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Association Study of 167 Candidate Genes for Schizophrenia Selected by a Multi-Domain Evidence-Based Prioritization Algorithm and Neurodevelopmental Hypothesis

Integrating evidence from multiple domains is useful in prioritizing disease candidate genes for subsequent testing. We ranked all known human genes (n = 3819) under linkage peaks in the Irish Study of High-Density Schizophrenia Families using three different evidence domains: 1) a meta-analysis of...

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Autores principales: Zhao, Zhongming, Webb, Bradley T., Jia, Peilin, Bigdeli, T. Bernard, Maher, Brion S., van den Oord, Edwin, Bergen, Sarah E., Amdur, Richard L., O'Neill, Francis A., Walsh, Dermot, Thiselton, Dawn L., Chen, Xiangning, Pato, Carlos N., Riley, Brien P., Kendler, Kenneth S., Fanous, Ayman H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3726675/
https://www.ncbi.nlm.nih.gov/pubmed/23922650
http://dx.doi.org/10.1371/journal.pone.0067776
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author Zhao, Zhongming
Webb, Bradley T.
Jia, Peilin
Bigdeli, T. Bernard
Maher, Brion S.
van den Oord, Edwin
Bergen, Sarah E.
Amdur, Richard L.
O'Neill, Francis A.
Walsh, Dermot
Thiselton, Dawn L.
Chen, Xiangning
Pato, Carlos N.
Riley, Brien P.
Kendler, Kenneth S.
Fanous, Ayman H.
author_facet Zhao, Zhongming
Webb, Bradley T.
Jia, Peilin
Bigdeli, T. Bernard
Maher, Brion S.
van den Oord, Edwin
Bergen, Sarah E.
Amdur, Richard L.
O'Neill, Francis A.
Walsh, Dermot
Thiselton, Dawn L.
Chen, Xiangning
Pato, Carlos N.
Riley, Brien P.
Kendler, Kenneth S.
Fanous, Ayman H.
author_sort Zhao, Zhongming
collection PubMed
description Integrating evidence from multiple domains is useful in prioritizing disease candidate genes for subsequent testing. We ranked all known human genes (n = 3819) under linkage peaks in the Irish Study of High-Density Schizophrenia Families using three different evidence domains: 1) a meta-analysis of microarray gene expression results using the Stanley Brain collection, 2) a schizophrenia protein-protein interaction network, and 3) a systematic literature search. Each gene was assigned a domain-specific p-value and ranked after evaluating the evidence within each domain. For comparison to this ranking process, a large-scale candidate gene hypothesis was also tested by including genes with Gene Ontology terms related to neurodevelopment. Subsequently, genotypes of 3725 SNPs in 167 genes from a custom Illumina iSelect array were used to evaluate the top ranked vs. hypothesis selected genes. Seventy-three genes were both highly ranked and involved in neurodevelopment (category 1) while 42 and 52 genes were exclusive to neurodevelopment (category 2) or highly ranked (category 3), respectively. The most significant associations were observed in genes PRKG1, PRKCE, and CNTN4 but no individual SNPs were significant after correction for multiple testing. Comparison of the approaches showed an excess of significant tests using the hypothesis-driven neurodevelopment category. Random selection of similar sized genes from two independent genome-wide association studies (GWAS) of schizophrenia showed the excess was unlikely by chance. In a further meta-analysis of three GWAS datasets, four candidate SNPs reached nominal significance. Although gene ranking using integrated sources of prior information did not enrich for significant results in the current experiment, gene selection using an a priori hypothesis (neurodevelopment) was superior to random selection. As such, further development of gene ranking strategies using more carefully selected sources of information is warranted.
