A simple and highly sensitive flexible sensor with extended-gate field-effect transistor for epinephrine detection utilizing InZnSnO sensing films.

This study introduces a straightforward method for depositing InZnSnO films onto flexible polyimide substrates at room temperature, enabling their application in electrochemical pH sensing and the detection of epinephrine. A comprehensive analysis of these sensing films, spanning structural, morphological, compositional, and profiling characteristics, was conducted using diverse techniques, including X-ray diffraction, atomic force microscopy, X-ray photoelectron spectroscopy, and secondary ion mass spectroscopy. The investigation into the influence of oxygen flow rates on the performance of InZnSnO sensitive films revealed a significant correlation between their structural properties and sensing capabilities. Notably, exposure to an oxygen flow rate of 30/2 (Ar/O2) the ratio of resulted in the InZnSnO sensitive film demonstrating outstanding pH sensitivity at 59.58 mV/pH within a broad pH range of 2-12, surpassing the performance observed with other oxygen flow rates. Moreover, under this specific condition, the film exhibited excellent stability, with a minimal drift rate of 0.14 mV/h at pH 7 and a low hysteresis voltage of 1.8 mV during a pH cycle of 7 â†’ 4→7 â†’ 10→7. Given the critical role of epinephrine in mammalian central nervous and hormone systems, monitoring its levels is essential for assessing human health. To facilitate the detection of epinephrine, we utilized the carboxyl group of 4-formylphenylboronic acid to enable a reaction with the amino group of the 3-aminopropyltriethoxysilane-coated InZnSnO film. Through optimization, the resulting InZnSnO-based flexible sensor displayed a broad and well-defined linear relationship within the concentration range of 10-7 to 0.1 µM. In practical applications, this sensor proved effective in analyzing epinephrine in human serum, showcasing notable selectivity, stability, and reproducibility. The promising outcomes of this study underscore the potential for future applications, leveraging the advantages of electrochemical sensors, including affordability, rapid response, and user-friendly operation.

The Seasonal Patterns, Ecological Function and Assembly Processes of Bacterioplankton Communities in the Danjiangkou Reservoir, China.

As the water source for the Middle Route Project of the South-to-North Water Diversion Project (MR-SNWD) of China, the Danjiangkou Reservoir (DJR) is in the process of ecosystem reassembly, but the composition, function, and assembly mechanisms of bacterioplankton communities are not yet clear. In this study, the composition, distribution characteristics and influencing factors of bacterioplankton communities were analyzed by high-throughput sequencing (HTS); PICRUSt2 was used to predict community function; a molecular ecological network was used to analyze bacterioplankton interactions; and the assembly process of bacterioplankton communities was estimated with a neutral model. The results indicated that the communities, function and interaction of bacterioplankton in the DJR had significant annual and seasonal variations and that the seasonal differences were greater than that the annual differences. Excessive nitrogen (N) and phosphorus (P) nutrients in the DJR are the most important factors affecting water quality in the reservoir, N and P nutrients are the main factors affecting bacterial communities. Season is the most important factor affecting bacterioplankton N and P cycle functions. Ecological network analysis indicated that the average clustering coefficient and average connectivity of the spring samples were lower than those of the autumn samples, while the number of modules for the spring samples was higher than that for the autumn samples. The neutral model explained 66.3%, 63.0%, 63.0%, and 70.9% of the bacterioplankton community variations in samples in the spring of 2018, the autumn of 2018, the spring of 2019, and the autumn of 2019, respectively. Stochastic processes dominate bacterioplankton community assembly in the DJR. This study revealed the composition, function, interaction, and assembly of bacterioplankton communities in the DJR, providing a reference for the protection of water quality and the ecological functions of DJR bacterioplankton.

Integrative Analysis of Randomized Clinical Trial and Observational Study Data to Inform Post-marketing Safety Decision-Making.

