From 15ad710f48742b03b0ae6b0393c5601f22b18ba0 Mon Sep 17 00:00:00 2001 From: UchicagoZchen138 Date: Fri, 16 Sep 2022 03:11:23 -0700 Subject: [PATCH] update va documentation to match portal relaese 3.33.0 --- .../documentation/_sources/index.rst.txt | 173 ++++++++++-------- .../Public/documentation/_sources/temp.md.txt | 64 ++++--- .../dashboard/Public/documentation/index.html | 171 +++++++++-------- .../Public/documentation/searchindex.js | 2 +- .../dashboard/Public/documentation/temp.html | 59 +++--- 5 files changed, 250 insertions(+), 219 deletions(-) diff --git a/va-testing.data-commons.org/dashboard/Public/documentation/_sources/index.rst.txt b/va-testing.data-commons.org/dashboard/Public/documentation/_sources/index.rst.txt index 709d8585a5..ec7dad116d 100644 --- a/va-testing.data-commons.org/dashboard/Public/documentation/_sources/index.rst.txt +++ b/va-testing.data-commons.org/dashboard/Public/documentation/_sources/index.rst.txt @@ -36,10 +36,11 @@ Table of Contents - `Steps to Generate a Cohort <#steps-to-generate-a-cohort>`__ - `Gen3 GWAS <#gen3-gwas>`__ - - `Genome-wide association studies (GWAS) for quantitative - phenotype. <#genome-wide-association-studies-gwas-for-quantitative-phenotype>`__ + - `Genome-Wide Association Studies (GWAS) for Quantitative + Phenotype. <#genome-wide-association-studies-gwas-for-quantitative-phenotype>`__ - `Genome-wide association studies (GWAS) for a case-control study. <#genome-wide-association-studies-gwas-for-a-case-control-study>`__ + - `GWAS Results <#gwas-results>`__ Getting Started =============== @@ -182,7 +183,8 @@ have access to. ATLAS is an open source software application developed as a part of `OHDSI `__ community intended to provide a -unified interface to patient level data and analytics. +unified interface to patient level data and analytics. Atlas software us +used to define cohorts, typically dichotomous variables, for analysis. ATLAS currently includes functionality for searching and navigating the vocabulary within the OMOP Common Data Model (CDM). In addition to the @@ -195,9 +197,11 @@ standardized to the OMOP Common Data Model V5 and can facilitate exchange of analysis designs with any other organizations across the OHDSI community. -The ATLAS user guide can be found -`here `__. (disclaimer: CTDS is not -responsible for the content). +Tutorial for the ATLAS tool can be found +`here `__. It is highly advisable +that you familiarize yourself with these resources before proceeding. We +have also provided a brief step-by-step guide to creating dichotomous +variables here: **Steps to Generate a Cohort** ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -229,8 +233,8 @@ concept variables. .. image:: _static/slide_14.png -Select desired concepts, click “Add To Concept Set”, then click “Concept -Sets”. +Select desired concepts, click “Add To Concept Set”. Repat Search and +Add steps as needed, then click “Concept Sets”. .. image:: _static/slide_15.png @@ -279,7 +283,8 @@ attribute…”, then click “Add Value as Number Criteria”. .. image:: _static/slide_23.png -Select “Greater or Equal To” and enter 2. This will allow for the +Select “Greater or Equal To”. In this specific case we added Heart +Failure concept Set and entered value “2”. This will allow for the collection of data from the Observation table of the MVP harmonization database. @@ -299,8 +304,14 @@ in the GWAS app when this cohort is selected. To complete the creation of the Cohort Definition, click “Generation”, then “Generate”. -Cohort size will be displayed. Use View Reports to see if you have -inclusion criteria that causes cohort attrition. +Cohort size will be displayed under the column “People”. Use View +Reports to see if you have inclusion criteria that causes cohort +attrition. + +We expect that this documentation in addition to the OHDSI tutorials are +sufficient for most analyses that users will attempt. If your phenotype +and analysis variables are more complex than this documentation covers, +please contact us for consultation at- craig.teerlink@va.gov **Gen3 GWAS** ------------- @@ -321,13 +332,17 @@ We offer two types of GWAS analysis- **Genome-wide association studies (GWAS) for a case-control study.** Each of these Gen3 GWAS options are available through the GWAS App, and -consists of several steps. To navigate between the steps- Cclick the -Next or Previous box in the lower corners of the screen. +consists of several steps. To navigate between the steps, click the Next +or Previous box in the lower corners of the screen. + +For more information about the web functionality of each step, please +refer to the Tutorial button. This tool will offer highlighted +explanations on different parts of the page. When entering the App, a user must first select the type of GWAS from the choices in the box on the screen. -**Genome-wide association studies (GWAS) for quantitative phenotype.** +**Genome-Wide Association Studies (GWAS) for Quantitative Phenotype.** ---------------------------------------------------------------------- Here, GWAS evaluates the statistical association between genetic @@ -353,16 +368,17 @@ Please select all variables you wish to use in your model, including both covariates and phenotype. (Note:- population PCs are not included in this step) -Please choose as many variables as you wish (need to choose at least -two), you may remove them later in the pipeline. Currently, only -continuous variables can be selected. All variables are harmonized. To -browse the table, please scroll down to the bottom. To search the table -please enter free text in the search box to search by cohort name. +You may choose as many variables as you wish in this step, with a +minimum of one, that will represent your outcome phenotype. You may +remove them later in the pipeline. Currently, only continuous variables +can be selected. To browse the table, please scroll down to the bottom. +To search the table please enter free text in the search box to search +by cohort name. **Step 3 Select which variable is your phenotype** -In this Sstep, you will determine your phenotype, using the selected -variables from Step 2. Please choose one of the selected variables to be +In this step, you will determine your phenotype, using the selected +variables from step 2. Please choose one of the selected variables to be the study’s phenotype. Here you may choose your phenotype. All data are harmonized from @@ -376,26 +392,26 @@ text in the search box to search by cohort name. **Step 4 Add custom dichotomous covariates** In this step, you may add custom dichotomous covariates by selecting two -cohorts. Please combine a cohort for YES and a cohort for NO. Once -cohorts are selected you may enter a name for the covariate. To commit -the changes please press ‘Add’ at the bottom (You must ‘Add’ the -variable before moving to the next screen if you want it to be a part of -your analysis). You may repeat this action as many times as you need, or -choose to not add any custom dichotomous covariates at all. +cohorts. This step is optional, and you may choose not to add any +dichotomous covariate at all. You may combine a cohort for YES and a +cohort for NO. Once cohorts are selected you may enter a name for the +covariate. To commit the changes please press ‘Add’ at the bottom (You +must ‘Add’ the variable before moving to the next screen if you want it +to be a part of your analysis). You may repeat this action as many times +as you need, or choose to not add any custom dichotomous covariates at +all. Please note that all given names must be unique. As you add covariates you may see them populate on the right hand side -of the screen as cards. The card contains the following information: -Your given name at the top of the card, Cohorts [X,Y] represents the -cohort’s ID number of the X-No/0 and Y-Yes/1 chosen as they intersect -with your initial population selected, and the ability to remove the -created covariate at the bottom of the card. +of the screen as cards. The card contains your given name at the top of +the card, and the ability to remove the created covariate at the bottom +of the card. **Step 5 Set workflow parameters and remove unwanted covariates** In this step, you will determine workflow parameters. Please adjust the number of population principal components (PCs) to control for population structure, minor allele frequency cutoff and imputation score -cutoff. You may also remove unwanted covariates. Please also choose the +cutoff. You may also remove unwanted covariates. Please also choose one ancestry population on which you would like to perform your study. Number of PCs- Population Principal components (PCs) refer to linear @@ -418,7 +434,7 @@ MAF Cutoff- Minor allele frequency (MAF) is the frequency at which the second most common allele occurs in a given population and can be used to filter out rare markers (scale of 0-0.5) -HARE dropdown manues- Please choose the ancestry population on which you +HARE