On the Aggregation of Multimarker Information for Marker-Set and Sequencing Data Analysis: Genotype Collapsing vs. Similarity Collapsing

Methods that collapse information across genetic markers when searching for association signals are gaining momentum in the literature. Although originally developed to achieve a better balance between retaining information and controlling degrees of freedom when performing multimarker association analysis, these methods have recently been proven to be a powerful tool for identifying rare variants that contribute to complex phenotypes. The information among markers can be collapsed at the genotype level, which focuses on the mean of genetic information, or the similarity level, which focuses on the variance of genetic information. The aim of this work is to understand the strengths and weaknesses of these two collapsing strategies. Our results show that neither collapsing strategy outperforms the other across all simulated scenarios. Two factors that dominate the performance of these strategies are the signal-to-noise ratio and the underlying genetic architecture of the causal variants. Genotype collapsing is more sensitive to the marker set being contaminated by noise loci than similarity collapsing. In addition, genotype collapsing performs best when the genetic architecture of the causal variants is not complex (e.g., causal loci with similar effects and similar frequencies). Similarity collapsing is more robust as the complexity of the genetic architecture increases and outperforms genotype collapsing when the genetic architecture of the marker set becomes more sophisticated (e.g., causal loci with various effect sizes or frequencies and potential non-linear or interactive effects). Because the underlying genetic architecture is not known a priori, we also considered a two-stage analysis that combines the two top-performing methods from different collapsing strategies. We find that it is reasonably robust across all simulated scenarios

Association between adrenergic receptor genotypes and beta-blocker dose in heart failure patients: analysis from the HF-ACTION DNA substudy

Beta-blockers reduce morbidity and mortality in chronic heart failure (HF) patients with reduced ejection fraction. However, there is heterogeneity in the response to these drugs, perhaps due to genetic variations in the β1-adrenergic receptor (ADRβ1). We examined whether the Arg389Gly polymorphism in ADRβ1 interacts with the dose requirements of beta-blockers in patients with systolic HF

Biomarkers of Myocardial Stress and Fibrosis as Predictors of Mode of Death in Patients With Chronic Heart Failure

To determine whether biomarkers of myocardial stress and fibrosis improve prediction of mode of death in patients with chronic heart failure

The effects of exercise on cardiovascular biomarkers in patients with chronic heart failure

Exercise training is recommended for chronic heart failure (HF) patients to improve functional status and reduce risk of adverse outcomes. Elevated plasma levels of amino-terminal pro-brain natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hs-CRP), and cardiac troponin T (cTnT) are associated with increased risk of adverse outcomes in this patient population. Whether exercise training leads to improvements in biomarkers and how such improvements relate to clinical outcomes are unclear

Soluble ST2 in Ambulatory Patients With Heart Failure: Association With Functional Capacity and Long-Term Outcomes

ST2 is involved in cardioprotective signaling in the myocardium and has been identified as a potentially promising biomarker in HF. We evaluated ST2 levels and their association with functional capacity and long-term clinical outcomes in a cohort of ambulatory heart failure (HF) patients enrolled in the HF-ACTION study—a multicenter, randomized study of exercise training in HF

Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmission

AbstractUnderstanding SARS-CoV-2 transmission in higher education settings is important to limit spread between students, and into at-risk populations. In this study, we sequenced 482 SARS-CoV-2 isolates from the University of Cambridge from 5 October to 6 December 2020. We perform a detailed phylogenetic comparison with 972 isolates from the surrounding community, complemented with epidemiological and contact tracing data, to determine transmission dynamics. We observe limited viral introductions into the university; the majority of student cases were linked to a single genetic cluster, likely following social gatherings at a venue outside the university. We identify considerable onward transmission associated with student accommodation and courses; this was effectively contained using local infection control measures and following a national lockdown. Transmission clusters were largely segregated within the university or the community. Our study highlights key determinants of SARS-CoV-2 transmission and effective interventions in a higher education setting that will inform public health policy during pandemics.</jats:p

Incidence and outcome of post-transplant lymphoproliferative disorders in lung transplant patients: analysis of ISHLT Registry

Post-transplant lymphoproliferative disorder (PTLD) is a life-threatening complication following lung transplant. We studied incidence and risk factors for PTLD in adult lung transplant recipients (LTRs) using the International Society for Heart and Lung Transplantation Registry.The International Society for Heart and Lung Transplantation Registry was used to identify adult, first-time, single and bilateral LTRs with at least 1 year of follow-up between 2006 and 2016. Kaplan-Meier method was used to describe the timing and distribution of PTLD. Univariable and multivariable Cox proportional hazards regression models were used to examine clinical characteristics associated with PTLD.Of 19,309 LTRs in the analysis cohort, we identified 454 cases of PTLD. Cumulative incidence of PTLD was 1.1% (95% CI = 1.0%-1.3%) at 1 year and 4.1%\ua0(95% CI = 3.6%-4.6%) at 10\ua0years. Of the PTLD cases, 47.4% occurred within the first year following lung transplantation. In the multivariable model, independent risk factors for PTLD included age, Epstein-Barr virus serostatus, restrictive lung diseases, and induction. Risk of PTLD during the first year after transplant increased with increasing age in patients between 45 and 62 years at time of transplantation; the inverse was true for ages 62 years. Finally, receiving a donor organ with human leukocyte antigen types A1 and A24 was associated with an increased risk of PTLD, whereas the recipient human leukocyte antigen type DR11 was associated with a decreased risk.Our study indicates that PTLD is a relatively rare complication among adult LTRs. We identified clinical characteristics that are associated with an increased risk of PTLD