Abstract
Accumulating evidence supports the polygenic nature of most complex diseases, suggesting the involvement of many susceptibility genes with small effect sizes. Although hundreds of genes may underlie the genetic architecture of complex diseases, those involved in a given disease are probably not randomly distributed, but likely to be functionally related. Protein-protein interaction networks have been used to evaluate the functional relatedness of susceptibility genes. However, these networks do not account for tissue specificity, are limited to protein-coding genes, and are typically biased by incomplete biological knowledge. Here, we present Gene Link Inspector Through Tissue-specific coExpRession (GLITTER), a web-based application for assessing the functional relatedness of susceptibility genes, either coding or noncoding, according to tissue-specific gene expression profiles. GLITTER can also shed light on the specific tissues in which susceptibility genes might exert their functions. We further demonstrate examples of how GLITTER can evaluate the functional relatedness of susceptibility genes underlying schizophrenia and breast cancer, and provide clues about etiology.
Highlights
We applied GLITTER to evaluate the functional relatedness of 97 schizophrenia candidate genes implicated by the 108 independent schizophrenia risk loci identified in a recent genome-wide association study of schizophrenia from the Psychiatric Genomics Consortium (PGC)[10]
We found that schizophrenia genes tend to be more connected than random gene sets in a number of brain regions, and this result remained significant after Bonferroni correction, suggesting their potentially important roles in the etiopathogenesis of the disease
We reason that gene relationships in the context of disease-relevant tissues may more closely reflect the functional relationships among susceptibility genes than those captured in a tissue-independent context through the general protein-protein interaction (PPI) network
Summary
The statistical significance is the proportion of random gene sets that show the same or a greater number of connected gene pairs than that of input genes. The statistical significance is estimated by generating a background distribution for the number of connected gene pairs within random gene sets, while matching for gene numbers, gene sizes and GC content based on the features found in the susceptibility gene list.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
