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    Author(s): Adam Thrash; Juliet D. Tang; Mason DeOrnellis; Daniel G. Peterson; Marilyn L. Warburton
    Date: 2020
    Source: Plants
    Publication Series: Scientific Journal (JRNL)
    Station: Forest Products Laboratory
    PDF: Download Publication  (1.0 MB)

    Description

    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 fnd signifcant 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-specifed options for maximum customization. Thus, to promote a powerful new pathway analysis methodology that interprets GWAS data to fnd 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.

    Publication Notes

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    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

    Citation

    Thrash, Adam; Tang, Juliet D.; DeOrnellis, Mason; Peterson, Daniel G.; Warburton, Marilyn L. 2020. PAST: the pathway association studies tool to infer biological meaning from GWAS datasets. Plants. 9(1): 58. https://doi.org/10.3390/plants9010058.

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    Keywords

    metabolic pathway analysis, genome-wide association study (GWAS), maize (Zea mays L.)

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