Weighted Selection Probability to Prioritize Susceptible Rare Variants in Multi-Phenotype Association Studies with Application to a Soybean Genetic Data Set.

Rare variant association studies with multiple traits or diseases have drawn a lot of attention since association signals of rare variants can be boosted if more than one phenotype outcome is associated with the same rare variants. Most of the existing statistical methods to identify rare variants associated with multiple phenotypes are based on a group test, where a pre-specified genetic region is tested one at a time. However, these methods are not designed to locate susceptible rare variants within the genetic region.

New statistical selection method for pleiotropic variants associated with both quantitative and qualitative traits.

Background: Identification of pleiotropic variants associated with multiple phenotypic traits has received increasing attention in genetic association studies. Overlapping genetic associations from multiple traits help to detect weak genetic associations missed by single-trait analyses. Many statistical methods were developed to identify pleiotropic variants with most of them being limited to quantitative traits when pleiotropic effects on both quantitative and qualitative traits have been observed.

A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study.

Background: Prognostic genes or gene signatures have been widely used to predict patient survival and aid in making decisions pertaining to therapeutic actions. Although some web-based survival analysis tools have been developed, they have several limitations.

Objective: Taking these limitations into account, we developed ESurv (Easy, Effective, and Excellent Survival analysis tool), a web-based tool that can perform advanced survival analyses using user-derived data or data from The Cancer Genome Atlas (TCGA).

Covariance thresholding to detect differentially co-expressed genes from microarray gene expression data.

Gene set analysis aims to identify differentially expressed or co-expressed genes within a biological pathway between two experimental conditions, so that it can eventually reveal biological processes and pathways involved in disease development. In the last few decades, various statistical and computational methods have been proposed to improve statistical power of gene set analysis. In recent years, much attention has been paid to differentially co-expressed genes since they can be potentially disease-related genes without significant difference in average expression levels between two conditions.

Hierarchical Cluster Analysis of Medical Chemicals Detected by a Bacteriophage-Based Colorimetric Sensor Array.

M13 bacteriophage-based colorimetric sensors, especially multi-array sensors, have been successfully demonstrated to be a powerful platform for detecting extremely small amounts of target molecules. Colorimetric sensors can be fabricated easily using self-assembly of genetically engineered M13 bacteriophage which incorporates peptide libraries on its surface. However, the ability to discriminate many types of target molecules is still required.

Risk of metastatic pheochromocytoma and paraganglioma in mutation carriers: a systematic review and updated meta-analysis.

Background: Pheochromocytoma and paraganglioma (PPGL) are tumours that arise from chromaffin cells. Some genetic mutations influence PPGL, among which, those in genes encoding subunits of succinate dehydrogenase (SDHA, SDHB, SDHC and SDHD) and assembly factor (SDHAF2) are the most relevant. However, the risk of metastasis posed by these mutations is not reported except for SDHB and SDHD mutations.

Incorporating genetic networks into case-control association studies with high-dimensional DNA methylation data.

Background: In human genetic association studies with high-dimensional gene expression data, it has been well known that statistical selection methods utilizing prior biological network knowledge such as genetic pathways and signaling pathways can outperform other methods that ignore genetic network structures in terms of true positive selection. In recent epigenetic research on case-control association studies, relatively many statistical methods have been proposed to identify cancer-related CpG sites and their corresponding genes from high-dimensional DNA methylation array data. However, most of existing methods are not designed to utilize genetic network information although methylation levels between linked genes in the genetic networks tend to be highly correlated with each other.

Disruption of the Myc-PDE4B regulatory circuitry impairs B-cell lymphoma survival.

A large body of evidence suggests that B-cell lymphomas with enhanced Myc expression are associated with an aggressive phenotype and poor prognosis, which makes Myc a compelling therapeutic target. Phosphodiesterase 4B (PDE4B), a main hydrolyzer of cyclic AMP (cAMP) in B cells, was shown to be involved in cell survival and drug resistance in diffuse large B cell lymphomas (DLBCL). However, the interrelationship between Myc and PDE4B remains unclear.

Dietary fat-associated osteoarthritic chondrocytes gain resistance to lipotoxicity through PKCK2/STAMP2/FSP27.

Free fatty acids (FFAs), which are elevated with metabolic syndrome, are considered the principal offender exerting lipotoxicity. Few previous studies have reported a causal relationship between FFAs and osteoarthritis pathogenesis. However, the molecular mechanism by which FFAs exert lipotoxicity and induce osteoarthritis remains largely unknown.

Anti-cancer effect of novel PAK1 inhibitor via induction of PUMA-mediated cell death and p21-mediated cell cycle arrest.

