Driven by the efficiency of DNA sequencing and related technologies, genome- and epigenome-wide association studies have already proven successful at producing specific results and general insights about the nature of genomic regulation. Discovery of expression Quantitative Trait Loci (eQTL), differentially methylated regions (DMRs), and other genomic and epigenetic features are proving integral to our understanding of how gene expression and DNA methylation (DNAm) are controlled throughout the human body and are changing how genomic and epigenetic data are analyzed in the study of cellular processes and complex diseases. Epistasis is the interaction among multiple genetic loci in their effect on gene expression. While epistasis is pervasive in biological systems and has the potential to account for heritability in traits that remain unexplained by the sum of main effects, the computational and statistical challenges of epistasis detection are daunting. We present the F-test of magnitude and concordance (Fomac) - a novel statistic that detects concordant epistasis across multiple datasets or co-expressed genes by constraining linear model parameters to be both significant and consistent. Simulations were carried out to compare the performance of Fomac to that of comparable methods for detecting single- and multi-trait epistasis, and they showed that Fomac is able to leverage concordant effects for improved statistical power. Fomac was also applied to gene expression from the Multiple Tissue Human Expression Resource (MuTHER) where a genome-wide analysis across 3 tissues identified 2754 examples of gene-wise Bonferroni-significant concordant epistasis. Epigenome-wide association studies (EWAS) are providing another angle from which to view genetic regulation. We performed an EWAS comparing the methylome of circulating monocytes in patients with and without Charcot foot (a devastating complication of diabetes.) Increased osteoclast activity has a role in the disease, and osteoclasts derived from monocytes are particularly well-suited for such a role. We observed that the methylome of these monocytes was significantly different in patients with and without Charcot foot, and identified specific genes with aberrant methylation. Together, the studies described in this dissertation serve the notion that by understanding relationships between and within omics data, we can both glean useful insights into specific regulatory mechanisms of the cell and apply patterns to accurately predict biological responses