Orion: Detecting regions of the human non-coding genome that are intolerant to variation using population genetics

There is broad agreement that genetic mutations occurring outside of the protein-coding regions play a key role in human disease. Despite this consensus, we are not yet capable of discerning which portions of non-coding sequence are important in the context of human disease. Here, we present Orion, an approach that detects regions of the non-coding genome that are depleted of variation, suggesting that the regions are intolerant of mutations and subject to purifying selection in the human lineage. We show that Orion is highly correlated with known intolerant regions as well as regions that harbor putatively pathogenic variation. This approach provides a mechanism to identify pathogenic variation in the human non-coding genome and will have immediate utility in the diagnostic interpretation of patient genomes and in large case control studies using whole-genome sequences

The intolerance to functional genetic variation of protein domains predicts the localization of pathogenic mutations within genes

Ranking human genes based on their tolerance to functional genetic variation can greatly facilitate patient genome interpretation. It is well established, however, that different parts of proteins can have different functions, suggesting that it will ultimately be more informative to focus attention on functionally distinct portions of genes. Here we evaluate the intolerance of genic sub-regions using two biological sub-region classifications. We show that the intolerance scores of these sub-regions significantly correlate with reported pathogenic mutations. This observation extends the utility of intolerance scores to indicating where pathogenic mutations are mostly likely to fall within genes

Epilepsy, hippocampal sclerosis and febrile seizures linked by common genetic variation around SCN1A

Epilepsy comprises several syndromes, amongst the most common being mesial temporal lobe epilepsy with hippocampal sclerosis. Seizures in mesial temporal lobe epilepsy with hippocampal sclerosis are typically drug-resistant, and mesial temporal lobe epilepsy with hippocampal sclerosis is frequently associated with important co-morbidities, mandating the search for better understanding and treatment. The cause of mesial temporal lobe epilepsy with hippocampal sclerosis is unknown, but there is an association with childhood febrile seizures. Several rarer epilepsies featuring febrile seizures are caused by mutations in SCN1A, which encodes a brain-expressed sodium channel subunit targeted by many anti-epileptic drugs. We undertook a genome-wide association study in 1018 people with mesial temporal lobe epilepsy with hippocampal sclerosis and 7552 control subjects, with validation in an independent sample set comprising 959 people with mesial temporal lobe epilepsy with hippocampal sclerosis and 3591 control subjects. To dissect out variants related to a history of febrile seizures, we tested cases with mesial temporal lobe epilepsy with hippocampal sclerosis with (overall n = 757) and without (overall n = 803) a history of febrile seizures. Meta-analysis revealed a genome-wide significant association for mesial temporal lobe epilepsy with hippocampal sclerosis with febrile seizures at the sodium channel gene cluster on chromosome 2q24.3 [rs7587026, within an intron of the SCN1A gene, P = 3.36 × 10−9, odds ratio (A) = 1.42, 95% confidence interval: 1.26-1.59]. In a cohort of 172 individuals with febrile seizures, who did not develop epilepsy during prospective follow-up to age 13 years, and 6456 controls, no association was found for rs7587026 and febrile seizures. These findings suggest SCN1A involvement in a common epilepsy syndrome, give new direction to biological understanding of mesial temporal lobe epilepsy with hippocampal sclerosis with febrile seizures, and open avenues for investigation of prognostic factors and possible prevention of epilepsy in some children with febrile seizure

Systems genetics identifies Sestrin 3 as a regulator of a proconvulsant gene network in human epileptic hippocampus

Peer reviewe

Genome-wide identification and phenotypic characterization of seizure-associated copy number variations in 741,075 individuals

