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• Quantifying novelty seeking is useful for translational psychiatric research. • Novelty seeking traits and behavior were assessed in healthy volunteers. • High novelty seekers had more motor activity and exploration. • Novelty-seeking traits did not strongly predict response to stimulants. • Novelty seeking can be sensitively quantified for translational research. Novelty seeking is a tendency to approach new situations, putatively driven by the brain's catecholaminergic system. It is traditionally measured via self-report, but a laboratory-based paradigm, the human Behavioral Pattern Monitor (hBPM), quantifies behavior in a novel environment and has utility in cross-species studies of neuropsychiatric disorders. Our primary aim assessed whether self-reported novelty-seeking traits were associated with novelty-seeking behavior in the hBPM. An existing sample of 106 volunteers were categorized as high vs. low novelty seekers using the Temperament and Character Inventory (TCI). Subjects had been randomized to one dose of amphetamine (10 or 20 mg) or modafinil (200 or 400 mg), allowing us to explore whether a pharmacological catecholamine challenge further enhanced novelty-seeking behavior. High TCI novelty-seekers had more hBPM motor activity and novel object interactions. The exploratory analyses, although limited by low power, suggested that amphetamine and modafinil did not markedly moderate novelty-seeking traits. The hBPM demonstrates construct validity as a lab-based measure of novelty seeking and thus useful in translational studies of neuropsychiatric conditions and treatment options. Further research may illuminate whether a biological predisposition towards higher catecholaminergic activity, combined with the novelty-seeking trait, may increase propensity for risky and addictive behaviors. [ABSTRACT FROM AUTHOR]

Background: We used RNA sequencing to analyze transcript profiles of ten autopsy brain regions from ten subjects. RNA sequencing techniques were designed to detect both coding and non-coding RNA, splice isoform composition, and allelic expression. Brain regions were selected from five subjects with a documented history of smoking and five non-smokers. Paired-end RNA sequencing was performed on SOLiD instruments to a depth of >40 million reads, using linearly amplified, ribosomally depleted RNA. Sequencing libraries were prepared with both poly-dT and random hexamer primers to detect all RNA classes, including long non-coding (lncRNA), intronic and intergenic transcripts, and transcripts lacking poly-A tails, providing additional data not previously available. The study was designed to generate a database of the complete transcriptomes in brain region for gene network analyses and discovery of regulatory variants. Results: Of 20,318 protein coding and 18,080 lncRNA genes annotated from GENCODE and lncipedia, 12 thousand protein coding and 2 thousand lncRNA transcripts were detectable at a conservative threshold. Of the aligned reads, 52 % were exonic, 34 % intronic and 14 % intergenic. A majority of protein coding genes (65 %) was expressed in all regions, whereas ncRNAs displayed a more restricted distribution. Profiles of RNA isoforms varied across brain regions and subjects at multiple gene loci, with neurexin 3 (NRXN3) a prominent example. Allelic RNA ratios deviating from unity were identified in > 400 genes, detectable in both protein-coding and non-coding genes, indicating the presence of cis-acting regulatory variants. Mathematical modeling was used to identify RNAs stably expressed in all brain regions (serving as potential markers for normalizing expression levels), linked to basic cellular functions. An initial analysis of differential expression analysis between smokers and nonsmokers implicated a number of genes, several previously associated with nicotine exposure. Conclusions: RNA sequencing identifies distinct and consistent differences in gene expression between brain regions, with non-coding RNA displaying greater diversity between brain regions than mRNAs. Numerous RNAs exhibit robust allele selective expression, proving a means for discovery of cis-acting regulatory factors with potential clinical relevance. [ABSTRACT FROM AUTHOR]

The article presents a study focusing on identification of a novel missense mutation at the collagenous domain of ectodermal dysplasia (EDA) gene in a Chinese family with X-linked hypohidrotic ectodermal dysplasia (XLHED). The findings of the study include the substitution of a proline with a leucine in EDA protein by mutation, the substitution mutation found only in the affected males and the carrier females with normal phenotypes, and the proline affecting the stability of collagen helix.

