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)

SYMBIOmatics: Synergies in Medical Informatics and Bioinformatics – exploring current scientific literature for emerging topics

Background: The SYMBIOmatics Specific Support Action (SSA) is "an information gathering and dissemination activity" that seeks "to identify synergies between the bioinformatics and the medical informatics" domain to improve collaborative progress between both domains (ref. to http://www.symbiomatics.org). As part of the project experts in both research fields will be identified and approached through a survey. To provide input to the survey, the scientific literature was analysed to extract topics relevant to both medical informatics and bioinformatics. Results: This paper presents results ofa systematic analysis of the scientific literature from medical informatics research and bioinformatics research. In the analysis pairs of words (bigrams) from the leading bioinformatics and medical informatics journals have been used as indication of existing and emerging technologies and topics over the period 2000-2005 ("recent") and 1990-1990 ("past"). We identified emerging topics that were equally important to bioinformatics and medical informatics in recent years such as microarray experiments, ontologies, open source, text mining and support vector machines. Emerging topics that evolved only in bioinformatics were system biology, protein interaction networks and statistical methods for microarray analyses, whereas emerging topics in medical informatics were grid technology and tissue microarrays. Conclusion: We conclude that although both fields have their own specific domains of interest, they share common technological developments that tend to be initiated by new developments in biotechnology and computer science

DAZL Relieves miRNA-Mediated Repression of Germline mRNAs by Controlling Poly(A) Tail Length in Zebrafish

BACKGROUND:During zebrafish embryogenesis, microRNA (miRNA) miR-430 contributes to restrict Nanos1 and TDRD7 to primordial germ cells (PGCs) by inducing mRNA deadenylation, mRNA degradation, and translational repression of nanos1 and tdrd7 mRNAs in somatic cells. The nanos1 and tdrd7 3'UTRs include cis-acting elements that allow activity in PGCs even in the presence of miRNA-mediated repression. METHODOLOGY/PRINCIPAL FINDINGS:Using a GFP reporter mRNA that was fused with tdrd7 3'UTR, we show that a germline-specific RNA-binding protein DAZ-like (DAZL) can relieve the miR-430-mediated repression of tdrd7 mRNA by inducing poly(A) tail elongation (polyadenylation) in zebrafish. We also show that DAZL enhances protein synthesis via the 3'UTR of dazl mRNA, another germline mRNA targeted by miR-430. CONCLUSIONS/SIGNIFICANCE:Our present study indicated that DAZL acts as an "anti-miRNA factor" during vertebrate germ cell development. Our data also suggested that miRNA-mediated regulation can be modulated on specific target mRNAs through the poly(A) tail control

L2-norm multiple kernel learning and its application to biomedical data fusion

<p>Abstract</p> <p>Background</p> <p>This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as <it>L</it>_∞, <it>L</it>₁, and <it>L</it>₂MKL. In particular, <it>L</it>₂MKL is a novel method that leads to non-sparse optimal kernel coefficients, which is different from the sparse kernel coefficients optimized by the existing <it>L</it>_∞MKL method. In real biomedical applications, <it>L</it>₂MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources.</p> <p>Results</p> <p>We provide a theoretical analysis of the relationship between the <it>L</it>₂optimization of kernels in the dual problem with the <it>L</it>₂coefficient regularization in the primal problem. Understanding the dual <it>L</it>₂problem grants a unified view on MKL and enables us to extend the <it>L</it>₂method to a wide range of machine learning problems. We implement <it>L</it>₂MKL for ranking and classification problems and compare its performance with the sparse <it>L</it>_∞and the averaging <it>L</it>₁MKL methods. The experiments are carried out on six real biomedical data sets and two large scale UCI data sets. <it>L</it>₂MKL yields better performance on most of the benchmark data sets. In particular, we propose a novel <it>L</it>₂MKL least squares support vector machine (LSSVM) algorithm, which is shown to be an efficient and promising classifier for large scale data sets processing.</p> <p>Conclusions</p> <p>This paper extends the statistical framework of genomic data fusion based on MKL. Allowing non-sparse weights on the data sources is an attractive option in settings where we believe most data sources to be relevant to the problem at hand and want to avoid a "winner-takes-all" effect seen in <it>L</it>_∞MKL, which can be detrimental to the performance in prospective studies. The notion of optimizing <it>L</it>₂kernels can be straightforwardly extended to ranking, classification, regression, and clustering algorithms. To tackle the computational burden of MKL, this paper proposes several novel LSSVM based MKL algorithms. Systematic comparison on real data sets shows that LSSVM MKL has comparable performance as the conventional SVM MKL algorithms. Moreover, large scale numerical experiments indicate that when cast as semi-infinite programming, LSSVM MKL can be solved more efficiently than SVM MKL.</p> <p>Availability</p> <p>The MATLAB code of algorithms implemented in this paper is downloadable from <url>http://homes.esat.kuleuven.be/~sistawww/bioi/syu/l2lssvm.html</url>.</p

DISCO-SCA and Properly Applied GSVD as Swinging Methods to Find Common and Distinctive Processes

