Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders

Personality is influenced by genetic and environmental factors1 and associated with mental health. However, the underlying genetic determinants are largely unknown. We identified six genetic loci, including five novel loci2,3, significantly associated with personality traits in a meta-analysis of genome-wide association studies (N = 123,132–260,861). Of these genomewide significant loci, extraversion was associated with variants in WSCD2 and near PCDH15, and neuroticism with variants on chromosome 8p23.1 and in L3MBTL2. We performed a principal component analysis to extract major dimensions underlying genetic variations among five personality traits and six psychiatric disorders (N = 5,422–18,759). The first genetic dimension separated personality traits and psychiatric disorders, except that neuroticism and openness to experience were clustered with the disorders. High genetic correlations were found between extraversion and attention-deficit– hyperactivity disorder (ADHD) and between openness and schizophrenia and bipolar disorder. The second genetic dimension was closely aligned with extraversion–introversion and grouped neuroticism with internalizing psychopathology (e.g., depression or anxiety)

Phenotype Restricted Genome-Wide Association Study Using a Gene-Centric Approach Identifies Three Low-Risk Neuroblastoma Susceptibility Loci

Neuroblastoma is a malignant neoplasm of the developing sympathetic nervous system that is notable for its phenotypic diversity. High-risk patients typically have widely disseminated disease at diagnosis and a poor survival probability, but low-risk patients frequently have localized tumors that are almost always cured with little or no chemotherapy. Our genome-wide association study (GWAS) has identified common variants within FLJ22536, BARD1, and LMO1 as significantly associated with neuroblastoma and more robustly associated with high-risk disease. Here we show that a GWAS focused on low-risk cases identified SNPs within DUSP12 at 1q23.3 (P = 2.07×10−6), DDX4 and IL31RA both at 5q11.2 (P = 2.94×10−6 and 6.54×10−7 respectively), and HSD17B12 at 11p11.2 (P = 4.20×10−7) as being associated with the less aggressive form of the disease. These data demonstrate the importance of robust phenotypic data in GWAS analyses and identify additional susceptibility variants for neuroblastoma

Genome-wide expression assay comparison across frozen and fixed postmortem brain tissue samples

<p>Abstract</p> <p>Background</p> <p>Gene expression assays have been shown to yield high quality genome-wide data from partially degraded RNA samples. However, these methods have not yet been applied to postmortem human brain tissue, despite their potential to overcome poor RNA quality and other technical limitations inherent in many assays. We compared cDNA-mediated annealing, selection, and ligation (DASL)- and <it>in vitro </it>transcription (IVT)-based genome-wide expression profiling assays on RNA samples from artificially degraded reference pools, frozen brain tissue, and formalin-fixed brain tissue.</p> <p>Results</p> <p>The DASL-based platform produced expression results of greater reliability than the IVT-based platform in artificially degraded reference brain RNA and RNA from frozen tissue-based samples. Although data associated with a small sample of formalin-fixed RNA samples were poor when obtained from both assays, the DASL-based platform exhibited greater reliability in a subset of probes and samples.</p> <p>Conclusions</p> <p>Our results suggest that the DASL-based gene expression-profiling platform may confer some advantages on mRNA assays of the brain over traditional IVT-based methods. We ultimately consider the implications of these results on investigations of neuropsychiatric disorders.</p

The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases

Genetic epidemiologists have taken the challenge to identify genetic polymorphisms involved in the development of diseases. Many have collected data on large numbers of genetic markers but are not familiar with available methods to assess their association with complex diseases. Statistical methods have been developed for analyzing the relation between large numbers of genetic and environmental predictors to disease or disease-related variables in genetic association studies. In this commentary we discuss logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimized neural networks (GPNN) and several non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted. Logistic regression and neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association studies using the case-control design, the application of a combination of several methods, including the set association approach, MDR and the random forests approach, will likely be a useful strategy to find the important genes and interaction patterns involved in complex diseases

Catecholamine Storage Vesicles: Role of Core Protein Genetic Polymorphisms in Hypertension

Hypertension is a complex trait with deranged autonomic control of the circulation. The sympathoadrenal system exerts minute-to-minute control over cardiac output and vascular tone. Catecholamine storage vesicles (or chromaffin granules) of the adrenal medulla contain remarkably high concentrations of chromogranins/secretogranins (or “granins”), catecholamines, neuropeptide Y, adenosine triphosphate (ATP), and Ca2+. Within secretory granules, granins are co-stored with catecholamine neurotransmitters and co-released upon stimulation of the regulated secretory pathway. The principal granin family members, chromogranin A (CHGA), chromogranin B (CHGB), and secretogranin II (SCG2), may have evolved from shared ancestral exons by gene duplication. This article reviews human genetic variation at loci encoding the major granins and probes the effects of such polymorphisms on blood pressure, using twin pairs to probe heritability and individuals with the most extreme blood pressure values in the population to study hypertension

