This guide will take approximately 5 minutes to complete.
In this guide you will learn:
- How to search for genetic alterations related to a disease of interest
- How to discover genetic variants from the GWAS Catalog
- How to identify disorders associated with mutations of a gene of interest from the GWAS Catalog
- How to validate and qualify genetic associations using data coming from the GWAS Catalog
Step 0: Login
Refer to https://med.causaly.com
Step 1: Search for genetic alterations related to a disease of interest
To explore mutations related to a disease of interest, use Intelligent Search on the Causaly homepage (Figure 1).
Figure 1. Intelligent Search on the Causaly homepage.
Begin your search by typing ‘mutations’ and the name of a specific disease, such as NSCLC (Non-Small Cell Lung Carcinoma), into the input box. Causaly will propose the relevant search topic: 'Genetic alterations related to NSCLC' (Figure 2).
Figure 2. Select ‘Genetic alterations related to NSCLC’ from the list of search topics.
By clicking on it, Causaly will initiate the search (link). The system searches for genetic variants and evidence from all our data sources by default, including the GWAS Catalog.
TIP!
- To find out what genetic alterations are covered in this search, click on the ‘Open in advanced search’ option, just under the input box (Figure 3).
Figure 3. Select ‘Open in advanced search’ to investigate how Causaly performed this query.
In the Advanced module, some keywords have been added by default in the free-text box to refine for genetic alterations and genetic evidence. You can add further keywords to specify your search, such as indel, deletion, rearrangement.
- Consider the free-text box like PubMed. Use keywords to refine your search or construct Boolean queries using the AND, OR, NOT operators. For more information, visit this page: How To Use the Keyword Search
Step 2: Discover genetic variants from the GWAS Catalog for a disease of interest
Causaly uses the GWAS Catalog as a data source, which contains information regarding gene variant-trait associations. The GWAS Catalog enriches our Knowledge Graph with 100,000+ SNPs and 250,000+ associations.
TIP! To account for the high chance of false positives in GWAS studies, a genome-wide significance threshold is applied at a p-value of 5 × 10^-8. This threshold helps to reduce the number of false positive associations that may arise from testing a large number of genetic variants.
You can specify results from the GWAS Catalog by making use of the Data Source filters on the left (link).
In this example search, the GWAS Catalog provides an additional 111 relationships for genetic alterations related to NSCLC (Figure 4).
Figure 4. The GWAS Catalog option is found under the Data Source filter.
You will find a snippet of the evidence from the GWAS Catalog for each result (Figure 5), allowing you to quickly identify the gene variant related to NSCLC as well as other key information about this evidence, such as:
- Functional consequence of the variant
- Trait
- P-value
- GWAS Study ID
Figure 5. A snippet showing the genetic variant related to NSCLC.
TIP! You can click on the title of the GWAS document to reach the Publication page in the GWAS Catalog.
Step 3: Identify disorders associated with mutations of a gene of interest from the GWAS Catalog
To find out which disorders are related to variants of your gene of interest, use Intelligent Search in the Causaly homepage by typing the name of a specific gene, such as BRCA2, and ‘variant’ into the input box. Causaly will propose the relevant search topic to find diseases associated with BRCA2 mutations (Figure 6).
Figure 6. Select ‘Diseases affected by BRCA2 mutations’ from the list of search topics.
Specify the GWAS Catalog filter to view results from this data source (link).
TIP!
- To limit your results to disorders associated with a specific organ system, such as reproductive system and breast disorders, use the MedDRA classification system in the filter panel and select the relevant category (link).
- Search for a specific gene variant by typing its name in Intelligent Search. Genetic variants that are well-established, such as the TP53 p.R337H mutation, exists in the UMLS ontologies and can be directly selected as a concept in Causaly (Figure 7).
Figure 7. The TP53 p.R337H mutation can be found as a concept in Causaly.
If the gene variant does not exist in the UMLS ontologies, such as the ApoE rs440446 SNP, use the free text option. Type ‘rs4400446’ (the gene locus) and select the proposed free text (Figure 8). To find the associated diseases, select effects of ‘rs4400446’ and refine results to the ‘Disorders’ category using the filter panel on the left.
Figure 8. Use the free text option for genetic variants with no concepts associated.
Step 4: Validate and qualify genetic associations using data coming from the GWAS Catalog
Targets that have been genetically linked to a disease exhibit a higher probability of success in the clinical development pipeline, with pre-clinical and clinical research demonstrating respectively four-fold and two-fold increases in success rates (link).
Use Intelligent Search to find out whether your target has a genetic association with your disease of interest. In this example, we explore the relationship between TERT and glioma (link) (Figure 9).
Figure 9. Explore the relationship between TERT and glioma.
In the filter panel on the left, under Data Sources, select ‘GWAS Catalog’ to see genetic evidence (link).
Using the Grid view, we find that there are genetic associations between TERT and glioma/glioblastoma multiforme supported by GWAS data. Click on a relationship to explore the supporting evidence in the sidebar (Figure 10).
Figure 10. Evidence supporting the genetic association between TERT and glioma.