The discovery of susceptibility genes for bipolar disorder and schizophrenia is thought to be one of the most intractable current problems in human genetics. The epidemiological data argue that a [...]

The integration of research on neuroimaging and pharmacogenetics holds promise for improving treatment for neuropsychiatric conditions. Neuroimaging may provide a more sensitive early measure of treatment response in genetically defined patient groups, and could facilitate development of novel therapies based on an improved understanding of pathogenic mechanisms underlying pharmacogenetic associations. This review summarizes progress in efforts to incorporate neuroimaging into genetics and treatment research on major psychiatric disorders, such as schizophrenia, major depressive disorder, bipolar disorder, attention-deficit/hyperactivity disorder, and addiction. Methodological challenges include: performing genetic analyses in small study populations used in imaging studies; inclusion of patients with psychiatric comorbidities; and the extensive variability across studies in neuroimaging protocols, neurobehavioral task probes, and analytic strategies. Moreover, few studies use pharmacogenetic designs that permit testing of genotype × drug effects. As a result of these limitations, few findings have been fully replicated. Future studies that pre-screen participants for genetic variants selected a priori based on drug metabolism and targets have the greatest potential to advance the science and practice of psychiatric treatment. [ABSTRACT FROM AUTHOR]

Recent results indicate that genome-wide association studies (GWAS) have the potential to explain much of the heritability of common complex phenotypes, but methods are lacking to reliably identify the remaining associated single nucleotide polymorphisms (SNPs). We applied stratified False Discovery Rate (sFDR) methods to leverage genic enrichment in GWAS summary statistics data to uncover new loci likely to replicate in independent samples. Specifically, we use linkage disequilibrium-weighted annotations for each SNP in combination with nominal p-values to estimate the True Discovery Rate (TDR = 12FDR) for strata determined by different genic categories. We show a consistent pattern of enrichment of polygenic effects in specific annotation categories across diverse phenotypes, with the greatest enrichment for SNPs tagging regulatory and coding genic elements, little enrichment in introns, and negative enrichment for intergenic SNPs. Stratified enrichment directly leads to increased TDR for a given p-value, mirrored by increased replication rates in independent samples. We show this in independent Crohn's disease GWAS, where we find a hundredfold variation in replication rate across genic categories. Applying a well-established sFDR methodology we demonstrate the utility of stratification for improving power of GWAS in complex phenotypes, with increased rejection rates from 20% in height to 300% in schizophrenia with traditional FDR and sFDR both fixed at 0.05. Our analyses demonstrate an inherent stratification among GWAS SNPs with important conceptual implications that can be leveraged by statistical methods to improve the discovery of loci. [ABSTRACT FROM AUTHOR]

Several lines of evidence suggest that genome-wide association studies (GWAS) have the potential to explain more of the "missing heritability" of common complex phenotypes. However, reliable methods to identify a larger proportion of single nucleotide polymorphisms (SNPs) that impact disease risk are currently lacking. Here, we use a genetic pleiotropy-informed conditional false discovery rate (FDR) method on GWAS summary statistics data to identify new loci associated with schizophrenia (SCZ) and bipolar disorders (BD), two highly heritable disorders with significant missing heritability. Epidemiological and clinical evidence suggest similar disease characteristics and overlapping genes between SCZ and BD. Here, we computed conditional Q-Q curves of data from the Psychiatric Genome Consortium (SCZ; n = 9,379 cases and n = 7,736 controls; BD: n = 6,990 cases and n = 4,820 controls) to show enrichment of SNPs associated with SCZ as a function of association with BD and vice versa with a corresponding reduction in FDR. Applying the conditional FDR method, we identified 58 loci associated with SCZ and 35 loci associated with BD below the conditional FDR level of 0.05. Of these, 14 loci were associated with both SCZ and BD (conjunction FDR). Together, these findings show the feasibility of genetic pleiotropy-informed methods to improve gene discovery in SCZ and BD and indicate overlapping genetic mechanisms between these two disorders. [ABSTRACT FROM AUTHOR]