BACKGROUND: In systems biology it is common to obtain for the same set of biological entities information from multiple sources. Examples include expression data for the same set of orthologous genes screened in different organisms and data on the same set of culture samples obtained with different high-throughput techniques. A major challenge is to find the important biological processes underlying the data and to disentangle therein processes common to all data sources and processes distinctive for a specific source. Recently, two promising simultaneous data integration methods have been proposed to attain this goal, namely generalized singular value decomposition (GSVD) and simultaneous component analysis with rotation to common and distinctive components (DISCO-SCA). RESULTS: Both theoretical analyses and applications to biologically relevant data show that: (1) straightforward applications of GSVD yield unsatisfactory results, (2) DISCO-SCA performs well, (3) provided proper pre-processing and algorithmic adaptations, GSVD reaches a performance level similar to that of DISCO-SCA, and (4) DISCO-SCA is directly generalizable to more than two data sources. The biological relevance of DISCO-SCA is illustrated with two applications. First, in a setting of comparative genomics, it is shown that DISCO-SCA recovers a common theme of cell cycle progression and a yeast-specific response to pheromones. The biological annotation was obtained by applying Gene Set Enrichment Analysis in an appropriate way. Second, in an application of DISCO-SCA to metabolomics data for Escherichia coli obtained with two different chemical analysis platforms, it is illustrated that the metabolites involved in some of the biological processes underlying the data are detected by one of the two platforms only; therefore, platforms for microbial metabolomics should be tailored to the biological question. CONCLUSIONS: Both DISCO-SCA and properly applied GSVD are promising integrative methods for finding common and distinctive processes in multisource data. Open source code for both methods is provided

Contribution of parental and school personnel smoking to health risk behaviours among Finnish adolescents

This study compared parental smoking with school personnel smoking in relation to adolescents' smoking behaviours, alcohol use, and illicit drug use

Genetic influences on the insulin response of the beta cell to different secretagogues

Aims/hypothesis: The aim of the present study was to estimate the heritability of the beta cell insulin response to glucose and to glucose combined with glucagon-like peptide-1 (GLP-1) or with GLP-1 plus arginine. Methods: This was a twin-family study that included 54 families from the Netherlands Twin Register. The participants were healthy twin pairs and their siblings of the same sex, aged 20 to 50 years. Insulin response of the beta cell was assessed by a modified hyperglycaemic clamp with additional GLP-1 and arginine. Insulin sensitivity index (ISI) was assessed by the euglycaemic-hyperinsulinaemic clamp. Multivariate structural equation modelling was used to obtain heritabilities and the genetic factors underlying individual differences in BMI, ISI and secretory responses of the beta cell. Results: The heritability of insulin levels in response to glucose was 52% and 77% for the first and second phase, respectively, 53% in response to glucose+GLP-1 and 80% in response to an additional arginine bolus. Insulin responses to the administration of glucose, glucose+GLP-1 and glucose+GLP-1+arginine were highly correlated (0.62<r<0.79). Heritability of BMI and ISI was 74% and 60% respectively. The genetic factors that influenced BMI and ISI explained about half of the heritability of insulin levels in response to the three secretagogues. The other half was due to genetic factors specific to the beta cell. Conclusions/interpretation: In healthy adults, genetic factors explain most of the individual differences in the secretory capacity of the beta cell. These genetic influences are partly independent from the genes that influence BMI and ISI. © 2009 Springer-Verlag

Candidate gene prioritization by network analysis of differential expression using machine learning approaches

<p>Abstract</p> <p>Background</p> <p>Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals.</p> <p>To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network.</p> <p>Results</p> <p>We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (<it>Simple Expression Ranking</it>). Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the <it>Heat Kernel Diffusion Ranking </it>leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%.</p> <p>Conclusion</p> <p>In this study we could identify promising candidate genes using network based machine learning approaches even if no knowledge is available about the disease or phenotype.</p

Root Canal Anatomy of Maxillary and Mandibular Teeth

It is a common knowledge that a comprehensive understanding of the complexity of the internal anatomy of teeth is imperative to ensure successful root canal treatment. The significance of canal anatomy has been emphasized by studies demonstrating that variations in canal geometry before cleaning, shaping, and obturation procedures had a greater effect on the outcome than the techniques themselves. In recent years, significant technological advances for imaging teeth, such as CBCT and micro-CT, respectively, have been introduced. Their noninvasive nature allows to perform in vivo anatomical studies using large populations to address the influence of several variables such as ethnicity, aging, gender, and others, on the root canal anatomy, as well as to evaluate, quantitatively and/or qualitatively, specific and fine anatomical features of a tooth group. The purpose of this chapter is to summarize the morphological aspects of the root canal anatomy published in the literature of all groups of teeth and illustrate with three-dimensional images acquired from micro-CT technology.info:eu-repo/semantics/publishedVersio

Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism

Neuroticism is a relatively stable personality trait characterized by negative emotionality (for example, worry and guilt)1; heritability estimated from twin studies ranges from 30 to 50%2, and SNP-based heritability ranges from 6 to 15%3,4,5,6. Increased neuroticism is associated with poorer mental and physical health7,8, translating to high economic burden9. Genome-wide association studies (GWAS) of neuroticism have identified up to 11 associated genetic loci3,4. Here we report 116 significant independent loci from a GWAS of neuroticism in 329,821 UK Biobank participants; 15 of these loci replicated at P &lt; 0.00045 in an unrelated cohort (N = 122,867). Genetic signals were enriched in neuronal genesis and differentiation pathways, and substantial genetic correlations were found between neuroticism and depressive symptoms (rg = 0.82, standard error (s.e.) = 0.03), major depressive disorder (MDD; rg = 0.69, s.e. = 0.07) and subjective well-being (rg = –0.68, s.e. = 0.03) alongside other mental health traits. These discoveries significantly advance understanding of neuroticism and its association with MDD