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\begin{document}

\title{Revisiting the clinical impact of variants in EFHC1 in patients with
different phenotypes of genetic generalized epilepsy}



\author[1]{Marina Gonsales}%
\author[1]{Patricia Ribeiro}%
\author[1]{Luiz Betting}%
\author[1]{Marina Alvim}%
\author[1]{Carlos Guerreiro}%
\author[1]{Clasissa Yassuda}%
\author[2]{Daniel Gitai}%
\author[1]{Fernando Cendes}%
\author[1]{Iscia Lopes-Cendes}%
\affil[1]{State University of Campinas}%
\affil[2]{Federal University of Alagoas}%


\vspace{-1em}



  
  \date{May 07, 2024}


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\selectlanguage{english}
\begin{abstract}
The most common form of genetic generalized epilepsy (GGE) is juvenile
myoclonic epilepsy (JME), which accounts for 5 to 10\% of all epilepsy
cases. The gene EFHC1 is associated with JME. However, it remains
debatable whether testing for EFHC1 mutations should be included in the
diagnostic epilepsy gene panels. To investigate the clinical utility of
EFHC1 testing, we studied 125 individuals: 100 with JME and 25 with
other GGEs. We amplified and sequenced all EFHC1 coding exons. Then, we
applied a revised version of the American College of Medical Genetics
and Genomics (ACMG)/Association for Molecular Pathology(AMP) guidelines
to predict the pathogenicity and benign impact of the variants. Mutation
screening revealed 11 missense variants in 44 probands with JME (44\%)
and in 1 of the 7 individuals with generalized tonic clinic seizures on
awakening (14\%). Overall, only the variant c.685T\textgreater{}C was
strictly classified as `pathogenic' (1/11, 9\%), five variants were
classified as `benign' (45\%), and the remaining five (45\%) were
considered variants of uncertain significance (VUS). There is currently
a limitation to test for genes that predispose an individual to complex,
non-monogenic phenotypes. Thus, we consider EFHC1 to be a risk factor
for JME but not currently useful for clinical purposes.%
\end{abstract}%



\sloppy


\textbf{INTRODUCTION}

Identifying the genes that influence the risk for epilepsies is crucial
to elucidate the mechanisms that underlie seizure susceptibility (Ottman
et al., 2010). However, the complex relationship between genotype and
phenotype poses considerable difficulties when evaluating the clinical
utility of genetic testing (Ottman et al., 2010). Thus, classifying the
pathogenicity of identified variants in complex disorders with any
degree of certainty is often challenging (Cooper, Krawczak,
Polychronakos, Tyler-Smith, \& Kehrer-Sawatzki, 2013). New genetic
technologies that involve massive parallel sequencing have influenced
the diagnostic practices in patients with intractable epilepsy; there
are several epilepsy gene panels that are currently commercially
available. Still, choosing a specific panel can be problematic for
clinicians (Chambers, Jansen, \& Dhamija, 2016). Therefore, even using
large-scale genomic tests, such as whole-exome sequencing and
whole-genome sequencing, genetic diagnosis is restricted mainly to
single-gene disorders (Boycott, Vastone, Bulma, \& MacKenzie, 2013).
Also, there has been limited success in identifying genes for complex
epilepsies, such as the genetic generalized epilepsies (GGE; Greenberg
\& Stewart, 2014; Ottman et al., 2010).

The most common form of GGE is juvenile myoclonic epilepsy (JME), which
accounts for 5 to 10\% of all epilepsies (Camfield, Striano, \&
Camfield, 2013). The clinical presentation begins between the ages of 9
and 27 years; it is characterized by myoclonic seizures (Fisher et al.,
2017), which may be followed by generalized tonic-clonic seizures (GTCS)
and absence seizures (Leppik, 2003). One of the genes associated with
JME is \emph{EFHC1} , which encodes the EFHC1 protein, also known as
myoclonin 1 (Medina et al., 2008). Although the function of the EFHC1
protein is still poorly understood, it is known to be associated with
microtubules and, consequently, involved in the regulation of cell
division, as well as associated with the process of radial migration
during the development of the central nervous system (CNS; Conte et al.,
2009; de Nijs et al., 2009). It is believed that mutations
in\emph{EFHC1} significantly impair apoptotic activity, which could
prevent the elimination of neurons with altered calcium homeostasis
during the development of the CNS, leading to JME (de Nijs et al.,
2009).

