Identifying interacting genetic variations by fish-swarm logic regression

Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR) is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR), which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds

Down-regulation of microRNA-23b aggravates LPS-induced inflammatory injury in chondrogenic ATDC5 cells by targeting PDCD4

Objective(s): Osteoarthritis (OA), characterized by degradation of articular cartilage, is a leading cause of disability. As the only cell type present in cartilage, chondrocytes play curial roles in the progression of OA. In our study, we aimed to explore the roles of miR-23b in the lipopolysaccharide (LPS)-induced inflammatory injury. Materials and Methods: LPS-induced cell injury of ATDC5 cells was evaluated by the loss of cell viability, enhancement of cell apoptosis, alteration of apoptosis-associated proteins, and release of inflammatory cytokines. Then, miR-23b level after LPS treatment was assessed by qRT-PCR. Next, the effects of aberrantly expressed miR-23b on the LPS-induced inflammatory injury were explored. The possible target genes of miR-23b were virtually screened by informatics and verified by luciferase assay. Subsequently, whether miR-23b functioned through regulating the target gene was validated. The involved signaling pathways were investigated finally.Results: Cell viability was decreased but cell apoptosis, as well as release of inflammatory cytokines, was enhanced by LPS treatment. MiR-23b was down-regulated by LPS and its overexpression alleviated LPS-induced inflammatory injury. PDCD4, negatively regulated by miR-23b expression, was verified as a target gene of miR-23b. Following experiments showed miR-23b alleviated LPS-induced cell injury through down-regulating PDCD4 expression. Phosphorylated levels of key kinases in the NF-κB pathway, as well as expressions of key kinases in the Notch pathways, were increased by PDCD4 overexpression.Conclusion: MiR-23b was down-regulated after LPS treatment, and its overexpression ameliorated LPS-induced inflammatory injury in ATDC5 cells by targeting PDCD4, which could activate the NF-κB/Notch pathways

An Efficient Algorithm for Sensitively Detecting Circular RNA from RNA-seq Data

Circular RNA (circRNA) is an important member of non-coding RNA family. Numerous computational methods for detecting circRNAs from RNA-seq data have been developed in the past few years, but there are dramatic differences among the algorithms regarding the balancing of the sensitivity and precision of the detection and filtering strategies. To further improve the sensitivity, while maintaining an acceptable precision of circRNA detection, a novel and efficient de novo detection algorithm, CIRCPlus, is proposed in this paper. CIRCPlus accurately locates circRNA candidates by identifying a set of back-spliced junction reads by comparing the local similar sequence of each pair of spanning junction reads. This strategy, thus, utilizes the important information provided by unbalanced spanning reads, which facilitates the detection especially when the expression levels of circRNA are unapparent. The performance of CIRCPlus was tested and compared to the existing de novo methods on the real datasets as well as a series of simulation datasets with different configurations. The experiment results demonstrated that the sensitivities of CIRCPlus were able to reach 90% in common simulation settings, while CIRCPlus held balanced sensitivity and reliability on the real datasets according to an objective assessment criteria based on RNase R-treated samples. The software tool is available for academic uses only

Analysis of the Mechanical Properties and Damage Mechanism of Carbon Fiber/Epoxy Composites under UV Aging

The UV durability of carbon fiber composites has been a concern. In this work, UV irradiation on carbon fiber-reinforced polymer (CFRP) materials was performed using an artificial accelerated UV aging chamber to investigate the effect of UV exposure on carbon fiber composites. UV aging caused some of the macromolecular chains on the surface resin to break, resulting in the loss of small molecules and loss of mass. After 80 days of UV irradiation exposure, a significant decline in the macroscopic mechanical properties occurred in the longitudinal direction, with the largest decrease of 23% in longitudinal compressive strength and a decreasing trend in the transverse mechanical properties at the later stage of aging. The microscopic mechanical properties of the CFRP specimens were characterized using nanoindentation, and it was found that UV aging had an embrittlement effect on the matrix, and its hardness/modulus values were higher than the initial values with UV exposure. The fibers were less affected by UV irradiation

A probabilistic method for identifying rare variants underlying complex traits

Abstract Background Identifying the genetic variants that contribute to disease susceptibilities is important both for developing methodologies and for studying complex diseases in molecular biology. It has been demonstrated that the spectrum of minor allelic frequencies (MAFs) of risk genetic variants ranges from common to rare. Although association studies are shifting to incorporate rare variants (RVs) affecting complex traits, existing approaches do not show a high degree of success, and more efforts should be considered. Results In this article, we focus on detecting associations between multiple rare variants and traits. Similar to RareCover, a widely used approach, we assume that variants located close to each other tend to have similar impacts on traits. Therefore, we introduce elevated regions and background regions, where the elevated regions are considered to have a higher chance of harboring causal variants. We propose a hidden Markov random field (HMRF) model to select a set of rare variants that potentially underlie the phenotype, and then, a statistical test is applied. Thus, the association analysis can be achieved without pre-selection by experts. In our model, each variant has two hidden states that represent the causal/non-causal status and the region status. In addition, two Bayesian processes are used to compare and estimate the genotype, phenotype and model parameters. We compare our approach to the three current methods using different types of datasets, and though these are simulation experiments, our approach has higher statistical power than the other methods. The software package, RareProb and the simulation datasets are available at: http://www.engr.uconn.edu/~jiw09003.</p

Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures

Gene fusion structure is a class of common somatic mutational events in cancer genomes, which are often formed by chromosomal mutations. Identifying the driver gene(s) in a fusion structure is important for many downstream analyses and it contributes to clinical practices. Existing computational approaches have prioritized the importance of oncogenes by incorporating prior knowledge from gene networks. However, different methods sometimes suffer different weaknesses when handling gene fusion data due to multiple issues such as fusion gene representation, network integration, and the effectiveness of the evaluation algorithms. In this paper, Synstable Fusion (SYN), an algorithm for computationally evaluating the fusion genes, is proposed. This algorithm uses network-based strategy by incorporating gene networks as prior information, but estimates the driver genes according to the destructiveness hypothesis. This hypothesis balances the two popular evaluation strategies in the existing studies, thereby providing more comprehensive results. A machine learning framework is introduced to integrate multiple networks and further solve the conflicting results from different networks. In addition, a synchronous stability model is established to reduce the computational complexity of the evaluation algorithm. To evaluate the proposed algorithm, we conduct a series of experiments on both artificial and real datasets. The results demonstrate that the proposed algorithm performs well on different configurations and is robust when altering the internal parameter settings

Toxic Metals in a Paddy Field System: A Review

The threat of toxic metals to food security and human health has become a high-priority issue in recent decades. As the world&rsquo;s main food crop source, the safe cultivation of rice has been the focus of much research, particularly the restoration of toxic metals in paddy fields. Therefore, in this paper, we focus on the effects of toxic metals on rice, as well as the removal or repair methods of toxic metals in paddy fields. We also provide a detailed discussion of the sources and monitoring methods of toxic metals pollution, the current toxic metal removal, and remediation methods in paddy fields. Finally, several important research issues related to toxic metals in paddy field systems are proposed for future work. The review has an important guiding role for the future of heavy metal remediation in paddy fields, safe production of rice, green ecological fish culture, and human food security and health

An improved burden-test pipeline for identifying associations from rare germline and somatic variants

Abstract Background Identifying rare germline and somatic variants associated with cancer progression is an important research topic in cancer genomics. Although many approaches are proposed for rare variant association study, they are not fit for cancer sequencing data due to multiple issues, such as overly relying on pre-selection, losing sight of interacting hotspots, etc. Results In this article, we propose an improved pipeline to identify germline variant and somatic mutation interactions influencing cancer susceptibility from pair-wise cancer sequencing data. The proposed pipeline, RareProb-C performs an algorithmic selection on the given variants by incorporating the variant allelic frequencies. The interactions among the variants are considered within the regions which are limited by a four-gamete test. Then it filters singular cases according to the posterior probability at each site. Finally, it outputs the selected candidates that pass a collapse test. Conclusions We apply RareProb-C on a series of carefully constructed simulation cases and it outperforms six existing genetic model-free approaches. We also test RareProb-C on 429 TCGA ovarian cancer cases, and RareProb-C successfully identifies the known highlighted variants which are considered increasing disease susceptibilities

Genetic Diversity of Chinese Longsnout Catfish (<i>Leiocassis longirostris</i>) in Four Farmed Populations Based on 20 New Microsatellite DNA Markers

Freshwater aquaculture has a long and vibrant tradition in China. The Chinese longsnout catfish (Leiocassis longirostris) is a popular economic freshwater fish native to China. Understanding the genetic structure of L. longirostris populations is important for ensuring the efficacy of management practices and the sustainability of future increases in production. In this study, we used Illumina sequencing technology to isolate 20 novel polymorphic microsatellites from the genome of L. longirostris. These microsatellites were used to analyze the genetic diversity of 240 L. longrostris individuals from four populations. Genetic diversity parameters (NA, HO, HE, I, PIC, and FST) of the four farmed L. longirostris populations were analyzed. The level of genetic differentiation among the four farmed L. longirostris populations (inferred by pairwise comparisons of FST values) was low, but the genetic diversity of these populations was high, indicating that they still provide useful sources of genetic variation that could aid in breeding efforts. The STRUCTURE and ADMIXTURE analyses indicated that admixture might be occurring in the four L. longirostris populations, especially between the MS and YB populations. Understanding the genetic diversity of farmed L. longirostris populations and inbreeding prevention could greatly aid in breeding and production. These newly isolated microsatellite markers and the high genetic diversity of L. longirostris populations in the main breeding areas have important implications for the breeding and stock management of L. longirostris