dropdown menu- Please choose the ancestry population on which you would like to perform your study. The numbers appearing in the dropdown represent the population size of your study, considering all of your previous selections. The codes are the HARE (Hharmonized Aancestry and @@ -427,25 +443,16 @@ Rrace/Eethnicity) codes. Imputation Score Cutoff- This value reflects the quality of imputed SNPs and can be used to remove low-quality imputed markers (scale of 0-1) -**Step 6 Submit GWAS job** +**Step 6 Submit GWAS Study** In this step, you may review the metadata selected for the study, give a name to the study, and submit the GWAS for analysis. To commit any changes please go back to the relevant step. -**Check Submission Status** +**Check Submission Status and Review Results** -Once your GWAS analysis is submitted, click the arrow in the **Submitted -Job Statuses** box to activate the drop- down menu and see the status of -your analysis. This menu will display a history of your submitted jobs -including: Run ID of your analysis, user given name for the analysis, -start time, and finish time for when the run is completed. This menu -will also display whether the analysis was a success or failed. Once -completed, you may download the results of the GWAS analysis from this -menu. By pressing the ‘Download’ link, a tar.gz file will start -downloading to your computer. The file contains the following: Manhattan -plot, QQ plot, metadata file containing all of your selections, -attrition table, and per-chromosome GWAS summary statistics. +Once your GWAS analysis is submitted, you can check the submission +status and review the results in the “GWAS Results” App. **Genome-wide association studies (GWAS) for a case-control study.** -------------------------------------------------------------------- @@ -466,7 +473,7 @@ down to the bottom. You may also see a button to create a new cohort. This button will open a new tab in your browser, outside of the Gen3 GWAS App and send you to -OHDSI Atlas App. +the OHDSI Atlas App. **Step 2 Select a control cohort for GWAS** @@ -485,36 +492,36 @@ OHDSI Atlas App. **Step 3 Select harmonized variables for covariates** -In this step, you will select covariates for your study. Please choose -as many covariates as you wish, you may remove them later in the +In this step, you may select covariates for your study. This step is +optional, and you may choose not to add any covariate at all. Please +choose as many covariates as you wish, you may remove them later in the pipeline. Currently, only continuous covariates are presented. All variables are harmonized. To browse the table please scroll down to the -bottom. You must select at least one covariate in order to move to the -next step. To search the table please enter free text in the search box -to search by cohort name. You must select at least one covariate in -order to move to the next step. +bottom. To search the table please enter free text in the search box to +search by cohort name. **Step 4 Assess % missing in selected covariates** In this step, you can review the covariates selection based on % missing -metrics. To adjust covariates please return to Step 3. +metrics. To adjust covariates please return to Step 3. If no covariates +were chosen in step 3, this step will be empty. **Step 5 Add custom dichotomous covariates** In this step, you may add custom dichotomous covariates by selecting two -cohorts. Please combine a cohort for YES and a cohort for NO. Once -cohorts are selected you may enter a name for the covariate. To commit -the changes please press ‘Add’ at the bottom (You must ‘Add’ the -variable before moving to the next screen if you want it to be a part of -your analysis). You may repeat this action as many times as you need, or -choose to not add any custom dichotomous covariates at all. +cohorts. This step is optional, and you may choose not to add any +dichotomous covariate at all. You may combine a cohort for YES and a +cohort for NO. Once cohorts are selected you may enter a name for the +covariate. To commit the changes please press ‘Add’ at the bottom (You +must ‘Add’ the variable before moving to the next screen if you want it +to be a part of your analysis). You may repeat this action as many times +as you need, or choose to not add any custom dichotomous covariates at +all. Please note that all given names must be unique. As you add covariates you may see them populate on the right hand side -of the screen as cards. The card contains the following information: -Your given name at the top of the card, Cohorts [X,Y] represents the -population size of the Yes and No cohorts chosen as they intersect with -your initial population selected, and the ability to remove the created -covariate at the bottom of the card. +of the screen as cards. The card contains your given name at the top of +the card and the ability to remove the created covariate at the bottom +of the card. **Step 6 Set workflow parameters and remove unwanted covariates** @@ -555,21 +562,27 @@ race/ethnicity) codes. Imputation Score Cutoff- This value reflects the quality of imputed SNPs and can be used to remove low-quality imputed markers (scale of 0-1) -**Step 7 Submit GWAS job** +**Step 7 Submit GWAS Study** In this step, you may review the metadata selected for the study, give a name to the study, and submit the GWAS for analysis. -**Check Submission Status** - -Once your GWAS analysis is submitted, click the arrow in the **Submitted -Job Statuses** box to activate the drop down menu and see the status of -your analysis. This menu will display a history of your submitted jobs -including the Run ID of your analysis, the start time, and the finish -time when the run is completed. This menu will also display whether the -analysis was a success or failed. Once completed, you may download the -results of the GWAS analysis from this menu. By pressing the ‘Download’ -link a tar.gz file will start downloading to your computer. The file -contains the following: Manhattan plot, QQ plot, metadata file -containing all of your selections, attrition table, and per-chromosome -GWAS summary statistics. +**Check Submission Status and Review Results** + +Once your GWAS analysis is submitted, you can check the Submission +Status and Review the Results in the “GWAS Results” app. + +**GWAS Results** +---------------- + +Use this App to view the status & results of submitted workflows. Click +the arrow in the Submitted Job Statuses box to activate the drop down +menu and see the status of your analysis. This menu will display a +history of your submitted jobs including the Run ID of your analysis, +the start time, and the finish time when the run is completed. This menu +will also display whether the analysis was a success or failed. Once +completed, you may download the results of the GWAS analysis from this +menu. By pressing the ‘Download’ link a tar.gz file will start +downloading to your computer. The file contains the following: Manhattan +plot, QQ plot, metadata file containing all of your selections, and +per-chromosome GWAS summary statistics. diff --git a/va-testing.data-commons.org/dashboard/Public/documentation/_sources/temp.md.txt b/va-testing.data-commons.org/dashboard/Public/documentation/_sources/temp.md.txt index 9c78d422ca..06a1613c3d 100644 --- a/va-testing.data-commons.org/dashboard/Public/documentation/_sources/temp.md.txt +++ b/va-testing.data-commons.org/dashboard/Public/documentation/_sources/temp.md.txt @@ -17,8 +17,9 @@ The Veterans Affairs (VA) Data Commons supports the research and analysis of US - [**OHDSI Atlas**](#ohdsi-atlas) - [**Steps to Generate a Cohort**](#steps-to-generate-a-cohort) - [**Gen3 GWAS**](#gen3-gwas) - - [**Genome-wide association studies (GWAS) for quantitative phenotype.**](#genome-wide-association-studies-gwas-for-quantitative-phenotype) + - [**Genome-Wide Association Studies (GWAS) for Quantitative Phenotype.**](#genome-wide-association-studies-gwas-for-quantitative-phenotype) - [**Genome-wide association studies (GWAS) for a case-control study.