Medicine (Austin & Northern Health) - Research Publications
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ItemIn silico prioritization based on coexpression can aid epileptic encephalopathy gene discovery(LIPPINCOTT WILLIAMS & WILKINS, 2016-02)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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ItemReanalysis and optimisation of bioinformatic pipelines is critical for mutation detection(WILEY-HINDAWI, 2019-04)Rapid advances in genomic technologies have facilitated the identification pathogenic variants causing human disease. We report siblings with developmental and epileptic encephalopathy due to a novel, shared heterozygous pathogenic 13 bp duplication in SYNGAP1 (c.435_447dup, p.(L150Vfs*6)) that was identified by whole genome sequencing (WGS). The pathogenic variant had escaped earlier detection via two methodologies: whole exome sequencing and high-depth targeted sequencing. Both technologies had produced reads carrying the variant, however, they were either not aligned due to the size of the insertion or aligned to multiple major histocompatibility complex (MHC) regions in the hg19 reference genome, making the critical reads unavailable for variant calling. The WGS pipeline followed different protocols, including alignment of reads to the GRCh37 reference genome, which lacks the additional MHC contigs. Our findings highlight the benefit of using orthogonal clinical bioinformatic pipelines and all relevant inheritance patterns to re-analyze genomic data in undiagnosed patients.
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ItemGenome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies(NATURE PUBLISHING GROUP, 2018-12-10)The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have diverse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology.
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ItemNo Preview AvailableMultiplex families with epilepsy Success of clinical and molecular genetic characterization(LIPPINCOTT WILLIAMS & WILKINS, 2016-02-23)OBJECTIVE: To analyze the clinical syndromes and inheritance patterns of multiplex families with epilepsy toward the ultimate aim of uncovering the underlying molecular genetic basis. METHODS: Following the referral of families with 2 or more relatives with epilepsy, individuals were classified into epilepsy syndromes. Families were classified into syndromes where at least 2 family members had a specific diagnosis. Pedigrees were analyzed and molecular genetic studies were performed as appropriate. RESULTS: A total of 211 families were ascertained over an 11-year period in Israel. A total of 169 were classified into broad familial epilepsy syndrome groups: 61 generalized, 22 focal, 24 febrile seizure syndromes, 33 special syndromes, and 29 mixed. A total of 42 families remained unclassified. Pathogenic variants were identified in 49/211 families (23%). The majority were found in established epilepsy genes (e.g., SCN1A, KCNQ2, CSTB), but in 11 families, this cohort contributed to the initial discovery (e.g., KCNT1, PCDH19, TBC1D24). We expand the phenotypic spectrum of established epilepsy genes by reporting a familial LAMC3 homozygous variant, where the predominant phenotype was epilepsy with myoclonic-atonic seizures, and a pathogenic SCN1A variant in a family where in 5 siblings the phenotype was broadly consistent with Dravet syndrome, a disorder that usually occurs sporadically. CONCLUSION: A total of 80% of families were successfully classified, with pathogenic variants identified in 23%. The successful characterization of familial electroclinical and inheritance patterns has highlighted the value of studying multiplex families and their contribution towards uncovering the genetic basis of the epilepsies.
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ItemHarnessing Gene Expression Networks to Prioritize Candidate Epileptic Encephalopathy Genes(PUBLIC LIBRARY SCIENCE, 2014-07-09)We apply a novel gene expression network analysis to a cohort of 182 recently reported candidate Epileptic Encephalopathy genes to identify those most likely to be true Epileptic Encephalopathy genes. These candidate genes were identified as having single variants of likely pathogenic significance discovered in a large-scale massively parallel sequencing study. Candidate Epileptic Encephalopathy genes were prioritized according to their co-expression with 29 known Epileptic Encephalopathy genes. We utilized developing brain and adult brain gene expression data from the Allen Human Brain Atlas (AHBA) and compared this to data from Celsius: a large, heterogeneous gene expression data warehouse. We show replicable prioritization results using these three independent gene expression resources, two of which are brain-specific, with small sample size, and the third derived from a heterogeneous collection of tissues with large sample size. Of the nineteen genes that we predicted with the highest likelihood to be true Epileptic Encephalopathy genes, two (GNAO1 and GRIN2B) have recently been independently reported and confirmed. We compare our results to those produced by an established in silico prioritization approach called Endeavour, and finally present gene expression networks for the known and candidate Epileptic Encephalopathy genes. This highlights sub-networks of gene expression, particularly in the network derived from the adult AHBA gene expression dataset. These networks give clues to the likely biological interactions between Epileptic Encephalopathy genes, potentially highlighting underlying mechanisms and avenues for therapeutic targets.