New technologies in molecular genetics: the impact on epilepsy research.

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New technologies in molecular genetics: the impact on epilepsy research

13 Ingo Helbig1

Division of Neurology, The Children’s Hospital of Philadelphia, Philadelphia, USA 1 Corresponding author: Tel.: +1 (215) 590-1000; Fax: +1 (215) 590-1771, e-mail address: [email protected]

Abstract Technical advances in the last decade have finally enabled researchers to identify epilepsyassociated genetic variants by querying virtually the entire genome. In the first decade of the twenty-first century, this technical revolution began with the advent of array comparative genomic hybridization and single nucleotide polymorphism arrays. These technologies made it possible for the first time to screen for common genetic variants and rare small deletions and duplications, referred to as microdeletions and microduplications. More recently, the repertoire of technologies has expanded to exome-wide and genome-wide sequencing approaches. These technologies led to a virtual explosion of gene identifications both in familial cases and in rare severe epilepsies, referred to as epileptic encephalopathies. This chapter aims to provide an overview of the achievements of these new technologies and the challenges that the field is currently facing.

Keywords epilepsy, seizures, genomics, exome, genome sequencing, SCN1A, 15q13.3 microdeletion, copy number variation

1 GENETICS VERSUS GENOMICS For a general discussion on the impact of the new technologies, it might be advantageous to contrast genetic and genomic approaches (Table 1). Given the effort required to derive genetic sequence from a person’s DNA, genetics has traditionally been a relatively data-poor field of science. Genetic information regarding a disease was often derived indirectly, for example, through associated markers such as restriction fragment length polymorphisms for linkage analysis. Identification of mutations Progress in Brain Research, Volume 213, ISSN 0079-6123, http://dx.doi.org/10.1016/B978-0-444-63326-2.00013-2 © 2014 Elsevier B.V. All rights reserved.

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CHAPTER 13 New technologies in molecular genetics

Table 1 Genetic versus genomic concepts Genetic concept (classic)

Genomic concepts (novel)

Association findings A significant association of a common variant even in small cohorts may indicate a contribution to disease Rare variants Rare genetic variation may be contributory to disease if not found in unaffected individuals De novo mutations De novo mutations and de novo deletions are considered pathogenic per se

Association findings Most association findings are false positives. Genome-wide significance (p 108) and independent validation is required to make findings credible Rare variants Rare genetic variation is frequent in patients and controls and is not necessarily related to disease De novo mutations Even unaffected individuals carry 1–2 de novo mutations in their coding sequence; additional information is needed to implicate a pathogenic role

Single base pairs

Microdeletions

Indels

Chromosomes

...GATC... C ...CTAG...

1 bp

10 bp

100 bp

1 kb

10 kb

100 kb

1 Mb

10 Mb

100 Mb

FIGURE 1 The range of genetic variants predisposing to human disease by size.

needed to be performed in a targeted fashion, for example, by sequencing the most likely candidate genes in a linkage interval. With the advent of massive parallel sequencing approaches, some of the traditional tools of genetics became redundant, as sequence information could be obtained directly. However, the new richness of data, be it on the level of common single nucleotide polymorphisms (SNPs), copy number variations (CNVs), or overall sequence, has led to the identification of a vast amount of benign variation in the human genome (Fig. 1). This complexity was already foreshadowed by studies in early CNV, which projected that up to 10% of the human genome is copy number variable in healthy individuals (Redon et al., 2006). This initial estimate has since been shown to be too generous, partly because some of the initial studies have relied on data from bacterial artificial chromosome (BAC)

2 Basics concepts and the genome in numbers

arrays. In fact, some pathogenic variants such as the 1q21.1 microdeletion were initially thought to be part of normal genetic variation until common copy number variants could be mapped more precisely (Sharp, 2009). Either way, the example of the 1q21.1 microdeletion exemplifies the difficulty of ongoing data curation in public databases that are often used as a reference for genetic studies (Mefford et al., 2008). A similar scenario can be seen with known genes implicated in human epilepsies. For example, one genetic variant in SCN1A previously found to be de novo in a patient with epileptic encephalopathy was also identified in a survey of normal genetic variation in the Exome Variant Server, one of the current reference databases for next-generation sequencing studies (Cherepanova et al., 2013). This example demonstrates a dramatic shift in the burden of proof in genetic research. Only a decade ago, the identification of rare genetic variants leading to functional changes in a credible candidate gene was considered sufficient to implicate this gene in the pathogenesis of epilepsy. In the meanwhile, this basic concept has been turned around. Given the vast amount of genomic variation, significant additional evidence is needed to ascribe a pathogenic role, even for mutations that arise de novo in affected individuals (Epi4K et al., 2013; Fromer et al., 2014; Neale et al., 2012; O’Roak et al., 2012). This paradigm shift impacts significantly on our naı¨ve assumption of what genetic information can tell us.

