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Merge pull request #5136 from uc-cdis/feat/update-va-doc
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update va documentation to match portal relaese 3.33.0
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UchicagoZchen138 authored Sep 16, 2022
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Expand Up @@ -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
===============
Expand Down Expand Up @@ -182,7 +183,8 @@ have access to.

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.
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
Expand All @@ -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 <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**
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Expand Down Expand Up @@ -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

Expand Down Expand Up @@ -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.

Expand All @@ -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- [email protected]

**Gen3 GWAS**
-------------
Expand All @@ -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
Expand All @@ -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
Expand All @@ -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
Expand All @@ -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
Expand All @@ -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.**
--------------------------------------------------------------------
Expand All @@ -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**

Expand All @@ -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**

Expand Down Expand Up @@ -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.
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