Fifty studies met eligibility criteria and were used for data extraction. With a single exception, all studies used a candidate gene approach, providing data on 229 polymorphisms in or near 55 different genes. Of variants investigating in independent data sets, only rs2029461 SNP in GRM4, rs3743123 in CX36 and rs3918149 in BRD2 showed a significant association with JME in at least two different background populations. The lack of consistent associations might be due to variations in experimental design and/or limitations of the approach.

The completion of the human genome sequences has allowed significant advance in association studies by using unbiased approaches such as genome-wide association studies (GWAS). In the last decade, this strategy has been used to investigate genetic variants associated with several diseases, including epilepsy[ 28 ]. Despite the high frequency of information yielded by genetic association studies of JME, the translation of these findings into clinical applications is still limited, requiring a critical appraisal of the existing information. The aim of this systematic review, therefore, was to report and evaluate the findings of existing genetic association studies that have examined the genetic variants underlying the JME phenotype.

The main hypothesis to explain genetic susceptibility in non-Mendelian JME is based on the interaction among multiple common and/or rare gene variations with modest or strong effects[ 23 , 24 ]. However, the identification of these susceptibility alleles is challenging[ 25 , 26 ]. One widely used experimental approach to investigate common variants is genetic association analysis of candidate genes selected according to their molecular function. Association analyses have mostly been used to assess whether the frequency of specific alleles differs between JME patients and controls more than would be predicted by chance[ 27 ]. Although such candidate gene approaches have been useful, they require prior knowledge of gene function.

As demonstrated by family and twin studies, genetic factors play a major role in JME[ 11 ]. Different heritability models have been used to explain the genetic basis of JME, including Mendelian inheritance of a few major genes or simultaneous involvement of multiple genes with minor effects inherited in non-Mendelian fashion[ 12 , 13 ]. Several methods have been developed over the past 40 years to identify JME causative/susceptibility genes. By using linkage analysis in affected families, researchers have identified genes carrying variations that co-segregate with Mendelian JME (as listed in “Online Mendelian Inheritance in Man”- http://omim.org and http://www.ncbi.nlm.nih.gov/omim/ ), including CACNB4 (calcium channel, voltage-dependent, beta 4 subunit)[ 14 ], CASR (calciumsensing receptor)[ 15 ], GABRA1 (gamma-aminobutyric acid A receptor, alpha 1)[ 16 ], GABRD (gamma-aminobutyric acid A receptor, delta)[ 17 ] and EFHC1 (EF-hand domain (C-terminal) containing 1[ 18 – 20 ]. Many more chromosome loci have been linked to JME, although their causative genes are still not known[ 21 ]. However, it should be noted that these findings only cover a small proportion of JME sufferers[ 22 ].

Juvenile Myoclonic Epilepsy (JME) has been recognized by the International League Against Epilepsy (ILAE) as an epileptic syndrome since 1989[ 1 , 2 ] and represents 5% to 10% of all epilepsies[ 3 ]. Initial reports indicated JME affects males and females equally, however, recent studies suggest that females outnumber males[ 4 ]. The onset of the condition usually occurs in the second decade, ranging from about 8 to 36 years[ 5 ]. Although diagnostic criteria differ between epileptologists, it is widely agreed that JME sufferers have early-morning myoclonic seizures (MC) with or without other seizure types (i.e., generalized tonic–clonic seizures and less frequent absences)[ 2 , 6 , 7 ]. Electroencephalography (EEG) has revealed interictal generalized spike-wave discharges (SWD) and normal background activity for patients with a typical history of JME[ 8 , 9 ]. Patients respond to pharmacological treatment, but with a high recurrence rate on discontinuation of antiepileptic drugs (AEDs)[ 6 , 10 ].

Methodological quality of the included studies was independently assessed by two reviewers (Bruna Santos and Thalita Marques), according to a set of predefined criteria ( S1 Table ) based on the scale of Thakkinstian et al.[ 30 ], which were amended compared to those used in the previously published meta-analytic studies[ 31 – 33 ]. The following factors were included in the criteria: representativeness of cases, representativeness of control, ascertainment of epileptic disorders, sample size (total number of cases and controls) and matching of case and control participants. Scores ranged from 0 (lowest) to 13 (highest). If the score was ≥7, the study was categorized as “high quality”; otherwise, the study was categorized as “low quality”. Disagreements were resolved by consensus. Due to high heterogeneity in study design and outcome measurements among the included articles, a meta-analysis was not performed. Instead, we conducted a narrative synthesis of the evidence.

