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Mood Disorders Center
Our team of computational scientists has been busy at work developing a number of bioinformatics and analytic resources and tools for studying the genetic causes of mood disorders and responses to their treatment. This site provides links to the various resources and tools.
Metamoodics is a bioinformatics resource that synthesizes the findings from on-going genetic studies of mood disorders, including depression and bipolar disorder, and displays the findings in their genomic context. It includes data from genetic association, expression and linkage studies, and will soon include new data from sequencing and methylation studies. Results of meta or mega- analyses of published studies are provided. The goal of Metamoodics is to create a central resource which investigators can explore to find out what we currently know about the genetic contribution to depression and bipolar disorder and to use this information to develop or evaluate hypotheses that advance our understanding of this contribution.
SynaptomeDB is an integrated database to retrieve, compile, and annotate genes comprising the synaptome. These genes encode components of the synapse, including neurotransmitters and their receptors, adhesion/cytoskeletal proteins, scaffold proteins, transporters and others. SynaptomeDB systematically queries existing bioinformatics resources and compiles available genomic and proteomic information about these gene products. Genes operating in the synapse are potentially important to psychiatric disorders; this resources provides a comprehensive catalogue of what genes these are and what they do.
The Bipolar Disorder Phenome Database
Johns Hopkins and NIMH researchers have jointly created this database, which posts the clinical phenotypes of over 5,000 people recruited for bipolar disorder genetics studies. The Bipolar Disorder Phenome database is meant to complement the large bodies of genetic data that are being generated through the Human Genome Project, The International HapMap Consortium, the Genetic Analysis Information Network, and similar efforts. The goal is to accelerate discovery of genes that contribute to this common and often disabling disease, by promoting the genetic analysis of clinical subtypes. Researchers can now explore connections between clinical variables and genetics with adequate numbers of subjects to detect even moderate genetic effects.
Chromosome 8 SNP Data
Research at Johns Hopkins University and University of Michigan collaborated to genotype 1,461 SNPs on chromosome 8q24 in 3,512 individuals, of whom 1,954 were affected with bipolar disorder. These individuals came from 737 families that consisted of 1,142 nuclear families and broke down into 1,840 parent affected offspring triads (573 with zero genotyped parents, 641 with one genotyped parent, and 626 with two genotyped parents) and 333 discordant sib-pairs. The individuals were ascertained and assessed through the Mood Disorders Research Program at Johns Hopkins University and as part of the National Institute of Mental Health Genetics Initiative on Bipolar Disorder. Chromosome 8q24 is one of the most promising regions of linkage in bipolar disorder, and this data was generated to fine map the region and search for associations. The genotype and phenotype data have been fully vetted and are made available to qualified investigators in order to stimulate research in this promising locus and to facilitate the development of statistical methods for analyzing this type of family data.
SVAw (or Surrogate Variable Analysis web application) is a web application dedicated to solving the problem of heterogeneity in gene expression data caused by measured and unmeasured factors such as environmental, demographic, genetic and technical factors. It provides a user-friendly and semi-automated web-based platform for carrying out surrogate variable analysis of gene expression data using the algorithm developed by Leek JT and Storey JD (PMID: 204228335).
QuickSNP was developed to help researchers select SNPs for association studies in a cost-effective and efficient manner. It allows for gene-centric SNP selection from a chromosomal region in an automated fashion, automated selection of coding non-synonymous SNPs, and SNP filtering based on inter-SNP distance. It also provides information on availability of genotyping assays for SNPs, and availability of SNPs on whole genome chips, and it produces user-friendly summary tables and results as well as a link to a UCSC Genome Browser track, illustrating the position of the selected tagSNPs in relation to genes and other genomic features.
Whole Exome Sequencing Pipeline
We have developed a pipeline consisting of a series of programming scripts for managing and analyzing data from whole exome sequencing studies. The semi-automated pipeline builds on several open-source technologies and uses standard file formats and multiple quality controls checks to generate variant calls from input fastq files. Variant calls are annotated and then prepared for downstream analyses.