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DNA methylation is one of the most important phenomena in epigenetics. Because the DNA methylation process regulates gene expression without altering the corresponding DNA sequences, it can explain many biological processes such as onset of cancer, aging, and differentiation of species. Therefore, detecting differentially methylated region (DMR) between groups has been considered one of important analyses in epigenetics. For the datasets generated by the methylated DNA immunoprecipitation (MeDIP) technique, DMR analysis has employed the previous methods developed for the microarray-based gene expression dataset. However, for the datasets generated by more recent bisulfite-sequencing (BS-seq) technologies, totally different approaches need to be used. Unfortunately, only a few methods are available for DMR analysis among which the Fisher's exact test is most widely used. We first propose a new DMR test for BS-seq data. Our approach employs Cochran-Mantel-Hantzel (CMH) test and extend it by incorporating correlation structures between adjacent CpG cytosine sites. Then we apply it to a real dataset of two subspecies of honeybee for detecting DMR regions.

There is an increasing interest in the pathway analysis of multiple genes and complex traits in association studies. Recently, a number of methods of pathway analysis have been developed to detect the novel pathways associated with human complex traits. In this paper, we propose a novel statistical approach for competitive pathway analysis based on Structural Equation Modeling (CPA-SEM), taking advantage of prior knowledge on existing relationships between genes in a pathway. Our CPA-SEM identifies pathways associated with traits of interest. The CPA-SEM approach is different from the previous SEM-based approaches in that it considers all possible sub-pathways into account and performs permutation based robust analysis. We applied the proposed CPA-SEM method to gene expression data of gastric cancer (GSE27342), and found that mTOR signaling pathway was significantly associated with gastric cancer. This pathway has previously been reported to be associated with gastric cancer. In conclusion, our CPA-SEM analysis provides a better understanding of biological mechanism by identifying pathways associated with a trait of interest.

Recent advances in genotyping technology have facilitated the use of genome-wide association studies (GWAS) to successfully identify genetic variants that are associated with common complex traits. Following the successes in identification of single variants, joint identification including gene-gene interaction has been studied vigorously and produced many novel results. However, most genome-wide association studies have been conducted by focusing on one trait of interest for identifying genetic variants associated with common complex traits. Since many complex diseases having severe influences on the public health are pleiotropic, simple univariate analysis focusing on a single trait does not well detect full genetic architecture of complex diseases. For example, hyperlipidemia is diagnosed by four multiple traits: Total cholesterol (Tchl), High density lipoprotein (HDL) cholesterol, Low density lipoprotein (LDL), and cholesterol and Triglycerides (TG). Surprisingly, however, only few studies handle multiple traits simultaneously so far. Therefore, in order to improve power and reflect biological association more expansively, we investigate a multivariate approach which considers multiple traits simultaneously. Especially for the gene-gene interaction analysis for the multiple traits, we extend original multifactor dimensionality reduction (MDR) to handle multiple traits. We then demonstrate its superiority to univariate analysis through simulation studies. We confirm that the multivariate approach provides more stable and precise accuracy measures compared to univariate analysis. We applied the multivariate MDR approach to a GWA dataset of 8,842 Korean individuals and detected genetic variants associated with hypertension traits using systolic blood pressure (SBP) and Diastolic blood pressure (DBP).

In genome-wide association studies (GWAS), logistic regression (LR) has been most commonly used for finding an association between a disease phenotype and genetic variants such as single nucleotide polymorphism (SNP). Since logistic regression model requires iterative algorithms to get the parameter estimates, its application to GWAS has been limited to the identification of the individual SNPs. Thus, there have been limited applications of LR to multiple SNP analysis including gene-gene interaction analysis in large scale GWAS data. To overcome this computational burden, we developed a logistic regression analysis tool named CUDA-LR, based on the new programming architecture using Graphics Processing Unit (GPU). CUDA-LR supports not only the simple model with single SNP but also more complex model with two SNPs including the interaction. In addition, CUDA-LR provides various parameters to gain more acceleration and perform specified analysis. In the comparison between our analysis and the other methods, CUDA-LR showed almost 700-folds of acceleration and highly reliable results by our GPU specified optimization techniques. We believe that the CUDA-LR now is a useful logistic regression analysis tool for interaction analysis of large scale GWAS datasets.

Current standard genome-wide association studies (GWAS) have relied on the simple analysis by focusing on the association between single genetic factor and one single common complex trait. However, since most common complex traits are associated with multiple genetic factors and their epistasis, this simple analysis is not powerful enough to detect multiple genetic factors. Furthermore, in many GWAS, one binary trait is commonly used and it is usually a summary trait derived from several quantitative traits. For example, a binary trait representing hyperlipidemia status is defined by combining four quantitative traits: Total cholesterol (Tchl), High density lipoprotein (HDL) cholesterol, Low density lipoprotein (LDL), and cholesterol and Triglycerides (TG). More information can be extracted from these quantitative traits than from one summary binary trait. However, not many methods have been proposed to account for the multiple traits simultaneously. In this study, we propose the following simple stepwise strategy to increase the power of detecting multiple genetic factors jointly for the multiple traits: (1) prescreening, (2) joint identification of putative SNPs based on elastic-net (EN) variable selection, and (3) collapsing. Joint identification of multiple genetic factors would be more powerful and provide better prediction on complex traits. We illustrated our approach with a large scale genome-wide dataset from a Korean population and identified the genetic factors associated with lipid-related traits.