A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
July 1st, 2020
By running the Pathway Association Study Tool (PAST), either through the Shiny application or through the R console, researchers can gain a deeper understanding of the biological meaning of their genome-wide association study (GWAS) results by investigating the metabolic pathways involved.
Genome-wide association analysis is a powerful tool for discovering genes associated with a trait, but misses many of them. PAST finds more of these genes and better completes the picture. PAST takes GWAS output and adds information about pathways that cells use to build metabolites and to create phenotypes in an organism.
Genes in these pathways are often missed by GWAS alone. After verifying that the GWAS data is tab-delimited, use the association file box to select an association file and the effects file selection box to select an effects file. To load GWAS data with PAST in the R console, modify and run the code as indicated.
To load linkage disequilibrium data with PAST Shiny, after verifying that the linkage disequilibrium data is tab-delimited and contains the appropriate types of data, select the file containing the linkage disequilibrium data. To load linkage disequilibrium data with PAST in the R console, modify and run the code as indicated. To assign SNPs to genes with PAST Shiny, locating annotations in general feature format, select the file containing the general feature format annotations and modify the settings according to the window size and R squared cutoff that are most suitable for the species being considered as necessary.
To assign SNPs to genes with PAST in the R console, modify and run the code as indicated. After verifying that the pathways file contains the appropriate data in tab-delimited format, select the file containing the pathways data and confirm that the mode is selected in the analysis options. If necessary, change the number of genes that must be in a pathway to retain the pathway for the analysis and change the number of permutations used to create the null distribution to test the significance of the effect.
To discover significant pathways with PAST in the R console, modify and run the code as indicated. To view rug plots with PAST Shiny, once all the inputs have been uploaded and set, click begin analysis. A progress bar will appear indicating which step of the analysis was last completed.
When the analysis is complete, PAST Shiny will switch to the results tab and a table of results and the rug plots will be displayed. Use the slider to control the filtering parameters. When the filtering level is satisfactory, click the download results button to individually download all of the images and tables to a zip file named with the analysis title.
To view the rug plots with PAST in the R console, modify and run the code to save the results. Here, a successful run using the sample data from the PAST package, which are based on a maze GWAS of grain color is shown. And here an example of an image that can be downloaded by clicking the download results button is shown.
This rug plot was produced from the pathway analysis of GWAS results created with the maze panel of 288 inbred lines that had been phenotype for grain color. As expected, the trans-lycopene biosynthesis pathway, which produced carotenoids was significantly associated with grain color. The genes and the trans-lycopene biosynthesis pathway are also marked in the gene rank of their effect as compared to all of the other genes in the analysis.
The apex of the running enrichment score line for the entire pathway is indicated by the dotted line. It is used by the program to determine if the pathway is chosen and presented as a rug plot. PAST around on all data, but only produces good results for high quality data.
Sequences must contain few errors and phenotypic data should be collected under vigorous statistical design. With PAST results, genes controlling important points and significant pathways can be identified and eventually manipulated via selection, gene editing, and drug therapy to improve a trait of interest.
Overview
The Pathway Association Study Tool (PAST) enhances genome-wide association study (GWAS) results by integrating metabolic pathway information. This tool allows researchers to identify genes associated with traits that may be overlooked by GWAS alone.
Key Study Components
Area of Science
- Genomics
- Metabolomics
- Bioinformatics
Background
- GWAS is effective for identifying trait-associated genes but often misses many.
- PAST complements GWAS by providing insights into metabolic pathways.
- Understanding these pathways can reveal additional genetic associations.
- High-quality data is crucial for reliable results.
Purpose of Study
- To improve the identification of genes linked to traits through pathway analysis.
- To utilize GWAS output for deeper biological insights.
- To facilitate the discovery of significant pathways related to traits.
Methods Used
- Data verification for tab-delimited formats.
- File selection for association and effects data.
- Modification and execution of R code for data loading.
- Analysis of linkage disequilibrium and pathway data.
Main Results
- Identification of significant pathways associated with traits.
- Visualization of results through rug plots in PAST Shiny.
- Successful application demonstrated with grain color GWAS data.
