Magic™ Anti-Dopamine Transporter (Phospho T53) polyclonal antibody

The dopamine transporter gene (SLC6A3) is a compelling candidate for pediatric bipolar disorder because (a) it has been associated with ADHD, (b) bipolar comorbidity with ADHD has been hypothesized to be an etiologically distinct familial subtype (c) blockade of the dopamine transporter with psychostimulants can induce mania in susceptible individuals and (d) previous studies have implicated the gene in bipolar disorder in adults. We conducted a family-based association study of SLC6A3 in 170 affected offspring trios defined by a child (12.9 +/- 5.3 years of age)with DSM-IV Bipolar-I disorder. Twenty-eight tag SNPs were chosen from the CEU (European) population of the International HapMap project (www.hapmap.org). Results indicated nominally positive association for 4 SNPs (rs40184, rs11133767, rs3776512, and rs464049), but only rs40184 survived correction for multiple statistical comparisons (P = 0.038). This is the first examination of the association with SLC6A3 and bipolar disorder in children and, like previous findings in adults with bipolar disorder, we found evidence of association with SNPs in the 3' region of the gene. These data provide suggestive evidence supporting a role for SLC6A3 in the etiology of pediatric bipolar disorder. (C) 2008 Wiley-Liss, Inc.

Motivation: Recent studies have shown that RNA-sequencing (RNA-seq) can be used to measure mRNA of sufficient quality extracted from formalin-fixed paraffin-embedded (FFPE) tissues to provide whole-genome transcriptome analysis. However, little attention has been given to the normalization of FFPE RNA-seq data, a key step that adjusts for unwanted biological and technical effects that can bias the signal of interest. Existing methods, developed based on fresh-frozen or similar-type samples, may cause suboptimal performance. Results: We proposed a new normalization method, labeled MIXnorm, for FFPE RNA-seq data. MIXnorm relies on a two-component mixture model, which models non-expressed genes by zero-inflated Poisson distributions and models expressed genes by truncated normal distributions. To obtain maximum likelihood estimates, we developed a nested EM algorithm, in which closed-form updates are available in each iteration. By eliminating the need for numerical optimization in the M-step, the algorithm is easy to implement and computationally efficient. We evaluated MIXnorm through simulations and cancer studies. MIXnorm makes a significant improvement over commonly used methods for RNA-seq expression data.