1. Setup
<ol>
	<li>Install R, if it is not already installed. 
	NOTE: PAST is written in R and, therefore, requires that its users have R installed. At the time of this writing, installing PAST directly from Bioconductor requires R4.0.&#160; Older versions of PAST can be&#160;installed from Bioconductor for R3.6, and PAST can be installed from Github for users with&#160;R3.5. R installation instructions can be downloaded from the following link:&#160;https://www.r-project.org/.</li>
	<li>Install the latest version of RStudio Desktop or update RStudio (optional). 
	NOTE: RStudio is a helpful environment for working with the R language. Its installation is recommended, especially for those who choose to run PAST in the command line rather than through the Shiny GUI application. RStudio and its installation instructions can be found at the following link:&#160;<a href="https://rstudio.com/products/rstudio/">https://rstudio.com/products/rstudio/</a>.&#160;</li>
	<li>Install PAST from Bioconductor11 by following the instructions on Bioconductor. 
	NOTE: Installation through Bioconductor should handle the installation of PAST&#39;s dependencies. Additionally, PAST can be installed from Github12, but installing from Github will not install dependencies automatically.</li>
	<li>Install PAST Shiny (optional). Download the file &#34;app.R&#34; from the Releases page of the Github repository:&#160;https://github.com/IGBB/PAST/releases/, and remember where the downloaded file is located.&#160; 
	NOTE: PAST can be used by calling its methods directly with R, but users who are less familiar with R can run the PAST Shiny application, which provides a guided user interface. PAST Shiny is an R script available in the shiny_app branch of the PAST Github repository. PAST Shiny will attempt to install its dependencies during the first run.</li>
	<li>Begin analysis by starting the application in one of the three ways described below.
	<ol>
		<li>PAST Shiny with RStudio
		<ol>
			<li>Using RStudio, create a new project in the folder where app.R is located. Click File | New Project and select that folder.</li>
			<li>Once a new project has been created, open the app.R file downloaded earlier. RStudio recognizes that app.R is a Shiny app and creates a Run App button on the bar above the displayed source code. Click Run App. RStudio will then launch a window that displays the PAST Shiny application.</li>
		</ol>
		</li>
		<li>PAST Shiny with R Console
		<ol>
			<li>Launch R and run the following code to start the PAST Shiny application: shiny::runApp(&#39;path/to/folder/with/shiny/app.R&#39;. Replace the text in quotes with the folder to which app.R was downloaded, and keep the quotes.</li>
		</ol>
		</li>
		<li>PAST without R Shiny
		<ol>
			<li>Run library(PAST) in an R Console to load PAST.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
2. Customize Shiny analysis (optional)
<ol>
	<li>Change the analysis title from &#34;New Analysis&#34; to something that better reflects the type of analysis being run which helps to keep track of multiple analyses (see Figure 1).</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61268/61268fig1.jpg" /> 
Figure 1. <a href="https://www.jove.com/files/ftp_upload/61268/61268fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>Modify the number of cores and the mode. Set the number of cores to any number between 1 and the total number on the machine but be aware that devoting more resources to PAST may slow down other operations on the machine. Set the mode based on the description in section 6.</li>
</ol>
3. Load GWAS data
NOTE: Verify that the GWAS data is tab delimited. Ensure that the association file contains the following columns: trait, marker name, locus or chromosome, position on the chromosome, p-value, and R2 value for the marker. Ensure that the effects file contains the following columns: trait, marker name, locus or chromosome, position on the chromosome, and effect. The order of these columns is not important, as the user can specify the names of the columns when loading the data. Any additional columns are ignored. TASSEL13 can be used to produce these files.
<ol>
	<li>Load GWAS data with PAST Shiny.
	<ol>
		<li>Select an association file and an effects file by using the Association File and Effects File selection boxes. Change the column names in the Association Column Name and Effects Columns Name input boxes below the file selection boxes to reflect the column names in the data.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61268/61268fig2.jpg" /> 
Figure 2. <a href="https://www.jove.com/files/ftp_upload/61268/61268fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>Load GWAS data with PAST in the R Console.
	<ol>
		<li>Modify and run the following code: 
		gwas_data = load_GWAS_data(&#34;path/to/association_file.tsv&#34;, &#34;path/to/effects_file.tsv&#34;, association_columns = c(&#34;Trait&#34;, &#34;Marker&#34;, &#34;Locus&#34;, &#34;Site&#34;, &#34;p&#34;, &#34;marker_R2&#34;), effects_columns = c(&#34;Trait&#34;, &#34;Marker&#34;, &#34;Locus&#34;, &#34;Site&#34;, &#34;Effect&#34;)</li>
	</ol>
	</li>
	<li>NOTE: Change the paths to the actual location of the GWAS files. The values provided for association_columns and effects_columns are the default values. If the names do not match the default values, specify the column names. Otherwise, these can be omitted.</li>
</ol>
4. Load linkage disequilibrium (LD) data
NOTE: Verify that the linkage disequilibrium (LD) data is tab delimited and contains the following types of data: Locus, Position1, Site1, Position2, Site2, Distance in base pairs between Position1 and Position2, and R2 value.
<ol>
	<li>Load LD data with PAST Shiny.
	<ol>
		<li>Select the file containing LD data. Change the column names in the LD Column Names input boxes below the file selection box to match the column names in the LD data if necessary.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61268/61268fig3.jpg" /> 
Figure 3. <a href="https://www.jove.com/files/ftp_upload/61268/61268fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>Load LD Data with PAST in the R Console.
	<ol>
		<li>Modify and run the following code to load LD data: 
		LD = load_LD(&#34;path/to/LD.tsv&#34;, LD_columns = c(&#34;Locus1&#34;, &#34;Position1&#34;, &#34;Site1&#34;, &#34;Position2&#34;, &#34;Site2&#34;, &#34;Dist_bp&#34;, &#34;R.2&#34;) 
		NOTE: Change the path to the actual location of the LD file. The values provided for LD_columns are the default values. If the names do not match these defaults, specify the correct names of the columns; otherwise, these can be omitted.</li>
	</ol>
	</li>
</ol>
5. Assign SNPs to genes
NOTE: Download or otherwise locate annotations in GFF format. These annotations can often be found in online databases for specific organisms. Be cautious about low quality annotations, as the quality of the annotations data will affect the quality of the pathway analysis. Confirm that the first column of these annotations (the chromosome) matches the format of the locus/chromosome in the association, effects, and LD data. For example, the annotations should not call the first chromosome &#34;chr1&#34; if the GWAS and LD data files call the first chromosome &#34;1&#34;.
<ol>
	<li>Assign SNPs to genes with PAST Shiny. 
	NOTE: More information about determining an appropriate R2 cutoff can be found in Tang et al.6, in the section called &#34;SNP to gene algorithm for the pathway analysis&#34;.
	<ol>
		<li>Select the file containing GFF annotations. Consider what window size and R2 cutoff are most suitable for the species being considered and modify if the defaults do not suit the uploaded data. 
		NOTE: Default values in PAST primarily reflect values appropriate for maize. The number of cores set at the beginning of the PAST Shiny analysis (Step 2.2) is used in this step.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61268/61268fig4.jpg" /> 
Figure 4. <a href="https://www.jove.com/files/ftp_upload/61268/61268fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>Assign SNPs to genes with PAST in the R Console.
	<ol>
		<li>Modify and run the following code to assign SNPs to genes: 
		genes = assign_SNPs_to_genes(gwas_data, LD, &#34;path/to/annotations.gff&#34;, c(&#34;gene&#34;), 1000, 0.8, 2) 
		NOTE: In this sample code, several default suggestions are provided: 1000 is the size of the window around the SNP to search for genes; 0.8 is the cutoff value for R2; 2 is the number of cores used for parallel processing. The path to the annotations should also be changed to the actual location of the annotations file.</li>
	</ol>
	</li>
</ol>
6. Discover significant pathways