Safety evaluation is a continual and iterative process throughout the drug development life cycle and requires long time horizons and large amounts of data to fully understand the safety profile of a medical product. Although randomized clinical trials (RCT) provide high-quality data for an initial assessment of safety signals, the safety signals may not all have been known at the time of approval because safety data collected from RCT only involve a relatively small number of subjects during a relatively short follow-up period. The increased accumulation of post-marketing real-world data (RWD) presents an opportunity to utilize them for safety decision-making; these include identifying new safety signals, further characterization of safety concerns that are raised in pre-marketing RCT, and further generalization of RCT findings to the broader patient populations not previously studied in RCT. In this paper, we use cardiovascular safety outcome trial for antidiabetic therapies as an illustrative example and discuss how integrative analysis of RCT and observational study data can answer regulatory concerns about cardiovascular risk in a post-marketing setting. A novel statistical analysis strategy is proposed to combine both sources of safety data in a data fusion approach. The proposed approach includes three stages: (1) feasibility analysis that uses an RCT to validate an observational study, applying estimand framework and emulating RCT with RWD; (2) integrative analysis that combines evidence from the RCT and observational study data cooperatively; and (3) sensitivity analysis that examines the consistency of the previous analyses. Two potential utilities of the proposed integrative analysis for the cardiovascular safety outcome trial are discussed.

[Shifts in Rhizosphere Bacterial Community Structure, Co-occurrence Network, and Function of Miscanthus Following Cadmium Exposure].

As a representative of second-generation bioenergy plants, Miscanthus has received increasing attention in the studies of heavy metal (HM)-contaminated soil remediation. Currently, few studies have examined the effects of using Miscanthus to remediate HM-contaminated soils on the composition and function of microbial communities. In this study, the Miscanthus cultivar M. saccariflorus was examined for its tolerance and enrichment abilities when grown in soils containing 100 mg ·kg-1 of cadmium (Cd). The structure, function, and co-occurrence network of their rhizosphere bacterial communities were analyzed during the remediation process. MiSeq sequencing showed that the Miscanthus rhizosphere bacterial community comprised 32 phyla and 425 genera, including plant growth-promoting rhizobacteria (PGPR), such as Sphingomonas, Bacillus, Gemmatimonas, and Streptomyces. The addition of Cd affected the Miscanthus rhizosphere bacterial community and reduced community diversity. Phylogenetic molecular ecological networks indicated that Cd addition reduced the interactions between Miscanthus rhizosphere bacteria to generate a simpler network structure, increased the number of negative-correlation links, enhanced the competition between rhizosphere bacterial species, and changed the composition of key bacteria. PICRUSt functional predictive analysis indicated that Cd stress reduced soil bacterial functions in the Miscanthus rhizosphere. The results of this study provide a reference for the subsequent regulation of efficient Miscanthus remediation by PGPRs or key bacteria.

Estimation of causal effect in integrating randomized clinical trial and observational data - An example application to cardiovascular outcome trial.

Safety evaluation of drug development is a comprehensive process across the product lifecycle. While a randomized clinical trial (RCT) can provide high-quality data to assess the efficacy and safety of a new intervention, the pre-marketing trials are limited in statistical power to detect causal elevation of rare but potentially serious adverse events. On the other hand, real-world data (RWD) sources play a critical role in further understanding the safety profile of the new intervention. Bringing together the breadth and strength of RWD and RCT data, we can maximize the utility of RWD and answer broader questions. In this manuscript, we propose a three-step statistical framework to corroborate findings from both RCT and RWD for evaluating important safety concerns identified in the pre-marketing setting. By the proposed approach, we first match the observational study to RCT, then the causal estimation is validated via the matched observational study with the target RCT by targeted maximum likelihood estimation (TMLE) method, and lastly the evidence from RCT and RWD can be combined in an integrative analysis. A potential application to cardiovascular outcome trials for type 2 diabetes mellitus is illustrated. Finally, simulation results suggest that the heterogeneity of patient population from RCT and RWD can lead to varying degrees of treatment effect estimation and the proposed approach may be able to mitigate such difference in the integrative analysis.

