Weighted selective collapsing strategy for detecting rare and common variants in genetic association study

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies (GWAS) have been used successfully in detecting associations between common genetic variants and complex diseases. However, common SNPs detected by current GWAS only explain a small proportion of heritable variability. With the development of next-generation sequencing technologies, researchers find more and more evidence to support the role played by rare variants in heritable variability. However, rare and common variants are often studied separately. The objective of this paper is to develop a robust strategy to analyze association between complex traits and genetic regions using both common and rare variants.</p> <p>Results</p> <p>We propose a weighted selective collapsing strategy for both candidate gene studies and genome-wide association scans. The strategy considers genetic information from both common and rare variants, selectively collapses all variants in a given region by a forward selection procedure, and uses an adaptive weight to favor more likely causal rare variants. Under this strategy, two tests are proposed. One test denoted by <it>B_wSC</it>is sensitive to the directions of genetic effects, and it separates the deleterious and protective effects into two components. Another denoted by <it>B_wSCd</it>is robust in the directions of genetic effects, and it considers the difference of the two components. In our simulation studies, <it>B_wSC</it>achieves a higher power when the casual variants have the same genetic effect, while <it>B_wSCd</it>is as powerful as several existing tests when a mixed genetic effect exists. Both of the proposed tests work well with and without the existence of genetic effects from common variants.</p> <p>Conclusions</p> <p>Two tests using a weighted selective collapsing strategy provide potentially powerful methods for association studies of sequencing data. The tests have a higher power when both common and rare variants contribute to the heritable variability and the effect of common variants is not strong enough to be detected by traditional methods. Our simulation studies have demonstrated a substantially higher power for both tests in all scenarios regardless whether the common SNPs are associated with the trait or not.</p

Combination of Sources of Evidence with Distinct Frames of Discernment

International audienceMulti-source information fusion strategies in target recognition have been widely applied. Generally, each source is defined and modelled over a common frame composed of the hypotheses to discern. However, in practice, the independent sources of evidence can refer to distinct frames of discernment in terms of the hypotheses they consider. Under this condition, the classical combination process cannot be applied directly. Working with distinct frames of discernment for information fusion is a problem often encountered in the development of recognition systems which requires a particular attention. In order to combine such sources, this paper presents a new combination method which splits the process of fusion into two steps: construction of granular structure, calculation of belief mass, followed by the fusion process. Our simulations results show that the proposed method can effectively solve the problem of fusion of sources defined on distinct frames

Rough Set Classifier Based on DSmT

International audienceThe classifier based on rough sets is widely used in pattern recognition. However, in the implementation of rough set-based classifiers, there always exist the problems of uncertainty. Generally, information decision table in Rough Set Theory (RST) always contains many attributes, and the classification performance of each attribute is different. It is necessary to determine which attribute needs to be used according to the specific problem. In RST, such problem is regarded as attribute reduction problems which aims to select proper candidates. Therefore, the uncertainty problem occurs for the classification caused by the choice of attributes. In addition, the voting strategy is usually adopted to determine the category of target concept in the final decision making. However, some classes of targets cannot be determined when multiple categories cannot be easily distinguished (for example, the number of votes of different classes is the same). Thus, the uncertainty occurs for the classification caused by the choice of classes. In this paper, we use the theory of belief functions to solve two above mentioned uncertainties in rough set classification and rough set classifier based on Dezert-Smarandache Theory (DSmT) is proposed. It can be experimentally verified that our proposed approach can deal efficiently with the uncertainty in rough set classifiers

Degradation or excretion of quantum dots in mouse embryonic stem cells

<p>Abstract</p> <p>Background</p> <p>Quantum dots (QDs) have been considered as a new and efficient probe for labeling cells non-invasively in vitro and in vivo, but fairly little is known about how QDs are eliminated from cells after labeling. The purpose of this study is to investigate the metabolism of QDs in different type of cells.</p> <p>Results</p> <p>Mouse embryonic stem cells (ESCs) and mouse embryonic fibroblasts (MEFs) were labeled with QD 655. QD-labeling was monitored by fluorescence microscopy and flow cytometry for 72 hours. Both types of cells were labeled efficiently, but a quick loss of QD-labeling in ESCs was observed within 48 hours, which was not prevented by inhibiting cell proliferation. Transmission electron microscope analysis showed a dramatic decrease of QD number in vesicles of ESCs at 24 hours post-labeling, suggesting that QDs might be degraded. In addition, supernatants collected from labeled ESCs in culture were used to label cells again, indicating that some QDs were excreted from cells.</p> <p>Conclusion</p> <p>This is the first study to demonstrate that the metabolism of QDs in different type of cells is different. QDs were quickly degraded or excreted from ESCs after labeling.</p

The Long-Term Impact of COVID-19 on Inbound Tourism from China: Using 2020/2022 Web-Based Survey Data