NOTE: Verify that the pathways file contains the following data in tab delimited format, with one line for every gene in each pathway: pathway ID - an identifier such as &#34;PWY-6475-1&#34;; pathway description - a lengthier description of what the pathways do such as &#34;trans-lycopene biosynthesis&#34;; gene - a gene in the pathway, which should match the names provided in the annotations. Pathway information can likely be found in online databases for specific organisms, such as MaizeGDB. The second user-specified option is the mode. &#34;Increasing&#34; refers to phenotypes that reflect when an increasing value of the measured trait is desirable, such as yield, while &#34;decreasing&#34; refers to a trait where a decrease in the measured values is beneficial, such as insect damage ratings. The significance of pathways is tested using previously described methods4,6,14.
<ol>
	<li>Discover significant pathways with PAST Shiny.
	<ol>
		<li>Select the file containing pathways data and be sure that the mode is selected in the analysis options. If necessary, change the number of genes that must be in a pathway to retain it for the analysis and the number of permutations used to create the null distribution to test significance of effect.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/61268/61268fig5.jpg" /> 
Figure 5. <a href="https://www.jove.com/files/ftp_upload/61268/61268fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
NOTE: The number of cores and the mode set at the beginning of the PAST Shiny analysis (Step 2.2) is used in this step. The default number of genes is currently set at 5 genes, so pathways with fewer known genes will be removed. The user can lower this value to 4 or 3, to include shorter pathways, but doing so will risk false positive results. Increasing this value can increase the power of the analysis but will remove more pathways from the analysis. Changing the number of permutations used&#160;increases and decreases the power of the test.
<ol start="2">
	<li>Discover significant pathways with PAST in the R Console.
	<ol>
		<li>Modify and run the following code to discover significant pathways: 
		rugplots_data &#60;- find_pathway_significance(genes, &#34;path/to/pathways.tsv&#34;, 5, &#34;increasing&#34;, 1000, 2) 
		&#8203;NOTE: In this sample code, several suggested defaults are provided. 5 is the minimum number of genes that must be in a pathway in order to keep the pathway in the analysis, increasing refers to an increasing amount of the measured trait (it is recommended that the user run both increasing and decreasing, regardless of trait; data interpretation will differ for the two, however), 1000 is the number of times to sample the effects to determine the null distribution, and 2 is the number of cores used for parallel processing. Change the path to the actual location of the pathways file.</li>
	</ol>
	</li>
</ol>
7. View Rugplots
<ol>
	<li>View Rugplots with PAST Shiny.
	<ol>
		<li>Once all inputs are uploaded and set, click Begin Analysis. A progress bar will appear and indicate which step of the analysis was last completed. When the analysis completes, PAST Shiny will switch to the Results tab. A table of results will be displayed in the left column (labeled &#34;pathways&#34;) and the Rugplots will be displayed in the right column (labeled &#34;plots&#34;).</li>
		<li>Use the slider to control the filtering parameters. When the filtering level is satisfactory, click the Download Results button at the bottom left to download all images and tables individually to a ZIP file that is named with the analysis title. This ZIP file contains the filtered table, the unfiltered table, and one image per pathway in the filtered table.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/61268/61268fig6.jpg" /> 
Figure 6. <a href="https://www.jove.com/files/ftp_upload/61268/61268fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/61268/61268fig7.jpg" /> 
Figure 7. <a href="https://www.jove.com/files/ftp_upload/61268/61268fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>View Rugplots with PAST in the R Console
	<ol>
		<li>Modify and run the following code to save the results: 
		plot_pathways(rugplots_data, &#34;pvalue&#34;, 0.02, &#34;increasing&#34;, &#34;output_folder&#34;) 
		NOTE: In this sample code, several suggested defaults are provided. pvalue provides the data that can be used for filtering insignificant pathways after a significance threshold is chosen by the user; 0.02 is the default value used in filtering, and increasing refers to an increasing amount of the measured trait (it is recommended that the user run both increasing and decreasing, regardless of trait; data interpretation will differ for the two, however); output_folder is the folder where the images and tables will be written (this folder must exist prior to running the function). A table of filtered results, the unfiltered results, and individual images for every pathway in the filtered results are written to this folder.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Rafalski, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Association+genetics+in+crop+improvement.">Association genetics in crop improvement.</a> Current Opinion in Plant Biology. 13 (2), 174-180 (2010).</li><li>Yan, J., Warburton, M., Crouch, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Association+Mapping+for+Enhancing+Maize+(Zea+mays+L.)+Genetic+Improvement.">Association Mapping for Enhancing Maize (Zea mays L.) Genetic Improvement.</a> Crop Science. 51 (2), 433-449 (2011).</li><li>Xiao, Y., Liu, H., Wu, L., Warburton, M., Yan, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+Association+Studies+in+Maize:+Praise+and+Stargaze.">Genome-wide Association Studies in Maize: Praise and Stargaze.</a> Molecular Plant. 10 (3), 359-374 (2017).</li><li>Wang, K., Li, M., Bucan, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pathway-Based+Approaches+for+Analysis+of+Genomewide+Association+Studies.">Pathway-Based Approaches for Analysis of Genomewide Association Studies.</a> The American Journal of Human Genetics. 81 (6), 1278-1283 (2007).</li><li>Weng, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SNP-based+pathway+enrichment+analysis+for+genome-wide+association+studies.">SNP-based pathway enrichment analysis for genome-wide association studies.</a> BMC Bioinformatics. 12 (1), 99 (2011).</li><li>Tang, J., Perkins, A., Williams, W., Warburton, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+genome-wide+associations+to+identify+metabolic+pathways+involved+in+maize+aflatoxin+accumulation+resistance.">Using genome-wide associations to identify metabolic pathways involved in maize aflatoxin accumulation resistance.</a> BMC Genomics. 16 (1), 673 (2015).</li><li>Warburton, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-Wide+Association+Mapping+of+Aspergillus+flavus+and+Aflatoxin+Accumulation+Resistance+in+Maize.">Genome-Wide Association Mapping of Aspergillus flavus and Aflatoxin Accumulation Resistance in Maize.</a> Crop Science. 55 (5), 1857-1867 (2015).</li><li>Warburton, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-Wide+Association+and+Metabolic+Pathway+Analysis+of+Corn+Earworm+Resistance+in+Maize.">Genome-Wide Association and Metabolic Pathway Analysis of Corn Earworm Resistance in Maize.</a> The Plant Genome. 11 (1), 170069 (2018).</li><li>Li, H., Thrash, A., Tang, J., He, L., Yan, J., Warburton, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Leveraging+GWAS+data+to+identify+metabolic+pathways+and+networks+involved+in+maize+lipid+biosynthesis.">Leveraging GWAS data to identify metabolic pathways and networks involved in maize lipid biosynthesis.</a> The Plant Journal. 98 (5), 853-863 (2019).</li><li>Thrash, A., Tang, J., DeOrnellis, M., Peterson, D., Warburton, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PAST:+The+Pathway+Association+Studies+Tool+to+Infer+Biological+Meaning+from+GWAS+Datasets.">PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS Datasets.</a> Plants. 9 (1), 58 (2020).</li><li>Adam, T., Mason, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> PAST: Pathway Association Study Tool (PAST). Bioconductor version: Release (3.10). , (2020).</li><li>Thrash, A., DeOrnellis, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> IGBB/PAST. , (2019).</li><li>Bradbury, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TASSEL:+software+for+association+mapping+of+complex+traits+in+diverse+samples.">TASSEL: software for association mapping of complex traits in diverse samples.</a> Bioinformatics. 23 (19), 2633-2635 (2007).</li><li>Subramanian, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+set+enrichment+analysis:+A+knowledge-based+approach+for+interpreting+genome-wide+expression+profiles.">Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles.</a> Proceedings of the National Academy of Sciences U.S.A. 102, 15545-15550 (2005).</li></ol>

