A genome-wide scan in 60 bipolar affective disorder (BPAD) affected sib-pairs (ASPs) identified linkage on chromosome 21 at 21q22 (D21S1446, NPL = 1.42, P = 0.08), a BPAD susceptibility locus supported by multiple studies. Although this linkage only approaches significance, the peak marker is located 12 Kb upstream of S100B, a neurotrophic factor implicated in the pathology of psychiatric disorders, including BPAD and schizophrenia. We hypothesized that the linkage signal at 21q22 may result from pathogenic disease variants within S100B and performed an association analysis of this gene in a collection of 125 BPAD type I trios. S100B single nucleotide polymorphisms (SNPs) rs2839350 (P = 0.022) and rs3788266 (P = 0.031) were significantly associated with BPAD. Since variants within S100B have also been associated with schizophrenia susceptibility, we reanalyzed the data in trios with a history of psychosis, a phenotype in common between the two disorders. SNPs rs2339350 (P = 0.016) and rs3788266 (P = 0.009) were more significantly associated in the psychotic subset. Increased significance was also obtained at the haplotype level. Interestingly, SNP rs3788266 is located within a consensus-binding site for Six-family transcription factors suggesting that this variant may directly affect S100B gene expression. Fine-mapping analyses of 21q22 have previously identified transient receptor potential gene melastatin 2 (TRPM2), which is 2 Mb upstream of S100B, as a possible BPAD susceptibility gene at 21q22. We also performed a family-based association analysis of TRPM2 which did not reveal any evidence for association of this gene with BPAD. Overall, our findings suggest that variants within the S100B gene predispose to a psychotic subtype of BPAD, possibly via alteration of gene expression.

BackgroundThe study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs.ObjectiveThe aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs.MethodsWe analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs.ResultsThe accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions.ConclusionsTo the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis.

Bipolar disorder (BPD) is a complex genetic disorder with cycling symptoms of depression and mania. Despite the extreme complexity of this psychiatric disorder, attempts to localize genes which confer vulnerability to the disorder have had some success. Chromosomal regions including 4p16, 12q24, 18p11, 18q22, and 21q21 have been repeatedly linked to BPD in different populations. Here we present the results of a whole genome scan for linkage to BPD in an Irish population. Our most significant result was at 14q24 which yielded a non-parametric LOD (NPL) score of 3.27 at the D14S588 marker with a nominal P-value of 0.0006 under a narrow (bipolar type I only) model of affection. We previously reported linkage to 14q22-24 in a subset of the families tested in this analysis. We also obtained suggestive evidence for linkage at 4q21, 9p21, 12q24, and 16p13, chromosomal regions that have all been previously linked to BPD. Additionally, we report on a novel approach to linkage analysis, STRUCTURE-Guided Linkage Analysis (SGLA), which is designed to reduce genetic heterogeneity and increase the power to detect linkage. Application of this technique resulted in more highly significant evidence for linkage of BPD to three regions including 16p13, a locus that has been repeatedly linked to numerous psychiatric disorders.

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