Copy number variants (CNV) are established risk factors for neurodevelopmental disorders with seizures or epilepsy. With the hypothesis that seizure disorders share genetic risk factors, we pooled CNV data from 10,590 individuals with seizure disorders, 16,109 individuals with clinically validated epilepsy, and 492,324 population controls and identified 25 genome-wide significant loci, 22 of which are novel for seizure disorders, such as deletions at 1p36.33, 1q44, 2p21-p16.3, 3q29, 8p23.3-p23.2, 9p24.3, 10q26.3, 15q11.2, 15q12-q13.1, 16p12.2, 17q21.31, duplications at 2q13, 9q34.3, 16p13.3, 17q12, 19p13.3, 20q13.33, and reciprocal CNVs at 16p11.2, and 22q11.21. Using genetic data from additional 248,751 individuals with 23 neuropsychiatric phenotypes, we explored the pleiotropy of these 25 loci. Finally, in a subset of individuals with epilepsy and detailed clinical data available, we performed phenome-wide association analyses between individual CNVs and clinical annotations categorized through the Human Phenotype Ontology (HPO). For six CNVs, we identified 19 significant associations with specific HPO terms and generated, for all CNVs, phenotype signatures across 17 clinical categories relevant for epileptologists. This is the most comprehensive investigation of CNVs in epilepsy and related seizure disorders, with potential implications for clinical practice

Bioinformatics: the application of multigenic models to predict disease and treatment outcomes

© 2011 Dr. Slave PetrovskiOne of the single most difficult aspects of treating newly diagnosed epilepsy, for patients and clinicians, is managing the uncertainty surrounding whether the anti-epileptic drug (AED) therapy will prevent further seizures. By investigating the pharmacogenomics of a population of Australian newly treated epilepsy patients, this thesis seeks to identify whether it is possible to develop risk prediction models that could improve personalising the treatment care provided to this population of patients by reducing the uncertainty and helping select the “right drug for the right patient”. The thesis explores the premise that developing predictive models for a complex phenotype such as seizure control in newly treated epilepsy, will require the utilization of multiple genomic variants, and also genomic and non-genomic factors need to be integrated. The results of the research demonstrated a proof-of-concept for this premise, showing that using the genetic profiles across five genetic variants improves the ability to significantly predict epilepsy pharmacoresponse in comparison to investigating each of the individual genomic components. This model was developed in an Australian population of newly treated epilepsy patients prospectively followed to determine the outcome of their drug treatment, and then validated in two other Australian cohorts. This model has potentially important clinical implications given that the current standard of care for newly diagnosed epilepsy patients does not provide clinicians with a meaningful tool to determine which of the patients are at a higher risk of not-responding to their initial AED therapy. To understand the broader clinical utility of the model, the Australian derived multigenic model was tested in two newly treated epilepsy cohorts from the United Kingdom. It was found that the Australian population did not significantly predict treatment outcome in the UK newly treated populations overall. However, the combination of five-SNPs identified as being relevant to pharmacoresponse in the Australian population treated with carbamazepine and valproate, were significantly predictive of pharmacoresponse in all the UK patients that were treated with these two drugs. This suggests that these five SNPs have drug specific predictive value. With a focus on developing a more accurate predictive model, non-genomic factors were also investigated and integrated with the genomic predictors into a unified predictive model. This model was found to have high predictive value for seizure control in newly treated epilepsy, and would potentially provide a clinically useful tool to assist in the ability to personalise treatment advice given to newly treated patients. The ability to determine non-responders improved from the currently generic 30% likelihood of not responding to initial carbamazepine or valproate treatment in the Australian newly treated population, to a 75% likelihood of not responding if the integrated, pre-treatment, model predicts the patient to be a “non-responder”, or conversely a 82% likelihood of responding if the model predicts the patient to be a “responder”. As a final task, the model development approach, which resulted in successfully identifying a multigenic predictive model for epilepsy pharmacoresponse based on a limited candidate gene dataset of approximately 4,000 carefully selected genetic markers, was applied to two genome-wide datasets. Based on this attempt to directly upscale the model development from candidate genes to a genome-wide scale, a number of limitations in the marker selection and model development stages of the approach were identified. These limitations emphasize the importance of designing tailored approaches to identifying multigenic models based on the differing contexts of datasets. Here, both in the epilepsy pharmacoresponse and HIV-1 susceptibility genome-wide attempts, the approach did not result in the development of significantly predictive models when applied to independent validation cohorts. However, possible future directions on how to overcome some of these direct upscale limitations are provided. Additionally, potential future directions that could result from this body of work are also explored