Bayesian hierarchical model for safety signal detection in multiple clinical trials.

Clinical safety signal detection is of great importance in establishing the safety profile of new drugs and biologics during drug development. Bayesian hierarchical meta-analysis has proven to be a very effective method of identifying potential safety signals by considering the hierarchical structure of clinical safety data from multiple randomized clinical trials conducted under an Investigational New Drug (IND) application or Biological License Application (BLA). This type of model can integrate information across studies, for instance by grouping related adverse events using the MedDRA system-organ-class (SOC) and preferred terms (PT). It therefore improves the precision of parameter estimates compared to models that do not consider the hierarchical structure of the safety data. We propose to extend an existing four-stage Bayesian hierarchical model and consider the exposure adjusted incidence rate, assuming the number of adverse events (AEs) follows a Poisson distribution. The proposed model is applied to a real-world example, using data from three randomized clinical trials of a neuroscience drug and examine in three simulation studies motivated by real-world examples. Comparison is made between the proposed method and other existing methods. The simulation results indicate that our proposed model outperforms other two candidate models in terms of power and false detection rate.

Meta-analysis of blinded and unblinded studies for ongoing aggregate safety monitoring and evaluation.

During the course of clinical development, ongoing aggregate safety monitoring and evaluation are needed to understand the evolving safety profile and to ensure effective risk-management strategies for medicinal products. CIOMS reports and global regulatory guidance (including from ICH, US FDA, and EMA) compel sponsors for assessment of safety based on aggregate data. To identify and characterize the risks of medicinal products at a program level in a more timely and informed manner, aggregate safety evaluations should combine all available information, including from ongoing blinded trials, completed unblinded trials, and other data sources. In this article, we propose two Bayesian meta-analytic approaches for synthesizing blinded and unblinded studies in order to characterize the evolving safety profile of medicinal products at the program level. With the proposed approaches, sponsors can dynamically update knowledge of their product safety profiles as data accrue. Application of the procedures to a real and a hypothetical clinical trial program are provided to illustrate how the proposed approaches can be used to analyze a pre-specified event of interest and to screen for risk-elevated events.

[Bacterial Community Composition in Cadmium-Contaminated Soils in Xinxiang City and Its Ability to Reduce Cadmium Bioaccumulation in Pak Choi (Brassica chinensis L.)].

There are many types of bacteria in heavy metal-contaminated soils, including plant growth-promoting bacteria and heavy metal-immobilizing bacteria, which may potentially be used for the bioremediation of contaminated soil. In this study, the Cd-contaminated soil around a battery factory in Xinxiang City was collected and its bacterial community composition was analyzed using high-throughput sequencing (HTS) and the plate lineation separation method. The traditional culture method showed that bacteria isolated from contaminated soil belonged to four phyla (Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes) and 30 genera including Bacillus, Arthrobacter, and Rhizobium. HTS showed that the soil contained 25 phyla including Proteobacteria, Actinobacteria, and Acidobacteria and 400 genera. The HTS analysis revealed a greater diversity of the bacterial community than the traditional culture method. Network analysis was performed using the relative abundances of bacteria based on the phylogenetic molecular ecological network (pMEN) method. Network analysis showed that the key bacteria included Arthrobacter, Marmoricola, Nocardioides, Ferruginibacter, Flavitalea, Nitrospira, and Lysobacter. The Cd fixation and adsorption abilities of the 159 cultivable strains were determined. The results showed that 30 strains from 11 genera, including Aneurinibacillus, Arthrobacter, and Bacillus, were highly efficient. Six high-efficiency strains were shown to increase the biomass of pak choi (Brassica chinensis L.) and reduce the content of Cd in different pak choi tissues. This study provides candidate bacterial species for the remediation of heavy metal-contaminated soil in Xinxiang City and provides references for the bacterial community and its function in heavy metal-contaminated soil.

