PAST - pathway association studies tool to infer biological meaning from GWAS datasets

Authors: THRASH, ADAM - Mississippi State University; Tang, Juliet; DEORNELLIS, MASON - Mississippi State University; PETERSON, DANIEL - Mississippi State University; Warburton, Marilyn

Submitted to: Plants
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/30/2019
Publication Date: 1/2/2020
Publication URL: https://handle.nal.usda.gov/10113/6837744
Citation: Thrash, A., Tang, J.D., DeOrnellis, M., Peterson, D.G., Warburton, M.L. 2020. PAST - pathway association studies tool to infer biological meaning from GWAS datasets. Plants. 9:58. https://doi.org/10.3390/plants9010058.
DOI: https://doi.org/10.3390/plants9010058

Interpretive Summary: Interpretive Studies The use of Genomewide Association Studies (GWAS) to identify genes affecting traits of interest in crop plants is widespread, but results are sometimes disappointing. Too many genes of very small effects each are usually identified. This situation is almost as useless as no genes identified, when crop scientists are trying to understand how the plant develops or creates the trait of interest, or geneticists are trying to choose the plants that have the best genes for that trait. In order to bring more clarity to this confusion, we have created a new analysis technique that takes the output of a GWAS study and identifies metabolic pathways that these genes belong to. This higher order analysis identifies a handful of pathways that can be studied in depth to understand how the plant creates the trait, and helps identify which genes are truly important, and can be selected by geneticists. Two methods of running this analysis are presented. One allows the user to work online with an easy-to-use, point-and-click graphical interface, and the other allows the user to download a unified set of R scripts and work from their own computer, changing any of the scripts and parameters they wish.

Technical Abstract: Abstract Background: In recent years, a bioinformatics method for interpreting 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 hours to process data. PAST (Pathway Association Study Tool), a new implementation of this method, has been developed to address these concerns. Results: PAST is 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 one hour by processing data in parallel. PAST has many user-specified options for maximum customization. PAST produces the same results as the previously developed method. Conclusions: In order to promote a powerful new method of pathway analysis that interprets GWAS data to find biological mechanisms associated with traits of interest, we developed a more accessible and user friendly tool. This tool is more efficient and requires less knowledge of programming languages to use than previous methods. Moreover, it produces similar results in significantly less time. These attributes make PAST accessible to researchers interested in associating metabolic pathways with GWAS datasets to better understand the genetic architecture and mechanisms affecting phenotype.