> For the complete documentation index, see [llms.txt](https://help.ica.illumina.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.ica.illumina.com/project/p-cohorts/cohorts-walkthrough-raredisease.md).

# Rare Genetic Disorders Walk-through

## Cohorts Walk-through: Rare Genetic Disorders

This walk-through is meant to represent a typical workflow when building and studying a cohort of rare genetic disorder cases.

## Login and Create a new Platform Core Project

Create a new Project to track your study:

1. Log in to Platform Core
2. Navigate to **Projects**.
3. Create a new project using the **+Create** button.
4. Give your project a name and region and click **Save**.
5. Navigate to the Platform Core Cohorts module by selecting **Projects > your\_project > Cohorts > Cohorts**.

## Create and Review a Rare Disease Cohort

1. Navigate to **Projects > your\_project > Cohorts > Cohorts**.
2. Click **Create Cohort** button.
3. Enter a name for your cohort, for example "Rare Disease + 1kGP" at the top, left of the pencil icon.
4. From the Public Data Sets list select:
   * DRAGEN-1kGP
   * All Rare genetic disease cohorts

{% hint style="info" %}
A cohort can also be created based on Technology, Disease Type and Tissue.
{% endhint %}

5. Under Selected Conditions in right panel, click on **Apply**
6. A new page opens with your cohort in a top-level tab.
7. Expand **Query Details** to see the study makeup of your cohort.
8. A set of 4 Charts will be open by default. If they are not, click **Show Charts**.
   * Use the gear icon in the top-right of the Charts pane to change chart settings.
9. The bottom section is demarcated by 8 tabs (Subjects, Marker Frequency, Genes, GWAS, PheWAS, Correlation, Molecular Breakdown, CNV).
10. The **Subjects** tab displays a list of exportable Subject IDs and attributes.
    * Clicking on a **Subject ID** link opens up a Subject details page.

## Analyze Your Rare Disease Cohort Data

1. A recent GWAS publication identified 10 risk genes for intellectual disability (ID) and autism. Our task is to evaluate them in Platform Core Cohorts: TTN, PKHD1, ANKRD11, ARID1B, ASXL3, SCN2A, FHL1, KMT2A, DDX3X, SYNGAP1.
2. First we **Hide charts** for more visual space.
3. Click the **Genes** tab where you need to query a gene to see and interact with results.
4. Type **SCN2A** into the Gene search field and select it from autocomplete dropdown options.
5. The **Gene Summary** tab now lists information and links to public resources about SCN2A.
6. Click on the **Variants** tab to see an interactive Legend and analysis tracks.
   * The Needle Plot displays **gnomAD Allele Frequency** for variants in your cohort. Some are in SCN2A conserved protein domains.
   * In Legend, switch the **Plot by** option to **Sample Count** in your cohort.
   * In Legend, uncheck all **Variant Types** except **Stop gained**. Now you should see 7 variants.
   * Hover over pin heads to see pop-up information about particular variants.
7. The **Primate AI** track displays Scores for potential missense variants, based on polymorphisms observed in primate species. Points above the dashed line for the 75th percentile may be considered "likely pathogenic" as cross-species sequence is highly conserved; you often see high conservancy at the functional domains. Points below the 25th percentile may be considered "likely benign".
8. The **Pathogenic variants** track displays markers from ClinVar color-coded by variant type. Hover over them to see pop-ups with more information.
9. The **Exons** track shows mRNA exon boundaries with click and zoom functionality at the ends.
10. Below the Needle Plot and analysis tracks is a list of Variants observed in the selected cohort.
    * The **Export Gene Variants** table icon is above the legend on right side.
11. Click on the **Gene Expression** tab to see a Bar chart of 50 normal tissues from GTEx in transcripts per million (TPM). SCN2A is highly expressed in certain brain tissues, indicating specificity to where good markers for intellectual disability and autism could be expected.
12. As a final exercise in discovering good markers, click on the tab for **Genetic Burden Test**. The table here associates **Phenotypes** with **Mutations Observed** in each Study selected for our cohort, alongside **Mutations Expected** to derive p-values. Given all considerations above, SCN2A is good marker for intellectual disability (p < 1.465 x 10 -22) and autism (p < 5.290 x 10 -9).
13. Continue to check the other genes of interest in step 1.


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