School of Mathematics and Statistics - Research Publications
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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 AvailableDominant KCNA2 mutation causes episodic ataxia and pharmacoresponsive epilepsy(LIPPINCOTT WILLIAMS & WILKINS, 2016-11-08)OBJECTIVE: To identify the genetic basis of a family segregating episodic ataxia, infantile seizures, and heterogeneous epilepsies and to study the phenotypic spectrum of KCNA2 mutations. METHODS: A family with 7 affected individuals over 3 generations underwent detailed phenotyping. Whole genome sequencing was performed on a mildly affected grandmother and her grandson with epileptic encephalopathy (EE). Segregating variants were filtered and prioritized based on functional annotations. The effects of the mutation on channel function were analyzed in vitro by voltage clamp assay and in silico by molecular modeling. KCNA2 was sequenced in 35 probands with heterogeneous phenotypes. RESULTS: The 7 family members had episodic ataxia (5), self-limited infantile seizures (5), evolving to genetic generalized epilepsy (4), focal seizures (2), and EE (1). They had a segregating novel mutation in the shaker type voltage-gated potassium channel KCNA2 (CCDS_827.1: c.765_773del; p.255_257del). A rare missense SCN2A (rs200884216) variant was also found in 2 affected siblings and their unaffected mother. The p.255_257del mutation caused dominant negative loss of channel function. Molecular modeling predicted repositioning of critical arginine residues in the voltage-sensing domain. KCNA2 sequencing revealed 1 de novo mutation (CCDS_827.1: c.890G>A; p.Arg297Gln) in a girl with EE, ataxia, and tremor. CONCLUSIONS: A KCNA2 mutation caused dominantly inherited episodic ataxia, mild infantile-onset seizures, and later generalized and focal epilepsies in the setting of normal intellect. This observation expands the KCNA2 phenotypic spectrum from EE often associated with chronic ataxia, reflecting the marked variation in severity observed in many ion channel disorders.
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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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ItemNo Preview AvailableDiagnosis and misdiagnosis of adult neuronal ceroid lipofuscinosis (Kufs disease)(LIPPINCOTT WILLIAMS & WILKINS, 2016-08-09)OBJECTIVE: To critically re-evaluate cases diagnosed as adult neuronal ceroid lipofuscinosis (ANCL) in order to aid clinicopathologic diagnosis as a route to further gene discovery. METHODS: Through establishment of an international consortium we pooled 47 unsolved cases regarded by referring centers as ANCL. Clinical and neuropathologic experts within the Consortium established diagnostic criteria for ANCL based on the literature to assess each case. A panel of 3 neuropathologists independently reviewed source pathologic data. Cases were given a final clinicopathologic classification of definite ANCL, probable ANCL, possible ANCL, or not ANCL. RESULTS: Of the 47 cases, only 16 fulfilled the Consortium's criteria of ANCL (5 definite, 2 probable, 9 possible). Definitive alternate diagnoses were made in 10, including Huntington disease, early-onset Alzheimer disease, Niemann-Pick disease, neuroserpinopathy, prion disease, and neurodegeneration with brain iron accumulation. Six cases had features suggesting an alternate diagnosis, but no specific condition was identified; in 15, the data were inadequate for classification. Misinterpretation of normal lipofuscin as abnormal storage material was the commonest cause of misdiagnosis. CONCLUSIONS: Diagnosis of ANCL remains challenging; expert pathologic analysis and recent molecular genetic advances revealed misdiagnoses in >1/3 of cases. We now have a refined group of cases that will facilitate identification of new causative genes.
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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.