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Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci

The analysis of gene sets is usually carried out based on gene ontology terms and known biological pathways. These approaches may not establish any formal relation between genotype and trait specific phenotype. In plant biology and breeding, analysis of gene sets with trait specific Quantitative Tra...

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Autores principales: Das, Samarendra, Rai, Anil, Mishra, D. C., Rai, Shesh N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5799309/
https://www.ncbi.nlm.nih.gov/pubmed/29402907
http://dx.doi.org/10.1038/s41598-018-19736-w
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author Das, Samarendra
Rai, Anil
Mishra, D. C.
Rai, Shesh N.
author_facet Das, Samarendra
Rai, Anil
Mishra, D. C.
Rai, Shesh N.
author_sort Das, Samarendra
collection PubMed
description The analysis of gene sets is usually carried out based on gene ontology terms and known biological pathways. These approaches may not establish any formal relation between genotype and trait specific phenotype. In plant biology and breeding, analysis of gene sets with trait specific Quantitative Trait Loci (QTL) data are considered as great source for biological knowledge discovery. Therefore, we proposed an innovative statistical approach called Gene Set Analysis with QTLs (GSAQ) for interpreting gene expression data in context of gene sets with traits. The utility of GSAQ was studied on five different complex abiotic and biotic stress scenarios in rice, which yields specific trait/stress enriched gene sets. Further, the GSAQ approach was more innovative and effective in performing gene set analysis with underlying QTLs and identifying QTL candidate genes than the existing approach. The GSAQ approach also provided two potential biological relevant criteria for performance analysis of gene selection methods. Based on this proposed approach, an R package, i.e., GSAQ (https://cran.r-project.org/web/packages/GSAQ) has been developed. The GSAQ approach provides a valuable platform for integrating the gene expression data with genetically rich QTL data.
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spelling pubmed-57993092018-02-14 Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci Das, Samarendra Rai, Anil Mishra, D. C. Rai, Shesh N. Sci Rep Article The analysis of gene sets is usually carried out based on gene ontology terms and known biological pathways. These approaches may not establish any formal relation between genotype and trait specific phenotype. In plant biology and breeding, analysis of gene sets with trait specific Quantitative Trait Loci (QTL) data are considered as great source for biological knowledge discovery. Therefore, we proposed an innovative statistical approach called Gene Set Analysis with QTLs (GSAQ) for interpreting gene expression data in context of gene sets with traits. The utility of GSAQ was studied on five different complex abiotic and biotic stress scenarios in rice, which yields specific trait/stress enriched gene sets. Further, the GSAQ approach was more innovative and effective in performing gene set analysis with underlying QTLs and identifying QTL candidate genes than the existing approach. The GSAQ approach also provided two potential biological relevant criteria for performance analysis of gene selection methods. Based on this proposed approach, an R package, i.e., GSAQ (https://cran.r-project.org/web/packages/GSAQ) has been developed. The GSAQ approach provides a valuable platform for integrating the gene expression data with genetically rich QTL data. Nature Publishing Group UK 2018-02-05 /pmc/articles/PMC5799309/ /pubmed/29402907 http://dx.doi.org/10.1038/s41598-018-19736-w Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Das, Samarendra
Rai, Anil
Mishra, D. C.
Rai, Shesh N.
Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci
title Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci
title_full Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci
title_fullStr Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci
title_full_unstemmed Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci
title_short Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci
title_sort statistical approach for gene set analysis with trait specific quantitative trait loci
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5799309/
https://www.ncbi.nlm.nih.gov/pubmed/29402907
http://dx.doi.org/10.1038/s41598-018-19736-w
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