Hyper-activation of PAK1 (p21-activated kinase 1) is frequently observed in human cancer and speculated as a target of novel anti-tumor drug. In previous, we also showed that PAK1 is highly activated in the Smad4-deficient condition and suppresses PUMA (p53 upregulated modulator of apoptosis) through direct binding and phosphorylation. On the basis of this result, we have tried to find novel PAK1-PUMA binding inhibitors.

Selection Probability for Rare Variant Association Studies.

In human genome research, genetic association studies of rare variants have been widely studied since the advent of high-throughput DNA sequencing platforms. However, detection of outcome-related rare variants still remains a statistically challenging problem because the number of observed genetic mutations is extremely rare. Recently, a power set-based statistical selection procedure has been proposed to locate both risk and protective rare variants within the outcome-related genes or genetic regions.

pETM: a penalized Exponential Tilt Model for analysis of correlated high-dimensional DNA methylation data.

Motivation: DNA methylation plays an important role in many biological processes and cancer progression. Recent studies have found that there are also differences in methylation variations in different groups other than differences in methylation means. Several methods have been developed that consider both mean and variance signals in order to improve statistical power of detecting differentially methylated loci.

Prevention effect of rare ginsenosides against stress-hormone induced MTOC amplification.

Stress has been suggested as one of important cause of human cancer without molecular biological evidence. Thus, we test the effect of stress-related hormones on cell viability and mitotic fidelity. Similarly to estrogen, stress hormone cortisol and its relative cortisone increase microtubule organizing center (MTOC) number through elevated expression of γ-tubulin and provide the Taxol resistance to human cancer cell lines.

NETWORK-REGULARIZED HIGH-DIMENSIONAL COX REGRESSION FOR ANALYSIS OF GENOMIC DATA.

We consider estimation and variable selection in high-dimensional Cox regression when a prior knowledge of the relationships among the covariates, described by a network or graph, is available. A limitation of the existing methodology for survival analysis with high-dimensional genomic data is that a wealth of structural information about many biological processes, such as regulatory networks and pathways, has often been ignored. In order to incorporate such prior network information into the analysis of genomic data, we propose a network-based regularization method for high-dimensional Cox regression; it uses an ℓ-penalty to induce sparsity of the regression coefficients and a quadratic Laplacian penalty to encourage smoothness between the coefficients of neighboring variables on a given network.

A power set-based statistical selection procedure to locate susceptible rare variants associated with complex traits with sequencing data.

Motivation: Existing association methods for rare variants from sequencing data have focused on aggregating variants in a gene or a genetic region because of the fact that analysing individual rare variants is underpowered. However, these existing rare variant detection methods are not able to identify which rare variants in a gene or a genetic region of all variants are associated with the complex diseases or traits. Once phenotypic associations of a gene or a genetic region are identified, the natural next step in the association study with sequencing data is to locate the susceptible rare variants within the gene or the genetic region.

Network-based regularization for matched case-control analysis of high-dimensional DNA methylation data.

The matched case-control designs are commonly used to control for potential confounding factors in genetic epidemiology studies especially epigenetic studies with DNA methylation. Compared with unmatched case-control studies with high-dimensional genomic or epigenetic data, there have been few variable selection methods for matched sets. In an earlier paper, we proposed the penalized logistic regression model for the analysis of unmatched DNA methylation data using a network-based penalty.

Robust Gaussian graphical modeling via l1 penalization.

Gaussian graphical models have been widely used as an effective method for studying the conditional independency structure among genes and for constructing genetic networks. However, gene expression data typically have heavier tails or more outlying observations than the standard Gaussian distribution. Such outliers in gene expression data can lead to wrong inference on the dependency structure among the genes.

Penalized logistic regression for high-dimensional DNA methylation data with case-control studies.

Motivation: DNA methylation is a molecular modification of DNA that plays crucial roles in regulation of gene expression. Particularly, CpG rich regions are frequently hypermethylated in cancer tissues, but not methylated in normal tissues. However, there are not many methodological literatures of case-control association studies for high-dimensional DNA methylation data, compared with those of microarray gene expression.

A Bayesian Approach for Graph-constrained Estimation for High-dimensional Regression.

Many different biological processes are represented by network graphs such as regulatory networks, metabolic pathways, and protein-protein interaction networks. Since genes that are linked on the networks usually have biologically similar functions, the linked genes form molecular modules to affect the clinical phenotypes/outcomes. Similarly, in large-scale genetic association studies, many SNPs are in high linkage disequilibrium (LD), which can also be summarized as a LD graph.