Bayesian Meta-analysis of Safety Outcomes Using Blinded Clinical Trial Data.

BACKGROUND: During the development of an investigational new drug, identifying potential safety risks is imperative. Early detection, as well as federal regulations, requires safety monitoring procedures during clinical trials while the data are still blinded. METHOD: We introduce a Bayesian meta-analytic approach for detecting a potential elevated safety risk conferred by an investigational treatment, as compared to control, using blinded data from multiple trials. RESULTS: Simulation studies of our approach demonstrate good operating characteristics when some relevant prior information is available. The utility of our procedure is demonstrated on data from a real clinical trial program with informative results based on blinded data compared with unblinded data. CONCLUSION: At any time during the drug development program, our approach provides easily interpretable posterior probability statements about the risk ratio for the safety event(s) of interest.

Miscanthus cultivation shapes rhizosphere microbial community structure and function as assessed by Illumina MiSeq sequencing combined with PICRUSt and FUNGUIld analyses.

Soil microbes play important roles in plant growth and in the biogeochemical cycling of earth's elements. However, the structure and functions of the microbial community associated with the growth of second-generation energy crops, such as Miscanthus, remain unclear. Thus, in this study, the composition and function of the bacterial and fungal communities associated with Miscanthus cultivation were analyzed by MiSeq sequencing combined with PICRUSt and FUNGUIld analyses. The results of community composition and diversity index analyses showed that Miscanthus cultivation significantly altered the bacterial and fungal community composition and reduced bacterial and fungal diversity. In addition, Miscanthus cultivation increased the soil organic matter (SOM) and total nitrogen (TN) contents. The correlation analysis between microbial community composition and environmental factors indicated that SOM and TN were the most important factors affecting bacterial and fungal communities. Miscanthus cultivation could enrich the abundances of Pseudomonas, Rhizobium, Luteibacter, Bradyrhizobium, Phenylobacterium and other common plant-promoting bacteria, while also increasing Cladophialophora, Hymenula, Magnaporthe, Mariannaea, etc., which predicted corresponded to the saprotrophic, plant pathogenic, and pathotrophic trophic modes. The PICRUSt predictive analysis indicated that Miscanthus cultivation altered the metabolic capabilities of bacterial communities, including the metabolism of carbon, nitrogen, and phosphorus cycle. In addition, FUNGUIld analysis indicated that Miscanthus cultivation altered the fungal trophic mode. The effects of Miscanthus on the communities and function of bacteria and fungi varied among Miscanthus species. Miscanthus specie Xiangdi NO 1 had the greatest impact on soil bacterial and fungal communities, whereas Miscanthus specie Wujiemang NO 1 had the greatest impact on soil bacteria and fungi functions. The results of this study provide a reference for the composition and function of microbial communities during the growth of Miscanthus.

Rhizosphere Bacterial Community Structure and Predicted Functional Analysis in the Water-Level Fluctuation Zone of the Danjiangkou Reservoir in China During the Dry Period.

The water-level fluctuation zone (WLFZ) is a transitional zone between terrestrial and aquatic ecosystems. Plant communities that are constructed artificially in the WLFZ can absorb and retain nutrients such as nitrogen (N) and phosphorus (P). However, the microbial community composition and function associated with this process have not been elucidated. In this study, four artificially constructed plant communities, including those of herbs (Cynodon dactylon and Chrysopogon zizanioides), trees (Metasequoia glyptostroboides), and shrubs (Salix matsudana) from the newly formed WLFZ of the Danjiangkou Reservoir were evaluated. The bacterial community compositions were analyzed by 16S rRNA gene sequencing using a MiSeq platform, and the functions of these communities were assessed via Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis. The results showed that the bacterial communities primarily comprised 362 genera from 24 phyla, such as Proteobacteria, Acidobacteria, Actinobacteria, and Gemmatimonadetes, showing the richness of the community composition. Planting altered the bacterial community composition, with varying effects observed among the different plant types. The bacterial community functional analysis revealed that these bacteria were primarily associated with six biological metabolic pathway categories (e.g., metabolism, genetic information processing, and environmental information processing) with 34 subfunctions, showing the richness of community functions. The planting of M. glyptostroboides, S. matsudana, and C. dactylon improved the metabolic capabilities of bacterial communities. N- and P-cycling gene analysis showed that planting altered the N- and P-cycling metabolic capacities of soil bacteria. The overall N- and P-metabolic capacity was highly similar between C. dactylon and C. zizanioides samples and between S. matsudana and M. glyptostroboides samples. The results of this study provide a preliminary analysis of soil bacterial community structure and function in the WLFZ of the Danjiangkou Reservoir and provides a reference for vegetation construction in this zone.