A hidden two-locus disease association pattern in genome-wide association studies

<p>Abstract</p> <p>Background</p> <p>Recent association analyses in genome-wide association studies (GWAS) mainly focus on single-locus association tests (marginal tests) and two-locus interaction detections. These analysis methods have provided strong evidence of associations between genetics variances and complex diseases. However, there exists a type of association pattern, which often occurs within local regions in the genome and is unlikely to be detected by either marginal tests or interaction tests. This association pattern involves a group of correlated single-nucleotide polymorphisms (SNPs). The correlation among SNPs can lead to weak marginal effects and the interaction does not play a role in this association pattern. This phenomenon is due to the existence of unfaithfulness: the marginal effects of correlated SNPs do not express their significant joint effects faithfully due to the correlation cancelation.</p> <p>Results</p> <p>In this paper, we develop a computational method to detect this association pattern masked by unfaithfulness. We have applied our method to analyze seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). The analysis for each data set takes about one week to finish the examination of all pairs of SNPs. Based on the empirical result of these real data, we show that this type of association masked by unfaithfulness widely exists in GWAS.</p> <p>Conclusions</p> <p>These newly identified associations enrich the discoveries of GWAS, which may provide new insights both in the analysis of tagSNPs and in the experiment design of GWAS. Since these associations may be easily missed by existing analysis tools, we can only connect some of them to publicly available findings from other association studies. As independent data set is limited at this moment, we also have difficulties to replicate these findings. More biological implications need further investigation.</p> <p>Availability</p> <p>The software is freely available at <url>http://bioinformatics.ust.hk/hidden_pattern_finder.zip</url>.</p

Mechanisms Underlying Hypoxia Tolerance in Drosophila melanogaster: hairy as a Metabolic Switch

Hypoxia-induced cell injury has been related to multiple pathological conditions. In order to render hypoxia-sensitive cells and tissues resistant to low O2 environment, in this current study, we used Drosophila melanogaster as a model to dissect the mechanisms underlying hypoxia-tolerance. A D. melanogaster strain that lives perpetually in an extremely low-oxygen environment (4% O2, an oxygen level that is equivalent to that over about 4,000 m above Mt. Everest) was generated through laboratory selection pressure using a continuing reduction of O2 over many generations. This phenotype is genetically stable since selected flies, after several generations in room air, survive at this low O2 level. Gene expression profiling showed striking differences between tolerant and naïve flies, in larvae and adults, both quantitatively and qualitatively. Up-regulated genes in the tolerant flies included signal transduction pathways (e.g., Notch and Toll/Imd pathways), but metabolic genes were remarkably down-regulated in the larvae. Furthermore, a different allelic frequency and enzymatic activity of the triose phosphate isomerase (TPI) was present in the tolerant versus naïve flies. The transcriptional suppressor, hairy, was up-regulated in the microarrays and its binding elements were present in the regulatory region of the specifically down-regulated metabolic genes but not others, and mutations in hairy significantly reduced hypoxia tolerance. We conclude that, the hypoxia-selected flies: (a) altered their gene expression and genetic code, and (b) coordinated their metabolic suppression, especially during development, with hairy acting as a metabolic switch, thus playing a crucial role in hypoxia-tolerance

Evaluation of inhaler technique and achievement and maintenance of mastery of budesonide/formoterol Spiromax® compared with budesonide/formoterol Turbuhaler® in adult patients with asthma: the Easy Low Instruction Over Time (ELIOT) study