We included population-based genetic association studies investigating any polymorphism with JME. Selected articles had to be original research containing independent data and case-control studies, including those that used candidate gene and GWAS approaches. Articles were filtered in three steps (see Fig 1 ): i) duplicated publications from the databases were excluded; ii) non-relevant studies (based on eligibility criteria) were excluded, such as reviews, non-genetic studies, non-human studies, case reports, and no access; iii): relevant studies were screened to exclude studies conducted with IGE patients without discriminating JME subgroup data and studies with related individuals in case or control groups.

We did a systematic review to identify genetic association studies with JME. We performed a systematic literature search of PubMed, Embase, Scopus, LILACS, epiGAD (Epilepsy Genetic Association Database), Google Scholar and SIGLE (System for Information on Grey Literature in Europe) up to February 12, 2016 using the following combinations of relevant keywords: “Juvenile Myoclonic Epilepsy” AND “Association Study”, “Juvenile Myoclonic Epilepsy” AND “Polymorphism”, “Idiopathic Generalized Epilepsy” AND “Association Study”, “Generalized Epilepsy” AND “Association Study”, “Juvenile Myoclonic Epilepsy” AND “Variants”, and “Generalized Epilepsy” AND “Variants”.

Only one study involved a genome-wide analysis of JME patients. The EPICURE study published a large GWAS in GGE, including 382 JME patients of North-Western European origin and 382 ethnically matched population controls. By combined analysis of the 2-stage, only SNP rs12059546 in M3 muscarinic acetylcholine receptor (CHRM3), reached genome-wide significance with JME ( S2 Table ). Furthermore, only 10 SNPs located at 8 different loci (1q43; 3q21.31; 5q12.3; 8q23.1, 11p15.4, 13q13.2, 18q11.2, 18q22.3) showed associations with JME exceeding the Stage-1 screening threshold of PLMM < 1.0 × 10−5 and none of them are among those included in this review.

Twenty-two polymorphisms were investigated, independently, in more than one study. For 14 polymorphisms, all independent investigations showed no association ( Table 1 ). Only rs2029461 SNP in GRM4, rs3743123 in CX36 and rs3918149 in BRD2 showed a significant association with JME in at least two different background populations. For 5 polymorphisms, the positive association was not confirmed in independent studies ( Table 1 ). For example, rs516535 in BRD2, which had reported analysis in several background populations, showed a significant association with JME in Northern American population[ 83 ], but no association in larger samples of West European[ 54 , 60 ].

The studies included in the review were conducted with different ethnic populations from Europe (n = 34), America (n = 5), Asia (n = 10), Africa (n = 2) and Oceania (n = 3). The number of patients ranged from 14 to 732, and their age varied from 2 to 25 years. The most used JME diagnostic criterion was based on the proposal by the Commission on Classification and Terminology of the International League Against Epilepsy. The vast major of polymorphisms failed to show associations with JME ( S2 Table ).

Discussion

To the best of our knowledge, this is the first systematic review of genetic association studies in JME. Our review provides an updated perspective on the accumulating evidence on common susceptibility alleles in this IGE subtype. In the 50 association studies reviewed, most polymorphisms were examined in one case–control study, of which just 17% had a positive association[34,43,47–49,51–53,55,58,61,62,64,66–69,71,77,80–83]. However, taking into account the high a priori risk of false positive results in candidate gene association studies[25], a discussion of the biological significance of these cases was precluded. In fact, genetic associations based on a single study cannot exclude the possibility of having been obtained by chance, and thus are not sufficient to establish a link with JME susceptibility. The rest of the discussion is therefore limited to data generated in more than one independent study.

Positive findings using variants from independent data sets could not be replicated in at least one of the studies, including GABRG2 (rs211037), HLA (DRB1), HLA (DQB1), BRD2 (rs3918149 and rs516535), KCNJ10 (rs1130183). As we discussed below, part of the reason for the lack of consistent patterns of association could be the experimental design: sample size, population stratification and phenotype definition.

Sample size: the recruitment of sufficiently large and homogeneous samples for robust genetic analysis is a long-standing weakness of association studies[25,85,86]. The using of small sample size reduces the statistical power to detect loci with a positive effect. On the other hand, larger sized samples may be more heterogeneous as a result of an effort to get larger cohorts. Population stratification: studies with discrepant results were often conducted on patients with different population backgrounds. For example, GABRG2 (rs211037) had a significant association with an Indian population but not in a Brazilian population. Interestingly, the allele and genotype frequencies of these polymorphisms show wide variation between the populations investigated, suggesting a role for ethnic differences in the distribution of this variant[63]. In these cases, the lack of replication could be caused by differences in the genetic structure of populations investigated. Population stratification could exist between treatment and control populations, even in well-designed studies. Such stratification could lead to spurious associations between a disease and genes that are biologically unrelated to the disease. In almost all the studies included in our sample, the only method to minimize stratification was by sampling and matching cases and controls from the same geographic region. Only four studies applied a complementary method by using genetic markers[52,53,56,57]. Thus, undetected population stratification could also be a cause of non-replicable studies[87–89], especially if the variant studied has variable penetrance and allele frequencies in different populations[90].