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spelling pubmed-37266752013-08-06 Association Study of 167 Candidate Genes for Schizophrenia Selected by a Multi-Domain Evidence-Based Prioritization Algorithm and Neurodevelopmental Hypothesis Zhao, Zhongming Webb, Bradley T. Jia, Peilin Bigdeli, T. Bernard Maher, Brion S. van den Oord, Edwin Bergen, Sarah E. Amdur, Richard L. O'Neill, Francis A. Walsh, Dermot Thiselton, Dawn L. Chen, Xiangning Pato, Carlos N. Riley, Brien P. Kendler, Kenneth S. Fanous, Ayman H. PLoS One Research Article Integrating evidence from multiple domains is useful in prioritizing disease candidate genes for subsequent testing. We ranked all known human genes (n = 3819) under linkage peaks in the Irish Study of High-Density Schizophrenia Families using three different evidence domains: 1) a meta-analysis of microarray gene expression results using the Stanley Brain collection, 2) a schizophrenia protein-protein interaction network, and 3) a systematic literature search. Each gene was assigned a domain-specific p-value and ranked after evaluating the evidence within each domain. For comparison to this ranking process, a large-scale candidate gene hypothesis was also tested by including genes with Gene Ontology terms related to neurodevelopment. Subsequently, genotypes of 3725 SNPs in 167 genes from a custom Illumina iSelect array were used to evaluate the top ranked vs. hypothesis selected genes. Seventy-three genes were both highly ranked and involved in neurodevelopment (category 1) while 42 and 52 genes were exclusive to neurodevelopment (category 2) or highly ranked (category 3), respectively. The most significant associations were observed in genes PRKG1, PRKCE, and CNTN4 but no individual SNPs were significant after correction for multiple testing. Comparison of the approaches showed an excess of significant tests using the hypothesis-driven neurodevelopment category. Random selection of similar sized genes from two independent genome-wide association studies (GWAS) of schizophrenia showed the excess was unlikely by chance. In a further meta-analysis of three GWAS datasets, four candidate SNPs reached nominal significance. Although gene ranking using integrated sources of prior information did not enrich for significant results in the current experiment, gene selection using an a priori hypothesis (neurodevelopment) was superior to random selection. As such, further development of gene ranking strategies using more carefully selected sources of information is warranted. Public Library of Science 2013-07-29 /pmc/articles/PMC3726675/ /pubmed/23922650 http://dx.doi.org/10.1371/journal.pone.0067776 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
spellingShingle Research Article
Zhao, Zhongming
Webb, Bradley T.
Jia, Peilin
Bigdeli, T. Bernard
Maher, Brion S.
van den Oord, Edwin
Bergen, Sarah E.
Amdur, Richard L.
O'Neill, Francis A.
Walsh, Dermot
Thiselton, Dawn L.
Chen, Xiangning
Pato, Carlos N.
Riley, Brien P.
Kendler, Kenneth S.
Fanous, Ayman H.
Association Study of 167 Candidate Genes for Schizophrenia Selected by a Multi-Domain Evidence-Based Prioritization Algorithm and Neurodevelopmental Hypothesis
title Association Study of 167 Candidate Genes for Schizophrenia Selected by a Multi-Domain Evidence-Based Prioritization Algorithm and Neurodevelopmental Hypothesis
title_full Association Study of 167 Candidate Genes for Schizophrenia Selected by a Multi-Domain Evidence-Based Prioritization Algorithm and Neurodevelopmental Hypothesis
title_fullStr Association Study of 167 Candidate Genes for Schizophrenia Selected by a Multi-Domain Evidence-Based Prioritization Algorithm and Neurodevelopmental Hypothesis
title_full_unstemmed Association Study of 167 Candidate Genes for Schizophrenia Selected by a Multi-Domain Evidence-Based Prioritization Algorithm and Neurodevelopmental Hypothesis
title_short Association Study of 167 Candidate Genes for Schizophrenia Selected by a Multi-Domain Evidence-Based Prioritization Algorithm and Neurodevelopmental Hypothesis
title_sort association study of 167 candidate genes for schizophrenia selected by a multi-domain evidence-based prioritization algorithm and neurodevelopmental hypothesis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3726675/
https://www.ncbi.nlm.nih.gov/pubmed/23922650
http://dx.doi.org/10.1371/journal.pone.0067776
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