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1.
Int J Methods Psychiatr Res ; 28(3): e1796, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31397039

RESUMO

OBJECTIVES: For many research cohorts, it is not practical to provide a "gold-standard" mental health diagnosis. It is therefore important for mental health research that potential alternative measures for ascertaining mental disorder status are understood. METHODS: Data from UK Biobank in those participants who had completed the online Mental Health Questionnaire (n = 157,363) were used to compare the classification of mental disorder by four methods: symptom-based outcome (self-complete based on diagnostic interviews), self-reported diagnosis, hospital data linkage, and self-report medication. RESULTS: Participants self-reporting any psychiatric diagnosis had elevated risk of any symptom-based outcome. Cohen's κ between self-reported diagnosis and symptom-based outcome was 0.46 for depression, 0.28 for bipolar affective disorder, and 0.24 for anxiety. There were small numbers of participants uniquely identified by hospital data linkage and medication. CONCLUSION: Our results confirm that ascertainment of mental disorder diagnosis in large cohorts such as UK Biobank is complex. There may not be one method of classification that is right for all circumstances, but an informed and transparent use of outcome measure(s) to suit each research question will maximise the potential of UK Biobank and other resources for mental health research.

2.
Nat Genet ; 51(8): 1207-1214, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31308545

RESUMO

Characterized primarily by a low body-mass index, anorexia nervosa is a complex and serious illness1, affecting 0.9-4% of women and 0.3% of men2-4, with twin-based heritability estimates of 50-60%5. Mortality rates are higher than those in other psychiatric disorders6, and outcomes are unacceptably poor7. Here we combine data from the Anorexia Nervosa Genetics Initiative (ANGI)8,9 and the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED) and conduct a genome-wide association study of 16,992 cases of anorexia nervosa and 55,525 controls, identifying eight significant loci. The genetic architecture of anorexia nervosa mirrors its clinical presentation, showing significant genetic correlations with psychiatric disorders, physical activity, and metabolic (including glycemic), lipid and anthropometric traits, independent of the effects of common variants associated with body-mass index. These results further encourage a reconceptualization of anorexia nervosa as a metabo-psychiatric disorder. Elucidating the metabolic component is a critical direction for future research, and paying attention to both psychiatric and metabolic components may be key to improving outcomes.

3.
Nat Genet ; 51(5): 793-803, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-31043756

RESUMO

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.


Assuntos
Transtorno Bipolar/genética , Loci Gênicos , Transtorno Bipolar/classificação , Estudos de Casos e Controles , Transtorno Depressivo Maior/genética , Feminino , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Masculino , Polimorfismo de Nucleotídeo Único , Transtornos Psicóticos/genética , Esquizofrenia/genética , Biologia de Sistemas
4.
BMC Bioinformatics ; 20(1): 116, 2019 Mar 07.
Artigo em Inglês | MEDLINE | ID: mdl-30845922

RESUMO

BACKGROUND: Principal component analysis (PCA) is a standard method to correct for population stratification in ancestry-specific genome-wide association studies (GWASs) and is used to cluster individuals by ancestry. Using the 1000 genomes project data, we examine how non-linear dimensionality reduction methods such as t-distributed stochastic neighbor embedding (t-SNE) or generative topographic mapping (GTM) can be used to provide improved ancestry maps by accounting for a higher percentage of explained variance in ancestry, and how they can help to estimate the number of principal components necessary to account for population stratification. GTM generates posterior probabilities of class membership which can be used to assess the probability of an individual to belong to a given population - as opposed to t-SNE, GTM can be used for both clustering and classification. RESULTS: PCA only partially identifies population clusters and does not separate most populations within a given continent, such as Japanese and Han Chinese in East Asia, or Mende and Yoruba in Africa. t-SNE and GTM, taking into account more data variance, can identify more fine-grained population clusters. GTM can be used to build probabilistic classification models, and is as efficient as support vector machine (SVM) for classifying 1000 Genomes Project populations. CONCLUSION: The main interest of probabilistic GTM maps is to attain two objectives with only one map: provide a better visualization that separates populations efficiently, and infer genetic ancestry for individuals or populations. This paper is a first application of GTM for ancestry classification models. Our code ( https://github.com/hagax8/ancestry_viz ) and interactive visualizations ( https://lovingscience.com/ancestries ) are available online.


