PCA-based bootstrap confidence interval tests for gene-disease association involving multiple SNPs.

BACKGROUND: Genetic association study is currently the primary vehicle for identification and characterization of disease-predisposing variant(s) which usually involves multiple single-nucleotide polymorphisms (SNPs) available. However, SNP-wise association tests raise concerns over multiple testing. Haplotype-based methods have the advantage of being able to account for correlations between neighbouring SNPs, yet assuming Hardy-Weinberg equilibrium (HWE) and potentially large number degrees of freedom can harm its statistical power and robustness. Approaches based on principal component analysis (PCA) are preferable in this regard but their performance varies with methods of extracting principal components (PCs). RESULTS: PCA-based bootstrap confidence interval test (PCA-BCIT), which directly uses the PC scores to assess gene-disease association, was developed and evaluated for three ways of extracting PCs, i.e., cases only(CAES), controls only(COES) and cases and controls combined(CES). Extraction of PCs with COES is preferred to that with CAES and CES. Performance of the test was examined via simulations as well as analyses on data of rheumatoid arthritis and heroin addiction, which maintains nominal level under null hypothesis and showed comparable performance with permutation test. CONCLUSIONS: PCA-BCIT is a valid and powerful method for assessing gene-disease association involving multiple SNPs.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

The resilience of interdependent transportation networks under targeted attack

Modern world builds on the resilience of interdependent infrastructures characterized as complex networks. Recently, a framework for analysis of interdependent networks has been developed to explain the mechanism of resilience in interdependent networks. Here we extend this interdependent network model by considering flows in the networks and study the system's resilience under different attack strategies. In our model, nodes may fail due to either overload or loss of interdependency. Under the interaction between these two failure mechanisms, it is shown that interdependent scale-free networks show extreme vulnerability. The resilience of interdependent SF networks is found in our simulation much smaller than single SF network or interdependent SF networks without flows.Comment: 5 pages, 4 figure

Multiple Solutions With Constant Sign of a Dirichlet Problem for a Class of Elliptic Systems With Variable Exponent Growth

We present here, in the system setting, a new set of growth conditions under which we manage to use a novel method to verify the Cerami compactness condition. By localization argument, decomposition technique and variational methods, we are able to show the existence of multiple solutions with constant sign for the problem without the well-known Ambrosetti--Rabinowitz type growth condition. More precisely, we manage to show that the problem admits four, six and infinitely many solutions respectively

A unified apparent porosity/permeability model of organic porous media: Coupling complex pore structure and multi-migration mechanism

 Shale gas resources are widely distributed and abundant in China, which is an important field for strategic replacement and development of oil and gas resources. Shale gas reservoirs has adsorption gas, free gas. The structure of different scale media, such as organic pores, are difficult to describe. Therefore, flow behavior cannot be simulated by conventional method. In this paper, the micro-scale fluid migration in shale gas reservoirs was established in a single pore, which coupled surface diffusion, slip flow, and viscous flow. On this basis, the fractal scale relationship was applied to describe the distribution of pore radius, tortuosity, and surface roughness. Based on the comprehensive characterization of static structure haracteristics of porous media, such as pore size distribution, pore shapes, tortuosity and surface roughness, and the dynamic pore size influenced by various stresses, the apparent porosity/permeability model of organic matter considering single-phase multi-migration mechanism was established. The gas migration in organic porous media was analyzed with the apparent porosity/permeability model. The results show that the small pores in organic matter are the main storage space of gas (more than 95% of the gas is stored in pores less than 10 nm), and the large pores are gas flow channel. At the same time, the apparent porosity/permeability model combined with conventional Darcy equation can be used to describe the single-phase gas flow in shale gas reservoirs.Cited as: Sheng, G., Su, Y., Zhao, H., Liu, J. A unified apparent porosity/permeability model of organic porous media: Coupling complex pore structure and multi-migration mechanism. Advances in Geo-Energy Research, 2020, 4(2): 115-125, doi: 10.26804/ager.2020.02.0

The impact of dissection and re-entry versus wire escalation techniques on long-term clinical outcomes in patients with chronic total occlusion lesions following percutaneous coronary intervention: An updated meta-analysis

Background: The meta-analysis was performed to evaluate the effect of dissection and re-entry (DR) vs. wire escalation (WE) techniques on long-term clinical outcomes in patients with chronic total occlusion (CTO) lesions undergoing percutaneous coronary intervention. Methods: Studies were searched in electronic databases from inception to September, 2019. Results were pooled using random effects model and fixed effects model and are presented as risk ratios (RR) with 95% confidence intervals (CI). Results: Pooled analyses revealed that patients with DR techniques had overall higher complexity CTO lesions than patients with WE techniques and required a greater number of stents and a greater mean stent length. The “extensive” DR techniques may have a higher incidence of target vessel revascularization (TVR) (RR = 2.30, 95% CI: 1.77–2.98), in-stent restenosis (RR = 1.71, 95% CI: 1.30–2.23), in-stent reocclusion (RR = 1.86, 95% CI: 1.03–3.3) and death/myocardial infarction/TVR (RR = 2.10, 95% CI: 1.71–2.58), when compared with WE techniques, during the long-term follow-up. However, “limited” DR techniques result in more promising outcomes, and are comparable to conventional WE techniques. Conclusions: Dissection and re-entry techniques were associated with increased risk of long-term negative clinical events, especially “extensive” DR techniques. However, “limited” DR techniques resulted in good long-term outcomes, comparable to WE techniques

Comparing Partial Least Square Approaches in Gene-or Region-based Association Study for Multiple Quantitative Phenotypes

On thinking quantitatively of complex diseases, there are at least three statistical strategies for association study: single SNP on single trait, gene-or region (with multiple SNPs) on single trait and on multiple traits. The third of which is the most general in dissecting the genetic mechanism underlying complex diseases underpinning multiple quantitative traits. Gene-or region association methods based on partial least square (PLS) approaches have been shown to have apparent power advantage. However, few attempts are developed for multiple quantitative phenotypes or traits underlying a condition or disease, and the performance of various PLS approaches used in association study for multiple quantitative traits had not been assessed. We, from regression perspective, exploit association between multiple SNPs and multiple phenotypes or traits through exhaustive scan statistics (sliding window) using PLS and sparse PLS (SPLS) regression. Simulations are conducted to assess the performance of the proposed scan statistics and compare them with the existed method. The proposed methods are applied to 12 regions of GWAS data from the European Prospective Investigation of Cancer (EPIC)-Norfolk study

Rumor Clarification, Digital Platform, and Stock Movement

Stock return is influenced by information release, dissemination, and acceptance. Rumor clarification is supposed to reduce asymmetric information and abnormal stock return. In this research, we extracted 4134 rumor-clarification pairs from 687,429 postings in social media, and quantified the language used in these messages, along with online firm behaviors, to study the effect of clarifications on stock returns. Our findings include (1) the digitalized rumor clarification messages affect the abnormal returns of the relevant stocks; (2) Such influence can be quantified and measured by the emotion polarity of rumor clarification; (3) Firm’s online clarification behaviors may have no influence on abnormal returns except for the total response number of rumor clarification for a listed company. In particular, investors prefer to trust the clarifications from the companies with frequent online interactive engagements