In silico prioritization based on coexpression can aid epileptic encephalopathy gene discovery
Karen L Oliver1, Vesna Lukic1, Saskia Freytag1
1Epilepsy Research Centre (K.L.O., I.E.S., S.F.B.), Department of Medicine, Austin Health, University of Melbourne, Heidelberg, Australia; Population Health and Immunity Division (V.L., S.F., M.B.), The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia; Florey Institute (I.E.S.), Melbourne, Australia; Department of Paediatrics (I.E.S.), University of Melbourne, Royal Children's Hospital, Melbourne, Australia; and Department of Mathematics and Statistics (M.B.) and Department of Medical Biology (M.B.), University of Melbourne, Australia.
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.
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