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A systems-genetics approach and data mining tool to assist in the discovery of genes underlying complex traits in Oryza sativa.

Ficklin SP, Feltus FA - PLoS ONE (2013)

Bottom Line: GeneNet Engine does not provide the exact set of genes underlying a given complex trait, but through the evidence of gene-marker correspondence, co-expression, and functional enrichment, site visitors can identify genes with potential shared causality for a trait which could then be used for experimental validation.A set of 2 million SNPs was incorporated into the database and serve as a potential set of testable biomarkers for genes in modules that overlap with genetic traits.Herein, we describe two modules found using GeneNet Engine, one with significant overlap with the trait amylose content and another with significant overlap with blast disease resistance.

View Article: PubMed Central - PubMed

Affiliation: Plant and Environmental Sciences, Clemson University, Clemson, South Carolina, United States of America.

ABSTRACT
Many traits of biological and agronomic significance in plants are controlled in a complex manner where multiple genes and environmental signals affect the expression of the phenotype. In Oryza sativa (rice), thousands of quantitative genetic signals have been mapped to the rice genome. In parallel, thousands of gene expression profiles have been generated across many experimental conditions. Through the discovery of networks with real gene co-expression relationships, it is possible to identify co-localized genetic and gene expression signals that implicate complex genotype-phenotype relationships. In this work, we used a knowledge-independent, systems genetics approach, to discover a high-quality set of co-expression networks, termed Gene Interaction Layers (GILs). Twenty-two GILs were constructed from 1,306 Affymetrix microarray rice expression profiles that were pre-clustered to allow for improved capture of gene co-expression relationships. Functional genomic and genetic data, including over 8,000 QTLs and 766 phenotype-tagged SNPs (p-value < = 0.001) from genome-wide association studies, both covering over 230 different rice traits were integrated with the GILs. An online systems genetics data-mining resource, the GeneNet Engine, was constructed to enable dynamic discovery of gene sets (i.e. network modules) that overlap with genetic traits. GeneNet Engine does not provide the exact set of genes underlying a given complex trait, but through the evidence of gene-marker correspondence, co-expression, and functional enrichment, site visitors can identify genes with potential shared causality for a trait which could then be used for experimental validation. A set of 2 million SNPs was incorporated into the database and serve as a potential set of testable biomarkers for genes in modules that overlap with genetic traits. Herein, we describe two modules found using GeneNet Engine, one with significant overlap with the trait amylose content and another with significant overlap with blast disease resistance.

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Jaccard vs Kappa Scatterplot.Jaccard (similarity of node composition) and Kappa (similarity of functional annotation) statistics were performed, pair-wise, for all modules across all GILs. A) The scatterplot of Jaccard coefficient vs Kappa κ for all modules with 30 or more nodes. B) Residual plot of Jaccard coefficient vs Kappa κ.
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pone-0068551-g002: Jaccard vs Kappa Scatterplot.Jaccard (similarity of node composition) and Kappa (similarity of functional annotation) statistics were performed, pair-wise, for all modules across all GILs. A) The scatterplot of Jaccard coefficient vs Kappa κ for all modules with 30 or more nodes. B) Residual plot of Jaccard coefficient vs Kappa κ.

Mentions: To obtain a measure of similarity between modules across all GILs, a correlation between Kappa scores (measuring functional similarity between two modules) and Jaccard indices (measuring similarity of node composition) was performed. First, functional enrichment analysis of the modules was performed using terms from the Gene Ontology (GO; [42]), InterPro [43] and KEGG [44]. Only terms enriched within a module with a Fisher’s p-value of 0.01 or less were considered enriched. Next, full pair-wise comparisons between modules with 30 or more nodes from all GILs were performed using both Kappa statistics and a Jaccard similarity test. Only enriched functional terms were used with the Kappa test. Kappa scores range from −1 to 1 with values less than 0 indicating no significant similarity of function and a score of 1 indicating identical similarity of function. A Jaccard index ranges from 0 (indicating no nodes in common) to 1 (all nodes in common). Figure 2 shows a scatterplot of Jaccard similarity coefficients versus Kappa scores with R2 = 0.5 (p-value <2.2e-16) indicating a good degree of correlation between the node composition of modules and the enriched function of modules.


