Applied Computational Techniques on Schizophrenia Using Genetic Mutations

[Abstract] Schizophrenia is a complex disease, with both genetic and environmental influence. Machine learning techniques can be used to associate different genetic variations at different genes with a (schizophrenic or non-schizophrenic) phenotype. Several machine learning techniques were applied to schizophrenia data to obtain the results presented in this study. Considering these data, Quantitative Genotype – Disease Relationships (QDGRs) can be used for disease prediction. One of the best machine learning-based models obtained after this exhaustive comparative study was implemented online; this model is an artificial neural network (ANN). Thus, the tool offers the possibility to introduce Single Nucleotide Polymorphism (SNP) sequences in order to classify a patient with schizophrenia. Besides this comparative study, a method for variable selection, based on ANNs and evolutionary computation (EC), is also presented. This method uses half the number of variables as the original ANN and the variables obtained are among those found in other publications. In the future, QDGR models based on nucleic acid information could be expanded to other diseases.Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT-0366Xunta de Galicia; 10SIN105004PRInstituto de Salud Carlos III; RD07/0067/0005Xunta de Galicia; Ref. 2009/5

Genetic influences on emotion/cognition interactions : from synaptic regulation to individual differences in working memory for emotional faces

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Association between oxytocin receptor gene polymorphisms and self-rated 'empathic concern' in schizophrenia

The nonapeptide oxytocin (OXT) and its receptor (OXTR) have been implicated in social cognition, empathy, emotion and stress regulation in humans. Previous studies reported associations between OXT and OXTR genetic polymorphisms and risk for disorders characterized by impaired socio-emotional functioning, such as schizophrenia and autism. Here we investigate the influence of two single nucleotide polymorphisms (SNPs) within the OXTR gene on a measure of socio-emotional functioning in schizophrenic patients. OXTR SNPs that were previously investigated in other studies were genotyped in 145 patients diagnosed with schizophrenia according to DSM-IV and 145 healthy controls matched for age and gender. The Interpersonal Reactivity Index (IRI) was used to assess cognitive ('perspective taking'), affective ('empathic concern') and self-related ('personal distress') dimensions of empathy. No group differences in genotype frequencies were observed. MANCOVA revealed a significant main (F [1,282] = 10.464; pGG) with 'empathic concern'. Within the schizophrenia group, linear regression analysis determined OXTR rs2254298 genotype, PANSS negative and general symptom score, and age of disease onset as being significantly associated with 'empathic concern'. OXTR rs2254298 significantly impacted PANSS general psychopathology scores. No associations were found for OXTR rs53576, IRI 'perspective taking' or 'personal distress' ratings. Our preliminary findings support hypotheses about an involvement of OXTR rs2254298 in emotional empathy in schizophrenic and healthy individuals, warranting independent replication

ZNF804A genotype modulates neural activity during working memory for faces

Copyright © 2013 S. Karger AG, Basel.Peer reviewedPublisher PD

Summaries of plenary, symposia, and oral sessions at the XXII World Congress of Psychiatric Genetics, Copenhagen, Denmark, 12-16 October 2014

The XXII World Congress of Psychiatric Genetics, sponsored by the International Society of Psychiatric Genetics, took place in Copenhagen, Denmark, on 12-16 October 2014. A total of 883 participants gathered to discuss the latest findings in the field. The following report was written by student and postdoctoral attendees. Each was assigned one or more sessions as a rapporteur. This manuscript represents topics covered in most, but not all of the oral presentations during the conference, and contains some of the major notable new findings reported

Genetic modifiers of cognitive maintenance among older adults.