2 BASICS CONCEPTS AND THE GENOME IN NUMBERS 2.1 EXOME—A TECHNICAL, NOT A PHILOSOPHICAL TERM Most studies using massive parallel sequencing have focused on the “exome.” The exome is the entirety of all coding regions of the human genome, i.e., the sum of all coding exons of the human genome. While this method has resulted in various breakthrough findings in the field, it should be noted that the term “exome” is used as a technical concept rather than a philosophical term, as it refers to the technical realization of a sequencing technology rather than to a general statement about the complete human coding sequence. For example, the first exons of many genes are notoriously hard to enrich and are therefore underrepresented on exome arrays. Also, some genes are not represented at all on available exome enrichment kits. It is assumed that 5–10% of the coding sequence in the human genome cannot be captured through present-day exome sequencing technologies (Fromer et al., 2014). Accordingly, the term “exome screening” is misleading, as screening technologies usually have a low false-negative rate while allowing for a higher falsepositive rate. In contrast, a negative exome sequencing result does not necessarily exclude that any of the genes still carries a pathogenic variant. In addition, 50 - and 30 -untranslated regions, regulatory regions, and repeat regions are poorly covered, if at all. Despite these limitations, there are no examples of pathogenic mutations so far in the field of epilepsy genetics that were missed by exome sequencing, but discovered through more comprehensive genome-wide approaches or other methods.

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2.2 THE GENOME IN NUMBERS Given the vast usage of exome sequence and the paucity of information on nonexonic genetic variation, exome sequencing has emerged as a standard for various genetic studies. Depending on the analysis platform, up to 50,000 genetic variants that differ from the reference human genome can be called in any given exome. Of those, up to 1000 genetic variants alter the amino acid sequence of the respective proteins and have never been observed before. Of those rare variants again, up to 40 variants might be truncating variants. While this number may be inflated through various technical artifacts or highly variable genes, a significant fraction of these variants are true positives. If the sequence of parents and children are compared, roughly 150–200 of these rare variants are transmitted from either parent to the child. In a parent–child trio, 4–6 genes are affected by homozygous mutations in the child transmitted from heterozygous parents as well as 8–10 genes affected by compound heterozygous mutations. In addition, every child carries 1–2 de novo mutations in his/ her exome that alter protein function (Conrad et al., 2011). All these estimates are independent of whether the offspring is affected or unaffected (Iossifov et al., 2012). This example demonstrates the level of genomic noise in the human genome. Causal genetic variation does not rise above this noise by numbers alone and must be identified through other means.

2.3 THE THIRD BEAST—RARE GENETIC VARIANTS Increasing sample sizes and the difficulties in distinguishing contributory variation from chance findings have resulted in large studies that assess the association with disease either on a candidate level or genome-wide level. These association studies can investigate the association of common genetic markers or rare genetic variants. In the field of epilepsy research, the first studies using large cohorts were association studies of microdeletions. For example, the 15q13.3 microdeletion was found to be significantly associated with IGE/GGE (Dibbens et al., 2009; Helbig et al., 2009), occurring in up to 1% of patients while virtually absent in controls. A similar, albeit weaker association due to higher frequency in controls was found for the 16p13.3 and 15q11.2 microdeletions (de Kovel et al., 2010). In addition to the general property of these variants as recurrent microdeletions due to the duplication architecture of the human genome, these variants provided a first glance at the property of rare genetic variants. The 15q13.3 microdeletion is known to have an odds ratio of >50, suggesting a roughly 50-fold increase of the risk of epilepsy in deletion carriers (Dibbens et al., 2009). However, given that these microdeletions can also be identified in controls to a certain extent, the genetic properties of these variants differ immensely when compared to clearly monogenic variants. With respect to their risk profile, the difference to monogenic variants becomes clear when the segregation in families is observed (de Kovel et al., 2010; Dibbens et al., 2009). While a clearly monogenic variant by definition follows the familial transmission of disease even if reduced penetrance can be seen, microdeletions as examples of rare genetic variants show a seemingly erratic transmission pattern at first glance with many nonpenetrant


carriers and a substantial amount of affected individuals not carrying the familial rare variant. While this apparent lack of segregation might be interpreted as a lack of pathogenicity, the observed segregation pattern is consistent with the risk conferred by the familial rare variant. Even variants with a relatively high odds ratio only have a penetrance of 20–40%, resulting in a high frequency of unaffected carriers. In addition, a certain proportion of affected family members is expected not to carry the familial microdeletion. The observed apparently inconclusive segregation pattern was found to be consistent with the strength of the association of rare variants (Helbig et al., 2013). In summary, rare genetic variants may be regarded as a third kind of genetic variation in addition to monogenic variants and common genetic variants. The seemingly inconsistent segregation pattern of rare genetic variants represents a difficult challenge for genetic counseling purposes. In addition, it provides a template for the segregation patterns expected for many of the rare genetic variants that will be identified in current and upcoming exome sequencing studies.

