ClinVar / GTR Conclusion

Conclusion

The analysis of ClinVar and the Genetic Testing Registry in terms of gene Symbols, number of ClinVar Submissions, and number of unique tests in GTR demonstrates a positive linear relationship between research submitted to ClinVar and number of clinical and/or research tests in the Genetic Testing Registry.

While the vast majority of genes reported in ClinVar and tests in GTR follow this positive linear relationship, as the graph below shows, certain notable outliers emerge from the data:

  • The LMNA gene appears to have a high number of GTR tests (272) while showing a relatively low number of ClinVar submissions (515) in proportion to other genes.
  • BRCA2 ranks high in both number of GTR tests (174) and number of ClinVar submissions (7584). Its nearest neighbor in terms of submissions and unique_tests is BRCA1. Together these genes comprise the most tested and most well-researched cancer-causing genes.
  • The gene region known as “TTN” (aka “Titan”) sits well above most genes with a ClinVar submission count at 4609, while showing only 6 tests in GTR.

Positive Linear Relationship

[Click to see source code]

This linear regression scatterplot demonstrates a positive linear relationship between ClinVar Submissions (the x axis) and number of unique tests in GTR (the y axis).

Scatterplot for x=Submissions (ClinVar) per gene and y=unique_tests per gene
Scatterplot for x=Submissions (ClinVar) per gene and y=unique_tests per gene

Clinvar Submissions: univariate distribution

The following skewed-right distribution graph of Submissions per gene Symbol shows that most genes cluster for ClinVar submissions around 1 to 1000, while some heavily-researched genes like TTN, BRCA1, and BRCA2 have many thousands of ClinVar Submissions.

The number of distinct genes in ClinVar is roughly 26,000. Since most of these genes have relatively low Submission counts, the values in the distribution, for the purposes of a more readable graph, have been log10 normalized.

Log10-normalized distribution of ClinVar Submissions per gene.
Log10-normalized distribution of ClinVar Submissions per gene.

GTR unique_tests: univariate distribution

The following skewed-right distribution graph of unique_tests per gene Symbol shows that most genes have few tests (under 25), while some heavily-researched genes like BRCA1 and LMNA have far more registered genetic tests (over 200).

GTR: distribution of unique_tests per gene Symbol (skewed right)
GTR: distribution of unique_tests per gene Symbol (skewed right)

A log10 normalization across the same data produces this graph:

Number of unique_tests per gene Symbol, log10 normalized.
GTR: distribution of unique_tests per gene Symbol, log10 normalized.

We might be able to explain the outliers by looking at the pattern of assignment of ClinicalSignificance to the genes recorded in ClinVar Submissions. For example, we might expect to see a very low rate of “pathogenic” calls on variants within the TTN gene, or a very high rate of “pathogenic” calls on variants in LMNA. (An exercise for another day.)

The above graph does not contain “NA” values; that is, genes noted in ClinVar without tests in GTR cannot be shown on this graph.

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ClinVar / GTR Conclusion

Clinvar / GTR Data Management

Collapsing and aggregating subgene regions into major gene Symbol groupings.

[Click to see source code]

The basic analysis completed in the previous post was good for a start, but several issues came to light as I examined the gene/submission lists:

  • Many gene Symbols appear in the form [gene]-[suffix]
  • These suffixed genes do not appear in the Genetic Testing Registry.
  • Many genes have significant Submissions quantities (over 10) attached to these subgene Symbols.

For example, the gene region known as “SNAR” contains many named subregions; here’s a listing of all of the subregions of SNAR known to the National Library of Medicine.

Since the Submission counts for various genes in ClinVar appear to be spread out across these subgene Symbols, the final analysis of whether any particular gene had a test in GTR could be significantly impacted by whether its subgene(s) were considered along with it.

I asked a top expert in the genetic testing field (a former coworker) whether it would be valid to “aggregate” the ClinVar Submission counts for each of these Symbols. Her gene specialty is TTN (aka “Titan”), for which a highly submitted ClinVar region is “TTN-AS1”. Her expert opinion was that combining the TTN-AS1 results with the TTN results for the purposes of cross-reference with the Genetic Testing Registry made good sense. (Emphasis on this aggregation being for these purposes only, not necessarily for any other type of analysis.)

The focus of my Data Management task thus became transforming the ClinVar gene-to-submission table by “collapsing” all genes appearing to be subgene regions.

A manual inspection of the highest submission count gene regions showed that we can “collapse” the Submission counts in this way with high enough confidence that we are not unduly amplifying signal, even if some false positives are included in the aggregate.

Below is are two generated tables containing gene Symbol, number of ClinVar Submissions for this gene region, and number of Unique Tests in GTR for this gene region. Since the length of this table is in the thousands, results in this write-up have been limited to the top 15 in each dimension, sorted in Figure 1 by Submissions (ClinVar) and in Figure 2 by unique_tests (GTR).

Continue reading “Clinvar / GTR Data Management”

Clinvar / GTR Data Management