Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies

<div><p>Genome-wide association studies (GWASs) have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test constructed based on the identified trait-relevant tissues for powerful association analysis. We refer to our analytic procedure as the Scalable Multiple Annotation integration for trait-Relevant Tissue identification and usage (SMART). With extensive simulations, we show how our method can make use of multiple complementary annotations to improve the accuracy for identifying trait-relevant tissues. In addition, our procedure allows us to make use of the inferred trait-relevant tissues, for the first time, to construct more powerful SNP set tests. We apply our method for an in-depth analysis of 43 traits from 28 GWASs using tissue-specific annotations in 105 tissues derived from ENCODE and Roadmap. Our results reveal new trait-tissue relevance, pinpoint important annotations that are informative of trait-tissue relationship, and illustrate how we can use the inferred trait-relevant tissues to construct more powerful association tests in the Wellcome trust case control consortium study.</p></div

A Prediction Model of Pressure Loss of Cement Slurry in Deep-Water HTHP Directional Wells

The exploitations of deep-water wells often use directional well drilling to reach the target layer. Affected by special environments in deep water, the prediction of pressure loss of cement slurry is particularly important. This paper presents a prediction model of pressure loss suitable for deep-water directional wells. This model takes the complex interaction between the temperature, pressure and hydration kinetics of cement slurry into account. Based on the initial and boundary conditions, the finite difference method is used to discretize and calculate the model to ensure the stability and convergence of the result calculated by this model. Finally, the calculation equation of the model is used to predict the transient temperature and pressure loss of Wells X1 and X2, and a comparison is made between the predicted value and the monitoring data. The comparison results show that the maximum error between the temperature and pressure predicted by the model and the field measured value is within 6%. Thus, this model is of high accuracy and can meet the needs of site construction. It is concluded that this result can provide reliable theoretical guidance for temperature and pressure prediction, as well as the anti-channeling design of HTHP directional wells

Heatmap displays the rank of 105 tissues (y-axis) in terms of their relevance for each of the 43 GWAS traits (x-axis).

<p>Traits are organized by hierarchical clustering. Tissues are organized into ten tissue groups.</p

Strata Movement of the Thick Loose Layer under Strip-Filling Mining Method: A Case Study

Filling mining plays an important role in controlling surface subsidence. To study the movement of overburdened rock in filling mining under thick loose layers, a numerical simulation combing field measurement in CT30101 working face in the Mahuangliang coal mine was tested. The results show that different filling rates and filling body strength have different influences on roof and surface movement. The filling rate has a greater impact, which is the main control factor. The filling stress and roof tensile stress decrease gradually with roadway filling. The filling body stress and roof tensile stress in the first and second rounds are far greater than in the fourth round. After the completion of filling, the first and second round of filling bodies mainly bear the overburden, and the total deformation of the surrounding rock of the main transport roadway is very small, and therefore the displacement of the overburdened rock is controllable. The field monitoring results also show that the overburdened rock became stable after several fillings rounds. Combing the numerical modeling and field tests results, this study can be a guideline for similar geological conditions especially for coal mining under thick loose layers and thin bedrock

Modelling the initial epidemic trends of COVID-19 in Italy, Spain, Germany, and France.

The Coronavirus Disease 2019 (COVID-19) has fast spread to over 200 countries and regions worldwide since its outbreak, while in March, Europe became the emerging epicentre. In this study, we aimed to model the epidemic trends and estimate the essential epidemic features of COVID-19 in Italy, Spain, Germany, and France at the initial stage. The numbers of daily confirmed cases and total confirmed cases were extracted from the Coronavirus disease (COVID-19) situation reports of WHO. We applied an extended Susceptible-Exposed-Infectious-Removed (SEIR) model to fit the epidemic trend and estimated corresponding epidemic features. The transmission rate estimates were 1.67 (95% credible interval (CrI), 1.64-1.71), 2.83 (2.72-2.85), 1.91 (1.84-1.98), and 1.89 (1.82-1.96) for Italy, Spain, Germany, and France, corresponding to the basic reproduction numbers (R0) 3.44 (3.35-3.54), 6.25 (5.97-6.55), 4.03 (3.84-4.23), and 4.00 (3.82-4.19), respectively. We found Spain had the lowest ascertainment rate of 0.22 (0.19-0.25), followed by France, Germany, and Italy of 0.45 (0.40-0.50), 0.46 (0.40-0.52), and 0.59 (0.55-0.64). The peaks of daily new confirmed cases would reach on April 16, April 5, April 21, and April 19 for Italy, Spain, Germany, and France if no action was taken by the authorities. Given the high transmissibility and high covertness of COVID-19, strict countermeasures, such as national lockdown and social distancing, were essential to be implemented to reduce the spread of the disease

Simulation results for comparing using multiple annotations versus a single annotation.