2.4 MICRODELETIONS—THE SEARCH FOR EPILEPSY-ASSOCIATED VARIANTS GOES GENOME WIDE The first wave of genomic technologies was made possible by the development of cost-effective SNP array and array comparative genomic hybridization (array CGH) platforms. SNP arrays genotype hundreds of thousands of predefined common genetic variants in the human genome, and the intensity of neighboring SNP markers on these arrays can be used to reliably assess small deletions and duplications of genomic material, which are referred to as microdeletions and microduplications (Redon et al., 2006). Array CGH technology accomplished the same goal by assessing the relative DNA quantity of a given genomic region by matching (hybridizing) hundreds of thousands of small DNA fragments that are represented on the array (Fig. 2). SNP arrays were initially designed for association studies. However, in the field of epilepsy and neurogenetics in general, they led to breakthrough findings on the role of CNVs. While microdeletions and microduplications were already known to be associated with various genetic syndromes, their role in common epilepsy was not anticipated. The investigations into the role of microdeletions were fueled by the discovery of the 15q13.3 microdeletion (Helbig et al., 2009; Sharp et al., 2008). This particular microdeletion was known to contain the CHRNA7 gene, a top candidate gene for seizure disorders due to its relatedness to CHRNA4, CHRNB2, and CHRNA2, known genes for the autosomal dominant nocturnal frontal lobe epilepsies (Steinlein, 2007; Steinlein et al., 2012). Also, this microdeletion mapped to a region with a known linkage finding (Neubauer et al., 1998). Given the availability of an already genotyped cohort of patients with idiopathic/generalized genetic epilepsy (IGE/GGE), we assessed the role of this microdeletion and found a strong association with the epilepsy phenotype (Helbig et al., 2009). In summary, the 15q13.3 microdeletion was present in 12/1226 patients with IGE/GGE (1%) while entirely absent in controls (0/3699). Further follow-up studies confirmed

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FIGURE 2 Example of a microdeletion identified through a single nucleotide polymorphism (SNP) array. This image shows an SNP array analyzed and normalized for SNP intensity in order to identify microdeletions. Each green (gray in the print version) dot represents an SNP marker. Between the inner gray bars, a “drop” in intensity can be observed that suggests a microdeletion, in this case a 15q13.3 microdeletion. On both sides of the microdeletion, there are small regions with few markers, signifying segmental duplications that can only be poorly covered with existing SNP platforms. These segmental duplications are default breakpoints for recurrent microdeletions.

these findings (Dibbens et al., 2009). As of 2014, a joint analysis of all published studies finds this variant in 19/1762 (1%) patients with IGE, while virtually absent in controls (8/50,115).

2.5 RECURRENT AND NONRECURRENT MICRODELETIONS The 15q13.3 microdeletion likely arises through nonallelic homologous recombination (NAHR), an unequal crossover between highly similar stretches of DNA referred to as segmental duplications (Mefford and Eichler, 2009). The 15q13.3 microdeletion is flanked by two segmental duplications, which are thought to mediate the deletion or duplication events. Given the peculiar mechanisms of CNV generation, 15q13.3 microdeletions are canonical CNVs, i.e., independent deletion or duplication events usually have the similar size as they occur between predefined breakpoints. This is in contrast to nonrecurrent CNVs that may occur through other mechanisms. Two properties make hotspot deletions suitable candidates for association studies. First, their identical size allows for a clear definition of the critical interval and a reliable detection through various platforms. Second, given the NAHR-mediated generation of these variants, they occur relatively frequently. In addition to the 15q13.3 microdeletion, microdeletions at 15q11.2 and 16p13.11 have been detected in association with common epilepsies (de Kovel et al., 2010) (Table 2). While the phenotypic spectrum of the 15q13.3 microdeletion appears to be limited to generalized epilepsies, the phenotypic range of 15q11.2 and 16p13.11 microdeletions is wider and their effect size is lower. For example, an excess of 16p13.11 microdeletions can be observed in patients with IGE/GGE and


Table 2 Some of the recurrent (genomic hotspots) and nonrecurrent microdeletions and microduplications implicated in human epilepsy Hotspot region

Deletion

Duplication

1p36 1q21.1 4p16.3 15q11.2 BP1-BP2 15q11-q13 BP2-BP3

1p36 deletion syndrome GGE/IGE, BECTS Wolf–Hirschhorn syndrome GGE, IC Angelman syndrome

15q13.3 BP3-BP4 15q13.3 BP4-BP5 16p11.2 proximal 16p11.2 distal 16p12.1 16p13.11 22q11.2 (DiGeorge)

MAE GGE/IGE (1%) GGE/IGE SIGEI, SGE BECTS GGE/IGE, focal epilepsy JME, GGE/IGE

Unknown Unknown Unknown Not pathogenic Infantile spasms (maternally inherited) Unknown Not pathogenic CAE, WS, EE Not pathogenic Not pathogenic Not pathogenic Myoclonic epilepsy