- Trans-lycopene biosynthesis pathway significantly linked to grain color.
Conclusions
- PAST effectively identifies additional genes and pathways missed by GWAS.
- Results can inform genetic manipulation strategies for trait improvement.
- High-quality data enhances the reliability of pathway analysis.
Frequently Asked Questions
Executive Industry Relevance
Pathway Association Study Tool (PAST) addresses a key limitation in genome-wide association studies by integrating metabolic pathway data to uncover additional trait-associated genes missed by standard GWAS. This enhances target validation confidence in early discovery by providing mechanistic context for genetic associations, supporting more informed go/no-go decisions in lead identification. The tool’s improved speed and user-friendliness enable scalable application across discovery workflows, reducing time-to-insight for pathway-based hypothesis generation.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Identifies genes within biologically relevant pathways that are overlooked by GWAS alone, strengthening therapeutic hypothesis interrogation.
- Operational Value: Enables rapid pathway-based analysis of GWAS outputs, accelerating target prioritization and de-risking.
- Predictive Value: Supports portfolio triage by highlighting pathways with strong phenotypic associations, improving confidence in target selection.
Screening & Assay Development
- Scientific Value: Prepares biologically contextualized gene sets for downstream screening, improving relevance of compound evaluation.
- Operational Value: Standardizes pathway analysis from GWAS data, ensuring reproducible inputs for assay development pipelines.
- Scalability: Reduced runtime from 24+ hours to under one hour enables high-throughput application across multiple traits or datasets.
Translational & Preclinical Research
- Translational Continuity: Links genetic findings to metabolic pathways, supporting biomarker alignment and mechanistic understanding of trait architecture.
- Preclinical Relevance: Identifies druggable pathways (e.g., trans-lycopene biosynthesis) that can be modulated via gene editing or pharmacological intervention.
- Risk Mitigation: Provides orthogonal validation of GWAS hits, reducing false positives and increasing confidence in preclinical advancement decisions.
Pipeline & Workflow Integration
PAST fits within the discovery continuum by enhancing GWAS-derived gene lists with pathway context, supporting progression from initial hit identification to lead optimization through mechanistic de-risking.
- Discovery Biology: Tests hypotheses about pathway-level trait associations, clarifying biological mechanisms behind GWAS signals.
- Screening: Generates pathway-enriched gene lists that improve the biological relevance of compound screening campaigns.
- Analytics: Produces quantitative pathway enrichment scores and significance metrics (via permutation testing) to compare and rank biological processes.
- Translational Research: Connects genetic variants to functional pathways, enabling biomarker-driven stratification in preclinical models.
- Enterprise Reuse: As a Bioconductor and GitHub-released tool, PAST supports standardized, reusable pathway analysis across projects and teams.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence by revealing mechanistic pathways underlying phenotypic traits.
- Operational Value: Delivers rapid, user-friendly analysis via Shiny or R console, improving accessibility for non-specialists.
- Strategic Value: Enhances decision-making in target validation by reducing biological ambiguity and improving target confidence.
- Portfolio Impact: Enables risk-adjusted prioritization of targets based on pathway-level evidence, supporting smarter resource allocation.
Implementation Considerations
- Requires expertise in GWAS data handling and pathway analysis interpretation.
- Depends on high-quality, tab-delimited input files (association, effects, linkage disequilibrium, and pathway data).
- Needs standardization of gene annotation files (GFF) and parameters like window size and R² cutoff for SNP-to-gene mapping.
- Relies on user-defined thresholds for pathway size (minimum genes) and permutation count for null distribution generation.
- Optimal performance contingent on rigorous phenotypic study design and low-error sequencing data.
Biopharma FAQs
Chapters in this video
0:05
Introduction
0:32
Genome-Wide Association Study (GWAS) Data Loading
0:57
Load Linkage Disequilibrium (LD) Data Loading
1:26
Single Nucleotide Polymorphism (SNP) Assignment
1:56
Significant Pathway Discovery
2:35
Rugplot Visualization
3:21
Results: Representative GWAS Analyses of Metabolic Pathway Information
4:21
Conclusion
