Clinvar / GTR Basic Analysis

Summary

[ Click to view: Source code | Data ]

Data analysis consisted of joining targeted Genetic Testing Registry and ClinVar tables making use of the ‘Symbol’ column in both tables as the shared key.

Frequency distributions for each gene in ClinVar and GTR were calculated, showing a fairly abnormal distribution for gene coverage in ClinVar in relationship to the tests available in GTR for each gene.

As a result of this analysis, some basic questions could then be posed and answered — namely, which genes are well-covered in ClinVar but not represented at all in GTR, and which genes reported as tests in GTR are the least well supported by evidence in ClinVar? The following 2 tables represent the top 10 genes in each category.

Top 10 ClinVar genes by Submissions having no tests available in GTR
Top 10 ClinVar genes by Submissions having no tests available in GTR
Top 10 GTR gene tests least well supported by ClinVar Submission evidence
Top 10 GTR gene tests least well supported by ClinVar Submission evidence.

Data Prep Discussion

Three variables were chosen for this analysis:

  • Symbol (i.e. standardized gene name)
  • Submissions (ClinVar submission count, i.e. number of times gene referenced in ClinVar)
  • unique_tests (GTR unique test count, i.e. number of separate tests represented for this gene in the Genetic Testing Registry)

The “Submissions” column comes from the clinvar.gene_specific_summary table generated by the NCBI. Since this table comes precomputed, no missing data can be detected or is suspected.

The “unique_tests” column was generated in the attached Python code by subselecting rows withing the GTR.test_condition_gene table to restrict to the following conditions:

  • concept_type=”gene”
  • Symbol contains a gene name (not NULL or ‘-‘)

Subselecting the GTR data in this way eliminated all rows that did not specify a Symbol (gene name) for their test.

Results from this initial data analysis can be found below. The code that produced this readout can be found here (bitbucket).

Data used in this experiment can be downloaded here (bitbucket).

Program Output

Continue reading “Clinvar / GTR Basic Analysis”

Advertisements
Clinvar / GTR Basic Analysis

Clinvar / GTR Research Questions

Clinvar and GTR: discussion

The subject of this data analysis experiment is a mashup of two datasets, Clinvar and the Genetic Testing Registry. Please see the linked blog posts for a detailed introduction to these datasets, as well as their technical details and links to formal documentation.

Gene (Symbol) based analysis

Does the research represented in ClinVar, indicated by the HUGO gene name symbols assigned to individual variant accessions, demonstrate a relationship with the frequency of distribution of genetic tests for these genes in the Genetic Testing Registry?

Which gene tests in GTR are backed by the most ClinVar submissions?

Are there genes well-represented in terms of ClinVar submissions that are not well represented in the GTR database in terms of gene panel coverage? Or are these two distributions fairly well aligned?

Condition (concept) based analysis

Graph the frequency of conditions (represented by regularized MedGen concept codes aka CUIs) cited in ClinVar versus the frequency of conditions tested for in GTR.

What is the apparent coverage for condition-based testing (GTR) in terms of numbers of accessioned variants for those conditions (ClinVar)?

Further analysis

How does the data landscape change when GTR test_type is restricted to “Clinical”?

Is there a correlation between frequency of pubmed citations for a particular variant and number of GTR tests for the gene in which that variant is found?

Hypotheses

  1. Genetic testing (as represented in GTR) follows a gene distribution pattern similar to the distribution of ClinVar submissions.
  2. The greater the number of pubmed citations for variants within particular genes, the greater the number of genetic tests for those genes.

Links to Relevant Research

The NIH genetic testing registry: a new, centralized database of genetic tests to enable access to comprehensive information and improve transparency

Database resources of the National Center for Biotechnology Information

Evaluating the NIH’s New Genetic Testing Registry

Free the Data: The End of Genetic Data as Trade Secrets

A general framework for estimating the relative pathogenicity of human genetic variants

ClinVitae: a unified database of clinically-observed genetic variants aggregated from public sources

ClinVar: public archive of relationships among sequence variation and human phenotype

In Tackling the VUS Challenge, Are Public Databases the Solution or a Liability for Labs?

Codebook

Continue reading “Clinvar / GTR Research Questions”

Clinvar / GTR Research Questions

Genetic Testing Registry Dataset

The Genetic Testing Registry (GTR) is an NCBI dataset (and like Clinvar, available via FTP and eutils) that publishes information on specific genetic tests provided by various institutions. Some tests focus on a particular gene (e.g. BRCA1); some tests comprise disease or condition “panels”; and some entries in GTR bundle the most commonly problematic genes (especially for cancer) into a single test.

For example, a typical disease testing panel for HHT (Hereditary hemorrhagic telangiectasia) should encompass at least the ENG and ACVR1 genes, and potentially also SMAD4. These genes are bundled into “panel” groupings — for example, this HHT Panel by GeneDX — to enable a genetic test provider to respond comprehensively to a doctor’s diagnostic indications for a patient.

As with the Clinvar dataset, the GTR data will be imported for analysis into MySQL using the medgen-mysql toolkit.

Technical Details: Access and Manipulation

Continue reading “Genetic Testing Registry Dataset”

Genetic Testing Registry Dataset

ClinVar Dataset

From the ClinVar NCBI home page:

ClinVar aggregates information about genomic variation and its relationship to human health.

As of September 14, 2015, there are 158,991 accessioned submissions in ClinVar. These data points represent cases of observation of a gene variation “in nature” — meaning human DNA variations read from sequenced genomic samples, analyzed by variant interpretation scientists and compared in the clinical literature for information pertaining to its pathogenicity and relevance to particular disease conditions.

The genetic testing industry has come to rely heavily on ClinVar for reporting on the (probable) pathogenicity of any given variant. These variants are described in ClinVar and (to varying degrees) within the clinical literature by a short piece of text formatted according to the HGVS (Human Genome Variability Society) standard.

Most ClinVar submissions contain reference to a gene, though many (over 50,000 of them) do not.

The gene names in ClinVar make use of the HUGO gene naming convention, which is the convention that this blog and research will use as well.

In the variant_summary table, ClinVar also makes reference to a “GeneID” variable; this field refers to a gene entry in the NCBI Gene database, which we will access programmatically via metapub.

Conveniently, the GTR database also uses HUGO gene names — a convenience we will exploit in this data exploration.

Technical Details: Access and Manipulation

Continue reading “ClinVar Dataset”

ClinVar Dataset