Candidate gene GABRD GJA8 CYFIP1 UBE3A

CHRNA7 SEZ6L2 SH2B1 NDE1

Recurrent copy number variants are usually due to genomic rearrangements at genomic hotspots, which result in canonical microdeletions and microduplications that usually have a similar size and similar breakpoints. Nonrecurrent microdeletions may have variable breakpoints and vary in size. BECTS, benign epilepsy with centrotemporal spikes; EE, epileptic encephalopathy; GGE, genetic generalized epilepsy (formerly idiopathic generalized epilepsy); ISs, infantile spasms; ICs, infantile convulsions; JME, juvenile myoclonic epilepsy; MAE, myoclonic astatic epilepsy; SIGEI, severe idiopathic generalized epilepsy of infancy; WS, West syndrome.

temporal lobe epilepsy (Heinzen et al., 2010). Nonrecurrent microdeletions, i.e., microdeletions not due to common breakpoints, were found in various epilepsies and are usually defined by a critical interval that covers the relevant candidate gene. Deletions with variable breakpoints associated with various human epilepsies include the 1q44 deletion and variations of the SHANK3, NRXN1, and RBFOX1 genes (Caliebe et al., 2010; Han et al., 2013; Lal et al., 2013; Moller et al., 2013) (Table 3). In addition, some epilepsy-associated genes have been identified through both microdeletion and next-generation sequencing studies. Those include STXBP1, GRIN2A, and C6orf70 (Conti et al., 2013; Lemke et al., 2013; Lesca et al., 2013; Reutlinger et al., 2010; Saitsu et al., 2008).

2.6 MICRODELETIONS FROM GENOMIC DISORDERS TO GENOME-FIRST When compared to classical association studies in the field of epilepsy research, association studies using microdeletions rapidly involved larger samples given the pregenotyped case and control cohorts that were available (Fig. 3). Given the fact that array CGH or SNP arrays are routinely performed on a diagnostic basis, the

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Table 3 Some of the genes affected by nonrecurrent structural genomic variants in human epilepsies in at least two patients reported in the literature Gene

Chromosomal location

Epilepsy phenotype

NRXN1 SLC26A1 GRID2 AUTS2 SYNGAP1 C6orf70 MAGI2 CACNA2D1 CNTNAP2 GRIN2A RBFOX1 CNTNAP4 CYTSB SHANK3

2p16.3 4p16 4q22.2 7q11.22 6p21.32 6q27 7q11 7q21 7q35 16p13.2 16p13.3 16q23.1 17p11.2 22q13.33

GGE/IGE EAS, IDa CAE JME, unclassified epilepsy EMA, ID, autism Periventricular heterotopia IS, EE EE NC, FE ESES, LKS, ABPE GGE/IGE GTCS only, IDa JME, GTCS alone ESES, various epilepsies

ABPE, atypical benign partial epilepsy; CAE, childhood absence epilepsy; EAS, epilepsy aphasia syndrome; EE, epileptic encephalopathy; EMA, epilepsy with myoclonic absences; ESES, electrical status epilepticus in slow-wave sleep, GTCSs, generalized tonic-clonic seizures; JME, juvenile myoclonic epilepsy; LKS, Landau–Kleffner syndrome; NCs, neonatal convulsions. a As annotated in Decipher database http://decipher.sanger.ac.uk/, most patients have additional syndromal features.

FIGURE 3 From genomic disorders to genotype-first strategies. This schematic graph demonstrates the evolution of microdeletions from variants first identified in defined genomic disorders with distinct phenotypes. Later, microdeletions were identified with variable penetrance, a wider phenotypic range, and, finally, a phenotypic spectrum that could no longer be subsumed under a common denominator. The later variants were identified through “genotype-first” strategies in large cohorts of patients with various disorders.


existing wealth of data has allowed for the discovery of epilepsy-related variants that are not necessarily related to a particular phenotype, but that show an excess in cases. The most dramatic finding is the 1q21.1 microdeletion that was found in cases with entirely unrelated developmental phenotypes and was found through a genome-first strategy (Mefford et al., 2008). This strategy suggests that some genetic variants may predispose to a range of developmental disorders that cannot be included under a common syndromal diagnosis in the traditional sense. For example, the case of the 1q21.1 microdeletion, the phenotypes of the patients may include phenotypes as diverse as intellectual disability, microcephaly, cardiac abnormalities, and cataracts (Mefford et al., 2008). Rare predisposing variants for such a broad range of conditions may only be possible by screening large patient cohorts with diverse phenotypes compared to large control cohorts. Rolandic epilepsies and IGE/GGE are phenotypes that can also be a presentation of the 1q21.1 microdeletion, which is usually not identified in patients without a developmental disease. In summary, microdeletions demonstrate the full spectrum of phenotypic associations expected in human disease, ranging from classical genomic disorders with clearly delineated phenotypes such as Angelman syndrome to microdeletions associated with a range of neurodevelopmental diseases such as 15q13.3 microdeletion to variants that cannot be defined by a coherent phenotypes such as the 1q21.1 microdeletion. Again, the research on microdeletions has paved the way for a novel understanding of rare genetic risk factors that will become crucial in the interpretation of next-generation sequencing studies.

