Epi4K: Gene discovery in 4,000 genomes

Sherr, Elliott H.; Berkovic, Samuel F.; Cossette, Patrick; Delanty, Norman; Dlugos, Dennis J.; Eichler, Evan E.; Epstein, Michael P.; Glauser, Tracy A.; Goldstein, David B.; Heinzen, Erin L.; Johnson, _______

doi:10.1111/j.1528-1167.2012.03511.x

Cited by 76 publications

(30 citation statements)

References 94 publications

(93 reference statements)

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“… 3–7 The role of rare variants in the common epilepsies is at present under exploration by deep-sequencing approaches. 11,58,59 A dual approach of identification of both rare and common variation will result in improved understanding of the genetic architecture for the overall population of people with epilepsy, necessary for precision medicine. Although our findings will not be of immediate clinical usefulness, they are an important first step to understand the genetic architecture of the epilepsies, which could lead to clinically relevant markers of prognosis and outcome.…”

Section: Discussionmentioning

confidence: 99%

“…The genetic determinants underlying the common epilepsies, for which clinical genetic data suggest complex inheritance, remain largely unknown. Some evidence suggests a role for rare sequence and copy number variants, 8–10 whereas the contribution of common polymorphisms is still unclear, 11,12 partly as a result of the relatively small sample sizes analysed to date.…”

Section: Introductionmentioning

confidence: 99%

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Genetic determinants of common epilepsies: a meta-analysis of genome-wide association studies

Anney¹,

Avberšek²,

Balding³

et al. 2014

The Lancet Neurology

262

104

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SummaryBackgroundThe epilepsies are a clinically heterogeneous group of neurological disorders. Despite strong evidence for heritability, genome-wide association studies have had little success in identification of risk loci associated with epilepsy, probably because of relatively small sample sizes and insufficient power. We aimed to identify risk loci through meta-analyses of genome-wide association studies for all epilepsy and the two largest clinical subtypes (genetic generalised epilepsy and focal epilepsy).MethodsWe combined genome-wide association data from 12 cohorts of individuals with epilepsy and controls from population-based datasets. Controls were ethnically matched with cases. We phenotyped individuals with epilepsy into categories of genetic generalised epilepsy, focal epilepsy, or unclassified epilepsy. After standardised filtering for quality control and imputation to account for different genotyping platforms across sites, investigators at each site conducted a linear mixed-model association analysis for each dataset. Combining summary statistics, we conducted fixed-effects meta-analyses of all epilepsy, focal epilepsy, and genetic generalised epilepsy. We set the genome-wide significance threshold at p<1·66 × 10−8.FindingsWe included 8696 cases and 26 157 controls in our analysis. Meta-analysis of the all-epilepsy cohort identified loci at 2q24.3 (p=8·71 × 10−10), implicating SCN1A, and at 4p15.1 (p=5·44 × 10−9), harbouring PCDH7, which encodes a protocadherin molecule not previously implicated in epilepsy. For the cohort of genetic generalised epilepsy, we noted a single signal at 2p16.1 (p=9·99 × 10−9), implicating VRK2 or FANCL. No single nucleotide polymorphism achieved genome-wide significance for focal epilepsy.InterpretationThis meta-analysis describes a new locus not previously implicated in epilepsy and provides further evidence about the genetic architecture of these disorders, with the ultimate aim of assisting in disease classification and prognosis. The data suggest that specific loci can act pleiotropically raising risk for epilepsy broadly, or can have effects limited to a specific epilepsy subtype. Future genetic analyses might benefit from both lumping (ie, grouping of epilepsy types together) or splitting (ie, analysis of specific clinical subtypes).FundingInternational League Against Epilepsy and multiple governmental and philanthropic agencies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genetic determinants of common epilepsies: a meta-analysis of genome-wide association studies