<p>(A) Power to detect trait-relevant tissues by different approaches in various settings at a fixed FDR of 0.1. x-axis shows the values of the two annotation coefficients used in the simulations. Settings where at least one annotation coefficient is zero are shaded in grey. The setting where the annotation coefficients equal to the median estimates from real data (i.e. <b><i>α</i></b> = <b>(0.1, 0.05)</b>) is shaded in gold. The first number for each method in the figure legend represents the number of times each method is ranked as the best in 25 simulation settings where none of the annotations have zero coefficients; while the second number represents the number of times each method is ranked as the best in 11 simulation settings where at least one annotation has a zero coefficient. (B) Annotation coefficient estimates by SMART are centered around the truth (horizontal dotted gold lines). (C) Mean power (y-axis) to detect trait-relevant tissues by different approaches at different FDR values (x-axis). Error bar shows the standard deviation computed across 10 simulation groups, each of which contains 1,000 simulation replicates (i.e. a total of 10,000 simulations). <i>p</i>-values from the paired t-test are used to compare methods at different FDR cutoffs. Note that the error bar is large due to the small number of simulation replicates within each simulation group. For (B) and (C), simulations were done at <b><i>α</i></b> = <b>(0.1, 0.05)</b>. FDR, false discovery rate.</p

Long-term adverse influence of smoking during pregnancy on height and body size of offspring at ten years old in the UK Biobank cohort

Background: To explore the long-term relationship between maternal smoking during pregnancy and early childhood growth in the UK Biobank cohort. Methods: To estimate the effect of maternal smoking during pregnancy on offspring height and body size at ten years old, we performed binary logistic analyses and reported odds ratios (OR) as well as 95% confidence intervals (95%CIs). We also implemented the cross-contextual comparison study to examine whether such influence could be repeatedly observed among three different ethnicities in the UK Biobank cohort (n = 22,140 for White, n = 7094 for South Asian, and n = 5000 for Black). In particular, we conducted the sibling cohort study in White sibling cohort (n = 9953 for height and n = 7239 for body size) to control for unmeasured familial confounders. Results: We discovered that children whose mothers smoked during pregnancy had greater risk of being shorter or plumper at age ten in the full UK Biobank White cohort, with 15.3% (95% CIs: 13.0%∼17.7%) higher risk for height and 32.4% (95%CIs: 29.5%∼35.4%) larger risk for body size. Similar associations were identified in the South Asian and Black ethnicities. These associations were robust and remained significant in the White sibling cohort (12.6% [95%CIs: 5.0%∼20.3%] for height and 36.1% [95%CIs: 26.3%∼45.9%] for body size) after controlling for family factors. Conclusion: This study robustly confirms that maternal smoking during pregnancy can promote height deficit and obesity for offspring at ten years old. Our findings strongly encourage mothers to quit smoking during pregnancy for improving growth and development of offspring

sj-xlsx-10-pie-10.1177_09544089221123408 - Supplemental material for Experimental study of centrifugal pump as turbine with S-blade impeller

Supplemental material, sj-xlsx-10-pie-10.1177_09544089221123408 for Experimental study of centrifugal pump as turbine with S-blade impeller by Xiaohui Wang, Zanxiu Wu, Hao Yang, Xingjie Zhang, Junhu Yang and Senchun Miao in Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering</p

sj-tif-2-pie-10.1177_09544089221123408 - Supplemental material for Experimental study of centrifugal pump as turbine with S-blade impeller

Supplemental material, sj-tif-2-pie-10.1177_09544089221123408 for Experimental study of centrifugal pump as turbine with S-blade impeller by Xiaohui Wang, Zanxiu Wu, Hao Yang, Xingjie Zhang, Junhu Yang and Senchun Miao in Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering</p