2.7 VARIANT CLASSIFICATION AND THE GLOBAL BURDEN OF MICRODELETIONS IN EPILEPSY When assessing rare structural genomic variants with a possibly pathogenic role, most deletions and duplications identified in patients are found to be singular events, which complicates the clinical interpretation of these findings. Given the large amount of array CGH analysis performed in a clinical and research setting, criteria have been established to assess the pathogenicity of identified microdeletions and microduplications (Mefford et al., 2011; Miller et al., 2010). For example, CNVs may be classified as pathogenic, likely pathogenic, or of unknown significance. Pathogenic CNVs are gene containing de novo deletions or deletions involving known epilepsy genes. De novo duplications or any CNVs larger than 1 Mb of unknown inheritance are considered likely pathogenic. This leaves the designation of a “variant of unknown significance” for the remainder of all other CNVs that have not been previously observed in controls and encompass genes. The clinical context is conservative given the flood of rare and often private deletions and duplications in the human genome (Cooper et al., 2011). In addition to the individual pathogenic role of specific recurrent or nonrecurrent CNVs in epilepsy, the overall burden of structural genomic variants has been assessed in various studies. Research on various developmental disorders including intellectual disability and autism demonstrates a higher load of structural genomic

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variants at the group level, suggesting that in addition to the clearly pathogenic variants, many likely pathogenic CNVs and variants of unknown significance contribute to the disease risk. In a first study on various epilepsy phenotypes, we were able to demonstrate that 9% of patients had rare CNVs not present in controls including 3% of patients with hotspot deletions (Mefford et al., 2010). A similar frequency was also observed in patients with epileptic encephalopathies, where 8% of patients were found to carry rare CNVs, half of which were classified as pathogenic or likely pathogenic (Mefford et al., 2011). In a study on various epilepsies with complex phenotypes that also included patients with symptomatic epilepsy due to a seemingly explanatory cause, we identified an overall attributable risk of 5% for structural genomic variants larger than 400 kB (Helbig et al., 2014). This study suggested that the pathogenic role of CNVs is not only limited to the nonlesional epilepsies but also extends to epilepsies that are usually considered symptomatic.

2.8 GENOME-WIDE ASSOCIATION STUDIES—THE LATE SUCCESS Genome-wide association studies (GWASs) query the human genome for the association of common genetic variants with a disease phenotype (Manolio et al., 2009). In contrast to genome-wide CNV studies, GWASs investigate common genetic variation that is also present in unaffected individuals, albeit to a lower extent. Consequently, the risk conferred by variants identified in these studies is relatively minor and usually not useful for clinical counseling. Nevertheless, GWAS was highly successful, as this method represented the first hypothesis-free approach to tackle the genetic contribution to many common diseases. In many neurodevelopmental disorders, however, GWASs were less successful and these studies required unexpectedly large sample sizes to detect associated variants (Table 4). In the field of epilepsy

Table 4 Candidate genes for human epilepsies identified through genome-wide association studies Gene

Chromosomal location

Phenotype

OR (p value)

VRK2 PNPO ZEB2 CHRM3 SCN1A SCN1A

2p16.1 17q21.32 2q22.3 1q43 2q24.3 2q24.3

GGE/IGE GGE/IGE AE (GGE/IGE) JME (GGE/IGE) GGE/IGE TLE + HS

1.23 1.30 1.47 1.42 1.30 1.42

(2.5 109) (9.3 109) (9.1 109) (4.1 108) (4.0 106) (3.4 109)

The candidate genes shown above were chosen from the existing publications in the literature that have identified common genetic variants in the epilepsies with a significance value consistent with genome-wide significance. For GGE/IGE, these variants were identified in the publication of the EPICURE Consortium et al. (2012); the association of the SCN1A variant with TLE and HS was identified by Kasperaviciute et al. (2013). AE, absence epilepsy; GGE, genetic generalized epilepsy; HS, hippocampal sclerosis; IGE, idiopathic generalized epilepsy; JME, juvenile myoclonic epilepsy; TLE, temporal lobe epilepsy.