Phenotype definition: the lack of diagnosis based on rigid standards or objective biomarkers is a critical issue in the genetic analysis of JME[12] and may explain the divergent results found in this study. Most of the studies classified patients according to those suggested by the Commission on Classification and Terminology of the International League Against Epilepsy[2] in their Proposal for Revised Classification of Epilepsies and Epileptic Syndromes from 1989. In this document, the League described a group of signs and symptoms to identify a JME patient, but did not establish a “diagnostic protocol. Thus, even though most researchers followed the ILAE clinical criteria, inconsistent interpretation of clinical parameters and electrographic findings could still contribute to the divergent results. For example, Balan et al.[71] only used abnormal findings on EEG recordings to support JME diagnosis, while Gitaí et al.[63] included generalized spike-wave discharges in their diagnosis. Moreover, JME is a heterogeneous electroclinical epilepsy syndrome[91,92]. Few studies have used a tight endophenotype criterion, grouping patients by seizure type, diurnal preferential seizure occurrence or electroencephalogram pattern. Thus, the clinical entities classified as JME display many differentiable symptoms[93] that may well reflect different underlying genetic influences. There is a subset of JME patients, for example, who evolved from childhood absence epilepsy (CAE)[6]. If samples are not divided into subclinical categories, the genetic signal may be masked. A more effective strategy to elucidate genetic markers associated with JME could be to narrowly and consistently identify phenotypes representing specific JME endophenotypes [94–97].

Thus, because of the difficulty in controlling genetic heterogeneity and all possible confounders across studies, the failure of replication does not prove a false-positive result. Although independent replication of association has been a normative criterion for weighing evidence, Pal et al.[98] suggest that evidence should also be judge by integrating results from different experimental approaches, including linkage analysis and mutation screening. Indeed, a positive allelic association found in a locus of prior linkage is more likely to be real[98,99]. Returning to the case of variants in BRD2, EJM1, a major JME susceptibility locus, was discovered by linkage analysis of three separate family collection[36,100–103]. In 2003, Pal et al.[83] suggested that BRD2 is responsible for the EJM1 linkage peak and that the rs3918149 (among others) variant is a risk factor for JME. The positive association of this variant with JME was confirmed by independent familial and populational-based case-control studies[77,83]. Furthermore, BRD2 (but not rs3918149) was associated with photoparoxysmal response (PPR)[104]. Therefore, although the relationship between BRD2 and JME has not been replicated across some populations[54,60,77] convergent evidence supports BRD2 contributions to epileptogenesis. In fact, functional assays with heterozygous BRD2 knockout mice showed an increase in seizure susceptibility to flurothyl and the occurrence of spontaneous seizures in female mice[105]. In this review, out of 39 variants with positive associations, 23 are located in areas linked to JME. An absence of replication for these polymorphisms, therefore, should not prevent their incorporation in functional studies.

Beyond the rs3918149 in BRD2, only two other polymorphisms showed significant associations with JME (rs2029461 in GRM4 and rs3743123 in CX36) which were replicated in at least one independent study. In fact, these studies showed higher quality scores. For example, to avoid the confounding effect of population admixture in case-control studies, at least, one of these studies applied a genomic control approach[52] or carried out a family-based association study in parent–child-trios[46,67,83]. CX36 is an integral membrane protein of neuronal gap junction channels that has a significant role in epileptogenesis[106–108]. rs3743123 is a C.T transition (c.588C.T) within exon two that has not been classified as biologically important. Two independent studies showed that subjects with the T/T genotype at position 588 had a significantly increased risk of JME in a German population (OR 4.3; 95% CI 1.49 to 12.3) and a mix of other European (OR1.62; 95% CI 1.02–2.57) populations. The GRM4 encoding the group III metabotropic glutamate receptor 4 (mGluR4) and several studies have indicated a functional importance for this gene in the genesis of epilepsy[109–111]. rs2029461 is an A/G change located in the 5`UTR. The minor allele (G) showed significant association with the JME phenotype in both Caucasian and Indian populations. Interestingly, both CX36 and GRM4 genes are located in two major susceptibility loci (EJM2) for JME: regions 15q14 and 6p21, respectively, and were therefore originally chosen as gene candidate due to positional and functional criteria. However, the mechanisms by which rs3743123 and rs2029461 predispose individuals to developing JME remain obscure.