Abstract
Background:Obsessive-compulsive disorder (OCD) is a debilitating neuropsychiatric condition estimated to afflict 1-3 % of the world population. Dozens of OCD candidate genes have been reported by an increased number of articles. Nevertheless, each patient/patient group may demonstrate unique etiologic characteristics that need personalized treatment. Methods:We integrated a sparse representation based variable selection (SRVS) approach with an OCD-gene ResNet relation data analysis to select top genes for a specific group of 118 subjects, including 16 OCD cases and 102 healthy controls. The gene expression profile were acquired from the dorsolateral prefrontal cortex (DLPFC) of postmortem tissue of these subjects. A 77 OCD candidate genes were acquired from ResNet relation data analysis. Pathway enrichment analysis (PEA), sub-network enrichment analysis (SNEA) and gene-gene Interaction analysis (GGI) were conducted to study the functional profile of the top genes selected by SRVS, and compared with previous reported genetic markers. Results:A significantly high classification accuracy (CR) of 79.66 % was acquired (permutation p-value = 0.0046) using the top 9 genes selected by SRVS, including HOXB8, HTR2C, CRHR2, GRIK3, HGF, OXT, TPH2, DRD2 and ADRA1A. These genes were enriched within multiple pathways and sub-networks that were previously implicated with OCD. In contrast, using the same number of most frequently reported, a CR of only 65.5 % is achieved. Moreover, GGI results showed that these genes demonstrated a strong functional correlation with the frequently reported OCD genes. Conclusion:Our study suggests that SRVS is an effective method for data driven variable selection for OCD, and that the genes that were frequently reported to associate with OCD might not be the best biomarkers for a specific OCD patient/ patient group
Highlights
Obsessive-Compulsive Disorder (OCD) is a mental disorder where people feel the need to check things repeatedly
We hypothesize that significant Obsessive-compulsive disorder (OCD) candidate gene/gene set should contribute to distinguishing OCD patients from healthy controls
To validate the effectiveness of the selected genes and the proposed sparse representation based variable selection (SRVS) approach, we performed a Euclidean distance-based multivariate classification [7] on the gene expression data set, followed by a leaveone-out (LOO) cross validation, using the overall gene set and the sub-sets selected by Sscore and Rscore as tentative markers
Summary
Obsessive-Compulsive Disorder (OCD) is a mental disorder where people feel the need to check things repeatedly. This order typically arises in late adolescence or early adulthood and, if left untreated, has a chronic course regardless of sex, race, intelligence, marital status, socioeconomic status, religion or nationality . We proposed a sparse representation based variable selection (SRVS) algorithm that selects significant biomarkers at different detection resolutions. This method has previously been shown effective in variable selection with SNP data and fMARI data [7]. Each patient/patient group may demonstrate unique etiologic characteristics that need personalized treatment
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