research, various GWASs have been performed on generalized and focal epilepsies. A GWAS on GGE/IGE including 3020 patients with GGEs and 3954 controls suggested common variants in CHRM3 at 1q43, VRK2 at 2p16.1, ZEB2 at 2q22.3, PNPO at 17q21.32, and SCN1A at 2q24.3 as common genetic risk factors (EPICURE Consortium et al., 2012). None of these variants had a particularly high effect size, suggesting that the genetic architecture of GGE/IGE is not predominantly influenced by strong and common genetic variants. Also, an association study on rare variants identified through exome sequencing in GGE/IGE that were followed up in 878 IGE patients and 1830 controls did not identify any “goldilocks variants,” i.e., rare, but still relatively frequent genetic variants that predispose to GGE/IGE (Heinzen et al., 2012). Interestingly, common variants in SCN1A were found to be associated with GGE/IGE in the initial study by the EPICURE Consortium et al. (2012). This gene was also implicated in a more recent GWAS on temporal lobe epilepsy with febrile seizures and hippocampal sclerosis (Kasperaviciute et al., 2013). These findings suggest that common genetic variants in SCN1A might predispose to an entire spectrum of epilepsies through both strong, monogenic variants and mild common genetic risk factors. In addition to the studies investigating the association of common variants with the epilepsy phenotype, secondary phenotypes such as the response to antiepileptic drugs (AEDs) have been of interest for GWASs. A recent study on 889 newly treated patients with epilepsies investigated the association of common genetic variants with the response to medication. No clear association was identified by the authors, who came to the conclusion that their data provide an upper limit to the possible effect of single common variants in AED response (Speed et al., 2014). In fact, less than 5% of the overall variation in drug response may be explained by a single gene variant, a finding that is in contrast to other medication response phenotypes that are more tightly linked to common genetic variants (Wei et al., 2012). For the field of AED side effects, association studies and genome-wide approaches were crucial in identifying the strong association between HLA-B*1502 and HLA-A*3101 and carbamazepine-related cutaneous side effects (Chen et al., 2011a, Man et al., 2007, McCormack et al., 2011). In summary, GWASs were crucial in identifying the first genetic risk factors in many types of common disorders. However, these studies only had limited success in seizure disorders. This observation might hint at differences in the genetic architecture with a predominance of rare genetic variants in neurodevelopmental and neuropsychiatric disorders. These genetic risk factors are increasingly assessed in massive parallel sequencing studies.

2.9 MASSIVE PARALLEL SEQUENCING STUDIES Massive parallel sequencing studies were made feasible in the last 3–5 years with the cost-efficient development of reliable platforms that allow for genotyping a large amount of target sequence. The application of these studies in epilepsy genetics can roughly be divided into three different fields including family studies, gene panel

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studies, and patient–parent trio studies. Family studies apply massive parallel sequencing studies to identify the causal monogenic variant in families with largely preexisting linkage data, resulting in a quantum leap of gene discovery in the field. Panel studies apply massive parallel sequencing technologies to reliably assess the coding sequence of a target gene panel in large cohorts, a technology that is increasingly translated into clinical practice. Finally, patient–parent trio studies focus on the genome-wide identification of de novo mutations, which has led to the discovery of various bona fide causal gene findings, largely in severe, treatment-resistant epilepsies, which are referred to as epileptic encephalopathies (Table 5).

2.9.1 Family Studies In 2010, TBC1D24 entered the stage as the first gene for human epilepsies identified through massive parallel sequencing technologies (Corbett et al., 2010; Falace et al., 2010). Recessive mutations in this gene were identified in parallel in two seemingly distinct phenotypes, a relatively benign familial infantile myoclonic epilepsy and a familial epilepsy–mental retardation syndrome (Corbett et al., 2010; Falace et al., 2010). Ever since the initial discovery, mutations in TBC1D24, coding for a brain expressed GTPase-interacting protein, were also identified in epilepsy with neurodegeneration (Guven and Tolun, 2013), malignant migrating partial seizure of Table 5 Some of the genes for human epilepsies identified through next-generation sequencing approaches Gene

Phenotype

Familial epilepsies TBC1D24 PRRT2 DEPDC5

Familial myoclonic epilepsy, DOORS Benign familial infantile seizures (BFISs) Familial focal epilepsy (variable foci)

Gene panel studies CHD2 SYNGAP1

MAE Epileptic encephalopathy, autism, ID

Trio sequencing studies GABRB3 ALG13 HDAC4 GRIN2B GNAO1 SLC35A2 GABRA1 IQSEC2

Infantile spasms, Lennox–Gastaut syndrome Infantile spasms, Lennox–Gastaut syndrome Infantile spasms, Lennox–Gastaut syndrome Infantile spasms, ID Ohtahara syndrome, infantile spasms EOEE Dravet syndrome, infantile spasms Infantile spasms

DOORS, deafness, onychodystrophy, osteodystrophy, mental retardation, and seizures; EOEE, early onset epileptic encephalopathy; ID, intellectual disability; MAE, myoclonic astatic epilepsy.