\emph{EFHC1} is currently included in 53 tests for diagnostic purposes
available in the Genetic Testing Registry
(https://www.ncbi.nlm.nih.gov/gtr; accessed in January 2020). Recently,
researchers have raised the possibility that some \emph{EFHC1} variants
might be pathogenic depending on specific genetic backgrounds in which
they are introduced (Subaran, Conte, Stewart, \& Greenberg, 2015).
However, there is a lack of studies in patients with a more diverse
ethnic background; most \emph{EFHC1} variants have been found either in
Hispanics, in patients from Central America, or in Japanese individuals.

Most importantly, there have been controversies as to whether genetic
testing for \emph{EFHC1} directly impacts therapeutic decision-making,
treatment, outcome, or other aspects in the context of medical care for
patients with GGEs. On the one hand, \emph{EFHC1} has been implicated in
JME (Bailey et al., 2017). On the other hand, \emph{EFHC1} is not listed
as an epilepsy-related genetic variant with implications for clinical
management (Poduri, Sheidley, Shostak, \& Ottman, 2014), and it has been
advised that prediction of epilepsy susceptibility in individuals who
harbor \emph{EFHC1} variants must be handled with caution (Subaran et
al., 2015). Therefore, it remains debatable whether \emph{EFHC1} is
clinically useful and should be included in the diagnostic gene panels
for GGEs. Thus, our study aimed to contribute to this ongoing debate.

\textbf{METHODS}

\textbf{Editorial policies and ethical considerations}

The patients or their parents signed an informed consent form, which was
approved by the research ethics committee on the clinical centers of the
University of Campinas and the Federal University of Alagoas, Brazil.

\textbf{Patients}

Our cohort comprised 125 individuals: 100 with JME, 10 with juvenile
absence epilepsy (JAE), 7 with GTCS on awakening, 3 with childhood
absence epilepsy (CAE), and 5 with unclassified GGE. These patients were
regularly followed in the outpatient epilepsy clinic of the university
hospitals at the University of Campinas and the Federal University of
Alagoas, Brazil. They fulfilled the clinical criteria for GGEs,
according to the International League Against Epilepsy (ILAE) guidelines
(Fisher et al., 2017; Scheffer et al., 2017; Wilmshurst et al., 2015).
Clinical and demographic features of these patients have been previously
published (Betting et al., 2006; Gitai et al., 2012).

\textbf{Mutation screening}

We obtained DNA samples for each patient from peripheral blood
lymphocytes by standard procedures (Sambrook, Russell, \& Fritsch,
2001). All 11 coding exons and intron-exon boundaries of
\emph{EFHC1}were amplified by polymerase chain reaction (PCR; primer
sequences available upon request). Sanger sequencing was performed by
capillary electrophoresis in an ABI 3500xL Genetic Analyzer using the
BigDye\selectlanguage{ngerman}® Terminator Cycle Sequencing Kit (Thermo Fisher Scientific,
Waltham, MA, USA). Sequence variants were described according to the
conventional nomenclature (den Dunnen \& Antonarakis, 2001; den Dunnen,
2019) based on the full-length \emph{EFHC1} isoform (NM\_018100) and
deposited in a public genomic database of GGEs
(http://bipmed.iqm.unicamp.br/GGE).

\textbf{Pathogenic classification}

We applied the American College of Medical Genetics and Genomics (ACMG)
and the Association for Molecular Pathology (AMP) guidelines (Richards
et al., 2015) in our study. We assessed the predicted pathogenicity and
benign impact of the variants found in our cohort. To do so, we revised
the ACMG/AMP rules to determine which criteria apply to our framework
for GGE-related \emph{EFHC1} variants.

We performed a series of analyses for each identified
\emph{EFHC1}variant. To address whether the identified amino acid
changes were already established as pathogenic or benign, we performed a
literature search using the online search engine PubMed
(https://www.ncbi.nlm.nih.gov/pubmed/) for the terms ´EFHC1 AND
(mutation OR variants) AND epilepsy´ up to April 2019. We further
assessed the frequency of the variants found in \emph{EFHC1} in
databases of individuals from different populations: BIPMed
(http://bipmed.org/; Secolin et al., 2019), ABraOM
(http://abraom.ib.usp.br/; Naslavsky et al., 2017), NHLBI Exome
Sequencing Project (ESP) (http://evs.gs.washington.edu/EVS/), gnomAD
(http://gnomad.broadinstitute.org/), and 1000 Genomes
(http://www.internationalgenome.org/). We also investigated
the\emph{EFHC1} variants found in our cohort in 100 unrelated Brazilian
individuals without a family history of epilepsy. To verify whether the
prevalence of these variants in affected individuals was statistically
increased over controls, we performed the unconditional exact
homogeneity/independence test (Z-pooled method, one-tailed). If the odds
ratio (OR) calculated is 1.00, then there is no association between the
variant and the risk for the disease. On the other hand, values greater
than 1.00 indicate that the variant increased the odds of having the
disease (Bailey et al., 2017). To avoid the possibility of population
stratification, we performed separate tests for each of the control
groups that contained admixed Brazilian individuals (our 100 individuals
control group, as well as the BIPMed and AbraOM databases).

