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CNV Deletions May Affect Schizophrenia Risks Through Lncrna Genes That Regulate Protein-Coding Genes
S418 GENERATING A QUENCES OF HUMAN TISSUES
Abstracts of the XXIV World Congress of Psychiatric Genetics (WCPG) 2016 DATABASE OF PHENOTYPIC CONSEGENE REGULATION ACROSS 40
CNV DELETIONS MAY AFFECT SCHIZOPHRENIA RISKS THROUGH LNCRNA GENES THAT REGULATE PROTEINCODING GENES
Hae Kyung Im1, Alvaro Barbeira1, Jiamao Zheng1, Jason Torres1, Scott Dickinson1, Heather Wheeler2, Graeme Bell1, Dan Nicolae1, Nancy Cox3
Chunyu Liu1, Qingtuan Meng2, Kangli Wang2, Yan Xia2, Chuan Jiao2, Chao Chen2
1
2
1
The University of Chicago 2 Loyola University Chicago 3 Vanderbilt University Abstract GWAS and sequencing studies have yielded thousands of genetic variants robustly associated genetic complex traits. However, the underlying biology of those associations needs to be further elucidated. To address this problem we developed PrediXcan, a mechanistically driven test, which was motivated by the accumulating evidence that the regulation of gene expression levels as well as splicing events have an important role in the genetic control of complex phenotypes. By using genotype to predict expression (or other intermediate molecular traits) and correlating them with the trait of interest, we can assess the phenotypic consequences of genetic variation through different intermediate processes. By collapsing the associations into functionally relevant units such as genes, we reduce the multiple testing burden. The method also provides direction of the effects. To implement the tissue specific analyses, we have developed prediction models for gene expression in 40 human tissues using the GTEx Consortium and Depression Genes Network data. Despite these advantages, the genetic architecture of psychiatric phenotypes is largely polygenic with modest effect sizes making discoveries only possible for very large sample sizes. Therefore we have extended our method so that only summary statistics are needed to infer PrediXcan results. This allows us to leverage the large meta analysis efforts that have collected hundreds of thousands of samples. Using this new method called MetaXcan we have generated results for 117 phenotypes with publicly available GWAS meta-analysis results. We validate our approach by re-identifying many established genes but in many cases, we find evidence that genes in the vicinity of reported ones are more likely mediators of the phenotype. Furthermore, we make this results database publicly available (http://gene2pheno.org). The database should be a valuable resource for the community to explore the phenotypic consequences of gene regulation. Software and all prediction models necessary to reproduce them or apply to new datasets are made publicly available on https://github.com/ hakyimlab/PrediXcan.
Disclosure Nothing to disclose. http://dx.doi.org/10.1016/j.euroneuro.2016.09.466
University of Illinois at Chicago Central South University
Abstract Schizophrenia is a complex psychiatric disorder with strong genetic background. Studies demonstrated that rare Copy Number Variations (CNVs) contribute to the risk of schizophrenia. More attention has been put to proteincoding genes residing in those CNVs. However, the exact mechanisms still remain unclear. In this study, we explored the potential roles of long non-coding RNAs (lncRNAs) inside CNV deletions (CNV-lncRNAs) in the risk of schizophrenia. We retrieved lncRNAs mapped to the CNV deletion regions known to increase risks of developing schizophrenia. Seven such regions were repeatedly reported by large case-control studies, including 1q21.1, 3q29, 15q11.2, 15q13.3, 17p12, 17q12, and 22q11.2. We carried out weighted gene coexpression network analysis (WGCNA) using RNA-seq data from Genotype-Tissue Expression (GTEx) and BrainSpan projects to look for co-expression modules that harbor CNV-lncRNAs. We identified one male reproduction-related module in male individuals and one neuronal functionsrelated module both in male and female individuals. In addition, the neuronal functions module comprised of protein-coding genes that were involved in several ion channel activities including calcium and potassium channel activities, which were known to be related to schizophrenia. Pathway analysis of these two modules further suggested that CNV-lncRNAs were involved in the olfactory transduction, neuroactive ligand-receptor and calcium signaling pathways. The CNV-lncRNA co-expression patterns were preserved through the different development and aging time points, and across different tissue types of human body. Our results suggested that lncRNAs inside those rare CNVs might play significant temporal and spatial roles in regulating other protein-coding genes and subsequently contribute to schizophrenia risk.
