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Please use this identifier to cite or link to this item: http://krishi.icar.gov.in/jspui/handle/123456789/68630
Title: Statistical Approach for Gene Set Analysis with Quantitative Trait Loci for Crop Gene Expression Data.
Other Titles: Not Available
Authors: Samarendra Das
S.N. Rai
ICAR Data Use Licennce: http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf
Author's Affiliated institute: ICAR::Indian Agricultural Statistics Research Institute
University of Louisville, USA
Published/ Complete Date: 2021-05-10
Project Code: Not Available
Keywords: gene set
RNA-seq
gene expression
gene set analysis
quantitative trait loci
false discovery rate
Publisher: MDPI
Citation: Das, S. and Rai, S.N. (2021). Statistical Approach for Gene Set Analysis with Quantitative Trait Loci for Crop Gene Expression Data. Entropy (Statistical Inference from High Dimensional Data II), 23(8), 945; doi.org/10.3390/e23080945
Series/Report no.: Not Available;
Abstract/Description: Genome-wide expression study is a powerful genomic technology to quantify expression dynamics of genes in a genome. In gene expression study, gene set analysis has become the first choice to gain insights into the underlying biology of diseases or stresses in plants. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results from the primary downstream differential expression analysis. The gene set analysis approaches are well developed in microarrays and RNA-seq gene expression data analysis. These approaches mainly focus on analyzing the gene sets with gene ontology or pathway annotation data. However, in plant biology, such methods may not establish any formal relationship between the genotypes and the phenotypes, as most of the traits are quantitative and controlled by polygenes. The existing Quantitative Trait Loci (QTL)-based gene set analysis approaches only focus on the over-representation analysis of the selected genes while ignoring their associated gene scores. Therefore, we developed an innovative statistical approach, GSQSeq, to analyze the gene sets with trait enriched QTL data. This approach considers the associated differential expression scores of genes while analyzing the gene sets. The performance of the developed method was tested on five different crop gene expression datasets obtained from real crop gene expression studies. Our analytical results indicated that the trait-specific analysis of gene sets was more robust and successful through the proposed approach than existing techniques. Further, the developed method provides a valuable platform for integrating the gene expression data with QTL data.
Description: Not Available
ISSN: Not Available
Type(s) of content: Research Paper
Sponsors: This study was fully supported by Netaji Subhas-ICAR International Fellowship, OM No. 18(02)/2016-EQR/Edn. (S.D.) of the Indian Council of Agricultural Research (ICAR), New Delhi, India. It was supported in part by Wendell Cherry Chair in Clinical Trial Research Fund (S.N.R.), multiple National Institutes of Health (NIH), USA grants (S.N.R.) (5P20GM113226, PI: McClain; 1P42ES023716, PI: Srivastava; 5P30GM127607-02, PI: Jones; 1P20GM125504-01, PI: Lamont; 2U54HL120163, PI: Bhatnagar/Robertson; 1P20GM135004, PI: Yan; 1R35ES0238373-01, PI: Cave; 1R01ES029846, PI: Bhatnagar; 1R01ES027778-01A1, PI: States;), and the Kentucky Council on Postsecondary Education grant (PON2 415 1900002934, PI: Chesney).
Language: English
Name of Journal: Entropy
Journal Type: research paper
NAAS Rating: 8.524
Impact Factor: 2.524
Volume No.: 23(8)
Page Number: 945
Name of the Division/Regional Station: Not Available
Source, DOI or any other URL: doi.org/10.3390/e23080945
URI: http://krishi.icar.gov.in/jspui/handle/123456789/68630
Appears in Collections:AEdu-IASRI-Publication

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