The authors have nothing to disclose.

A primary goal of PAST is to bring metabolic pathway analyses of GWAS data to a wider audience, especially for non-human and non-animal organisms. Alternative methods to PAST are often command-line programs that focus on humans or animals. User-friendliness was a primary goal in the development of PAST, both in choosing to develop a Shiny application and in choosing to use R and Bioconductor to release the application. Users do not need to learn how to compile programs in order to use PAST.
As with most types of analysis software, the results of PAST are only as good as the input data; if the input data has errors or is incorrectly formatted, PAST will fail to run or produce uninformative results. Ensuring that the GWAS data, LD data, annotations, and pathways files are correctly formatted is critical to receiving correct output from PAST. PAST only analyzes bi-allelic markers and can run only one trait for each set of input data. In addition, GWAS data produced by poor genotyping or incorrect or imprecise phenotyping is not likely to produce clear or repeatable results either. PAST can aid in the biological interpretation of GWAS results but is unlikely to clarify chaotic data sets if environmental variation, experimental error, or population structure was not properly accounted for.
Users can choose to change some parameters of the analysis, both in the Shiny application and by passing those parameters to PAST&#39;s functions in the R console. These parameters can change the results reported by PAST, and users should take care when modifying these from the defaults. Because LD is measured by the users, typically using the same marker data set that was also used in the GWAS, the LD measurements are specific to the population. For all studies, especially for species other than maize, (particularly self-pollinating, polyploid, or highly heterogenous species), changes in the defaults may be warranted.