This study discusses the long-term impact of the COVID-19 pandemic on inbound tourism from China, aiming to investigate its prospects during the post-pandemic period. After briefly reviewing trends concerning COVID-19 impact studies at home and abroad, basic results from two cross-sections of web-based data in 2020 and 2022 are introduced to identify how the pandemic impacted not only daily activity and travel patterns but also the intentions of visiting Japan in the post-pandemic period. Finally, we summarize the challenges that we should verify to support inbound tourism restoration policies

Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs

Determining the target genes that interact with drugs—drug–target interactions—plays an important role in drug discovery. Identification of drug–target interactions through biological experiments is time consuming, laborious, and costly. Therefore, using computational approaches to predict candidate targets is a good way to reduce the cost of wet-lab experiments. However, the known interactions (positive samples) and the unknown interactions (negative samples) display a serious class imbalance, which has an adverse effect on the accuracy of the prediction results. To mitigate the impact of class imbalance and completely exploit the negative samples, we proposed a new method, named DTIGBDT, based on gradient boosting decision trees, for predicting candidate drug–target interactions. We constructed a drug–target heterogeneous network that contains the drug similarities based on the chemical structures of drugs, the target similarities based on target sequences, and the known drug–target interactions. The topological information of the network was captured by random walks to update the similarities between drugs or targets. The paths between drugs and targets could be divided into multiple categories, and the features of each category of paths were extracted. We constructed a prediction model based on gradient boosting decision trees. The model establishes multiple decision trees with the extracted features and obtains the interaction scores between drugs and targets. DTIGBDT is a method of ensemble learning, and it effectively reduces the impact of class imbalance. The experimental results indicate that DTIGBDT outperforms several state-of-the-art methods for drug–target interaction prediction. In addition, case studies on Quetiapine, Clozapine, Olanzapine, Aripiprazole, and Ziprasidone demonstrate the ability of DTIGBDT to discover potential drug–target interactions

Improved power by collapsing rare and common variants based on a data-adaptive forward selection strategy

Genome-wide association studies have been used successfully to detect associations between common genetic variants and complex diseases, but common single-nucleotide polymorphisms (SNPs) detected by these studies explain only 5–10% of disease heritability. Alternatively, the common disease/rare variants hypothesis suggests that complex diseases are often caused by multiple rare variants with moderate to high effects. Under this hypothesis, the analysis of the cumulative effect of rare variants may thus help us discover the missing genetic variations. Collapsing all rare variants across a functional region is currently a popular method to find rare variants that may have a causal effect on certain diseases. However, the power of tests based on collapsing methods is often impaired by misclassification of functional variants. We develop a data-adaptive forward selection procedure that selectively chooses only variants that improve the association signal between functional regions and the disease risk. We apply our strategy to the Genetic Analysis Workshop 17 unrelated individuals data with quantitative traits. The type I error rate and the power of different collapsing functions are evaluated. The substantially higher power of the proposed strategy was demonstrated. The new method provides a useful strategy for the association study of sequencing data by taking advantage of the selection of rare variants

Effective natural inhibitors targeting granzyme B in rheumatoid arthritis by computational study

BackgroundRheumatoid arthritis (RA) is an autoimmune disease characterized by erosive arthritis, and current treatments for RA fall short of the outcomes expected by clinicians and patients.ObjectivesThis study aimed to identify novel therapeutic and prognostic targets in RA at the genomic level and to screen desirable compounds with potential inhibitory effects on GZMB.MethodsWe performed differential gene analysis on GSE55235 and GSE55457 from Gene Expression Omnibus (GEO) and then obtained the intersection of the two differentially expressed genes (DEGs) lists by drawing Venn diagrams. Then we performed protein-protein interaction (PPI) network analysis, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on the DEGs of the intersection. Next, we downloaded the crystal structure of Granzyme B (GZMB). Molecular docking technology was used to screen potential inhibitors of GZMB in subsequent experiments, and we then analyzed the toxicity and water solubility of these potential inhibitors for future drug experiments. Finally, whether the docking of these small molecules with GZMB is stable is tested by molecular dynamics.ResultsA total of 352 mutual DEGs were identified. Twenty hub genes were obtained according to PPI network analysis, among which the GZMB gene attracted the attention of our research. Three potent natural compounds, ZINC000004557101, ZINC000012495776, and ZINC000038143593, bound to GZMB, show better binding affinity. Furthermore, they are predicted to own low Ames mutagenicity, developmental toxicity potential, rodent carcinogenicity, and high tolerance to cytochrome P4502D6. Molecular dynamics simulations show that ZINC000004557101 and GZMB have more advantageous potential energy and can exist stably in a natural environment. Moreover, we finally verified the inhibitory effect of ZINC000004557101 on granzyme B by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and Western blotting experiment.ConclusionRA patients showed increased GZMB expression. ZINC000004557101 is a potential drug targeting GZMB for treating RA