**](#genome-wide-association-studies-gwas-for-a-case-control-study) + - [**GWAS Results**](#gwas-results) # Getting Started ## **Login Page** @@ -75,15 +76,15 @@ For more information about the Gen3 Workspace, refer to [Data Analysis in a Gen3 ## **Apps** -The [Apps page](https://va.data-commons.org/analysis) displays the option of two apps, OHDSI Atlas and Gen3 GWAS. Using these Apps, a user may perform a genomic analysis on available data from projects that they have access to. +The [Apps page](https://va.data-commons.org/analysis) displays the option of two apps, OHDSI Atlas and Gen3 GWAS. Using these Apps, a user may perform a genomic analysis on available data from projects that they have access to. ## **OHDSI Atlas** -ATLAS is an open source software application developed as a part of [OHDSI](https://www.ohdsi.org/) community intended to provide a unified interface to patient level data and analytics. +ATLAS is an open source software application developed as a part of [OHDSI](https://www.ohdsi.org/) community intended to provide a unified interface to patient level data and analytics. Atlas software us used to define cohorts, typically dichotomous variables, for analysis. ATLAS currently includes functionality for searching and navigating the vocabulary within the OMOP Common Data Model (CDM). In addition to the search and navigation capabilities, it also provides features to curate and export custom sets of concept identifiers for use in cohort definitions. These will automatically populate on the Gen3 GWAS App. In general, ATLAS is an analytics platform that can be used to perform analyses across one or more observational databases which have been standardized to the OMOP Common Data Model V5 and can facilitate exchange of analysis designs with any other organizations across the OHDSI community. -The ATLAS user guide can be found [here](https://github.com/OHDSI/Atlas/wiki). (disclaimer: CTDS is not responsible for the content). +Tutorial for the ATLAS tool can be found [here](https://github.com/OHDSI/Atlas/wiki). It is highly advisable that you familiarize yourself with these resources before proceeding. We have also provided a brief step-by-step guide to creating dichotomous variables here: ### **Steps to Generate a Cohort** @@ -107,7 +108,7 @@ Enter the concept name or code into the search bar to find relevant concept vari ![](_static/slide_14.png) -Select desired concepts, click “Add To Concept Set”, then click “Concept Sets”. +Select desired concepts, click “Add To Concept Set”. Repat Search and Add steps as needed, then click “Concept Sets”. ![](_static/slide_15.png) @@ -146,8 +147,7 @@ To access the table in the MVP harmonization database, click “+ Add attribute ![](_static/slide_23.png) -Select “Greater or Equal To” and enter 2. -This will allow for the collection of data from the Observation table of the MVP harmonization database. +Select “Greater or Equal To”. In this specific case we added Heart Failure concept Set and entered value "2". This will allow for the collection of data from the Observation table of the MVP harmonization database. If you would like to add additional inclusion criteria, click “New inclusion criteria” select criteria or import another configuration. To complete the Cohort Definition, click the green Save icon. @@ -160,8 +160,9 @@ people are in the cohort. This is the initial number that will be used in the GW To complete the creation of the Cohort Definition, click “Generation”, then “Generate”. -Cohort size will be displayed. Use View Reports to see if you have inclusion criteria that causes cohort attrition. +Cohort size will be displayed under the column “People”. Use View Reports to see if you have inclusion criteria that causes cohort attrition. +We expect that this documentation in addition to the OHDSI tutorials are sufficient for most analyses that users will attempt. If your phenotype and analysis variables are more complex than this documentation covers, please contact us for consultation at- craig.teerlink@va.gov ## **Gen3 GWAS** Use this app to perform a high throughput GWAS on MVP data using the University of Washington Genesis pipeline. @@ -174,11 +175,13 @@ We offer two types of GWAS analysis- **Genome-wide association studies (GWAS) for a case-control study.** -Each of these Gen3 GWAS options are available through the GWAS App, and consists of several steps. To navigate between the steps- Cclick the Next or Previous box in the lower corners of the screen. +Each of these Gen3 GWAS options are available through the GWAS App, and consists of several steps. To navigate between the steps, click the Next or Previous box in the lower corners of the screen. + +For more information about the web functionality of each step, please refer to the Tutorial button. This tool will offer highlighted explanations on different parts of the page. When entering the App, a user must first select the type of GWAS from the choices in the box on the screen. -## **Genome-wide association studies (GWAS) for quantitative phenotype.** +## **Genome-Wide Association Studies (GWAS) for Quantitative Phenotype.** Here, GWAS evaluates the statistical association between genetic variation and a continuous phenotype. A phenotype, also called a trait, can be any measured or observed property of an individual. **Step 1 Select a cohort for GWAS** @@ -191,23 +194,23 @@ You may also see a button to create a new cohort. This button will open a new ta In this step, you will select the harmonized variables for your study. Please select all variables you wish to use in your model, including both covariates and phenotype. (Note:- population PCs are not included in this step) -Please choose as many variables as you wish (need to choose at least two), you may remove them later in the pipeline. Currently, only continuous variables can be selected. All variables are harmonized. To browse the table, please scroll down to the bottom. To search the table please enter free text in the search box to search by cohort name. +You may choose as many variables as you wish in this step, with a minimum of one, that will represent your outcome phenotype. You may remove them later in the pipeline. Currently, only continuous variables can be selected. To browse the table, please scroll down to the bottom. To search the table please enter free text in the search box to search by cohort name. **Step 3 Select which variable is your phenotype** -In this Sstep, you will determine your phenotype, using the selected variables from Step 2. Please choose one of the selected variables to be the study’s phenotype. +In this step, you will determine your phenotype, using the selected variables from step 2. Please choose one of the selected variables to be the study’s phenotype. Here you may choose your phenotype. All data are harmonized from different projects through the collaborative development of a data dictionary. In the right hand side of the table a missing % is calculated. This is to reflect how many subjects of the chosen population do not have this information available. To browse the table please scroll down to the bottom.To search the table please enter free text in the search box to search by cohort name. **Step 4 Add custom dichotomous covariates** -In this step, you may add custom dichotomous covariates by selecting two cohorts. Please combine a cohort for YES and a cohort for NO. Once cohorts are selected you may enter a name for the covariate. To commit the changes please press 'Add' at the bottom (You must ‘Add’ the variable before moving to the next screen if you want it to be a part of your analysis). You may repeat this action as many times as you need, or choose to not add any custom dichotomous covariates at all. +In this step, you may add custom dichotomous covariates by selecting two cohorts. This step is optional, and you may choose not to add any dichotomous covariate at all. You may combine a cohort for YES and a cohort for NO. Once cohorts are selected you may enter a name for the covariate. To commit the changes please press 'Add' at the bottom (You must ‘Add’ the variable before moving to the next screen if you want it to be a part of your analysis). You may repeat this action as many times as you need, or choose to not add any custom dichotomous covariates at all. Please note that all given names must be unique. -As you add covariates you may see them populate on the right hand side of the screen as cards. The card contains the following information: Your given name at the top of the card, Cohorts [X,Y] represents the cohort’s ID number of the X-No/0 and Y-Yes/1 chosen as they intersect with your initial population selected, and the ability to remove the created covariate at the bottom of the card. +As you add covariates you may see them populate on the right hand side of the screen as cards. The card contains your given name at the top of the card, and the ability to remove the created covariate at the bottom of the card. **Step 5 Set workflow parameters and remove unwanted covariates** -In this step, you will determine workflow parameters. Please adjust the number of population principal components (PCs) to control for population structure, minor allele frequency cutoff and imputation score cutoff. You may also remove unwanted covariates. Please also choose the ancestry population on which you would like to perform your study. +In this step, you will determine workflow parameters. Please adjust the number of population principal components (PCs) to control for population structure, minor allele frequency cutoff and imputation score cutoff. You may also remove unwanted covariates. Please also choose one ancestry population on which you would like to perform your study. Number of PCs- Population Principal components (PCs) refer to linear combinations of genome-wide genotyping data to control for