An erratum was issued for: A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information. One of the affiliations was updated.
The second affiliation was updated from:
USDA-ARS Corn Host Plant Resistance Research Unit, Mississippi State University
to:
Corn Host Plant Resistance Research Unit, USDA-ARS

An erratum was issued for:&#160;<a href="https://www.jove.com/t/61268">A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information</a>. One of the affiliations was updated.

Erratum: A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Genome-wide association studies (GWAS) are a popular method of studying complex traits and the genomic regions associated with them1,2,3. In this type of study, hundreds of thousands of single nucleotide polymorphism (SNP) markers are tested for their association with the trait, and the significance of the associations is assessed. Marker-trait associations that meet the false discovery rate (FDR) threshold (or some other type of significance threshold) are retained for the study, but true associations may be filtered out. For complex, polygenic traits, the effect of each gene might be small (and thus filtered out), and some alleles are only expressed in specific conditions that might not be present in the study3. Thus, while many SNPs may be retained as associated with the trait, each may have a very small effect. Too many SNP calls will be missing, and an interpretation of the biological meaning and genetic architecture of the trait may be incomplete and confusing. Metabolic pathway analysis can help to address some of these issues by focusing on the combined effects of genes grouped according to their biological function4,5,6.
Several studies were completed using a previous implementation of the method described in this article. Aflatoxin accumulation7, corn earworm resistance8, and oil biosynthesis9 were all studied with the previous implementation. While these analyses were successful, the process of analysis was complicated, time-consuming, and cumbersome, because the analysis tools were written in a combination of R, Perl, and Bash, and the pipeline was not automated. Because of the specialized knowledge required to modify this method for each analysis, a new method has now been developed that can be shared with other researchers.
The Pathway Association Study Tool (PAST)10 was designed to address the shortcomings of the previous method by requiring less knowledge of programming languages and by running analyses in a shorter period. While the method was tested with maize, PAST makes no species-specific assumptions. PAST can be run through the R console, as a Shiny app, and an online version is expected to soon be available on MaizeGDB.

