Hokeun Sun

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. One key feature of DNA methylation data is a grouped structure among CpG sites from a gene that are possibly highly correlated. In this article, we proposed a penalized logistic regression model for correlated DNA methylation CpG sites within genes from high-dimensional array data. Our regularization procedure is based on a combination of the l(1) penalty and squared l(2) penalty on degree-scaled differences of coefficients of CpG sites within one gene, so it induces both sparsity and smoothness with respect to the correlated regression coefficients. We combined the penalized procedure with a stability selection procedure such that a selection probability of each regression coefficient was provided which helps us make a stable and confident selection of methylation CpG sites that are possibly truly associated with the outcome. RESULTS Using simulation studies we demonstrated that the proposed procedure outperforms existing main-stream regularization methods such as lasso and elastic-net when data is correlated within a group. We also applied our method to identify important CpG sites and corresponding genes for ovarian cancer from over 20 000 CpGs generated from Illumina Infinium HumanMethylation27K Beadchip. Some genes identified are potentially associated with cancers.

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. However, for popularly applied matched designs in epigenetic studies that compare DNA methylation between tumor and adjacent non-tumor tissues or between pre-treatment and post-treatment conditions, applying ordinary logistic regression ignoring matching is known to bring serious bias in estimation. In this paper, we developed a penalized conditional logistic model using the network-based penalty that encourages a grouping effect of (1) linked Cytosine-phosphate-Guanine (CpG) sites within a gene or (2) linked genes within a genetic pathway for analysis of matched DNA methylation data. In our simulation studies, we demonstrated the superiority of using conditional logistic model over unconditional logistic model in high-dimensional variable selection problems for matched case-control data. We further investigated the benefits of utilizing biological group or graph information for matched case-control data. We applied the proposed method to a genome-wide DNA methylation study on hepatocellular carcinoma (HCC) where we investigated the DNA methylation levels of tumor and adjacent non-tumor tissues from HCC patients by using the Illumina Infinium HumanMethylation27 Beadchip. Several new CpG sites and genes known to be related to HCC were identified but were missed by the standard method in the original paper.

Estimating the Entropy of a Weibull Distribution under Generalized Progressive Hybrid Censoring

Recently, progressive hybrid censoring schemes have become quite popular in a life-testing problem and reliability analysis. However, the limitation of the progressive hybrid censoring scheme is that it cannot be applied when few failures occur before time T. Therefore, a generalized progressive hybrid censoring scheme was introduced. In this paper, the estimation of the entropy of a two-parameter Weibull distribution based on the generalized progressively censored sample has been considered. The Bayes estimators for the entropy of the Weibull distribution based on the symmetric and asymmetric loss functions, such as the squared error, linex and general entropy loss functions, are provided. The Bayes estimators cannot be obtained explicitly, and Lindley’s approximation is used to obtain the Bayes estimators. Simulation experiments are performed to see the effectiveness of the different estimators. Finally, a real dataset has been analyzed for illustrative purposes.

An Estimation of the Entropy for a Rayleigh Distribution Based on Doubly-Generalized Type-II Hybrid Censored Samples

In this paper, based on a doubly generalized Type II censored sample, the maximum likelihood estimators (MLEs), the approximate MLE and the Bayes estimator for the entropy of the Rayleigh distribution are derived. We compare the entropy estimators’ root mean squared error (RMSE), bias and Kullback–Leibler divergence values. The simulation procedure is repeated 10,000 times for the sample size n = 10, 20, 40 and 100 and various doubly generalized Type II hybrid censoring schemes. Finally, a real data set has been analyzed for illustrative purposes.

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. Moreover, as methylation levels of neighboring CpG sites are known to be strongly correlated, methods that incorporate correlations have also been developed. We previously developed a network‐based penalized logistic regression for correlated methylation data, but only focusing on mean signals. We have also developed a generalized exponential tilt model that captures both mean and variance signals but only examining one CpG site at a time. Results: In this article, we proposed a penalized Exponential Tilt Model (pETM) using network‐based regularization that captures both mean and variance signals in DNA methylation data and takes into account the correlations among nearby CpG sites. By combining the strength of the two models we previously developed, we demonstrated the superior power and better performance of the pETM method through simulations and the applications to the 450K DNA methylation array data of the four breast invasive carcinoma cancer subtypes from The Cancer Genome Atlas (TCGA) project. The developed pETM method identifies many cancer‐related methylation loci that were missed by our previously developed method that considers correlations among nearby methylation loci but not variance signals. Availability and Implementation: The R package ‘pETM’ is publicly available through CRAN: http://cran.r‐project.org. Contact : sw2206@columbia.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Statistical Selection Strategy for Risk and Protective Rare Variants Associated with Complex Traits

In genetic association studies with deep sequencing data, it is a challenging statistical problem to precisely locate rare variants associated with complex diseases or traits due to the limited number of observed genetic mutations. In particular, both risk and protective rare variants can be present in the same gene or genetic region. There currently exist very few statistical methods to separate casual rare variants from noncausal variants within a disease/trait-related gene or a genetic region, while there are relatively many statistical tests to detect a phenotypic association of a group of rare variants such as a gene or a genetic region. In this article, we propose a new statistical selection strategy that is able to locate causal rare variants within the disease/trait-related gene or a genetic region. The proposed procedure is to linearly combine potential risk and protective variants in order to find the optimal combination of rare variants that can have the strongest association signal. It is also computationally very efficient since the procedure is based on forward selection. In simulation studies we demonstrate that the selection performance of the proposed procedure is more powerful than other existing methods when both risk and protective variants are present. We also applied it to the real sequencing data on the ANGPTL gene family from the Dallas Heart Study.