A systems-genetics approach and data mining tool to assist in the discovery of genes underlying complex traits in Oryza sativa.

Ficklin SP, Feltus FA - PLoS ONE (2013)

Jaccard vs Kappa Scatterplot.Jaccard (similarity of node composition) and Kappa (similarity of functional annotation) statistics were performed, pair-wise, for all modules across all GILs. A) The scatterplot of Jaccard coefficient vs Kappa κ for all modules with 30 or more nodes. B) Residual plot of Jaccard coefficient vs Kappa κ.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3713027&req=5

pone-0068551-g002: Jaccard vs Kappa Scatterplot.Jaccard (similarity of node composition) and Kappa (similarity of functional annotation) statistics were performed, pair-wise, for all modules across all GILs. A) The scatterplot of Jaccard coefficient vs Kappa κ for all modules with 30 or more nodes. B) Residual plot of Jaccard coefficient vs Kappa κ.
Mentions: To obtain a measure of similarity between modules across all GILs, a correlation between Kappa scores (measuring functional similarity between two modules) and Jaccard indices (measuring similarity of node composition) was performed. First, functional enrichment analysis of the modules was performed using terms from the Gene Ontology (GO; [42]), InterPro [43] and KEGG [44]. Only terms enriched within a module with a Fisher’s p-value of 0.01 or less were considered enriched. Next, full pair-wise comparisons between modules with 30 or more nodes from all GILs were performed using both Kappa statistics and a Jaccard similarity test. Only enriched functional terms were used with the Kappa test. Kappa scores range from −1 to 1 with values less than 0 indicating no significant similarity of function and a score of 1 indicating identical similarity of function. A Jaccard index ranges from 0 (indicating no nodes in common) to 1 (all nodes in common). Figure 2 shows a scatterplot of Jaccard similarity coefficients versus Kappa scores with R2 = 0.5 (p-value <2.2e-16) indicating a good degree of correlation between the node composition of modules and the enriched function of modules.

Bottom Line: GeneNet Engine does not provide the exact set of genes underlying a given complex trait, but through the evidence of gene-marker correspondence, co-expression, and functional enrichment, site visitors can identify genes with potential shared causality for a trait which could then be used for experimental validation.A set of 2 million SNPs was incorporated into the database and serve as a potential set of testable biomarkers for genes in modules that overlap with genetic traits.Herein, we describe two modules found using GeneNet Engine, one with significant overlap with the trait amylose content and another with significant overlap with blast disease resistance.

View Article: PubMed Central - PubMed

Affiliation: Plant and Environmental Sciences, Clemson University, Clemson, South Carolina, United States of America.

ABSTRACT
Many traits of biological and agronomic significance in plants are controlled in a complex manner where multiple genes and environmental signals affect the expression of the phenotype. In Oryza sativa (rice), thousands of quantitative genetic signals have been mapped to the rice genome. In parallel, thousands of gene expression profiles have been generated across many experimental conditions. Through the discovery of networks with real gene co-expression relationships, it is possible to identify co-localized genetic and gene expression signals that implicate complex genotype-phenotype relationships. In this work, we used a knowledge-independent, systems genetics approach, to discover a high-quality set of co-expression networks, termed Gene Interaction Layers (GILs). Twenty-two GILs were constructed from 1,306 Affymetrix microarray rice expression profiles that were pre-clustered to allow for improved capture of gene co-expression relationships. Functional genomic and genetic data, including over 8,000 QTLs and 766 phenotype-tagged SNPs (p-value < = 0.001) from genome-wide association studies, both covering over 230 different rice traits were integrated with the GILs. An online systems genetics data-mining resource, the GeneNet Engine, was constructed to enable dynamic discovery of gene sets (i.e. network modules) that overlap with genetic traits. GeneNet Engine does not provide the exact set of genes underlying a given complex trait, but through the evidence of gene-marker correspondence, co-expression, and functional enrichment, site visitors can identify genes with potential shared causality for a trait which could then be used for experimental validation. A set of 2 million SNPs was incorporated into the database and serve as a potential set of testable biomarkers for genes in modules that overlap with genetic traits. Herein, we describe two modules found using GeneNet Engine, one with significant overlap with the trait amylose content and another with significant overlap with blast disease resistance.

Show MeSH