<table border="0" cellpadding="0" cellspacing="0" style="width:719px;" width="717">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">Computer</td>
			<td style="width:74px;">NA</td>
			<td style="width:123px;">NA</td>
			<td style="width:300px;">Any computer with 8GB RAM should be sufficient</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">R</td>
			<td style="width:74px;">R Project</td>
			<td style="width:123px;">NA</td>
			<td>R 3.6 or greater is required to install from Bioconductor</td>
		</tr>
	</tbody>
</table>

a pathway association study tool for gwas analyses of metabolic pathway information

Recently, a new implementation of a previously described method for interpreting genome-wide association study (GWAS) data using metabolic pathway analysis has been developed and released. The Pathway Association Study Tool (PAST) was developed to address concerns with user-friendliness and slow-running analyses. This new user-friendly tool has been released on Bioconductor and Github. In testing, PAST ran analyses in less than one hour that previously required twenty-four or more hours. In this article, we present the protocol for using either the Shiny application or the R console to run PAST.

If results are not produced following a run of the PAST software tool, check to be sure that all input files are correctly formatted. A successful run using the example data in the PAST package, which are based on a maize GWAS of grain color, is shown in Figure 8. This table and the resulting image can be downloaded using the Download Results button. An example of the downloaded image is shown in Figure 210. Incorrect settings might lead to results that do not make biological sense, but determining incorrectness must be up to the researcher, who should double check the validity of the chosen settings and consider all known evidence regarding the trait of interest.
Figure 910 shows the rugplot produced from the pathway analysis of GWAS results created with a maize panel of 288 inbred lines that had been phenotyped for grain color. This simplistic example, where the phenotypes were either &#34;white&#34; or &#34;yellow&#34;, was used because the pathway responsible for creating the bright yellow carotenoid pigments is known and should be responsible for most of the phenotype. Thus, we expected to see the trans-lycopene biosynthesis pathway (which produce carotenoids) to be significantly associated with grain color, which it is. Pathway ID and name are listed at the top of the graph. The horizontal axis of the graph ranks all genes that were included in the analysis, arranged from left to right in order of largest effect on the trait to smallest. However, only the genes in the trans-lycopene biosynthesis pathway are marked (at the top of the graph, as hatch marks, appearing in the gene rank of their effect as compared to all other genes in the analysis). There are 7 genes in this pathway. The running enrichment score (ES) is plotted along the vertical axis. The ES for each gene is added into the running total in order of effect and the total is adjusted to the number of genes analyzed. Thus, the score changes as one moves right along the horizontal axis and tends to increase as the larger effect genes are included, but at some point, the increase in the effect is smaller than the adjustment for having added another gene, and the entire score begins to decrease. The apex of the running ES line is marked with a dotted vertical line; this is the ES for the entire pathway and is used by the program to determine if the pathway is chosen and presented as a rugplot.&#160;
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/61268/61268fig8.jpg" /> 
Figure 8: Completed run of PAST Shiny. <a href="https://www.jove.com/files/ftp_upload/61268/61268fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/61268/61268fig9.jpg" /> 
Figure 9: Pathway image from completed run of PAST (or downloaded from Shiny). This figure has been cited from Thrash et al.10. <a href="https://www.jove.com/files/ftp_upload/61268/61268fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information at JoVE.com

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

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.

Recently, a new implementation of a previously described method for interpreting genome-wide association study (GWAS) data using metabolic pathway ...

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Research

JoVE Journal

Genetics

3.1K Views.  Mississippi State University. 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. 

Video: A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Ultralow Input Genome Sequencing Library Preparation from a Single Tardigrade Specimen

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state when water is supplied. The genomic sequencing of microscopic animals such as tardigrades risks bacterial contamination that sometimes leads to erroneous interpretations, for example, regarding the extent of horizontal gene transfer in these animals. Here, we provide an ultralow input method to sequence the genome of the tardigrade, Hypsibius dujardini, from a single specimen. By employing rigorous washing and contaminant exclusion along with an efficient extraction of the 50 ~ 200 pg genomic DNA from a single individual, we constructed a library sequenced with a DNA sequencing instrument. These libraries were highly reproducible and unbiased, and an informatics analysis of the sequenced reads with other H. dujardini genomes showed a minimal amount of contamination. This method can be applied to unculturable tardigrades that could not be sequenced using previous methods.