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. We here observed that oleate at the usual clinical range does not exert lipotoxicity while oleate at high pathological ranges exerted lipotoxicity through apoptosis in articular chondrocytes. By investigating the differential effect of oleate at toxic and nontoxic concentrations, we revealed that lipid droplet (LD) accumulation confers articular chondrocytes, the resistance to lipotoxicity. Using high fat diet-induced osteoarthritis models and articular chondrocytes treated with oleate alone or oleate plus palmitate, we demonstrated that articular chondrocytes gain resistance to lipotoxicity through protein kinase casein kinase 2 (PKCK2)—six-transmembrane protein of prostate 2 (STAMP2)—and fat-specific protein 27 (FSP27)-mediated LD accumulation. We further observed that the exertion of FFAs-induced lipotoxicity was correlated with the increased concentration of cellular FFAs freed from LDs, whether FFAs are saturated or not. In conclusion, PKCK2/STAMP2/FSP27-mediated sequestration of FFAs in LD rescues osteoarthritic chondrocytes. PKCK2/STAMP2/FSP27 should be considered for interventions against metabolic OA.Oil droplets protect cartilage from toxic fatty acidsCartilage tissue deals with the stress of exposure to free fatty acids by sequestering the toxic molecules into sub-cellular oil droplets. Young Hyun Yoo from Dong-A University College of Medicine in Busan, South Korea, and coworkers exposed rat cartilage cells to increasing levels of a fatty acid called oleate, a by-product of fat metabolism, and observed that the accumulation of oil droplets conferred resistance to oleate-induced toxicity. In these rat cells and in experiments involving mouse models of osteoarthritis fed a high-fat diet, the researchers then identified three of the protective proteins needed for cartilage tissue to properly quarantine fatty acids into oil droplets. Those proteins — and their connected regulatory networks — could now serve as drug targets for treating metabolic syndrome-associated osteoarthritis.

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. Through ELISA-based blind chemical library screening, we isolated single compound, IPP-14 (IPP; Inhibitor of PAK1-PUMA), which selectively blocks the PAK1-PUMA binding and also suppresses cell proliferation via PUMA-dependent manner. Indeed, in PUMA-deficient cells, this chemical did not show anti-proliferating effect. This chemical possessed very strong PAK1 inhibition activity that it suppressed BAD (Bcl-2-asoociated death promoter) phosphorylation and meta-phase arrest via Aurora kinase inactivation in lower concentration than that of previous PAK1 kinase, FRAX486 and AG879. Moreover, our chemical obviously induced p21/WAF1/CIP1 (Cyclin-dependent kinase inhibitor 1A) expression by releasing from Bcl-2 (B-cell lymphoma-2) and by inhibition of AKT-mediated p21 suppression. Considering our result, IPP-14 and its derivatives would be possible candidates for PAK1 and p21 induction targeted anti-cancer drug.

Multiple Group Testing Procedures for Analysis of High-Dimensional Genomic Data

In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistical testing procedures based on an individual test suffer from multiple testing issues such as the control of family-wise error rate and dependent tests. Moreover, detecting only a few of genes associated with a phenotype outcome among tens of thousands of genes is of main interest in genetic association studies. In this reason regularization procedures, where a phenotype outcome regresses on all genomic markers and then regression coefficients are estimated based on a penalized likelihood, have been considered as a good alternative approach to analysis of high-dimensional genomic data. But, selection performance of regularization procedures has been rarely compared with that of statistical group testing procedures. In this article, we performed extensive simulation studies where commonly used group testing procedures such as principal component analysis, Hotellings T2 test, and permutation test are compared with group lasso (least absolute selection and shrinkage operator) in terms of true positive selection. Also, we applied all methods considered in simulation studies to identify genes associated with ovarian cancer from over 20,000 genetic sites generated from Illumina Infinium HumanMethylation27K Beadchip. We found a big discrepancy of selected genes between multiple group testing procedures and group lasso.

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. However, these effects are achieved by glucocorticoid hormone receptor (GR) but not by estrogen receptor (ER). Since ginsenosides possess steroid-like structure, we hypothesized that it would block the stress or estrogen-induced MTOC amplification and Taxol resistance. Among tested chemicals, rare ginsenoside, CSH1 (Rg6) shows obvious effect on inhibition of MTOC amplification, γ-tubulin induction and Taxol resistance. Comparing to Fulvestant (FST), ER-α specific inhibitor, this chemical can block the cortisol/cortisone-induced MTOC deregulation as well as ER-α signaling. Our results suggest that stress hormone induced tumorigenesis would be achieved by MTOC amplification, and CSH1 would be useful for prevention of stress-hormone or steroid hormone-induced chromosomal instability.