Assuntos
Grupo com Ancestrais do Continente Africano/genética , Grupo com Ancestrais do Continente Asiático/genética , Genética Populacional , Modelos Estatísticos , Arabidopsis/genética , Análise por Conglomerados , Estudo de Associação Genômica Ampla , Humanos , Análise de Componente Principal , Processos Estocásticos
5.
Transl Psychiatry ; 9(1): 117, 2019 03 15.
Artigo em Inglês | MEDLINE | ID: mdl-30877270

RESUMO

The major depressive disorder (MDD) working group of the Psychiatric Genomics Consortium (PGC) has published a genome-wide association study (GWAS) for MDD in 130,664 cases, identifying 44 risk variants. We used these results to investigate potential drug targets and repurposing opportunities. We built easily interpretable bipartite drug-target networks integrating interactions between drugs and their targets, genome-wide association statistics, and genetically predicted expression levels in different tissues, using the online tool Drug Targetor ( drugtargetor.com ). We also investigated drug-target relationships that could be impacting MDD. MAGMA was used to perform pathway analyses and S-PrediXcan to investigate the directionality of tissue-specific expression levels in patients vs. controls. Outside the major histocompatibility complex (MHC) region, 153 protein-coding genes are significantly associated with MDD in MAGMA after multiple testing correction; among these, five are predicted to be down or upregulated in brain regions and 24 are known druggable genes. Several drug classes were significantly enriched, including monoamine reuptake inhibitors, sex hormones, antipsychotics, and antihistamines, indicating an effect on MDD and potential repurposing opportunities. These findings not only require validation in model systems and clinical examination, but also show that GWAS may become a rich source of new therapeutic hypotheses for MDD and other psychiatric disorders that need new-and better-treatment options.

6.
Am J Med Genet B Neuropsychiatr Genet ; 180(6): 428-438, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-30593698

RESUMO

Anorexia nervosa (AN) occurs nine times more often in females than in males. Although environmental factors likely play a role, the reasons for this imbalanced sex ratio remain unresolved. AN displays high genetic correlations with anthropometric and metabolic traits. Given sex differences in body composition, we investigated the possible metabolic underpinnings of female propensity for AN. We conducted sex-specific GWAS in a healthy and medication-free subsample of the UK Biobank (n = 155,961), identifying 77 genome-wide significant loci associated with body fat percentage (BF%) and 174 with fat-free mass (FFM). Partitioned heritability analysis showed an enrichment for central nervous tissue-associated genes for BF%, which was more prominent in females than males. Genetic correlations of BF% and FFM with the largest GWAS of AN by the Psychiatric Genomics Consortium were estimated to explore shared genomics. The genetic correlations of BF%male and BF%female with AN differed significantly from each other (p < .0001, δ = -0.17), suggesting that the female preponderance in AN may, in part, be explained by sex-specific anthropometric and metabolic genetic factors increasing liability to AN.

7.
Bioinformatics ; 2018 Dec 04.
Artigo em Inglês | MEDLINE | ID: mdl-30517594

RESUMO

Summary: Results from hundreds of genome-wide association studies (GWAS) are now freely available and offer a catalogue of the association between phenotypes across medicine with variants in the genome. With the aim of using this data to better understand therapeutic mechanisms, we have developed Drug Targetor, a web interface that allows the generation and exploration of drug-target networks of hundreds of phenotypes using GWAS data. Drug Targetor networks consist of drug and target nodes ordered by genetic association and connected by drug-target or drug-gene relationship. We show that Drug Targetor can help prioritize drugs, targets and drug-target interactions for a specific phenotype based on genetic evidence. Availability and implementation: Drug Targetor v1.21 is a web application freely available online at drugtargetor.com and under MIT license. The source code can be found at https://github.com/hagax8/drugtargetor. Supplementary information: Supplementary data are available at Bioinformatics online.