Contamination during the genomic sequencing of microscopic organisms remains a large problem. Here, we show a method to sequence the genome of a tardigrade from a single specimen with as little as 50 pg of genomic DNA without whole genome amplification to minimize the risk of contamination.

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state ...

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Quantification of DNA methylation can be achieved using bisulfite sequencing, which takes advantage of the property of sodium bisulfite to convert unmethylated cytosine into uracil, in a single-stranded DNA context. Bisulfite sequencing can be targeted (using PCR) or performed on the whole genome and provides absolute quantification of cytosine methylation at the single base-resolution. Given the distinct nature of nuclear- and mitochondrial DNA, notably in the secondary structure, adaptions of bisulfite sequencing methods for investigating cytosine methylation in mtDNA should be made. Secondary and tertiary structure of mtDNA can indeed lead to bisulfite sequencing artifacts leading to false-positives due to incomplete denaturation poor access of bisulfite to single-stranded DNA. Here, we describe a protocol using an enzymatic digestion of DNA with BamHI coupled with bioinformatic analysis pipeline to allow accurate quantification of cytosine methylation levels in mtDNA. In addition, we provide guidelines for designing the bisulfite sequencing primers specific to mtDNA, in order to avoid targeting undesirable NUclear MiTochondrial segments (NUMTs) inserted into the nuclear genome.

Here, we present a protocol to allow accurate quantification of mitochondrial DNA (mtDNA) methylation. In this protocol, we describe an enzymatic digestion of DNA with BamHI coupled with a bioinformatic analysis pipeline which can be used to avoid overestimation of mtDNA methylation levels caused by the secondary structure of mtDNA.

Quantification of DNA methylation can be achieved using bisulfite sequencing, which takes advantage of the property of sodium bisulfite to convert ...

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and effect sizes of regulatory elements to a target gene. Some progress has been made with the bioinformatic prediction of the existence and function of proximal epigenetic modifications associated with activated gene expression using conserved transcription factor binding sites. Chromatin conformation capture studies have revolutionized our ability to discover physical chromatin contacts between sequences and even within an entire genome. Circular chromatin conformation capture coupled with next-generation sequencing (4C-seq), in particular, is designed to discover all possible physical chromatin interactions for a given sequence of interest (viewpoint), such as a target gene or a regulatory enhancer. Current 4C-seq strategies directly sequence from within the viewpoint but require numerous and diverse viewpoints to be simultaneously sequenced to avoid the technical challenges of uniform base calling (imaging) with next generation sequencing platforms. This volume of experiments may not be practical for many laboratories. Here, we report a modified approach to the 4C-seq protocol that incorporates both an additional restriction enzyme digest and qPCR-based amplification steps that are designed to facilitate a greater capture of diverse sequence reads and mitigate the potential for PCR bias, respectively. Our modified 4C method is amenable to the standard molecular biology lab for assessing chromatin architecture.

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

The identification of physical interactions between genes and regulatory elements is challenging but has been facilitated by chromosome conformation capture methods. This modification to the 4C-seq protocol mitigates PCR bias by minimizing over-amplification of PCR templates and maximizes the mappability of reads by incorporating an addition restriction enzyme digest step.

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and ...

Chromogenic In Situ Hybridization as a Tool for HPV-Related Head and Neck Cancer Diagnosis

Human papillomavirus (HPV) infection is a major risk factor for a subtype of oropharyngeal squamous cell carcinoma (OPSCC), which tends to be associated with a better outcome than alcohol- and tobacco-related OPSCC. Chromogenic in situ hybridization (CISH) of HPV viral RNA could allow the semiquantitative evaluation of viral transcripts of the oncogenic proteins E6 and E7 and an in situ visualization with a good spatial resolution. This technique allows the diagnosis of an active infection with the visualization of HPV transcription in the tumoral HPV-infected cells. An advantage of this technique is the avoidance of contamination from nonneoplastic HPV-infected cells adjacent to the tumor. Overall, its good diagnosis performances have it considered to be the gold standard for active HPV infection identification. Since E6 and E7 viral protein interaction with cell proteins pRb and p53 is mandatory for cell transformation, HPV RNA CISH is functionally relevant and acutely reflects active oncogenic HPV infection. This technique is clinically relevant as well since &#34;low&#34; or &#34;high&#34; HPV transcription levels helped the identification of two prognosis groups among HPV-related p16-positive head and neck cancer patients. Here we present the protocol for manual HPV RNA CISH performed on formalin-fixed paraffin-embedded (FFPE) slides with a kit obtained from the manufacturer. Instead of chromogenic revelation, RNA in situ hybridization may also be performed with fluorescent revelation (RNA FISH). It may also be combined with conventional immunostaining.