population structure/stratification (select up to 10 PCs) @@ -224,30 +227,28 @@ Please see here the phenotype chosen in step 3. To adjust please go back (at the MAF Cutoff- Minor allele frequency (MAF) is the frequency at which the second most common allele occurs in a given population and can be used to filter out rare markers (scale of 0-0.5) -HARE dropdown manues- +HARE dropdown menu- Please choose the ancestry population on which you would like to perform your study. The numbers appearing in the dropdown represent the population size of your study, considering all of your previous selections. The codes are the HARE (Hharmonized Aancestry and Rrace/Eethnicity) codes. Imputation Score Cutoff- This value reflects the quality of imputed SNPs and can be used to remove low-quality imputed markers (scale of 0-1) -**Step 6 Submit GWAS job** +**Step 6 Submit GWAS Study** In this step, you may review the metadata selected for the study, give a name to the study, and submit the GWAS for analysis. To commit any changes please go back to the relevant step. -**Check Submission Status** +**Check Submission Status and Review Results** -Once your GWAS analysis is submitted, click the arrow in the **Submitted Job Statuses** box to activate the drop- down menu and see the status of your analysis. This menu will display a history of your submitted jobs including: Run ID of your analysis, user given name for the analysis, start time, and finish time for when the run is completed. This menu will also display whether the analysis was a success or failed. Once completed, you may download the results of the GWAS analysis from this menu. By pressing the 'Download' link, a tar.gz file will start downloading to your computer. The file contains the following: Manhattan plot, QQ plot, metadata file containing all of your selections, attrition table, and per-chromosome GWAS summary statistics. +Once your GWAS analysis is submitted, you can check the submission status and review the results in the “GWAS Results” App. ## **Genome-wide association studies (GWAS) for a case-control study.** Here, the genotypes of a roughly equal number of diseased (“cases”) and healthy (“controls”) people are compared to determine which genetic variants are associated with the disease. Cases are encoded as ‘1’ while controls are encoded as ‘0’ and a binary model is used. - - **Step 1 Select a case cohort for GWAS** In this step, you will begin to define the study population. To begin, select the cohort that you would like to define as your study “cases” population. You may only see cohorts that you have access to. Please select only one cohort. The size of the cohort population is indicated in the right hand side of the table. To browse the table please scroll down to the bottom. -You may also see a button to create a new cohort. This button will open a new tab in your browser, outside of the Gen3 GWAS App and send you to OHDSI Atlas App. +You may also see a button to create a new cohort. This button will open a new tab in your browser, outside of the Gen3 GWAS App and send you to the OHDSI Atlas App. **Step 2 Select a control cohort for GWAS** @@ -257,17 +258,17 @@ You may also see a button to create a new cohort. This button will open a new ta **Step 3 Select harmonized variables for covariates** -In this step, you will select covariates for your study. Please choose as many covariates as you wish, you may remove them later in the pipeline. Currently, only continuous covariates are presented. All variables are harmonized. To browse the table please scroll down to the bottom. You must select at least one covariate in order to move to the next step. To search the table please enter free text in the search box to search by cohort name. You must select at least one covariate in order to move to the next step. +In this step, you may select covariates for your study. This step is optional, and you may choose not to add any covariate at all. Please choose as many covariates as you wish, you may remove them later in the pipeline. Currently, only continuous covariates are presented. All variables are harmonized. To browse the table please scroll down to the bottom. To search the table please enter free text in the search box to search by cohort name. **Step 4 Assess % missing in selected covariates** -In this step, you can review the covariates selection based on % missing metrics. To adjust covariates please return to Step 3. +In this step, you can review the covariates selection based on % missing metrics. To adjust covariates please return to Step 3. If no covariates were chosen in step 3, this step will be empty. **Step 5 Add custom dichotomous covariates** -In this step, you may add custom dichotomous covariates by selecting two cohorts. Please combine a cohort for YES and a cohort for NO. Once cohorts are selected you may enter a name for the covariate. To commit the changes please press 'Add' at the bottom (You must ‘Add’ the variable before moving to the next screen if you want it to be a part of your analysis). You may repeat this action as many times as you need, or choose to not add any custom dichotomous covariates at all. +In this step, you may add custom dichotomous covariates by selecting two cohorts. This step is optional, and you may choose not to add any dichotomous covariate at all. You may combine a cohort for YES and a cohort for NO. Once cohorts are selected you may enter a name for the covariate. To commit the changes please press 'Add' at the bottom (You must ‘Add’ the variable before moving to the next screen if you want it to be a part of your analysis). You may repeat this action as many times as you need, or choose to not add any custom dichotomous covariates at all. Please note that all given names must be unique. -As you add covariates you may see them populate on the right hand side of the screen as cards. The card contains the following information: Your given name at the top of the card, Cohorts [X,Y] represents the population size of the Yes and No cohorts chosen as they intersect with your initial population selected, and the ability to remove the created covariate at the bottom of the card. +As you add covariates you may see them populate on the right hand side of the screen as cards. The card contains your given name at the top of the card and the ability to remove the created covariate at the bottom of the card. **Step 6 Set workflow parameters and remove unwanted covariates** @@ -294,11 +295,14 @@ Please choose the ancestry population on which you would like to perform your st Imputation Score Cutoff- This value reflects the quality of imputed SNPs and can be used to remove low-quality imputed markers (scale of 0-1) -**Step 7 Submit GWAS job** +**Step 7 Submit GWAS Study** In this step, you may review the metadata selected for the study, give a name to the study, and submit the GWAS for analysis. -**Check Submission Status** +**Check Submission Status and Review Results** + +Once your GWAS analysis is submitted, you can check the Submission Status and Review the Results in the “GWAS Results” app. + +## **GWAS Results** -Once your GWAS analysis is submitted, click the arrow in the **Submitted Job Statuses** box to activate the drop down menu and see the status of your analysis. This menu will display a history of your submitted jobs including the Run ID of your analysis, the start time, and the finish time when the run is completed. This menu will also display whether the analysis was a success or failed. Once completed, you may download the results of the GWAS analysis from this menu. -By pressing the 'Download' link a tar.gz file will start downloading to your computer. The file contains the following: Manhattan plot, QQ plot, metadata file containing all of your selections, attrition table, and per-chromosome GWAS summary statistics. +Use this App to view the status & results of submitted workflows. Click the arrow in the Submitted Job Statuses box to activate the drop down menu and see the status of your analysis. This menu will display a history of your submitted jobs including the Run ID of your analysis, the start time, and the finish time when the run is completed. This menu will also display whether the analysis was a success or failed. Once completed, you may download the results of the GWAS analysis from this menu. By pressing the ‘Download’ link a tar.gz file will start downloading to your computer. The file contains the following: Manhattan plot, QQ plot, metadata file containing all of your selections, and per-chromosome GWAS summary statistics. \ No newline at end of file diff --git a/va-testing.data-commons.org/dashboard/Public/documentation/index.html b/va-testing.data-commons.org/dashboard/Public/documentation/index.html index e59bbe8759..aad90eda4c 100644 --- a/va-testing.data-commons.org/dashboard/Public/documentation/index.html +++ b/va-testing.data-commons.org/dashboard/Public/documentation/index.html @@ -73,10 +73,11 @@