Disclosure Nothing to disclose. http://dx.doi.org/10.1016/j.euroneuro.2016.09.467
Abstracts of the XXIV World Congress of Psychiatric Genetics (WCPG) 2016 DATABASE OF PHENOTYPIC CONSEGENE REGULATION ACROSS 40
CNV DELETIONS MAY AFFECT SCHIZOPHRENIA RISKS THROUGH LNCRNA GENES THAT REGULATE PROTEINCODING GENES
Hae Kyung Im1, Alvaro Barbeira1, Jiamao Zheng1, Jason Torres1, Scott Dickinson1, Heather Wheeler2, Graeme Bell1, Dan Nicolae1, Nancy Cox3
Chunyu Liu1, Qingtuan Meng2, Kangli Wang2, Yan Xia2, Chuan Jiao2, Chao Chen2
1
2
1
The University of Chicago 2 Loyola University Chicago 3 Vanderbilt University Abstract GWAS and sequencing studies have yielded thousands of genetic variants robustly associated genetic complex traits. However, the underlying biology of those associations needs to be further elucidated. To address this problem we developed PrediXcan, a mechanistically driven test, which was motivated by the accumulating evidence that the regulation of gene expression levels as well as splicing events have an important role in the genetic control of complex phenotypes. By using genotype to predict expression (or other intermediate molecular traits) and correlating them with the trait of interest, we can assess the phenotypic consequences of genetic variation through different intermediate processes. By collapsing the associations into functionally relevant units such as genes, we reduce the multiple testing burden. The method also provides direction of the effects. To implement the tissue specific analyses, we have developed prediction models for gene expression in 40 human tissues using the GTEx Consortium and Depression Genes Network data. Despite these advantages, the genetic architecture of psychiatric phenotypes is largely polygenic with modest effect sizes making discoveries only possible for very large sample sizes. Therefore we have extended our method so that only summary statistics are needed to infer PrediXcan results. This allows us to leverage the large meta analysis efforts that have collected hundreds of thousands of samples. Using this new method called MetaXcan we have generated results for 117 phenotypes with publicly available GWAS meta-analysis results. We validate our approach by re-identifying many established genes but in many cases, we find evidence that genes in the vicinity of reported ones are more likely mediators of the phenotype. Furthermore, we make this results database publicly available (http://gene2pheno.org). The database should be a valuable resource for the community to explore the phenotypic consequences of gene regulation. Software and all prediction models necessary to reproduce them or apply to new datasets are made publicly available on https://github.com/ hakyimlab/PrediXcan.
Disclosure Nothing to disclose. http://dx.doi.org/10.1016/j.euroneuro.2016.09.466
University of Illinois at Chicago Central South University
Abstract Schizophrenia is a complex psychiatric disorder with strong genetic background. Studies demonstrated that rare Copy Number Variations (CNVs) contribute to the risk of schizophrenia. More attention has been put to proteincoding genes residing in those CNVs. However, the exact mechanisms still remain unclear. In this study, we explored the potential roles of long non-coding RNAs (lncRNAs) inside CNV deletions (CNV-lncRNAs) in the risk of schizophrenia. We retrieved lncRNAs mapped to the CNV deletion regions known to increase risks of developing schizophrenia. Seven such regions were repeatedly reported by large case-control studies, including 1q21.1, 3q29, 15q11.2, 15q13.3, 17p12, 17q12, and 22q11.2. We carried out weighted gene coexpression network analysis (WGCNA) using RNA-seq data from Genotype-Tissue Expression (GTEx) and BrainSpan projects to look for co-expression modules that harbor CNV-lncRNAs. We identified one male reproduction-related module in male individuals and one neuronal functionsrelated module both in male and female individuals. In addition, the neuronal functions module comprised of protein-coding genes that were involved in several ion channel activities including calcium and potassium channel activities, which were known to be related to schizophrenia. Pathway analysis of these two modules further suggested that CNV-lncRNAs were involved in the olfactory transduction, neuroactive ligand-receptor and calcium signaling pathways. The CNV-lncRNA co-expression patterns were preserved through the different development and aging time points, and across different tissue types of human body. Our results suggested that lncRNAs inside those rare CNVs might play significant temporal and spatial roles in regulating other protein-coding genes and subsequently contribute to schizophrenia risk.
Disclosure Nothing to disclose. http://dx.doi.org/10.1016/j.euroneuro.2016.09.467