Anney¹,

Avberšek²,

Balding³

et al. 2014

The Lancet Neurology

262

104

View full text Add to dashboard Cite

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“…Analysis of genetic contributions to these presumed polygenic forms of epilepsy using genotypes recorded over single nucleotide polymorphisms (SNPs) revealed that common variants collectively explain substantial phenotypic variation of epilepsy and suggested that at least 400 variants (and potentially many thousands) influence disease susceptibility [9]. For these common epilepsies, there is an unresolved debate about whether genetic susceptibility arises as a result of polygenic contributions from common variants or whether these epilepsies comprise a large number of discrete diseases arising from rare monogenic variation tagged by SNPs (reviewed in [9, 16]).…”

Section: Introductionmentioning

confidence: 99%

Rare and common epilepsies converge on a shared gene regulatory network providing opportunities for novel antiepileptic drug discovery

et al. 2016

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BackgroundThe relationship between monogenic and polygenic forms of epilepsy is poorly understood and the extent to which the genetic and acquired epilepsies share common pathways is unclear. Here, we use an integrated systems-level analysis of brain gene expression data to identify molecular networks disrupted in epilepsy.ResultsWe identified a co-expression network of 320 genes (M30), which is significantly enriched for non-synonymous de novo mutations ascertained from patients with monogenic epilepsy and for common variants associated with polygenic epilepsy. The genes in the M30 network are expressed widely in the human brain under tight developmental control and encode physically interacting proteins involved in synaptic processes. The most highly connected proteins within the M30 network were preferentially disrupted by deleterious de novo mutations for monogenic epilepsy, in line with the centrality-lethality hypothesis. Analysis of M30 expression revealed consistent downregulation in the epileptic brain in heterogeneous forms of epilepsy including human temporal lobe epilepsy, a mouse model of acquired temporal lobe epilepsy, and a mouse model of monogenic Dravet (SCN1A) disease. These results suggest functional disruption of M30 via gene mutation or altered expression as a convergent mechanism regulating susceptibility to epilepsy broadly. Using the large collection of drug-induced gene expression data from Connectivity Map, several drugs were predicted to preferentially restore the downregulation of M30 in epilepsy toward health, most notably valproic acid, whose effect on M30 expression was replicated in neurons.ConclusionsTaken together, our results suggest targeting the expression of M30 as a potential new therapeutic strategy in epilepsy.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1097-7) contains supplementary material, which is available to authorized users.

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“…Whereas targeted gene sequencing and Genome Wide Association Studies (GWAS) at predetermined loci used to be the cutting edge,6,7 new studies aim to identify single nucleotide variations (SNVs) in all genes and to analyze their association with disease 8. There are now thousands of sequenced exomes encompassing phenotypes both rare (e.g., Joubert syndrome,9 myofibrillar myopathy10), and relatively common (e.g., cancer11–16 and epilepsy17,18). Many of these exome projects have been catalogued and made available for analysis through the Database of Genotypes and Phenotypes (dbGaP),19,20 and multi-center efforts like the NHLBI Exome Sequencing Project21 are actively gathering more data.…”

Section: Introductionmentioning

confidence: 99%

Single nucleotide variations: Biological impact and theoretical interpretation

Koire²,

et al. 2014

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Genome-wide association studies (GWAS) and whole-exome sequencing (WES) generate massive amounts of genomic variant information, and a major challenge is to identify which variations drive disease or contribute to phenotypic traits. Because the majority of known disease-causing mutations are exonic non-synonymous single nucleotide variations (nsSNVs), most studies focus on whether these nsSNVs affect protein function. Computational studies show that the impact of nsSNVs on protein function reflects sequence homology and structural information and predict the impact through statistical methods, machine learning techniques, or models of protein evolution. Here, we review impact prediction methods and discuss their underlying principles, their advantages and limitations, and how they compare to and complement one another. Finally, we present current applications and future directions for these methods in biological research and medical genetics.

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Epi4K: Gene discovery in 4,000 genomes

Cited by 76 publications

References 94 publications

Genetic determinants of common epilepsies: a meta-analysis of genome-wide association studies

Genetic determinants of common epilepsies: a meta-analysis of genome-wide association studies

Rare and common epilepsies converge on a shared gene regulatory network providing opportunities for novel antiepileptic drug discovery

Single nucleotide variations: Biological impact and theoretical interpretation

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