Table of ContentsGen3 GWAS

-

  • Genome-wide association studies (GWAS) for quantitative -phenotype.

    • +

  • Genome-Wide Association Studies (GWAS) for Quantitative +Phenotype.

  • Genome-wide association studies (GWAS) for a case-control study.

    • +

  • GWAS Results

    • @@ -146,7 +147,8 @@

    WorkspaceJupyter.org.

    +Jupyter.org (disclaimer: CTDS is not +responsible for the content).

    There are five workspace options available. The options include two @@ -191,7 +193,8 @@

    Apps

    ATLAS is an open source software application developed as a part of OHDSI community intended to provide a -unified interface to patient level data and analytics.

    +unified interface to patient level data and analytics. Atlas software us +used to define cohorts, typically dichotomous variables, for analysis.

    ATLAS currently includes functionality for searching and navigating the vocabulary within the OMOP Common Data Model (CDM). In addition to the search and navigation capabilities, it also provides features to curate @@ -202,9 +205,11 @@

    OHDSI Atlas -

    The ATLAS user guide can be found -here. (disclaimer: CTDS is not -responsible for the content).

    +

    Tutorial for the ATLAS tool can be found +here. It is highly advisable +that you familiarize yourself with these resources before proceeding. We +have also provided a brief step-by-step guide to creating dichotomous +variables here:

    Steps to Generate a Cohort

    Step 1 Create a Concept Set

    @@ -224,8 +229,8 @@

    Steps to Generate a Cohort -

    Select desired concepts, click “Add To Concept Set”, then click “Concept -Sets”.

    +

    Select desired concepts, click “Add To Concept Set”. Repat Search and +Add steps as needed, then click “Concept Sets”.

    _images/slide_15.png

    Make sure all desired concepts are included, then click the save icon to the right of the name of the Concept Set.

    @@ -255,7 +260,8 @@

    Steps to Generate a Cohort -

    Select “Greater or Equal To” and enter 2. This will allow for the +

    Select “Greater or Equal To”. In this specific case we added Heart +Failure concept Set and entered value “2”. This will allow for the collection of data from the Observation table of the MVP harmonization database.

    If you would like to add additional inclusion criteria, click “New @@ -269,8 +275,13 @@

    Steps to Generate a Cohort

    To complete the creation of the Cohort Definition, click “Generation”, then “Generate”.

    -

    Cohort size will be displayed. Use View Reports to see if you have -inclusion criteria that causes cohort attrition.

    +

    Cohort size will be displayed under the column “People”. Use View +Reports to see if you have inclusion criteria that causes cohort +attrition.

    +

    We expect that this documentation in addition to the OHDSI tutorials are +sufficient for most analyses that users will attempt. If your phenotype +and analysis variables are more complex than this documentation covers, +please contact us for consultation at- craig.teerlink@va.gov

    @@ -286,15 +297,16 @@

    Gen3 GWAS -

    Genome-wide association studies (GWAS) for quantitative phenotype.

    +

    Genome-Wide Association Studies (GWAS) for Quantitative Phenotype.

    Here, GWAS evaluates the statistical association between genetic variation and a continuous phenotype. A phenotype, also called a trait, can be any measured or observed property of an individual.

    @@ -312,14 +324,15 @@

    Genome-wide association studies (GWAS) for quantitative phenotype. -

    Please choose as many variables as you wish (need to choose at least -two), you may remove them later in the pipeline. Currently, only -continuous variables can be selected. All variables are harmonized. To -browse the table, please scroll down to the bottom. To search the table -please enter free text in the search box to search by cohort name.

    +

    You may choose as many variables as you wish in this step, with a +minimum of one, that will represent your outcome phenotype. You may +remove them later in the pipeline. Currently, only continuous variables +can be selected. To browse the table, please scroll down to the bottom. +To search the table please enter free text in the search box to search +by cohort name.

    Step 3 Select which variable is your phenotype

    In this step, you will determine your phenotype, using the selected -variables from Step 2. Please choose one of the selected variables to be +variables from step 2. Please choose one of the selected variables to be the study’s phenotype.

    Here you may choose your phenotype. All data are harmonized from different projects through the collaborative development of a data @@ -330,23 +343,23 @@

    Genome-wide association studies (GWAS) for quantitative phenotype.

    Step 4 Add custom dichotomous covariates

    In this step, you may add custom dichotomous covariates by selecting two -cohorts. Please combine a cohort for YES and a cohort for NO. Once -cohorts are selected you may enter a name for the covariate. To commit -the changes please press ‘Add’ at the bottom (You must ‘Add’ the -variable before moving to the next screen if you want it to be a part of -your analysis). You may repeat this action as many times as you need, or -choose to not add any custom dichotomous covariates at all.

    +cohorts. This step is optional, and you may choose not to add any +dichotomous covariate at all. You may combine a cohort for YES and a +cohort for NO. Once cohorts are selected you may enter a name for the +covariate. To commit the changes please press ‘Add’ at the bottom (You +must ‘Add’ the variable before moving to the next screen if you want it +to be a part of your analysis). You may repeat this action as many times +as you need, or choose to not add any custom dichotomous covariates at +all. Please note that all given names must be unique.

    As you add covariates you may see them populate on the right hand side -of the screen as cards. The card contains the following information: -Your given name at the top of the card, Cohorts [X,Y] represents the -cohort’s ID number of the X-No/0 and Y-Yes/1 chosen as they intersect -with your initial population selected, and the ability to remove the -created covariate at the bottom of the card.

    +of the screen as cards. The card contains your given name at the top of +the card, and the ability to remove the created covariate at the bottom +of the card.

    Step 5 Set workflow parameters and remove unwanted covariates

    In this step, you will determine workflow parameters. Please adjust the number of population principal components (PCs) to control for population structure, minor allele frequency cutoff and imputation score -cutoff. You may also remove unwanted covariates. Please also choose the +cutoff. You may also remove unwanted covariates. Please also choose one ancestry population on which you would like to perform your study.

