Location: Corn Host Plant Resistance ResearchTitle: PAST: the Pathway Association Study Tool for interpreting GWAS data in light of metabolic pathway data
Submitted to: Maize Genetics Conference Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 6/22/2020
Publication Date: 6/25/2020
Citation: Warburton, M.L., Thrash, A., Andorf, C.M. 2020. PAST: the Pathway Association Study Tool for interpreting GWAS data in light of metabolic pathway data. 62nd Maize Genetics Conference Abstracts. P222.
Interpretive Summary: The new computer analysis tool, PAST (the Pathway Association Study Tool) is now available online at MaizeGDB, Bioconductor, and Github. It is user friendly and offers a new interpretation of GWAS data. GWAS has become a very popular analysis to map the genes that influence traits of importance in plants, animals, and humans, but often provides results that are not straight-forward to interpret. PAST allows a better understanding of the biological mechanisms that the organism uses during development to express these traits. The output from PAST is easy to understand, useful, and relevant to breeding programs that aim to improve the trait. This poster shows where to find PAST and its user manual, and provides some examples where PAST was used in maize for interpreting GWAS data sets.
Technical Abstract: We present a recently developed bioinformatics method for interpreting genome-wide association study (GWAS) data using metabolic pathway analysis. PAST (Pathway Association Study Tool) has been used in several studies to find significant pathways and mechanisms explaining phenotypic traits of interest in plants. 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. An online, maize specific version of PAST has been (will be?) added to MaizeGDB. In testing, PAST completed analyses in up to one hour by processing data in parallel. PAST has many user-specified options for maximum customization. It has been used to analyze data from multiple independent maize GWAS panels on grain color, oil and fatty acid grain concentrations, corn earworm resistance, and aflatoxin accumulation resistance, with results that make clear and biological sense. PAST has also been used with GWAS data from other plant species. The user-friendliness and unequivocal results make PAST accessible and useful to researchers interested in associating metabolic pathways with GWAS datasets to better understand the genetic architecture and mechanisms affecting phenotypes. Links to Bioconductor, Github, and MaizeGDB, where PAST and the user manual can be accessed, are presented in this poster.