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1.
Mol Psychiatry ; 22(6): 792-801, 2017 06.
Article in English | MEDLINE | ID: mdl-28348379

ABSTRACT

The most recent genome-wide association studies (GWAS) of schizophrenia (SCZ) identified hundreds of risk variants potentially implicated in the disease. Further, novel statistical methodology designed for polygenic architecture revealed more potential risk variants. This can provide a link between individual genetic factors and the mechanistic underpinnings of SCZ. Intriguingly, a large number of genes coding for ionotropic and metabotropic receptors for various neurotransmitters-glutamate, γ-aminobutyric acid (GABA), dopamine, serotonin, acetylcholine and opioids-and numerous ion channels were associated with SCZ. Here, we review these findings from the standpoint of classical neurobiological knowledge of neuronal synaptic transmission and regulation of electrical excitability. We show that a substantial proportion of the identified genes are involved in intracellular cascades known to integrate 'slow' (G-protein-coupled receptors) and 'fast' (ionotropic receptors) neurotransmission converging on the protein DARPP-32. Inspection of the Human Brain Transcriptome Project database confirms that that these genes are indeed expressed in the brain, with the expression profile following specific developmental trajectories, underscoring their relevance to brain organization and function. These findings extend the existing pathophysiology hypothesis by suggesting a unifying role of dysregulation in neuronal excitability and synaptic integration in SCZ. This emergent model supports the concept of SCZ as an 'associative' disorder-a breakdown in the communication across different slow and fast neurotransmitter systems through intracellular signaling pathways-and may unify a number of currently competing hypotheses of SCZ pathophysiology.


Subject(s)
Receptors, Ionotropic Glutamate/genetics , Receptors, Metabotropic Glutamate/genetics , Schizophrenia/genetics , Brain/metabolism , Dopamine/metabolism , Dopamine and cAMP-Regulated Phosphoprotein 32/metabolism , Genetic Predisposition to Disease/genetics , Genome-Wide Association Study , Genotype , Humans , Multifactorial Inheritance/genetics , Polymorphism, Single Nucleotide/genetics , Receptors, Ionotropic Glutamate/metabolism , Receptors, Metabotropic Glutamate/metabolism , Risk Factors , Signal Transduction/genetics , Synaptic Transmission/genetics , gamma-Aminobutyric Acid/metabolism
2.
Mol Psychiatry ; 20(12): 1588-95, 2015 Dec.
Article in English | MEDLINE | ID: mdl-25687773

ABSTRACT

We investigated the genetic overlap between Alzheimer's disease (AD) and Parkinson's disease (PD). Using summary statistics (P-values) from large recent genome-wide association studies (GWAS) (total n=89 904 individuals), we sought to identify single nucleotide polymorphisms (SNPs) associating with both AD and PD. We found and replicated association of both AD and PD with the A allele of rs393152 within the extended MAPT region on chromosome 17 (meta analysis P-value across five independent AD cohorts=1.65 × 10(-7)). In independent datasets, we found a dose-dependent effect of the A allele of rs393152 on intra-cerebral MAPT transcript levels and volume loss within the entorhinal cortex and hippocampus. Our findings identify the tau-associated MAPT locus as a site of genetic overlap between AD and PD, and extending prior work, we show that the MAPT region increases risk of Alzheimer's neurodegeneration.


Subject(s)
Alzheimer Disease/genetics , Parkinson Disease/genetics , tau Proteins/genetics , Aged , Aged, 80 and over , Alleles , Apolipoproteins E/genetics , Brain/pathology , Chromosomes, Human, Pair 17 , Female , Genetic Loci , Genetic Pleiotropy , Genome-Wide Association Study , Humans , Male , Middle Aged , Polymorphism, Single Nucleotide
3.
Neural Comput ; 22(11): 2924-61, 2010 Nov.
Article in English | MEDLINE | ID: mdl-20804387

ABSTRACT

A variety of modifications have been employed to learning vector quantization (LVQ) algorithms using either crisp or soft windows for selection of data. Although these schemes have been shown in practice to improve performance, a theoretical study on the influence of windows has so far been limited. Here we rigorously analyze the influence of windows in a controlled environment of gaussian mixtures in high dimensions. Concepts from statistical physics and the theory of online learning allow an exact description of the training dynamics, yielding typical learning curves, convergence properties, and achievable generalization abilities. We compare the performance and demonstrate the advantages of various algorithms, including LVQ 2.1, generalized LVQ (GLVQ), Learning from Mistakes (LFM) and Robust Soft LVQ (RSLVQ). We find that the selection of the window parameter highly influences the learning curves but not, surprisingly, the asymptotic performances of LVQ 2.1 and RSLVQ. Although the prototypes of LVQ 2.1 exhibit divergent behavior, the resulting decision boundary coincides with the optimal decision boundary, thus yielding optimal generalization ability.


Subject(s)
Algorithms , Learning , Neural Networks, Computer
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