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Open AccessArticle

PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS Datasets

1
Institute for Genomics, Biocomputing & Biotechnology, Mississippi State University, Mississippi State, MS 39762, USA
2
USDA-FS Forest Products Laboratory, Starkville, MS 39759, USA
3
Humanities and Fine Arts Division, East Mississippi Community College, Mayhew, MS 39752, USA
4
Department of Computer Science and Engineering, Mississippi State University, Mississippi State, MS 39762, USA
5
USDA-ARS Corn Host Plant Resistance Research Unit, Mississippi State, MS 39762, USA
*
Authors to whom correspondence should be addressed.
Plants 2020, 9(1), 58; https://doi.org/10.3390/plants9010058
Received: 18 December 2019 / Revised: 27 December 2019 / Accepted: 30 December 2019 / Published: 2 January 2020
(This article belongs to the Special Issue Plant Bioinformatics)
In recent years, a bioinformatics method for interpreting genome-wide association study (GWAS) data using metabolic pathway analysis has been developed and successfully used to find significant pathways and mechanisms explaining phenotypic traits of interest in plants. However, the many scripts implementing this method were not straightforward to use, had to be customized for each project, required user supervision, and took more than 24 h to process data. PAST (Pathway Association Study Tool), a new implementation of this method, has been developed to address these concerns. PAST has been implemented as a package for the R language. Two user-interfaces are provided; PAST can be run by loading the package in R and calling its methods, or by using an R Shiny guided user interface. In testing, PAST completed analyses in approximately half an hour to one hour by processing data in parallel and produced the same results as the previously developed method. PAST has many user-specified options for maximum customization. Thus, to promote a powerful new pathway analysis methodology that interprets GWAS data to find biological mechanisms associated with traits of interest, we developed a more accessible, efficient, and user-friendly tool. These attributes make PAST accessible to researchers interested in associating metabolic pathways with GWAS datasets to better understand the genetic architecture and mechanisms affecting phenotypes. View Full-Text
Keywords: metabolic pathway analysis; genome-wide association study (GWAS); maize (Zea mays L.) metabolic pathway analysis; genome-wide association study (GWAS); maize (Zea mays L.)
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Thrash, A.; Tang, J.D.; DeOrnellis, M.; Peterson, D.G.; Warburton, M.L. PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS Datasets. Plants 2020, 9, 58.

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