Imperial College London
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DrNathanSkene

Faculty of Medicine, Department of Brain Sciences

Lecturer in Dementia Research, UK DRI Group Leader
 
 
 
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Contact

 

n.skene Website

 
 
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Location

 

515Burlington DanesHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Nguyen:2018:10.1101/410100,
author = {Nguyen, HT and Dobbyn, A and Charney, AW and Bryois, J and Kim, A and Mcfadden, W and Skene, NG and Huckins, LM and Wang, W and Ruderfer, DM and Xu, X and Fromer, M and Purcell, SM and Lage, K and Verhage, M and Smit, AB and Hjerling-Leffler, J and Buxbaum, JD and Pinto, D and He, X and Sullivan, PF and Stahl, EA},
doi = {10.1101/410100},
title = {Integrative analysis of rare variants and pathway information shows convergent results between immune pathways, drug targets and epilepsy genes},
url = {http://dx.doi.org/10.1101/410100},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:title>Abstract</jats:title><jats:p>Trio family and case-control studies of next-generation sequencing data have proven integral to understanding the contribution of rare inherited and<jats:italic>de novo</jats:italic>single-nucleotide variants to the genetic architecture of complex disease. Ideally, such studies should identify individual risk genes of moderate to large effect size to generate novel treatment hypotheses for further follow-up. However, due to insufficient power, gene set enrichment analyses have come to be relied upon for detecting differences between cases and controls, implicating sets of hundreds of genes rather than specific targets for further investigation. Here, we present a Bayesian statistical framework, termed gTADA, that integrates gene-set membership information with gene-level<jats:italic>de novo</jats:italic>and rare inherited case-control counts, to prioritize risk genes with excess rare variant burden within enriched gene sets. Applying gTADA to available whole-exome sequencing datasets for several neuropsychiatric conditions, we replicated previously reported gene set enrichments and identified novel risk genes. For epilepsy, gTADA prioritized 40 risk genes (posterior probabilities > 0.95), 6 of which replicate in an independent whole-genome sequencing study. In addition, 30/40 genes are novel genes. We found that epilepsy genes had high protein-protein interaction (PPI) network connectivity, and show specific expression during human brain development. Some of the top prioritized EPI genes were connected to a PPI subnetwork of immune genes and show specific expression in prenatal microglia. We also identified multiple enriched drug-target gene sets for EPI which included immunostimulants as well as known antiepileptics. Immune biology was supported specifically by case-control variants from familial epilepsies rather than do novo mutations in generalized encephalitic epilepsy.<
AU  - Nguyen,HT
AU  - Dobbyn,A
AU  - Charney,AW
AU  - Bryois,J
AU  - Kim,A
AU  - Mcfadden,W
AU  - Skene,NG
AU  - Huckins,LM
AU  - Wang,W
AU  - Ruderfer,DM
AU  - Xu,X
AU  - Fromer,M
AU  - Purcell,SM
AU  - Lage,K
AU  - Verhage,M
AU  - Smit,AB
AU  - Hjerling-Leffler,J
AU  - Buxbaum,JD
AU  - Pinto,D
AU  - He,X
AU  - Sullivan,PF
AU  - Stahl,EA
DO  - 10.1101/410100
PY  - 2018///
TI  - Integrative analysis of rare variants and pathway information shows convergent results between immune pathways, drug targets and epilepsy genes
UR  - http://dx.doi.org/10.1101/410100
ER  -
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