Integrative network analysis is powerful in helping understand the underlying mechanisms of genetic and epigenetic perturbations for disease studies. Although it becomes clear that microRNAs, one type of epigenetic factors, have direct effect on target genes, it is unclear how microRNAs perturb downstream genetic neighborhood. Hence, we propose a network community approach to integrate microRNA and gene expression profiles, to construct an integrative genetic network perturbed by microRNAs. We apply this approach to an ovarian cancer dataset from The Cancer Genome Atlas project to identify the fluctuation of microRNA expression and its effects on gene expression. First, we perform expression quantitative loci analysis between microRNA and gene expression profiles via both a classical regression framework and a sparse learning model. Then, we apply the spin glass community detection algorithm to find genetic neighborhoods of the microRNAs and their associated genes. Finally, we construct an integrated network between microRNA and gene expression based on their community structure. Various disease related microRNAs and genes, particularly related to ovarian cancer, are identified in this network. Such an integrative network allows us to investigate the genetic neighborhood affected by microRNA expression that may lead to disease manifestation and progression.

Gene expression is a critical process in biological system that is influenced and modulated by many factors including genetic variation. Expression Quantitative Trait Loci (eQTL) analysis provides a powerful way to understand how genetic variants affect gene expression. For genome wide eQTL analysis, the number of genetic variants and that of genes are large and thus the search space is tremendous. Therefore, eQTL analysis brings about computational and statistical challenges. In this paper, we provide a comprehensive review of recent advances in methods for eQTL analysis in population-based studies. We first present traditional pairwise association methods, which are widely used in human genetics. To account for expression heterogeneity, we investigate the methods for correcting confounding factors. Next, we discuss newly developed statistical learning methods including Lasso-based models. In the conclusion, we provide an overview of future method development in analyzing eQTL associations. Although we focus on human genetics in this review, the methods are applicable to many other organisms.