Identification of Novel Schizophrenia Loci by Homozygosity Mapping Using DNA Microarray Analysis

Kurotaki, Naohiro; Tasaki, Shinya; Mishima, Hiroyuki; Ono, Shinji; Imamura, Akira; Kikuchi, Taeko; Nishida, Nao; Tokunaga, Katsushi; Yoshiura, Koh-ichiro; Ozawa, Hiroki

doi:10.1371/journal.pone.0020589

Cited by 23 publications

(18 citation statements)

References 28 publications

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“…The substantial G component was in accordance with previous studies that indicated the genetic heterogeneity of schizophrenia and the contributions of many rare (possibly dominant) variants in schizophrenia (McClellan et al 2007;Malhotra and Sebat 2012;Gratten et al 2013). Consanguinity induces neuropsychological disorders due to homozygosity (Kurotaki et al 2011), possibly suggesting many recessive genes for the causation of schizophrenia. From the posterior distributions of latent variables, the multiple-hit model with a majority of dominant genes, similar to the previous two-hit model (Girirajan et al 2010), was the most likely explanation for the gene-gene interaction component of schizophrenia presentation.…”

Section: Discussionsupporting

confidence: 90%

A Novel Approach for Identifying Causal Models of Complex Diseases from Family Data

Park

Kim

2015

Genetics

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Causal models including genetic factors are important for understanding the presentation mechanisms of complex diseases. Familial aggregation and segregation analyses based on polygenic threshold models have been the primary approach to fitting genetic models to the family data of complex diseases. In the current study, an advanced approach to obtaining appropriate causal models for complex diseases based on the sufficient component cause (SCC) model involving combinations of traditional genetics principles was proposed. The probabilities for the entire population, i.e., normal-normal, normal-disease, and disease-disease, were considered for each model for the appropriate handling of common complex diseases. The causal model in the current study included the genetic effects from single genes involving epistasis, complementary gene interactions, gene-environment interactions, and environmental effects. Bayesian inference using a Markov chain Monte Carlo algorithm (MCMC) was used to assess of the proportions of each component for a given population lifetime incidence. This approach is flexible, allowing both common and rare variants within a gene and across multiple genes. An application to schizophrenia data confirmed the complexity of the causal factors. An analysis of diabetes data demonstrated that environmental factors and geneenvironment interactions are the main causal factors for type II diabetes. The proposed method is effective and useful for identifying causal models, which can accelerate the development of efficient strategies for identifying causal factors of complex diseases.KEYWORDS complex disease; causal model; relative pair; Bayesian Markov chain Monte Carlo (MCMC); population lifetime incidence M OST complex diseases involve a large number of genes and intricate patterns of inheritance. These heterogeneities result in difficulties in identifying genetic models using segregation analyses (Demenais and Elston 1981;Karunaratne and Elston 1998; SAGE 1994). The flexible framework based on variance components has enabled many extensions for fitting genetic models, with major causal factors of additive genetic effects, shared environment, and unique environment (Morton and MacLean 1974;Falconer and Mackay 1996;Rabe-Hesketh et al. 2008). Genetic models based on familial aggregation using relative risk and covariance could provide partial assessment of relevant parameters such as the number of loci and/or the disease allele frequencies (Elston and Campbell 1970;McGue et al. 1983;Risch 1990;Lange 2002;Slatkin 2008).These genetic models are based on linear models that search the linear relationships between the trait and the causal components. The linear models in genetics were developed to be applicable to most kinds of genetics problems (Mackay 2014). While genetic epidemiologists have focused on the development of modern statistical technologies derived from Fisher's variance components (Fisher 1918), the focus of epidemiologists has been the fundamental concept of causation. A cause is an even...

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Section: Discussionsupporting

confidence: 90%

A Novel Approach for Identifying Causal Models of Complex Diseases from Family Data

Park

Kim

2015

Genetics

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“…The genome-wide ROH overlap pattern was detected using in-house Ruby script (available on request) (44).…”

Section: Methodsmentioning

confidence: 99%

Proteasome assembly defect due to a proteasome subunit beta type 8 (PSMB8) mutation causes the autoinflammatory disorder, Nakajo-Nishimura syndrome

Arima

Kinoshita

Mishima

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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287

270

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Nakajo-Nishimura syndrome (NNS) is a disorder that segregates in an autosomal recessive fashion. Symptoms include periodic fever, skin rash, partial lipomuscular atrophy, and joint contracture. Here, we report a mutation in the human proteasome subunit beta type 8 gene (PSMB8) that encodes the immunoproteasome subunit β5i in patients with NNS. This G201V mutation disrupts the β-sheet structure, protrudes from the loop that interfaces with the β4 subunit, and is in close proximity to the catalytic threonine residue. The β5i mutant is not efficiently incorporated during immunoproteasome biogenesis, resulting in reduced proteasome activity and accumulation of ubiquitinated and oxidized proteins within cells expressing immunoproteasomes. As a result, the level of interleukin (IL)-6 and IFN-γ inducible protein (IP)-10 in patient sera is markedly increased. Nuclear phosphorylated p38 and the secretion of IL-6 are increased in patient cells both in vitro and in vivo, which may account for the inflammatory response and periodic fever observed in these patients. These results show that a mutation within a proteasome subunit is the direct cause of a human disease and suggest that decreased proteasome activity can cause inflammation.

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“…HINT1 levels are increased in dorsolateral prefrontal cortex of patients with major depression disorder (Martins-de-Souza et al , 2012) and, adversely, decreased in the same brain regions of patients with schizophrenia (Varadarajulu et al , 2012). Furthermore, association studies reveal HINT1 as a susceptibility gene for schizophrenia (Chen et al , 2008; Kurotaki et al , 2011), bipolar disorder (Elashoff et al , 2007) and nicotine dependence (Jackson et al , 2011; Fang et al , 2014). So far, CMT patients carrying HINT1 mutations have not been neuropsychiatrically evaluated.…”

Section: Introductionmentioning

confidence: 99%

Axonal neuropathy with neuromyotonia: there is a HINT

Peeters

Chamova

Tournev

et al. 2016

Brain

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Mutations in HINT1 cause a common, autosomal-recessive, axonal Charcot-Marie-Tooth neuropathy, often with neuromyotonia. Peeters et al. summarize neurological aspects of the disease, epidemiology and mutation spectrum, and structural and functional characteristics of the affected protein. They propose guidelines to recognize and differentiate HINT1-neuropathy and suggest strategies to treat common symptoms.

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Identification of Novel Schizophrenia Loci by Homozygosity Mapping Using DNA Microarray Analysis

Cited by 23 publications

References 28 publications

A Novel Approach for Identifying Causal Models of Complex Diseases from Family Data

A Novel Approach for Identifying Causal Models of Complex Diseases from Family Data

Proteasome assembly defect due to a proteasome subunit beta type 8 (PSMB8) mutation causes the autoinflammatory disorder, Nakajo-Nishimura syndrome

Axonal neuropathy with neuromyotonia: there is a HINT

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