    Number of PCs- Population Principal components (PCs) refer to linear combinations of genome-wide genotyping data to control for population @@ -363,29 +376,20 @@

    Genome-wide association studies (GWAS) for quantitative phenotype.MAF Cutoff- Minor allele frequency (MAF) is the frequency at which the second most common allele occurs in a given population and can be used to filter out rare markers (scale of 0-0.5)

    -

    HARE dropdown manues- Please choose the ancestry population on which you +

    HARE dropdown menu- Please choose the ancestry population on which you would like to perform your study. The numbers appearing in the dropdown represent the population size of your study, considering all of your previous selections. The codes are the HARE (Hharmonized Aancestry and Rrace/Eethnicity) codes.

    Imputation Score Cutoff- This value reflects the quality of imputed SNPs and can be used to remove low-quality imputed markers (scale of 0-1)

    -

    Step 6 Submit GWAS job

    +

    Step 6 Submit GWAS Study

    In this step, you may review the metadata selected for the study, give a name to the study, and submit the GWAS for analysis. To commit any changes please go back to the relevant step.

    -

    Check Submission Status

    -

    Once your GWAS analysis is submitted, click the arrow in the Submitted -Job Statuses box to activate the drop- down menu and see the status of -your analysis. This menu will display a history of your submitted jobs -including: Run ID of your analysis, user given name for the analysis, -start time, and finish time for when the run is completed. This menu -will also display whether the analysis was a success or failed. Once -completed, you may download the results of the GWAS analysis from this -menu. By pressing the ‘Download’ link, a tar.gz file will start -downloading to your computer. The file contains the following: Manhattan -plot, QQ plot, metadata file containing all of your selections, -attrition table, and per-chromosome GWAS summary statistics.

    +

    Check Submission Status and Review Results

    +

    Once your GWAS analysis is submitted, you can check the submission +status and review the results in the “GWAS Results” App.

    Genome-wide association studies (GWAS) for a case-control study.

    @@ -402,7 +406,7 @@

    Genome-wide association studies (GWAS) for a case-control study.

    You may also see a button to create a new cohort. This button will open a new tab in your browser, outside of the Gen3 GWAS App and send you to -OHDSI Atlas App.

    +the OHDSI Atlas App.

    Step 2 Select a control cohort for GWAS

    In this step, you will continue to define the study population. Please select the cohort that you would like to define as your study “control” @@ -416,31 +420,31 @@

    Genome-wide association studies (GWAS) for a case-control study.

    Step 3 Select harmonized variables for covariates

    -

    In this step, you will select covariates for your study. Please choose -as many covariates as you wish, you may remove them later in the +

    In this step, you may select covariates for your study. This step is +optional, and you may choose not to add any covariate at all. Please +choose as many covariates as you wish, you may remove them later in the pipeline. Currently, only continuous covariates are presented. All variables are harmonized. To browse the table please scroll down to the -bottom. You must select at least one covariate in order to move to the -next step. To search the table please enter free text in the search box -to search by cohort name. You must select at least one covariate in -order to move to the next step.

    +bottom. To search the table please enter free text in the search box to +search by cohort name.

    Step 4 Assess % missing in selected covariates

    In this step, you can review the covariates selection based on % missing -metrics. To adjust covariates please return to Step 3.

    +metrics. To adjust covariates please return to Step 3. If no covariates +were chosen in step 3, this step will be empty.

    Step 5 Add custom dichotomous covariates

    In this step, you may add custom dichotomous covariates by selecting two -cohorts. Please combine a cohort for YES and a cohort for NO. Once -cohorts are selected you may enter a name for the covariate. To commit -the changes please press ‘Add’ at the bottom (You must ‘Add’ the -variable before moving to the next screen if you want it to be a part of -your analysis). You may repeat this action as many times as you need, or -choose to not add any custom dichotomous covariates at all.

    +cohorts. This step is optional, and you may choose not to add any +dichotomous covariate at all. You may combine a cohort for YES and a +cohort for NO. Once cohorts are selected you may enter a name for the +covariate. To commit the changes please press ‘Add’ at the bottom (You +must ‘Add’ the variable before moving to the next screen if you want it +to be a part of your analysis). You may repeat this action as many times +as you need, or choose to not add any custom dichotomous covariates at +all. Please note that all given names must be unique.

    As you add covariates you may see them populate on the right hand side -of the screen as cards. The card contains the following information: -Your given name at the top of the card, Cohorts [X,Y] represents the -population size of the Yes and No cohorts chosen as they intersect with -your initial population selected, and the ability to remove the created -covariate at the bottom of the card.

    +of the screen as cards. The card contains your given name at the top of +the card and the ability to remove the created covariate at the bottom +of the card.

    Step 6 Set workflow parameters and remove unwanted covariates

    In this step, you will determine workflow parameters. Please adjust the number of population principal components to control for population @@ -471,21 +475,26 @@

    Genome-wide association studies (GWAS) for a case-control study.

    Imputation Score Cutoff- This value reflects the quality of imputed SNPs and can be used to remove low-quality imputed markers (scale of 0-1)

    -

    Step 7 Submit GWAS job

    +

    Step 7 Submit GWAS Study

    In this step, you may review the metadata selected for the study, give a name to the study, and submit the GWAS for analysis.

    -

    Check Submission Status

    -

    Once your GWAS analysis is submitted, click the arrow in the Submitted -Job Statuses box to activate the drop down menu and see the status of -your analysis. This menu will display a history of your submitted jobs -including the Run ID of your analysis, the start time, and the finish -time when the run is completed. This menu will also display whether the -analysis was a success or failed. Once completed, you may download the -results of the GWAS analysis from this menu. By pressing the ‘Download’ -link a tar.gz file will start downloading to your computer. The file -contains the following: Manhattan plot, QQ plot, metadata file -containing all of your selections, attrition table, and per-chromosome -GWAS summary statistics.

    +

    Check Submission Status and Review Results

    +

    Once your GWAS analysis is submitted, you can check the Submission +Status and Review the Results in the “GWAS Results” app.

    +

    +
    +

    GWAS Results

    +

    Use this App to view the status & results of submitted workflows. Click +the arrow in the Submitted Job Statuses box to activate the drop down +menu and see the status of your analysis. This menu will display a +history of your submitted jobs including the Run ID of your analysis, +the start time, and the finish time when the run is completed. This menu +will also display whether the analysis was a success or failed. Once +completed, you may download the results of the GWAS analysis from this +menu. By pressing the ‘Download’ link a tar.gz file will start +downloading to your computer. The file contains the following: Manhattan +plot, QQ plot, metadata file containing all of your selections, and +per-chromosome GWAS summary statistics.

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    Table of ContentsGen3 GWAS

    -

  • Genome-wide association studies (GWAS) for quantitative phenotype.

    • +

  • Genome-Wide Association Studies (GWAS) for Quantitative Phenotype.

  • Genome-wide association studies (GWAS) for a case-control study.

    • +

  • GWAS Results

    • @@ -127,10 +128,10 @@

    Apps

    OHDSI Atlas

    -

    ATLAS is an open source software application developed as a part of OHDSI community intended to provide a unified interface to patient level data and analytics.

    +

    ATLAS is an open source software application developed as a part of OHDSI community intended to provide a unified interface to patient level data and analytics. Atlas software us used to define cohorts, typically dichotomous variables, for analysis.