eHealth as a Facilitator of Precision Medicine in Epilepsy

Epilepsy is a chronic neurological condition that affects approximately 50 million people worldwide. Current treatments are inadequate and around a third of patients continue to experience uncontrolled seizures. The genetic architecture of many of the epilepsies makes them amenable to next-generation sequencing technologies, enabling a molecular diagnosis in an increasing proportion of patients. As a result, rare but remarkable examples of precision therapeutics in epilepsy are emerging. Coordinated research efforts are required to increase the diagnostic yield of sequencing and translate diagnosis to improved prognosis. This review explores the potential of eHealth technologies in facilitating and accelerating precision therapeutics in epilepsy. We describe the state of the art in precision diagnostics and therapeutics in epilepsy and identify opportunities for eHealth to accelerate the realisation of precision therapeutics via patient registries, research-enabled electronic health records, and connected health solutions.</p

Anti-epileptic effects of an anaplerotic diet in two chronic mouse epilepsy models

Abstract not available

Evaluating whole genome sequence data from the Genetic Absence Epilepsy Rat from Strasbourg and its related non-epileptic strain

Objective The Genetic Absence Epilepsy Rats from Strasbourg (GAERS) are an inbreed Wistar rat strain widely used as a model of genetic generalised epilepsy with absence seizures. As in humans, the genetic architecture that results in genetic generalized epilepsy in GAERS is poorly understood. Here we present the strain-specific variants found among the epileptic GAERS and their related Non-Epileptic Control (NEC) strain. The GAERS and NEC represent a powerful opportunity to identify neurobiological factors that are associated with the genetic generalised epilepsy phenotype. Methods We performed whole genome sequencing on adult epileptic GAERS and adult NEC rats, a strain derived from the same original Wistar colony. We also generated whole genome sequencing on four double-crossed (GAERS with NEC) F2 selected for high-seizing (n = 2) and non-seizing (n = 2) phenotypes. Results Specific to the GAERS genome, we identified 1.12 million single nucleotide variants, 296.5K short insertion-deletions, and 354 putative copy number variants that result in complete or partial loss/duplication of 41 genes. Of the GAERS-specific variants that met high quality criteria, 25 are annotated as stop codon gain/loss, 56 as putative essential splice sites, and 56 indels are predicted to result in a frameshift. Subsequent screening against the two F2 progeny sequenced for having the highest and two F2 progeny for having the lowest seizure burden identified only the selected Cacna1h GAERS-private protein-coding variant as exclusively co-segregating with the two high-seizing F2 rats. Significance This study highlights an approach for using whole genome sequencing to narrow down to a manageable candidate list of genetic variants in a complex genetic epilepsy animal model, and suggests utility of this sequencing design to investigate other spontaneously occurring animal models of human disease

Multidrug-resistant genotype (ABCB1) and seizure recurrence in newly treated epilepsy: data from international pharmacogenetic cohorts

Purpose: The association between a specific polymorphism (3435C&gt;T) in the ABCB1 gene, coding for the membrane drug transporter P‐glycoprotein (PgP), and pharmacoresistance to seizure control is controversial. Studies have been limited by multiple drug use, chronic cohorts with varying definitions, and retrospective clinical data. Herein we examine the relationship of this polymorphism with seizure recurrence in three independent international cohorts of patients newly treated for epilepsy. Methods: Data were collected on demographics, medication details, and seizure control after 12 months of treatment. The distribution of ABCB1 3435C&gt;T genotypes was compared between patients with and without recurrent unprovoked seizures. Results: Five hundred forty‐two newly treated patients were enrolled (212 from Australia, 285 from Scotland, and 45 from Hong Kong). A total of 38.4% had recurrent unprovoked seizures after starting antiepileptic drug (AED) treatment. Genotype frequencies and ethnicity did not differ between the Scottish and Australian cohorts, but both were significantly different in the Hong Kong cohort. There was no significant relationship between the ABCB1 3435C&gt;T genotype and the rate of recurrence of unprovoked seizures in the three cohorts individually or combined; however the epilepsy syndrome and a greater number of seizures pretreatment was associated with an increased risk of seizure recurrence. Conclusions: The ABCB1 3435C&gt;T genotype does not have a major role in determining the efficacy of seizure control with initial AED therapy. The study highlights issues that arise in combining pharmacogenetic datasets from different ethnic regions and health systems, an approach that is essential to advance this field