Abstract
Objective: To evaluate the performance of an in silico prioritization approach that was applied to 179 epileptic encephalopathy candidate genes in 2013 and to expand the application of this approach to the whole genome based on expression data from the Allen Human Brain Atlas.
Methods: PubMed searches determined which of the 179 epileptic encephalopathy candidate genes had been validated. For validated genes, it was noted whether they were 1 of the 19 of 179 candidates prioritized in 2013. The in silico prioritization approach was applied genome-wide; all genes were ranked according to their coexpression strength with a reference set (i.e., 51 established epileptic encephalopathy genes) in both adult and developing human brain expression data sets. Candidate genes ranked in the top 10% for both data sets were cross-referenced with genes previously implicated in the epileptic encephalopathies due to a de novo variant.
Results: Five of 6 validated epileptic encephalopathy candidate genes were among the 19 prioritized in 2013 (odds ratio = 54, 95% confidence interval [7,∞], p = 4.5 × 10−5, Fisher exact test); one gene was false negative. A total of 297 genes ranked in the top 10% for both the adult and developing brain data sets based on coexpression with the reference set. Of these, 9 had been previously implicated in the epileptic encephalopathies (FBXO41, PLXNA1, ACOT4, PAK6, GABBR2, YWHAG, NBEA, KNDC1, and SELRC1).
Conclusions: We conclude that brain gene coexpression data can be used to assist epileptic encephalopathy gene discovery and propose 9 genes as strong epileptic encephalopathy candidates worthy of further investigation.
Footnotes
Funding information and disclosures are provided at the end of the article. Go to Neurology.org/ng for full disclosure forms. The Article Processing Charge was paid by the authors.
Supplemental data at Neurology.org/ng
- Received September 28, 2015.
- Accepted in final form December 10, 2015.
- © 2016 American Academy of Neurology
This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND), which permits downloading and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially.
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