PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS Datasets
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 impl...
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doaj-5f5568ef716f4db9be70977a628234462020-11-25T01:35:18ZengMDPI AGPlants2223-77472020-01-01915810.3390/plants9010058plants9010058PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS DatasetsAdam Thrash0Juliet D. Tang1Mason DeOrnellis2Daniel G. Peterson3Marilyn L. Warburton4Institute for Genomics, Biocomputing & Biotechnology, Mississippi State University, Mississippi State, MS 39762, USAUSDA-FS Forest Products Laboratory, Starkville, MS 39759, USAHumanities and Fine Arts Division, East Mississippi Community College, Mayhew, MS 39752, USAInstitute for Genomics, Biocomputing & Biotechnology, Mississippi State University, Mississippi State, MS 39762, USAUSDA-ARS Corn Host Plant Resistance Research Unit, Mississippi State, MS 39762, USAIn 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.https://www.mdpi.com/2223-7747/9/1/58metabolic pathway analysisgenome-wide association study (gwas)maize (<i>zea mays</i> l.) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Adam Thrash Juliet D. Tang Mason DeOrnellis Daniel G. Peterson Marilyn L. Warburton |
spellingShingle |
Adam Thrash Juliet D. Tang Mason DeOrnellis Daniel G. Peterson Marilyn L. Warburton PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS Datasets Plants metabolic pathway analysis genome-wide association study (gwas) maize (<i>zea mays</i> l.) |
author_facet |
Adam Thrash Juliet D. Tang Mason DeOrnellis Daniel G. Peterson Marilyn L. Warburton |
author_sort |
Adam Thrash |
title |
PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS Datasets |
title_short |
PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS Datasets |
title_full |
PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS Datasets |
title_fullStr |
PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS Datasets |
title_full_unstemmed |
PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS Datasets |
title_sort |
past: the pathway association studies tool to infer biological meaning from gwas datasets |
publisher |
MDPI AG |
series |
Plants |
issn |
2223-7747 |
publishDate |
2020-01-01 |
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 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. |
topic |
metabolic pathway analysis genome-wide association study (gwas) maize (<i>zea mays</i> l.) |
url |
https://www.mdpi.com/2223-7747/9/1/58 |
work_keys_str_mv |
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