Nitrogen loss by anaerobic ammonium oxidation in a mangrove wetland of the Zhangjiang Estuary, China.

Anaerobic ammonium oxidation (anammox), a microbial process in which NH4+ is oxidized to N2 gas, is considered a significant nitrogen cycle process, but its significance in mangrove wetland sediments, particularly its depth- and genus-specific distribution and activity have remained uncertain. Here we report the vertical distribution, abundance, activity and role of anammox bacteria in mangrove sediments of Zhangjiang Estuary, China. We used stable isotope-tracer techniques, 16S rRNA and anammox bacterial functional gene (Hydrazine synthase B: hzsB) clone libraries and quantitative polymerase chain reaction (qPCR) assays, along with an assessment of nutrient profiles of sediment core samples. We observed a widespread occurrence of anammox bacteria at different depths of mangrove sediments. The abundance of anammox bacterial 16S rRNA and hzsB genes ranged from 0.41×107 to 9.74×107 and from 0.42×106 to 6.44×106 copies per gram of dry soil and peaked in the upper layer of mangrove sediments. We also verified the co-occurrence of different genera of anammox microorganisms in mangrove sediments, with Candidatus Scalindua and Candidatus Kuenenia being the dominant genera. Potential anammox rates ranged from 4.83 to 277.36 nmolN2·g-1·d-1 at different depths of sediment cores, and the highest rates were found in the deeper layer (70-100cm) of mangrove sediments. Scaling our findings up to the entire mangrove system, we estimated that anammox hotspots accounted for a loss of 751 gN·m-2·y-1, and contributed to over 12% of the nitrogen lost from the deeper layer of mangrove sediments in this region.

Bridging blinded and unblinded analysis for ongoing safety monitoring and evaluation.

In order to better characterize the safety profile of investigational new drugs (INDs) during clinical development, more interest and attention have been paid to ongoing safety monitoring and evaluation. The 2015 US FDA IND safety reporting draft guidance compels sponsors to periodically evaluate unblinded safety data. However, maintaining the trial blind is necessary to avoid jeopardizing the validity of study findings. In this article, we propose an innovative new approach which includes analyzing both blinded and unblinded data. The proposed two-stage framework incorporates periodic analyses of blinded safety data to detect and flag adverse events that may have potential risk elevation related to experimental treatment, as well as planned unblinded analyses to quantify associations between the drug and adverse events, and to determine thresholds for referring adverse events for medical review and safety reporting.

Different Height Forms of Spartina alterniflora Might Select Their Own Rhizospheric Bacterial Communities in Southern Coast of China.