infancy (Milh et al., 2013), and, more recently, DOORS syndrome (Campeau et al., 2014), including a combination of deafness, onychodystrophy, osteodystrophy, mental retardation, and seizures. This broad and apparently unrelated phenotypic spectrum indicated that modern parallel sequencing strategies are capable of identifying risk factors linking a broad range of phenotypes, made possible through the generation and accumulation of large amounts of sequence information. Multiple familial epilepsies were identified using next-generation sequencing technologies. These include, most prominently, PRRT2 and DEPDC5. The PRRT2 gene is the long-missing gene for benign familial infantile seizures (BFISs). While other forms of benign familial epilepsies of the first year of life had already been identified genetically more than a decade ago (Helbig et al., 2008), it seemed difficult to pin down the causative gene for BFIS despite a well-established linkage peak on chromosome 16 (Striano et al., 2006). Eventually, PRRT2 was identified as the culprit gene for this condition in 2012 (Heron et al., 2012), based on the identification of mutations in this gene causing paroxysmal kinesigenic dyskinesia (Chen et al. 2011b), a movement disorder that was known to cooccur with BFIS relatively frequently. The DEPDC5 gene was identified in 2013 as the causative gene for a subset of familial focal epilepsies (Dibbens et al., 2013; Ishida et al., 2013), particularly the familial epilepsy phenotype of familial focal epilepsy with variable foci (Scheffer et al., 1998). The identification of DEPDC5 has raised interest in the epilepsy community as this gene may explain up to 10% of all familial focal epilepsies and might hint at an unanticipated connection between nonlesional focal epilepsies and the mTOR pathway, which is implicated in tuberous sclerosis complex and cortical malformations (Bar-Peled et al., 2013). In summary, TBC1D24, PRRT2, and DEPDC5 are only a few of the genes for familial epilepsies identified through modern massive parallel sequencing studies in common epilepsies. With increasing popularity and a reduction in price and effort, these technologies will be increasingly applied to familial epilepsies, hopefully leading to an increasing fraction of familial epilepsies explained.

2.9.2 Panel Studies Gene panel studies apply massive parallel sequencing approaches to a list of predefined candidate genes. In contrast to exome sequencing studies, which can be performed using the same technical platforms, gene panel studies trade additional genetic information outside the selected genes for deeper coverage of the selected genes. Gene panels are increasingly used in a diagnostic setting, replacing the traditional gene-by-gene approach with a panel approach of various candidate genes. Two studies have been published on gene panel analysis in the field of epilepsy genetics so far. The initial study by Lemke and collaborators applied a gene panel of 256 genes to 33 patients with likely genetic epilepsies (Lemke et al., 2012). In 16 of 33 patients, the authors were able to identify a causal mutation, suggesting that gene panel approaches may identify a causal mutation in a large subset of patients with epilepsy. Interestingly, causative mutations were also identified in mutations

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with atypical phenotypes, in which the respective candidate gene would not have been tested clinically at first sight. Also, three SCN1A mutations were identified in patients with reportedly SCN1A-negative Dravet syndrome, demonstrating the power of massive parallel sequencing approaches in comparison with classical Sanger sequencing technologies. A second study by Carvill and collaborators used a gene panel to follow up 19 known and 46 candidate genes in 500 patients with various epileptic encephalopathies (Carvill et al., 2013). Their study implicated mutations in CHD2 and SYNGAP1 in 1% of patients with epileptic encephalopathies, adding to an overall frequency of 10% for patients explained by the candidate genes represented on the panel. Both studies demonstrate that gene panels may help to identify the causal mutation in patients with various epilepsies in a significant fraction of cases. Given the fact that the prices and efforts for genome sequencing are still extensive and given that exome sequencing still struggles with up to 5–10% of insufficiently covered target regions, gene panel studies are predicted to still dominate the diagnostic setting for genetic epilepsies in the years to come.

2.9.3 Trio Studies Trio sequencing requires sequencing of both parents and the proband, which provides researchers with the unique possibility to assess de novo mutations on a global scale. In most studies published to date, exome sequencing is performed, covering a significant proportion of the coding sequence in the human genome. The analysis of de novo mutations is particularly attractive for genetic studies, as it reduces the plethora of genomic information found in any given exome to 1–2 exonic de novo mutations that affect protein function (Conrad et al., 2011). Also, using de novo mutation in genes such as SCN1A or CDKL5 as examples, the de novo disease model in severe epilepsies has many precedents and presents a promising hypothesis. Accordingly, this technology has been extensively used in various neurodevelopmental disorders including autism, intellectual disability, and schizophrenia (de Ligt et al., 2012; Fromer et al., 2014; Neale et al., 2012). While many of these studies initially conveyed significant excitement with regard to their findings, the fractions of cases explained by newly discovered genes soon stagnated. A wide array of genes affected by de novo mutations was identified in each study, but genes affected more than once (double hits) were found to be a rarity. One of the most extreme examples of this tremendous genetic heterogeneity was found in schizophrenia in a 2014 study (Fromer et al., 2014). In 623 patient–parent trios with schizophrenia, only 18 genes were found to be affected by de novo mutations twice. All other genes were single hits. Even though a common pattern emerged that linked many of these genes to some aspects of neuronal function, the involvement of any gene in the pathogenesis of schizophrenia was hard to establish using statistical approaches alone. In addition to the vast genetic heterogeneity, 1–2 de novo mutations are also observed in unaffected individuals and the rate of de novo mutation in patients with neurodevelopmental disorders is not significantly higher. This suggests that only a fraction of the de novo mutations in patients are causal, calling for new approaches to separate out genomic noise from causal genetic variants (Fig. 4).


