Metamoodics: meta-analysis and bioinformatics resource for mood disorders

Abstract

Mood disorders, including Major Depression (MD) and Bipolar Disorder (BP), are among the most common psychiatric disorders1. Although genetic factors play an important role in their etiology2, 3, the heritability of these disorders remains largely unexplained. Due to advances in high-throughput genomic technologies, it is becoming increasingly feasible to investigate the genetic architecture of these disorders at an unprecedented resolution. The challenge is how to make sense of the enormous flood of data generated by the diverse genomic studies that use these new technologies. It is essential to integrate the findings from such studies in order to develop a more comprehensive understanding of the genetic contribution to these disorders that can be readily translated into new and more rational therapeutic interventions. Towards this end, we have developed a bioinformatics resource called Metamoodics (www.metamoodics.org), which integrate results from various genomic studies of mood disorders and enhance their interpretation by placing them within an enriched genomic context. Metamoodics was created as a resource for the research community to explore what is currently known about the genetic contribution to mood disorders. Metamoodics synthesizes data from existing genome-wide linkage, expression and association studies for both MD and BP. It brings together results from up-to-date meta- or mega-analyses of these studies, and presents them in both tabular and graphical modes. The genome-wide linkage data comes from among the largest and most comprehensive of such studies with MD and BP. Data for BP comes from a mega-analysis carried out with 972 informative pedigrees of European ancestry pooled from eight research groups in North America and the United Kingdom3. Linkage statistics for twelve different phenotype-by-genotype models were calculated for a genome-wide panel of 6090 SNPs. For MD, data from the Genetics of Recurrent Early-Onset Major Depression (GenRED) collaborative are provided4. This included data on 418 microsatellite markers genotyped in 656 families consisting of 1,494 all possible informative affective relative pairs. Data for genome-wide association studies (GWAS) were obtained from the psychiatric GWAS consortium (PGC). For BP, the sample included 7,481 cases and 9,250 controls, all of European ancestry, with data on ~2.5M genotyped and/or imputed SNPs5. Data on MD consisted of more than 1.2 million SNPs in 9,240 cases and 9,519 controls of recent European ancestry6. Gene expression data was obtained through a systematic review of studies on BP (n=13) and MD (n=7) using post-mortem brain tissue. Mega-analyses of the most comprehensive and rigorous of these studies were carried out using samples from all regions of the brain, the pre-frontal cortex, and the hippocampus, where sufficient data was available7. Finally, Metamoodics includes a comprehensive meta-analysis of published candidate gene association studies of BP. Data on 33 polymorphisms in 18 genes reported on by three or more case-control studies were included in the meta-analysis8. A similar meta-analysis for MD is forthcoming. Metamoodics includes a variety of online applications and tools for visualization and further analysis of the genomics data. Data visualization makes use of NCBI SeqView API and a local mirror of the UCSC genome browser. The results can be queried by gene or chromosomal position to obtain either a gene level view via chromosomal or regional plots, or a more global view via the genome browser to investigate the genomic context of the findings. User-defined data can be uploaded to the genome browser using the custom track option to visualize alongside the Metamoodics tracks. Analysis tools include SVAw9 for surrogate variable analysis, METAw for meta-analysis and a gene expression package for individual and mega analysis of expression data. Metamoodics presents the latest results from genomic studies of mood disorders in a user-friendly web interface that enables users to rapidly compare findings from different experimental platforms across the genome, as well as drill down to specific findings to visualize how these relate to underlying genomic structure and function. Metamoodics provides the entire set of results of a given study rather than just significant ones, thus providing a complete and unbiased report of the findings. We will continue to update Metamoodics with findings from the latest genomic experiments of mood disorders, such as whole exome or genome sequencing and epigenetic studies. Metamoodics is a vital bioinformatics tool for investigating the genetic etiology of mood disorders that will only grow more powerful as new data are added.