8.
Nat Genet ; 50(6): 825-833, 2018 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-29785013

RESUMO

With few exceptions, the marked advances in knowledge about the genetic basis of schizophrenia have not converged on findings that can be confidently used for precise experimental modeling. By applying knowledge of the cellular taxonomy of the brain from single-cell RNA sequencing, we evaluated whether the genomic loci implicated in schizophrenia map onto specific brain cell types. We found that the common-variant genomic results consistently mapped to pyramidal cells, medium spiny neurons (MSNs) and certain interneurons, but far less consistently to embryonic, progenitor or glial cells. These enrichments were due to sets of genes that were specifically expressed in each of these cell types. We also found that many of the diverse gene sets previously associated with schizophrenia (genes involved in synaptic function, those encoding mRNAs that interact with FMRP, antipsychotic targets, etc.) generally implicated the same brain cell types. Our results suggest a parsimonious explanation: the common-variant genetic results for schizophrenia point at a limited set of neurons, and the gene sets point to the same cells. The genetic risk associated with MSNs did not overlap with that of glutamatergic pyramidal cells and interneurons, suggesting that different cell types have biologically distinct roles in schizophrenia.

9.
Nat Genet ; 50(5): 668-681, 2018 May.
Artigo em Inglês | MEDLINE | ID: mdl-29700475

RESUMO

Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.

11.
Mol Psychiatry ; 2018 Mar 08.
Artigo em Inglês | MEDLINE | ID: mdl-29520040

RESUMO

Variance in IQ is associated with a wide range of health outcomes, and 1% of the population are affected by intellectual disability. Despite a century of research, the fundamental neural underpinnings of intelligence remain unclear. We integrate results from genome-wide association studies (GWAS) of intelligence with brain tissue and single cell gene expression data to identify tissues and cell types associated with intelligence. GWAS data for IQ (N = 78,308) were meta-analyzed with a study comparing 1247 individuals with mean IQ ~170 to 8185 controls. Genes associated with intelligence implicate pyramidal neurons of the somatosensory cortex and CA1 region of the hippocampus, and midbrain embryonic GABAergic neurons. Tissue-specific analyses find the most significant enrichment for frontal cortex brain expressed genes. These results suggest specific neuronal cell types and genes may be involved in intelligence and provide new hypotheses for neuroscience experiments using model systems.

12.
Nat Neurosci ; 19(11): 1392-1396, 2016 10 26.
Artigo em Inglês | MEDLINE | ID: mdl-27786187

RESUMO

Genome-wide association studies (GWAS) in psychiatry, once they reach sufficient sample size and power, have been enormously successful. The Psychiatric Genomics Consortium (PGC) aims for mega-analyses with sample sizes that will grow to >1 million individuals in the next 5 years. This should lead to hundreds of new findings for common genetic variants across nine psychiatric disorders studied by the PGC. The new targets discovered by GWAS have the potential to restart largely stalled psychiatric drug development pipelines, and the translation of GWAS findings into the clinic is a key aim of the recently funded phase 3 of the PGC. This is not without considerable technical challenges. These approaches complement the other main aim of GWAS studies, risk prediction approaches for improving detection, differential diagnosis, and clinical trial design. This paper outlines the motivations, technical and analytical issues, and the plans for translating PGC phase 3 findings into new therapeutics.


Assuntos
Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Transtornos Mentais/genética , Polimorfismo de Nucleotídeo Único/genética , Psiquiatria , Animais , Humanos , Medição de Risco
13.
J Chem Inf Model ; 55(11): 2403-10, 2015 Nov 23.
Artigo em Inglês | MEDLINE | ID: mdl-26458083

