Integrative Analysis of DiseaseLand Omics Database for Disease Signatures and Treatments: A Bipolar Case Study

Transcriptomics technologies such as next-generation sequencing and microarray platforms provide exciting opportunities for improving diagnosis and treatment of complex diseases. Transcriptomics studies often share similar hypotheses, but are carried out on different platforms, in different conditions, and with different analysis approaches. These factors, in addition to small sample sizes, can result in a lack of reproducibility. A clear understanding and unified picture of many complex diseases are still elusive, highlighting an urgent need to effectively integrate multiple transcriptomic studies for disease signatures. We have integrated more than 3,000 high-quality transcriptomic datasets in oncology, immunology, neuroscience, cardiovascular and metabolic disease, and from both public and internal sources (DiseaseLand database). We established a systematic data integration and meta-analysis approach, which can be applied in multiple disease areas to create a unified picture of the disease signature and prioritize drug targets, pathways, and compounds. In this bipolar case study, we provided an illustrative example using our approach to combine a total of 30 genome-wide gene expression studies using postmortem human brain samples. First, the studies were integrated by extracting raw FASTQ or CEL files, then undergoing the same procedures for preprocessing, normalization, and statistical inference. Second, both p-value and effect size based meta-analysis algorithms were used to identify a total of 204 differentially expressed (DE) genes (FDR < 0.05) genes in the prefrontal cortex. Among these were BDNF, VGF, WFS1, DUSP6, CRHBP, MAOA, and RELN, which have previously been implicated in bipolar disorder. Finally, pathway enrichment analysis revealed a role for GPCR, MAPK, immune, and Reelin pathways. Compound profiling analysis revealed MAPK and other inhibitors may modulate the DE genes. The ability to robustly combine and synthesize the information from multiple studies enables a more powerful understanding of this complex disease.