Complex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies. We developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package. Mergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits.

Background: Coronary artery disease (CAD) is a multifactorial condition with both genetic and exogenous causes. The contribution of tissue-specific functional networks to the development of atherosclerosis remains largely unclear. The aim of this study was to identify and characterize central regulators and networks leading to atherosclerosis. Methods: Based on several hundred genes known to affect atherosclerosis risk in mouse (as demonstrated in knockout models) and human (as shown by genome-wide association studies), liver gene regulatory networks were modeled. The hierarchical order and regulatory directions of genes within the network were based on Bayesian prediction models, as well as experimental studies including chromatin immunoprecipitation DNA-sequencing, chromatin immunoprecipitation mass spectrometry, overexpression, small interfering RNA knockdown in mouse and human liver cells, and knockout mouse experiments. Bioinformatics and correlation analyses were used to clarify associations between central genes and CAD phenotypes in both human and mouse. Results: The transcription factor MAFF (MAF basic leucine zipper transcription factor F) interacted as a key driver of a liver network with 3 human genes at CAD genome-wide association studies loci and 11 atherosclerotic murine genes. Most importantly, expression levels of the low-density lipoprotein receptor (LDLR) gene correlated with MAFF in 600 CAD patients undergoing bypass surgery (STARNET [Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task]) and a hybrid mouse diversity panel involving 105 different inbred mouse strains. Molecular mechanisms of MAFF were tested in noninflammatory conditions and showed positive correlation between MAFF and LDLR in vitro and in vivo. Interestingly, after lipopolysaccharide stimulation (inflammatory conditions), an inverse correlation between MAFF and LDLR in vitro and in vivo was observed. Chromatin immunoprecipitation mass spectrometry revealed that the human CAD genome-wide association studies candidate BACH1 (BTB domain and CNC homolog 1) assists MAFF in the presence of lipopolysaccharide stimulation with respective heterodimers binding at the MAF recognition element of the LDLR promoter to transcriptionally downregulate LDLR expression. Conclusions: The transcription factor MAFF was identified as a novel central regulator of an atherosclerosis/CAD-relevant liver network. MAFF triggered context-specific expression of LDLR and other genes known to affect CAD risk. Our results suggest that MAFF is a missing link between inflammation, lipid and lipoprotein metabolism, and a possible treatment target.

Cardiovascular diseases (CVD) and type 2 diabetes (T2D) are closely interrelated complex diseases likely sharing overlapping pathogenesis driven by aberrant activities in gene networks. However, the molecular circuitries underlying the pathogenic commonalities remain poorly understood. We sought to identify the shared gene networks and their key intervening drivers for both CVD and T2D by conducting a comprehensive integrative analysis driven by five multi-ethnic genome-wide association studies (GWAS) for CVD and T2D, expression quantitative trait loci (eQTLs), ENCODE, and tissue-specific gene network models (both co-expression and graphical models) from CVD and T2D relevant tissues. We identified pathways regulating the metabolism of lipids, glucose, and branched-chain amino acids, along with those governing oxidation, extracellular matrix, immune response, and neuronal system as shared pathogenic processes for both diseases. Further, we uncovered 15 key drivers including HMGCR, CAV1, IGF1 and PCOLCE, whose network neighbors collectively account for approximately 35% of known GWAS hits for CVD and 22% for T2D. Finally, we cross-validated the regulatory role of the top key drivers using in vitro siRNA knockdown, in vivo gene knockout, and two Hybrid Mouse Diversity Panels each comprised of >100 strains. Findings from this in-depth assessment of genetic and functional data from multiple human cohorts provide strong support that common sets of tissue-specific molecular networks drive the pathogenesis of both CVD and T2D across ethnicities and help prioritize new therapeutic avenues for both CVD and T2D. Cardiovascular disease (CVD) and type 2 diabetes (T2D) are two tightly interrelated diseases that are leading epidemics and causes of deaths around the world. Elucidating the mechanistic connections between the two diseases will offer critical insights for the development of novel therapeutic avenues to target both simultaneously. Because of the challenging complexity of CVD and T2D, involving numerous risk factors, multiple tissues, and multidimensional molecular alterations, few have attempted such an investigation. We herein report a comprehensive and in-depth data-driven assessment of the shared mechanisms between CVD and T2D by integrating genomics data from diverse human populations including African Americans, Caucasian Americans, and Hispanic Americans with tissue-specific functional genomics information. We identified shared pathways and gene networks informed by CVD and T2D genetic risks across populations, confirming the importance of well-established processes, as well as unraveling previously under-appreciated processes such as extracellular matrix, branched-chain amino acid metabolism, and neuronal system for both diseases. Further incorporation of tissue-specific regulatory networks pinpointed potential key regulators that orchestrate the biological processes shared between the two diseases, which were cross-validated using cell culture and mouse models. This study suggests potential new therapeutic targets that warrant further investigation for both CVD and T2D.

Non-alcoholic fatty liver disease (NAFLD) encompasses benign steatosis and more severe conditions such as non-alcoholic steatohepatitis (NASH), cirrhosis, and liver cancer. This chronic liver disease has a poorly understood etiology and demonstrates sexual dimorphisms. We aim to examine the molecular mechanisms underlying sexual dimorphisms in NAFLD pathogenesis through a comprehensive multi-omics study. We integrated genomics (DNA variations), transcriptomics of liver and adipose tissue, and phenotypic data of NAFLD derived from female mice of ~ 100 strains included in the hybrid mouse diversity panel (HMDP) and compared the NAFLD molecular pathways and gene networks between sexes. We identified both shared and sex-specific biological processes for NAFLD. Adaptive immunity, branched chain amino acid metabolism, oxidative phosphorylation, and cell cycle/apoptosis were shared between sexes. Among the sex-specific pathways were vitamins and cofactors metabolism and ion channel transport for females, and phospholipid, lysophospholipid, and phosphatidylinositol metabolism and insulin signaling for males. Additionally, numerous lipid and insulin-related pathways and inflammatory processes in the adipose and liver tissue appeared to show more prominent association with NAFLD in male HMDP. Using data-driven network modeling, we identified plausible sex-specific and tissue-specific regulatory genes as well as those that are shared between sexes. These key regulators orchestrate the NAFLD pathways in a sex- and tissue-specific manner. Gonadectomy experiments support that sex hormones may partially underlie the sexually dimorphic genes and pathways involved in NAFLD. Our multi-omics integrative study reveals sex- and tissue-specific genes, processes, and networks underlying sexual dimorphism in NAFLD and may facilitate sex-specific precision medicine.