Human papillomavirus (HPV) RNA chromogenic in situ hybridization is considered to be one of the gold standards for active human papillomavirus infection detection within tumors. It allows the visualization of HPV E6-E7 mRNA expression with localization and semiquantitative evaluation of its signal.

Human papillomavirus (HPV) infection is a major risk factor for a subtype of oropharyngeal squamous cell carcinoma (OPSCC), which tends to be associated ...

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Through whole-exome/genome sequencing, human geneticists identify rare variants that segregate with disease phenotypes. To assess if a specific variant is pathogenic, one must query many databases to determine whether the gene of interest is linked to a genetic disease, whether the specific variant has been reported before, and what functional data is available in model organism databases that may provide clues about the gene&#8217;s function in human. MARRVEL (Model organism Aggregated Resources for Rare Variant ExpLoration) is a one-stop data collection tool for human genes and variants and their orthologous genes in seven model organisms including in mouse, rat, zebrafish, fruit fly, nematode worm, fission yeast, and budding yeast. In this Protocol, we provide an overview of what MARRVEL can be used for and discuss how different datasets can be used to assess whether a variant of unknown significance (VUS) in a known disease-causing gene or a variant in a gene of uncertain significance (GUS) may be pathogenic. This protocol will guide a user through searching multiple human databases simultaneously starting with a human gene with or without a variant of interest. We also discuss how to utilize data from OMIM, ExAC/gnomAD, ClinVar, Geno2MP, DGV and DECHIPHER. Moreover, we illustrate how to interpret a list of ortholog candidate genes, expression patterns, and GO terms in model organisms associated with each human gene. Furthermore, we discuss the value protein structural domain annotations provided and explain how to use the multiple species protein alignment feature to assess whether a variant of interest affects an evolutionarily conserved domain or amino acid. Finally, we will discuss three different use-cases of this website. MARRVEL is an easily accessible open access website designed for both clinical and basic researchers and serves as a starting point to design experiments for functional studies.

Here, we present a protocol to access and analyze many human and model organism databases efficiently. This protocol demonstrates the use of MARRVEL to analyze candidate disease-causing variants identified from next-generation sequencing efforts.

Through whole-exome/genome sequencing, human geneticists identify rare variants that segregate with disease phenotypes. To assess if a specific variant is ...

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame (ORF) annotation impose two arbitrary criteria: a minimum length of 100 codons and a single ORF per transcript. However, a growing number of studies report expression of proteins from allegedly non-coding regions, challenging the accuracy of current genome annotations. These novel proteins were found encoded either within non-coding RNAs, 5' or 3' untranslated regions (UTRs) of mRNAs, or overlapping a known coding sequence (CDS) in an alternative ORF. OpenProt is the first database that enforces a polycistronic model for eukaryotic genomes, allowing annotation of multiple ORFs per transcript. OpenProt is freely accessible and offers custom downloads of protein sequences across 10 species. Using OpenProt database for proteomic experiments enables novel proteins discovery and highlights the polycistronic nature of eukaryotic genes. The size of OpenProt database (all predicted proteins) is substantial and need be taken in account for the analysis. However, with appropriate false discovery rate (FDR) settings or the use of a restricted OpenProt database, users will gain a more realistic view of the proteomic landscape. Overall, OpenProt is a freely available tool that will foster proteomic discoveries.

OpenProt is a freely accessible database that enforces a polycistronic model of eukaryotic genomes. Here, we present a protocol for the use of OpenProt databases when interrogating mass spectrometry datasets. Using OpenProt database for analysis of proteomic experiments allows for discovery of novel and previously undetectable proteins.

Genome annotation is central to today's proteomic research as it draws the outlines of the proteomic landscape. Traditional models of open reading frame ...

A Method to Study de novo Formation of Chromatin Domains

The organization and structure of chromatin domains are unique to individual cell lineages. Their misregulation might lead to a loss in cellular identity and/or disease. Despite tremendous efforts, our understanding of the formation and propagation of chromatin domains is still limited. Chromatin domains have been studied under steady-state conditions, which are not conducive to following the initial events during their establishment. Here, we present a method to inducibly reconstruct chromatin domains and follow their re-formation as a function of time. Although, first applied to the case of PRC2-mediated repressive chromatin domain formation, it could be easily adapted to other chromatin domains. The modification of and/or the combination of this method with genomics and imaging technologies will provide invaluable tools to study the establishment of chromatin domains in great detail. We believe that this method will revolutionize our understanding of how chromatin domains form and interact with each other.

A Method to Study de novo Formation of Chromatin Domains

This method is designed to follow formation of PRC2-mediated chromatin domains in cell lines, and the method can be adapted to many other systems.

The organization and structure of chromatin domains are unique to individual cell lineages. Their misregulation might lead to a loss in cellular identity ...