In the southernmost part of coast of China, two height forms of Spartina alterniflora, tall and short, have invaded Leizhou Peninsula within the last decade. However, the effect of different height forms of Spartina alterniflora on plant-microbe interaction has not been clarified. Here, the community structures of rhizosphere bacteria and the abundance of N- and S-cycling functional genes associated with selected S. alterniflora were investigated in the field and a common garden. The community structure of tall-form S. alterniflora was distinct from short-form S. alterniflora at OTU level in the field, even after transplantation into a common garden. The abundance of bacterial amoA, nirS, and nosZ in tall S. alterniflora was significantly greater than those in short S. alterniflora in the field; however, this difference disappeared in a 1-year common garden experiment. These results suggested that compared with the tall-form S. alterniflora, the rhizosphere of short-form S. alterniflora harbored fewer nitrification-denitrification related microorganisms, which might benefit from conserving N in an N limited habitat. Together, our results suggested that tall- and short-form S. alterniflora can host their specific rhizosphere microbial communities and had different strategies of N usage via selecting the composition of rhizosphere bacterial assemblages, which in turn might determine the growth and invasiveness of S. alterniflora in its introduced range.

Litorivita pollutaquae gen. nov., sp. nov., a marine bacterium in the family Rhodobacteraceae isolated from surface seawater of Xiamen Port, China.

A Gram-stain-negative Rhodobacterales strain, designated as FSX-11T, was isolated from surface seawater of Xiamen port in China. Strain FSX-11T showed less than 96.5 % 16S rRNA gene sequence similarity to the type strains of species with validly published names. Phylogenetic analysis based on 16S rRNA gene sequences revealed that the novel isolate formed a distinct monophyletic clade within the family Rhodobacteriaceae and clustered distantly with the genera Thalassobius and Marivita. Cells of strain FSX-11T were non-motile, oval-shaped and facultative anaerobic. Optimal growth occurred at 20-30 °C, at pH 7.0-8.0 and in the presence of 2-3 % NaCl (w/v). The major respiratory quinone was ubiquinone-10. Summed feature 8 (C18 : 1ω7c and/or C18 : 1ω6c), 11-methyl C18 : 1ω7c and C16 : 0 were the major fatty acids. The DNA G+C content of strain FSX-11T was 58.7 mol%. On the basis of phylogenetic analysis, phenotypic and chemotaxonomic characteristics and 16S rRNA gene signature nucleotide patterns, strain FSX-11T represents a novel species in a novel genus within the family Rhodobacteraceae, for which the name Litorivita pollutaquae gen. nov., sp. nov. is proposed. The type strain is FSX-11T (=JCM 32715T=MCCC 1K03503T).

Bayesian regression model for recurrent event data with event-varying covariate effects and event effect.

In the course of hypertension, cardiovascular disease events (e.g., stroke, heart failure) occur frequently and recurrently. The scientific interest in such study may lie in the estimation of treatment effect while accounting for the correlation among event times. The correlation among recurrent event times come from two sources: subject-specific heterogeneity (e.g., varied lifestyles, genetic variations, and other unmeasurable effects) and event dependence (i.e., event incidences may change the risk of future recurrent events). Moreover, event incidences may change the disease progression so that there may exist event-varying covariate effects (the covariate effects may change after each event) and event effect (the effect of prior events on the future events). In this article, we propose a Bayesian regression model that not only accommodates correlation among recurrent events from both sources, but also explicitly characterizes the event-varying covariate effects and event effect. This model is especially useful in quantifying how the incidences of events change the effects of covariates and risk of future events. We compare the proposed model with several commonly used recurrent event models and apply our model to the motivating lipid-lowering trial (LLT) component of the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) (ALLHAT-LLT).

Molecular and stable isotopic evidence for the occurrence of nitrite-dependent anaerobic methane-oxidizing bacteria in the mangrove sediment of Zhangjiang Estuary, China.