SCN2A

4

GPR98 (unaff)

GABRB3 SCN1A STXBP1 CDKL5

2

KCNT1

PathScore

HDAC4 ALG13

MUC6 (unaff) PDE4DIP (unaff) TTN (unaff)

SPTAN1 TSC2 CHD2

0

NEB (unaff)

−2

−4 −10

−5

0 RVIS

5

10

FIGURE 4 Genic intolerance in genes found to be affected by de novo mutations in epileptic encephalopathies (red; dark gray dots in the print version) and unaffected individuals (green; white dots in the print version). Approximately 17,000 human genes are plotted with respect to their genic intolerance (Petrovski et al., 2013) and PathScore (Campbell et al., 2013). The genic intolerance score or residual variation intolerance score (RVIS) assesses the mutation intolerance in genes based on the data available in large exome databases. The scoring for mutation intolerance investigates whether a given gene has more or less functional genetic variation than expected when compared to the neutral variation in the gene. Some of the genes implicated in epileptic encephalopathies are relatively mutation-intolerant (e.g., SCN1A), while genes like MUC6, TTN, PDEDIP4, or GPR98 are relatively mutation-tolerant. The PathScore is a pathogenicity scoring system that assesses the relatedness of a given gene to genes implicated in human epilepsy. In contrast to many other scores, this ranking is derived from large-scale genomic data fusion including data comprising gene ontology, protein–protein interactions, and various other annotation systems. Genes for human epilepsies have a higher PathScore and are more intolerant to mutations than genes found to be affected by de novo mutations in unaffected individuals.

In contrast to the widespread skepticism in the field of de novo mutations in neurodevelopmental disorders, this method has worked remarkably well in the field of epilepsy genetics in last 2 years. In addition to smaller studies that already implicated particular candidate genes such as KCNT1 in malignant migrating partial seizures of infancy (Barcia et al., 2012) and CHD2 mutations in patients with SCN1A-negative Dravet syndrome (Suls et al., 2013), trio exome sequencing has also been used systematically in large cohorts of patients with West syndrome and Lennox– Gastaut syndrome (Epi4K et al., 2013). The Epi4K/EPGP studies systematically performed trio exome sequencing in 264 patient–parents trios with West syndrome or

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Lennox–Gastaut syndrome. The authors could identify causal mutations in previously identified genes for epileptic encephalopathies in up to 15% of patients. The most frequent gene found to be affected by de novo mutations was SCN1A (n ¼ 7), followed by STXBP1 (n ¼ 5) and CDKL5 (n ¼ 3). In addition to the finding in genes known to be implicated in epileptic encephalopathies, the Epi4K/EPGP study generated a large list of possible candidate genes. For some of these genes, there was sufficient statistical evidence to implicate them in the pathogenesis of EE. This was true for GABRB3, ALG13, and HDAC4, which are now considered bona fide genes for epileptic encephalopathies. In addition, several of the candidate genes suggested by the Epi4K/EPGP study have been independently confirmed shortly afterward, suggesting that a significant proportion of candidate genes affected by de novo mutations may cross the threshold to becoming securely implicated genes for epileptic encephalopathies. As of February 2014, CHD2, GABRA1, GRIN2B, GNAO1, SLC35A2, and IQSEC2 have accumulated independent evidence for their role in the etiology of the epileptic encephalopathies through de novo mutations (Carvill et al., 2013; Gandomi et al, 2014; Kodera et al., 2013; Lemke et al., 2014; Nakamura et al., 2013). Accordingly, the genetic architecture of the epileptic encephalopathies may be tangible and less complicated than in other neurodevelopmental diseases. With the recent flurry of confirmatory gene findings, the epileptic encephalopathies clearly set themselves apart from conditions such as autism, intellectual disability, and schizophrenia with respect to the prominent role of de novo mutations. In addition to the newly identified role for novel genes, several old candidate genes have returned as genes for epileptic encephalopathies. Most prominently, this includes KCNQ2 and SCN2A (Carvill et al., 2013; Ogiwara et al., 2009; Weckhuysen et al., 2012). Both genes were initially described to be inherited in an autosomal dominant manner in benign familial seizure disorders of the first year of life (Helbig et al., 2008). In the area of epileptic encephalopathies, de novo mutations in both genes can be found in epileptic encephalopathies, suggesting a clear spectrum of phenotypic consequences due to mutations in both genes. As SCN2A de novo mutations are also identified in patients with autism, intellectual disability, and schizophrenia, phenotypic spectra may sometimes vary as widely as seen with microdeletions. It will be part of future studies to establish whether the resulting phenotypic differences may be accounted for by particular sites in the gene affected by mutations (genotype/ phenotype correlation). In addition to the positive gene findings, the larger systematic studies also provide an overview over the genes that are not affected by mutations. So far, de novo mutations in classical genes such as ARX or SLC2A1 have not been identified as frequently as might have been expected initially.

3 SUMMARY The new genomic technologies hit the field of epilepsy genetics in two waves. First, the identification of epilepsy-related microdeletions led to the identification of the first genetic risk factors for common epilepsies and an unexpected overlap between

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