In order to predict the deleterious effect of \emph{EFHC1} missense
variants in protein function, we used 13 of the 16 computer algorithms
recommended by the ACMG/AMP guidelines: FATHMM
(http://fathmm.biocompute.org.uk; Shihab et al., 2015), Condel
(http://bg.upf.edu/condel; González-Pérez \& Lopez-Bigas, 2011),
MutationTaster (http://www.mutationtaster.org; Schwarz, Rodelsperger,
Schuelke, \& Seelow, 2010), PANTHER (http://www.pantherdb.org/tools; Mi,
Muruganuian, \& Thomas, 2013), SNPs\&GO
(http://snps.biofold.org/snps-and-go/snps-and-go.html; Calabrese
Capriotti, Fariselli, Martelli, \& Casadio, 2009), MutPred2
(http://mutpred.mutdb.org; Pejaver et al., 2017), PROVEAN
(http://provean.jcvi.org; Choi, Sims, Murphy, Miller, \& Chan, 2012),
CADD (http://cadd.gs.washington.edu; Rentzsch, Witten, Cooper, Shendure,
\& Kircher, 2019), PolyPhen2 (http://genetics.bwh.harvard.edu/pph2;
Adzhubei, Jordan, \& Suyaev, 2013), MutationAssessor
(http://mutationassessor.org/r3; Reva, Antipin, \& Sander, 2011), SIFT
(http://siftdna.org; Sim et al., 2012), Align GVGD
(http://agvgd.hci.utah.edu; Tavtigian et al., 2006), and PhD-SNP
(http://snps.biofold.org/phd-snp/phd-snp.html; Capriotti, Calabrese, \&
Cadadio, 2006).

\textbf{RESULTS}

Mutation screening in \emph{EFHC1} in our cohort of GGE patients
revealed 11 heterozygous missense variants. They were found in 44 of the
100 probands with JME (44\%), and one of the variants was also
identified in 1 of the 7 individuals with GTCS on awakening (14\%).
Table 1 shows all 11 variants, along with their predicted protein
repercussions and the clinical features of the carriers. All these
variants have been deposited and are publicly available at
http://bipmed.iqm.unicamp.br/GGE.

We revised and adapted the ACMG/AMP guidelines to our framework. For the
pathogenic classification, we eliminated the following four
criteria:\textbf{PM1} (variant in mutational hot spot or well-studied
functional domain without benign variation) because no mutational hot
spots have been reported for \emph{EFCH1} , and the variants found in
patients with GGEs are scattered throughout the gene; \textbf{PM3}
(variant detected in \emph{trans} with a pathogenic variant), which is
exclusive for recessive disorders; \textbf{PP2} (missense variant in
gene with low rate of benign missense variants and pathogenic missenses
common) because \emph{EFHC1} has a low Z score for missense variants
(0.14 according to gnomAD), indicating that the deviation of observed
counts is not far from the expected number, and thus the gene is
tolerant to missense variants; and \textbf{PP4} (patient's phenotype or
family history highly specific for the gene) because GGEs are
genetically heterogeneous (Zifkin, Andermann, \& Andermann, 2005). We
then organized the remaining 12 suitable criteria for a comprehensive
classification scheme (Figure 1). We used the corresponding evidence of
pathogenicity to meet the ACMG/AMP rules for combining criteria to
classify variants (Table 2).

For the benign classification, we considered two ACMG/AMP criteria not
applicable to our framework: \textbf{BS2} (variant observed in a healthy
adult with full penetrance expected at an early age) because
GGE-related\emph{EFHC1} variants do not have full penetrance (Suzuki et
al., 2004); and \textbf{BP1} (missense variant in a gene for which
primarily truncating variants are known to cause disease) because
truncating variants in \emph{EFHC1} is rarely associated with JME
(Bailey et al., 2017). We used the remaining 10 criteria in another
classification scheme (Figure 2), an action that fulfills the ACMG/AMP
rules for combining criteria to classify genetic variants (Table 2).

We assessed the different criteria for each of the 11
\emph{EFHC1}variants found in our cohort. From the literature search, we
observed that two variants, namely c.1765G\textgreater{}A and
c.1820A\textgreater{}G, had not been reported. Next, we assessed the
allele frequency of the variants in databases of individuals from
different populations and computed the values (Table S1). All 11
variants were identified in at least one database. