Dataset Information

Duplications in RB1CC1 are associated with schizophrenia; identification in large European sample sets.

ABSTRACT: Schizophrenia (SCZ) is a severe and debilitating neuropsychiatric disorder with an estimated heritability of ~80%. Recently, de novo mutations, identified by next-generation sequencing (NGS) technology, have been suggested to contribute to the risk of developing SCZ. Although these studies show an overall excess of de novo mutations among patients compared with controls, it is not easy to pinpoint specific genes hit by de novo mutations as actually involved in the disease process. Importantly, support for a specific gene can be provided by the identification of additional alterations in several independent patients. We took advantage of existing genome-wide single-nucleotide polymorphism data sets to screen for deletions or duplications (copy number variations, CNVs) in genes previously implicated by NGS studies. Our approach was based on the observation that CNVs constitute part of the mutational spectrum in many human disease-associated genes. In a discovery step, we investigated whether CNVs in 55 candidate genes, suggested from NGS studies, were more frequent among 1637 patients compared with 1627 controls. Duplications in RB1CC1 were overrepresented among patients. This finding was followed-up in large, independent European sample sets. In the combined analysis, totaling 8461 patients and 112 871 controls, duplications in RB1CC1 were found to be associated with SCZ (P=1.29 × 10(-5); odds ratio=8.58). Our study provides evidence for rare duplications in RB1CC1 as a risk factor for SCZ.

SUBMITTER: Degenhardt F

PROVIDER: S-EPMC3849960 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Duplications in RB1CC1 are associated with schizophrenia; identification in large European sample sets.

Degenhardt F F Priebe L L Meier S S Lennertz L L Streit F F Witt S H SH Hofmann A A Becker T T Mössner R R Maier W W Nenadic I I Sauer H H Mattheisen M M Buizer-Voskamp J J Ophoff R A RA Rujescu D D Giegling I I Ingason A A Wagner M M Delobel B B Andrieux J J Meyer-Lindenberg A A Heinz A A Walter H H Moebus S S Corvin A A Rietschel M M Nöthen M M MM Cichon S S

Translational psychiatry 20131126

Schizophrenia (SCZ) is a severe and debilitating neuropsychiatric disorder with an estimated heritability of ~80%. Recently, de novo mutations, identified by next-generation sequencing (NGS) technology, have been suggested to contribute to the risk of developing SCZ. Although these studies show an overall excess of de novo mutations among patients compared with controls, it is not easy to pinpoint specific genes hit by de novo mutations as actually involved in the disease process. Importantly, s ...[more]

PMID: 26151896

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Project description:MotivationWe have proposed a mixture model based approach to the concordant integrative analysis of multiple large-scale two-sample expression datasets. Since the mixture model is based on the transformed differential expression test P-values (z-scores), it is generally applicable to the expression data generated by either microarray or RNA-seq platforms. The mixture model is simple with three normal distribution components for each dataset to represent down-regulation, up-regulation and no differential expression. However, when the number of datasets increases, the model parameter space increases exponentially due to the component combination from different datasets.ResultsIn this study, motivated by the well-known generalized estimating equations (GEEs) for longitudinal data analysis, we focus on the concordant components and assume that the proportions of non-concordant components follow a special structure. We discuss the exchangeable, multiset coefficient and autoregressive structures for model reduction, and their related expectation-maximization (EM) algorithms. Then, the parameter space is linear with the number of datasets. In our previous study, we have applied the general mixture model to three microarray datasets for lung cancer studies. We show that more gene sets (or pathways) can be detected by the reduced mixture model with the exchangeable structure. Furthermore, we show that more genes can also be detected by the reduced model. The Cancer Genome Atlas (TCGA) data have been increasingly collected. The advantage of incorporating the concordance feature has also been clearly demonstrated based on TCGA RNA sequencing data for studying two closely related types of cancer.Availability and implementationAdditional results are included in a supplemental file. Computer program R-functions are freely available at http://home.gwu.edu/?ylai/research/Concordance.Contactylai@gwu.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Duplications in RB1CC1 are associated with schizophrenia; identification in large European sample sets.

Publications

Duplications in RB1CC1 are associated with schizophrenia; identification in large European sample sets.

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