    ATLAS currently includes functionality for searching and navigating the vocabulary within the OMOP Common Data Model (CDM). In addition to the search and navigation capabilities, it also provides features to curate and export custom sets of concept identifiers for use in cohort definitions. These will automatically populate on the Gen3 GWAS App. In general, ATLAS is an analytics platform that can be used to perform analyses across one or more observational databases which have been standardized to the OMOP Common Data Model V5 and can facilitate exchange of analysis designs with any other organizations across the OHDSI community.

    -

    The ATLAS user guide can be found here. (disclaimer: CTDS is not responsible for the content).

    +

    Tutorial for the ATLAS tool can be found here. It is highly advisable that you familiarize yourself with these resources before proceeding. We have also provided a brief step-by-step guide to creating dichotomous variables here:

    Steps to Generate a Cohort

    Step 1 Create a Concept Set

    @@ -143,7 +144,7 @@

    Steps to Generate a Cohort

    Enter the concept name or code into the search bar to find relevant concept variables.

    -

    Select desired concepts, click “Add To Concept Set”, then click “Concept Sets”.

    +

    Select desired concepts, click “Add To Concept Set”. Repat Search and Add steps as needed, then click “Concept Sets”.

    Make sure all desired concepts are included, then click the save icon to the right of the name of the Concept Set.

    Step 2 Create Cohort Definition

    @@ -162,15 +163,15 @@

    Steps to Generate a Cohort

    To access the table in the MVP harmonization database, click “+ Add attribute…”, then click “Add Value as Number Criteria”.

    -

    Select “Greater or Equal To” and enter 2. -This will allow for the collection of data from the Observation table of the MVP harmonization database.

    +

    Select “Greater or Equal To”. In this specific case we added Heart Failure concept Set and entered value “2”. This will allow for the collection of data from the Observation table of the MVP harmonization database.

    If you would like to add additional inclusion criteria, click “New inclusion criteria” select criteria or import another configuration. To complete the Cohort Definition, click the green Save icon.

    Step 3 Generate Cohort

    Once created, the cohort can then serve as the basis of inputs for your subsequent analyses. Use the cohort definition to identify how many people are in the cohort. This is the initial number that will be used in the GWAS app when this cohort is selected.

    To complete the creation of the Cohort Definition, click “Generation”, then “Generate”.

    -

    Cohort size will be displayed. Use View Reports to see if you have inclusion criteria that causes cohort attrition.

    +

    Cohort size will be displayed under the column “People”. Use View Reports to see if you have inclusion criteria that causes cohort attrition.

    +

    We expect that this documentation in addition to the OHDSI tutorials are sufficient for most analyses that users will attempt. If your phenotype and analysis variables are more complex than this documentation covers, please contact us for consultation at- craig.teerlink@va.gov

    @@ -180,26 +181,27 @@

    Gen3 GWAS -

    Genome-wide association studies (GWAS) for quantitative phenotype.

    +

    Genome-Wide Association Studies (GWAS) for Quantitative Phenotype.

    Here, GWAS evaluates the statistical association between genetic variation and a continuous phenotype. A phenotype, also called a trait, can be any measured or observed property of an individual.

    Step 1 Select a cohort for GWAS

    In this step, you will determine the study population. To begin, select the cohort that you would like to define your study population with. You may only see cohorts that you have access to. Please select only one cohort. The size of the cohort population is indicated in the right hand side of the table. To browse the table please scroll down to the bottom.

    You may also see a button to create a new cohort. This button will open a new tab in your browser, outside of the Gen3 GWAS App and send you to the OHDSI Atlas App.

    Step 2 Select harmonized variables for phenotypes and covariates

    In this step, you will select the harmonized variables for your study. Please select all variables you wish to use in your model, including both covariates and phenotype. (Note:- population PCs are not included in this step)

    -

    Please choose as many variables as you wish (need to choose at least two), you may remove them later in the pipeline. Currently, only continuous variables can be selected. All variables are harmonized. To browse the table, please scroll down to the bottom. To search the table please enter free text in the search box to search by cohort name.

    +

    You may choose as many variables as you wish in this step, with a minimum of one, that will represent your outcome phenotype. You may remove them later in the pipeline. Currently, only continuous variables can be selected. To browse the table, please scroll down to the bottom. To search the table please enter free text in the search box to search by cohort name.

    Step 3 Select which variable is your phenotype

    -

    In this Sstep, you will determine your phenotype, using the selected variables from Step 2. Please choose one of the selected variables to be the study’s phenotype.

    +

    In this step, you will determine your phenotype, using the selected variables from step 2. Please choose one of the selected variables to be the study’s phenotype.

    Here you may choose your phenotype. All data are harmonized from different projects through the collaborative development of a data dictionary. In the right hand side of the table a missing % is calculated. This is to reflect how many subjects of the chosen population do not have this information available. To browse the table please scroll down to the bottom.To search the table please enter free text in the search box to search by cohort name.

    Step 4 Add custom dichotomous covariates

    -

    In this step, you may add custom dichotomous covariates by selecting two cohorts. Please combine a cohort for YES and a cohort for NO. Once cohorts are selected you may enter a name for the covariate. To commit the changes please press ‘Add’ at the bottom (You must ‘Add’ the variable before moving to the next screen if you want it to be a part of your analysis). You may repeat this action as many times as you need, or choose to not add any custom dichotomous covariates at all.

    -

    As you add covariates you may see them populate on the right hand side of the screen as cards. The card contains the following information: Your given name at the top of the card, Cohorts [X,Y] represents the cohort’s ID number of the X-No/0 and Y-Yes/1 chosen as they intersect with your initial population selected, and the ability to remove the created covariate at the bottom of the card.

    +

    In this step, you may add custom dichotomous covariates by selecting two cohorts. This step is optional, and you may choose not to add any dichotomous covariate at all. You may combine a cohort for YES and a cohort for NO. Once cohorts are selected you may enter a name for the covariate. To commit the changes please press ‘Add’ at the bottom (You must ‘Add’ the variable before moving to the next screen if you want it to be a part of your analysis). You may repeat this action as many times as you need, or choose to not add any custom dichotomous covariates at all. Please note that all given names must be unique.

    +

    As you add covariates you may see them populate on the right hand side of the screen as cards. The card contains your given name at the top of the card, and the ability to remove the created covariate at the bottom of the card.

    Step 5 Set workflow parameters and remove unwanted covariates

    -

    In this step, you will determine workflow parameters. Please adjust the number of population principal components (PCs) to control for population structure, minor allele frequency cutoff and imputation score cutoff. You may also remove unwanted covariates. Please also choose the ancestry population on which you would like to perform your study.

    +

    In this step, you will determine workflow parameters. Please adjust the number of population principal components (PCs) to control for population structure, minor allele frequency cutoff and imputation score cutoff. You may also remove unwanted covariates. Please also choose one ancestry population on which you would like to perform your study.

    Number of PCs- Population Principal components (PCs) refer to linear combinations of genome-wide genotyping data to control for population structure/stratification (select up to 10 PCs)

    Covariates- @@ -210,31 +212,31 @@

    Genome-wide association studies (GWAS) for quantitative phenotype.

    MAF Cutoff- Minor allele frequency (MAF) is the frequency at which the second most common allele occurs in a given population and can be used to filter out rare markers (scale of 0-0.5)

    -

    HARE dropdown manues- +

    HARE dropdown menu- Please choose the ancestry population on which you would like to perform your study. The numbers appearing in the dropdown represent the population size of your study, considering all of your previous selections. The codes are the HARE (Hharmonized Aancestry and Rrace/Eethnicity) codes.