ObjectiveIdentify genetic factors associated with cognitive maintenance in late life and assess their association with gray matter (GM) volume in brain networks affected in aging.MethodsWe conducted a genome-wide association study of ∼2.4 M markers to identify modifiers of cognitive trajectories in Caucasian participants (N = 7,328) from two population-based cohorts of non-demented elderly. Standardized measures of global cognitive function (z-scores) over 10 and 6 years were calculated among participants and mixed model regression was used to determine subject-specific cognitive slopes. "Cognitive maintenance" was defined as a change in slope of ≥ 0 and was compared with all cognitive decliners (slope < 0). In an independent cohort of cognitively normal older Caucasians adults (N = 122), top association findings were then used to create genetic scores to assess whether carrying more cognitive maintenance alleles was associated with greater GM volume in specific brain networks using voxel-based morphometry.ResultsThe most significant association was on chromosome 11 (rs7109806, P = 7.8 × 10(-8)) near RIC3. RIC3 modulates activity of α7 nicotinic acetylcholine receptors, which have been implicated in synaptic plasticity and beta-amyloid binding. In the neuroimaging cohort, carrying more cognitive maintenance alleles was associated with greater volume in the right executive control network (RECN; PFWE  = 0.01).ConclusionsThese findings suggest that there may be genetic loci that promote healthy cognitive aging and that they may do so by conferring robustness to GM in the RECN. Future work is required to validate top candidate genes such as RIC3 for involvement in cognitive maintenance

Identification of SNP barcode biomarkers for genes associated with facial emotion perception using particle swarm optimization algorithm

BACKGROUND: Facial emotion perception (FEP) can affect social function. We previously reported that parts of five tested single-nucleotide polymorphisms (SNPs) in the MET and AKT1 genes may individually affect FEP performance. However, the effects of SNP-SNP interactions on FEP performance remain unclear. METHODS: This study compared patients with high and low FEP performances (n = 89 and 93, respectively). A particle swarm optimization (PSO) algorithm was used to identify the best SNP barcodes (i.e., the SNP combinations and genotypes that revealed the largest differences between the high and low FEP groups). RESULTS: The analyses of individual SNPs showed no significant differences between the high and low FEP groups. However, comparisons of multiple SNP-SNP interactions involving different combinations of two to five SNPs showed that the best PSO-generated SNP barcodes were significantly associated with high FEP score. The analyses of the joint effects of the best SNP barcodes for two to five interacting SNPs also showed that the best SNP barcodes had significantly higher odds ratios (2.119 to 3.138; P < 0.05) compared to other SNP barcodes. In conclusion, the proposed PSO algorithm effectively identifies the best SNP barcodes that have the strongest associations with FEP performance. CONCLUSIONS: This study also proposes a computational methodology for analyzing complex SNP-SNP interactions in social cognition domains such as recognition of facial emotion

The protocadherin 17 gene affects cognition, personality, amygdala structure and function, synapse development and risk of major mood disorders

Major mood disorders, which primarily include bipolar disorder and major depressive disorder, are the leading cause of disability worldwide and pose a major challenge in identifying robust risk genes. Here, we present data from independent large-scale clinical data sets (including 29 557 cases and 32 056 controls) revealing brain expressed protocadherin 17 (PCDH17) as a susceptibility gene for major mood disorders. Single-nucleotide polymorphisms (SNPs) spanning the PCDH17 region are significantly associated with major mood disorders; subjects carrying the risk allele showed impaired cognitive abilities, increased vulnerable personality features, decreased amygdala volume and altered amygdala function as compared with non-carriers. The risk allele predicted higher transcriptional levels of PCDH17 mRNA in postmortem brain samples, which is consistent with increased gene expression in patients with bipolar disorder compared with healthy subjects. Further, overexpression of PCDH17 in primary cortical neurons revealed significantly decreased spine density and abnormal dendritic morphology compared with control groups, which again is consistent with the clinical observations of reduced numbers of dendritic spines in the brains of patients with major mood disorders. Given that synaptic spines are dynamic structures which regulate neuronal plasticity and have crucial roles in myriad brain functions, this study reveals a potential underlying biological mechanism of a novel risk gene for major mood disorders involved in synaptic function and related intermediate phenotypes