RESUMO

Predicting the activity profile of a molecule or discovering structures possessing a specific activity profile are two important goals in chemoinformatics, which could be achieved by bridging activity and molecular descriptor spaces. In this paper, we introduce the "Stargate" version of the Generative Topographic Mapping approach (S-GTM) in which two different multidimensional spaces (e.g., structural descriptor space and activity space) are linked through a common 2D latent space. In the S-GTM algorithm, the manifolds are trained simultaneously in two initial spaces using the probabilities in the 2D latent space calculated as a weighted geometric mean of probability distributions in both spaces. S-GTM has the following interesting features: (1) activities are involved during the training procedure; therefore, the method is supervised, unlike conventional GTM; (2) using molecular descriptors of a given compound as input, the model predicts a whole activity profile, and (3) using an activity profile as input, areas populated by relevant chemical structures can be detected. To assess the performance of S-GTM prediction models, a descriptor space (ISIDA descriptors) of a set of 1325 GPCR ligands was related to a B-dimensional (B = 1 or 8) activity space corresponding to pKi values for eight different targets. S-GTM outperforms conventional GTM for individual activities and performs similarly to the Lasso multitask learning algorithm, although it is still slightly less accurate than the Random Forest method.


Assuntos
Algoritmos , Projeto Auxiliado por Computador , Desenho de Drogas , Inteligência Artificial , Humanos , Probabilidade , Relação Quantitativa Estrutura-Atividade
14.
J Chem Inf Model ; 55(1): 84-94, 2015 Jan 26.
Artigo em Inglês | MEDLINE | ID: mdl-25423612

RESUMO

This paper is devoted to the analysis and visualization in 2-dimensional space of large data sets of millions of compounds using the incremental version of generative topographic mapping (iGTM). The iGTM algorithm implemented in the in-house ISIDA-GTM program was applied to a database of more than 2 million compounds combining data sets of 36 chemicals suppliers and the NCI collection, encoded either by MOE descriptors or by MACCS keys. Taking advantage of the probabilistic nature of GTM, several approaches to data analysis were proposed. The chemical space coverage was evaluated using the normalized Shannon entropy. Different views of the data (property landscapes) were obtained by mapping various physical and chemical properties (molecular weight, aqueous solubility, LogP, etc.) onto the iGTM map. The superposition of these views helped to identify the regions in the chemical space populated by compounds with desirable physicochemical profiles and the suppliers providing them. The data sets similarity in the latent space was assessed by applying several metrics (Euclidean distance, Tanimoto and Bhattacharyya coefficients) to data probability distributions based on cumulated responsibility vectors. As a complementary approach, data sets were compared by considering them as individual objects on a meta-GTM map, built on cumulated responsibility vectors or property landscapes produced with iGTM. We believe that the iGTM methodology described in this article represents a fast and reliable way to analyze and visualize large chemical databases.


Assuntos
Algoritmos , Bases de Dados de Compostos Químicos , Entropia , Bibliotecas de Moléculas Pequenas , Solubilidade , Interface Usuário-Computador
15.
J Chem Inf Model ; 53(12): 3318-25, 2013 Dec 23.
Artigo em Inglês | MEDLINE | ID: mdl-24320683

RESUMO

Earlier (Kireeva et al. Mol. Inf. 2012, 31, 301-312), we demonstrated that generative topographic mapping (GTM) can be efficiently used both for data visualization and building of classification models in the initial D-dimensional space of molecular descriptors. Here, we describe the modeling in two-dimensional latent space for the four classes of the BioPharmaceutics Drug Disposition Classification System (BDDCS) involving VolSurf descriptors. Three new definitions of the applicability domain (AD) of models have been suggested: one class-independent AD which considers the GTM likelihood and two class-dependent ADs considering respectively, either the predominant class in a given node of the map or informational entropy. The class entropy AD was found to be the most efficient for the BDDCS modeling. The predominant class AD can be directly visualized on GTM maps, which helps the interpretation of the model.


Assuntos
Produtos Biológicos/classificação , Drogas em Investigação/classificação , Modelos Estatísticos , Medicamentos sob Prescrição/classificação , Software , Algoritmos , Produtos Biológicos/química , Biofarmácia , Bases de Dados de Produtos Farmacêuticos , Drogas em Investigação/química , Entropia , Humanos , Medicamentos sob Prescrição/química , Solubilidade
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