3D Multicolor DNA FISH Tool to Study Nuclear Architecture in Human Primary Cells

A major question in cell biology is genomic organization within the nuclear space and how chromatin architecture can influence processes such as gene expression, cell identity and differentiation. Many approaches developed to study the 3D architecture of the genome can be divided into two complementary categories: chromosome conformation capture based technologies (C-technologies) and imaging. While the former is based on capturing the chromosome conformation and proximal DNA interactions in a population of fixed cells, the latter, based on DNA fluorescence in situ hybridization (FISH) on 3D-preserved nuclei, allows contemporary visualization of multiple loci at a single cell level (multicolor), examining their interactions and distribution within the nucleus (3D multicolor DNA FISH). The technique of 3D multicolor DNA FISH has a limitation of visualizing only a few predetermined loci, not permitting a comprehensive analysis of the nuclear architecture. However, given the robustness of its results, 3D multicolor DNA FISH in combination with 3D-microscopy and image reconstruction is a possible method to validate C-technology based results and to unambiguously study the position and organization of specific loci at a single cell level. Here, we propose a step by step method of 3D multicolor DNA FISH suitable for a wide range of human primary cells and discuss all the practical actions, crucial steps, notions of 3D imaging and data analysis needed to obtained a successful and informative 3D multicolor DNA FISH within different biological contexts.

3D multicolor DNA FISH represents a tool to visualize multiple genomic loci within 3D preserved nuclei, unambiguously defining their reciprocal interactions and localization within the nuclear space at a single cell level. Here, a step by step protocol is described for a wide spectrum of human primary cells.

A major question in cell biology is genomic organization within the nuclear space and how chromatin architecture can influence processes such as gene ...

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

Different types of cardiac hypertrophy have been associated with an increased volume of cardiac myocytes (CMs), along with changes in CM morphology. While the effects of cell volume on gene expression are well known, the effects of cell shape are not well understood. This paper describes a method that has been designed to systematically analyze the effects of CM morphology on gene expression. It details the development of a novel single-cell trapping strategy that is then followed by single-cell mRNA sequencing. A micropatterned chip has also been designed, which contains 3000 rectangular-shaped fibronectin micropatterns. This makes it possible to grow CMs in distinct length:width aspect ratios (AR), corresponding to different types of heart failure (HF). The paper also describes a protocol that has been designed to pick up single cells from their pattern, using a semi-automated micro-pipetting cell picker, and individually inject them into a separate lysis buffer. This has made it possible to profile the transcriptomes of single CMs with defined geometrical morphotypes and characterize them according to a range of normal or pathological conditions: hypertrophic cardiomyopathy (HCM) or afterload/concentric versus dilated cardiomyopathy (DCM) or preload/eccentric. In summary, this paper presents methods for growing CMs with different shapes, which represent different pathologies, and sorting these adherent CMs based on their morphology at a single-cell level. The proposed platform provides a novel approach to high throughput and drug screening for different types of HF.

This paper presents methods for growing cardiac myocytes with different shapes, which represent different pathologies, and sorting these adherent cardiac myocytes based on their morphology at a single cell level. The proposed platform provides a novel approach to high throughput and drug screening for different types of heart failure.

Different types of cardiac hypertrophy have been associated with an increased volume of cardiac myocytes (CMs), along with changes in CM morphology. While ...

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Obesity is directly connected to lifestyle and has been associated with DNA methylation changes that may cause alterations in the adipogenesis and lipid storage processes contributing to the development of the disease. We demonstrate a complete protocol from selection to epigenetic data analysis of patients with and without obesity. All steps from the protocol were tested and validated in a pilot study. 32 women participated in the study, in which 15 individuals were classified with obesity according to Body Mass Index (BMI) (45.1 &#177; 5.4 kg/m2); and 17 individuals were classified without obesity according to BMI (22.6 &#177; 1.8 kg/m2). In the group with obesity, 564 CpG sites related to fat mass were identified by linear regression analysis. The CpG sites were in the promoter regions. The differential analysis found 470 CpGs hypomethylated and 94 hypermethylated sites in individuals with obesity. The most hypomethylated enriched pathwayswere in the RUNX, WNT signaling, and response to hypoxia. The hypermethylated pathways were related to insulin secretion, glucagon signaling, and Ca2+. We conclude that the protocol effectively identified DNA methylation patterns and trait-related DNA methylation. These patterns could be associated with altered gene expression, affecting adipogenesis and lipid storage. Our results confirmed that an obesogenic lifestyle could promote epigenetic changes in human DNA.

The present study describes the workflow to manage DNA methylation data obtained by microarray technologies. The protocol demonstrates steps from sample preparation to data analysis. All procedures are described in detail, and the video shows the significant steps.

Obesity is directly connected to lifestyle and has been associated with DNA methylation changes that may cause alterations in the adipogenesis and lipid ...