk, guide prevention and potentially inform surgical outcomes. [ABSTRACT FROM AUTHOR]

Abstract: It can be difficult for both patients and staff to find particular locations within large, modern hospital building complexes. Interactive way-finding information on a touch-screen kiosk might remedy this, but numerous design issues face those developing appropriate interfaces. This paper discusses the decisions underlying a design which provided routes to 16 destinations in the UK’s third largest hospital, for both stair users and those wishing to avoid stairs. All routes included an animated map, photographs and text boxes with optional spoken output. Assessment methods included unobtrusive observation of kiosk users, analysis of computer logs, and interviews with reception staff who normally answered way-finding queries. Observation confirmed that people using the touch-screen reached their destinations. The computer logs over 10weeks showed a stable daily average of 82 people interacting with the kiosk. Most way-finders (72%) retained the voice output but 28% turned it off, suggesting that modality choice is needed in multimedia interfaces for the public. This study highlighted beneficial side-effects of interactive way-finding kiosks, such as enabling patients to access relevant route information before visiting the hospital. This information could be provided via the internet or by including a printout of the relevant route with the appointment letter. [Copyright &y& Elsevier]