Una herramienta de estudio de asociación de vías para los análisis GWAS de la información de las vías metabólicas

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.