Nitrite-dependent anaerobic methane oxidation (n-damo), which is mediated by "Candidatus Methylomirabilis oxyfera-like" bacteria, is unique in linking the carbon and nitrogen cycles. However, the niche and activity of n-damo bacteria in the mangrove ecosystem have not been confirmed. Here, we report the occurrence of the n-damo process in the mangrove wetland of the Zhangjiang Estuary, China. The widespread occurrence of n-damo bacteria in mangrove wetland was confirmed using real-time quantitative polymerase chain reaction (qPCR) assay, which showed that the abundance of Methylomirabilis oxyfera-like bacterial 16S rRNA and pmoA genes ranged from 2.43 × 106 to 2.09 × 107 and 2.07 × 106 to 3.38 × 107copies per gram of dry soil in the examined sediment cores. The highest amount of targeting genes was all detected in the upper layer (0-20 cm). Phylogenetic analyses of n-damo bacterial 16S rRNA and pmoA genes illustrated the depth-specific distribution and high diversity of n-damo bacteria in the mangrove wetland. Stable isotope experiments further confirmed the occurrence of n-damo in the examined mangrove sediments, and the potential n-damo rates ranged from 25.93 to 704.08 nmol CO2 per gram of dry soil per day at different depths of the sediment cores, with the n-damo being more active in the upper layer of the mangrove sediments. These results illustrate the existence of active M. oxyfera-like bacteria and indicate that the n-damo process is a previously overlooked microbial methane sink in the mangrove wetlands.

Bayesian analysis of multi-type recurrent events and dependent termination with nonparametric covariate functions.

Multi-type recurrent event data occur frequently in longitudinal studies. Dependent termination may occur when the terminal time is correlated to recurrent event times. In this article, we simultaneously model the multi-type recurrent events and a dependent terminal event, both with nonparametric covariate functions modeled by B-splines. We develop a Bayesian multivariate frailty model to account for the correlation among the dependent termination and various types of recurrent events. Extensive simulation results suggest that misspecifying nonparametric covariate functions may introduce bias in parameter estimation. This method development has been motivated by and applied to the lipid-lowering trial component of the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial.

Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci.

Meta-analyses of association results for blood pressure using exome-centric single-variant and gene-based tests identified 31 new loci in a discovery stage among 146,562 individuals, with follow-up and meta-analysis in 180,726 additional individuals (total n = 327,288). These blood pressure-associated loci are enriched for known variants for cardiometabolic traits. Associations were also observed for the aggregation of rare and low-frequency missense variants in three genes, NPR1, DBH, and PTPMT1. In addition, blood pressure associations at 39 previously reported loci were confirmed. The identified variants implicate biological pathways related to cardiometabolic traits, vascular function, and development. Several new variants are inferred to have roles in transcription or as hubs in protein-protein interaction networks. Genetic risk scores constructed from the identified variants were strongly associated with coronary disease and myocardial infarction. This large collection of blood pressure-associated loci suggests new therapeutic strategies for hypertension, emphasizing a link with cardiometabolic risk.

An Empirical Comparison of Joint and Stratified Frameworks for Studying G × E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene-Lifestyle Interactions Working Group.

Studying gene-environment (G × E) interactions is important, as they extend our knowledge of the genetic architecture of complex traits and may help to identify novel variants not detected via analysis of main effects alone. The main statistical framework for studying G × E interactions uses a single regression model that includes both the genetic main and G × E interaction effects (the "joint" framework). The alternative "stratified" framework combines results from genetic main-effect analyses carried out separately within the exposed and unexposed groups. Although there have been several investigations using theory and simulation, an empirical comparison of the two frameworks is lacking. Here, we compare the two frameworks using results from genome-wide association studies of systolic blood pressure for 3.2 million low frequency and 6.5 million common variants across 20 cohorts of European ancestry, comprising 79,731 individuals. Our cohorts have sample sizes ranging from 456 to 22,983 and include both family-based and population-based samples. In cohort-specific analyses, the two frameworks provided similar inference for population-based cohorts. The agreement was reduced for family-based cohorts. In meta-analyses, agreement between the two frameworks was less than that observed in cohort-specific analyses, despite the increased sample size. In meta-analyses, agreement depended on (1) the minor allele frequency, (2) inclusion of family-based cohorts in meta-analysis, and (3) filtering scheme. The stratified framework appears to approximate the joint framework well only for common variants in population-based cohorts. We conclude that the joint framework is the preferred approach and should be used to control false positives when dealing with low-frequency variants and/or family-based cohorts.