    Imputation Score Cutoff- This value reflects the quality of imputed SNPs and can be used to remove low-quality imputed markers (scale of 0-1)

    -

    Step 6 Submit GWAS job

    +

    Step 6 Submit GWAS Study

    In this step, you may review the metadata selected for the study, give a name to the study, and submit the GWAS for analysis. To commit any changes please go back to the relevant step.

    -

    Check Submission Status

    -

    Once your GWAS analysis is submitted, click the arrow in the Submitted Job Statuses box to activate the drop- down menu and see the status of your analysis. This menu will display a history of your submitted jobs including: Run ID of your analysis, user given name for the analysis, start time, and finish time for when the run is completed. This menu will also display whether the analysis was a success or failed. Once completed, you may download the results of the GWAS analysis from this menu. By pressing the ‘Download’ link, a tar.gz file will start downloading to your computer. The file contains the following: Manhattan plot, QQ plot, metadata file containing all of your selections, attrition table, and per-chromosome GWAS summary statistics.

    +

    Check Submission Status and Review Results

    +

    Once your GWAS analysis is submitted, you can check the submission status and review the results in the “GWAS Results” App.

    Genome-wide association studies (GWAS) for a case-control study.

    Here, the genotypes of a roughly equal number of diseased (“cases”) and healthy (“controls”) people are compared to determine which genetic variants are associated with the disease. Cases are encoded as ‘1’ while controls are encoded as ‘0’ and a binary model is used.

    Step 1 Select a case cohort for GWAS

    In this step, you will begin to define the study population. To begin, select the cohort that you would like to define as your study “cases” population. You may only see cohorts that you have access to. Please select only one cohort. The size of the cohort population is indicated in the right hand side of the table. To browse the table please scroll down to the bottom.

    -

    You may also see a button to create a new cohort. This button will open a new tab in your browser, outside of the Gen3 GWAS App and send you to OHDSI Atlas App.

    +

    You may also see a button to create a new cohort. This button will open a new tab in your browser, outside of the Gen3 GWAS App and send you to the OHDSI Atlas App.

    Step 2 Select a control cohort for GWAS

    In this step, you will continue to define the study population. Please select the cohort that you would like to define as your study “control” population. You may only see cohorts that you have access to. Please select only one cohort. The size of the cohort population is indicated in the right hand side of the table. To browse the table please scroll down to the bottom. To search the table please enter free text in the search box to search by cohort name. To search the table please enter free text in the search box to search by cohort name.

    You may also see a button to create a new cohort. This button will open a new tab in your browser, outside of the Gen3 GWAS App and send you to OHDSI Atlas App.

    Step 3 Select harmonized variables for covariates

    -

    In this step, you will select covariates for your study. Please choose as many covariates as you wish, you may remove them later in the pipeline. Currently, only continuous covariates are presented. All variables are harmonized. To browse the table please scroll down to the bottom. You must select at least one covariate in order to move to the next step. To search the table please enter free text in the search box to search by cohort name. You must select at least one covariate in order to move to the next step.

    +

    In this step, you may select covariates for your study. This step is optional, and you may choose not to add any covariate at all. Please choose as many covariates as you wish, you may remove them later in the pipeline. Currently, only continuous covariates are presented. All variables are harmonized. To browse the table please scroll down to the bottom. To search the table please enter free text in the search box to search by cohort name.

    Step 4 Assess % missing in selected covariates

    -

    In this step, you can review the covariates selection based on % missing metrics. To adjust covariates please return to Step 3.

    +

    In this step, you can review the covariates selection based on % missing metrics. To adjust covariates please return to Step 3. If no covariates were chosen in step 3, this step will be empty.

    Step 5 Add custom dichotomous covariates

    -

    In this step, you may add custom dichotomous covariates by selecting two cohorts. Please combine a cohort for YES and a cohort for NO. Once cohorts are selected you may enter a name for the covariate. To commit the changes please press ‘Add’ at the bottom (You must ‘Add’ the variable before moving to the next screen if you want it to be a part of your analysis). You may repeat this action as many times as you need, or choose to not add any custom dichotomous covariates at all.

    -

    As you add covariates you may see them populate on the right hand side of the screen as cards. The card contains the following information: Your given name at the top of the card, Cohorts [X,Y] represents the population size of the Yes and No cohorts chosen as they intersect with your initial population selected, and the ability to remove the created covariate at the bottom of the card.

    +

    In this step, you may add custom dichotomous covariates by selecting two cohorts. This step is optional, and you may choose not to add any dichotomous covariate at all. You may combine a cohort for YES and a cohort for NO. Once cohorts are selected you may enter a name for the covariate. To commit the changes please press ‘Add’ at the bottom (You must ‘Add’ the variable before moving to the next screen if you want it to be a part of your analysis). You may repeat this action as many times as you need, or choose to not add any custom dichotomous covariates at all. Please note that all given names must be unique.

    +

    As you add covariates you may see them populate on the right hand side of the screen as cards. The card contains your given name at the top of the card and the ability to remove the created covariate at the bottom of the card.

    Step 6 Set workflow parameters and remove unwanted covariates

    In this step, you will determine workflow parameters. Please adjust the number of population principal components to control for population structure, minor allele frequency cutoff and imputation score cutoff. You may also remove unwanted covariates. Please also choose the ancestry population on which you would like to perform your study.

    Number of PCs- @@ -251,11 +253,14 @@

    Genome-wide association studies (GWAS) for a case-control study.

    Imputation Score Cutoff- This value reflects the quality of imputed SNPs and can be used to remove low-quality imputed markers (scale of 0-1)

    -

    Step 7 Submit GWAS job

    +

    Step 7 Submit GWAS Study

    In this step, you may review the metadata selected for the study, give a name to the study, and submit the GWAS for analysis.

    -

    Check Submission Status

    -

    Once your GWAS analysis is submitted, click the arrow in the Submitted Job Statuses box to activate the drop down menu and see the status of your analysis. This menu will display a history of your submitted jobs including the Run ID of your analysis, the start time, and the finish time when the run is completed. This menu will also display whether the analysis was a success or failed. Once completed, you may download the results of the GWAS analysis from this menu. -By pressing the ‘Download’ link a tar.gz file will start downloading to your computer. The file contains the following: Manhattan plot, QQ plot, metadata file containing all of your selections, attrition table, and per-chromosome GWAS summary statistics.

    +

    Check Submission Status and Review Results

    +

    Once your GWAS analysis is submitted, you can check the Submission Status and Review the Results in the “GWAS Results” app.

    +

    +
    +

    GWAS Results

    +

    Use this App to view the status & results of submitted workflows. Click the arrow in the Submitted Job Statuses box to activate the drop down menu and see the status of your analysis. This menu will display a history of your submitted jobs including the Run ID of your analysis, the start time, and the finish time when the run is completed. This menu will also display whether the analysis was a success or failed. Once completed, you may download the results of the GWAS analysis from this menu. By pressing the ‘Download’ link a tar.gz file will start downloading to your computer. The file contains the following: Manhattan plot, QQ plot, metadata file containing all of your selections, and per-chromosome GWAS summary statistics.