Background: Incorrect inhaler technique is a common cause of poor asthma control. This two-phase pragmatic study evaluated inhaler technique mastery and maintenance of mastery with DuoResp® (budesonide-formoterol [BF]) Spiromax® compared with Symbicort® (BF) Turbuhaler® in patients with asthma who were receiving inhaled corticosteroids/long-acting β2-agonists. Methods: In the initial cross-sectional phase, patients were randomized to a 6-step training protocol with empty Spiromax and Turbuhaler devices. Patients initially demonstrating ≥1 error with their current device, and then achieving mastery with both Spiromax and Turbuhaler (absence of healthcare professional [HCP]-observed errors), were eligible for the longitudinal phase. In the longitudinal phase, patients were randomized to BF Spiromax or BF Turbuhaler. Co-primary endpoints were the proportions of patients achieving device mastery after three training steps and maintaining device mastery (defined as the absence of HCP-observed errors after 12 weeks of use). Secondary endpoints included device preference, handling error frequency, asthma control, and safety. Exploratory endpoints included assessment of device mastery by an independent external expert reviewing video recordings of a subset of patients. Results: Four hundred ninety-three patients participated in the cross-sectional phase, and 395 patients in the longitudinal phase. In the cross-sectional phase, more patients achieved device mastery after three training steps with Spiromax (94%) versus Turbuhaler (87%) (odds ratio [OR] 3.77 [95% confidence interval (CI) 2.05–6.95], p < 0.001). Longitudinal phase data indicated that the odds of maintaining inhaler mastery at 12 weeks were not statistically significantly different (OR 1.26 [95% CI 0.80–1.98], p = 0.316). Asthma control improved in both groups with no significant difference between groups (OR 0.11 [95% CI -0.09–0.30]). An exploratory analysis indicated that the odds of maintaining independent expert-verified device mastery were significantly higher for patients using Spiromax versus Turbuhaler (OR 2.11 [95% CI 1.25–3.54]). Conclusions: In the cross-sectional phase, a significantly greater proportion of patients using Spiromax versus Turbuhaler achieved device mastery; in the longitudinal phase, the proportion of patients maintaining device mastery with Spiromax versus Turbuhaler was similar. An exploratory independent expert-verified analysis found Spiromax was associated with higher levels of device mastery after 12 weeks. Asthma control was improved by treatment with both BF Spiromax and BF Turbuhaler

Global Developmental Gene Expression and Pathway Analysis of Normal Brain Development and Mouse Models of Human Neuronal Migration Defects

Heterozygous LIS1 mutations are the most common cause of human lissencephaly, a human neuronal migration defect, and DCX mutations are the most common cause of X-linked lissencephaly. LIS1 is part of a protein complex including NDEL1 and 14-3-3ε that regulates dynein motor function and microtubule dynamics, while DCX stabilizes microtubules and cooperates with LIS1 during neuronal migration and neurogenesis. Targeted gene mutations of Lis1, Dcx, Ywhae (coding for 14-3-3ε), and Ndel1 lead to neuronal migration defects in mouse and provide models of human lissencephaly, as well as aid the study of related neuro-developmental diseases. Here we investigated the developing brain of these four mutants and wild-type mice using expression microarrays, bioinformatic analyses, and in vivo/in vitro experiments to address whether mutations in different members of the LIS1 neuronal migration complex lead to similar and/or distinct global gene expression alterations. Consistent with the overall successful development of the mutant brains, unsupervised clustering and co-expression analysis suggested that cell cycle and synaptogenesis genes are similarly expressed and co-regulated in WT and mutant brains in a time-dependent fashion. By contrast, focused co-expression analysis in the Lis1 and Ndel1 mutants uncovered substantial differences in the correlation among pathways. Differential expression analysis revealed that cell cycle, cell adhesion, and cytoskeleton organization pathways are commonly altered in all mutants, while synaptogenesis, cell morphology, and inflammation/immune response are specifically altered in one or more mutants. We found several commonly dysregulated genes located within pathogenic deletion/duplication regions, which represent novel candidates of human mental retardation and neurocognitive disabilities. Our analysis suggests that gene expression and pathway analysis in mouse models of a similar disorder or within a common pathway can be used to define novel candidates for related human diseases

Functional Role of the Polymorphic 647 T/C Variant of ENT1 (SLC29A1) and Its Association with Alcohol Withdrawal Seizures

Adenosine is involved in several neurological and behavioral disorders including alcoholism. In cultured cell and animal studies, type 1 equilibrative nucleoside transporter (ENT1, slc29a1), which regulates adenosine levels, is known to regulate ethanol sensitivity and preference. Interestingly, in humans, the ENT1 (SLC29A1) gene contains a non-synonymous single nucleotide polymorphism (647 T/C; rs45573936) that might be involved in the functional change of ENT1. Our functional analysis showed that prolonged ethanol exposure increased adenosine uptake activity of mutant cells (ENT1-216Thr) compared to wild-type (ENT1-216Ile) transfected cells, which might result in reduced extracellular adenosine levels. We found that mice lacking ENT1 displayed increased propensity to ethanol withdrawal seizures compared to wild-type littermates. We further investigated a possible association of the 647C variant with alcoholism and the history of alcohol withdrawal seizures in subjects of European ancestry recruited from two independent sites. Analyses of the combined data set showed an association of the 647C variant and alcohol dependence with withdrawal seizures at the nominally significant level. Together with the functional data, our findings suggest a potential contribution of a genetic variant of ENT1 to the development of alcoholism with increased risk of alcohol withdrawal-induced seizures in humans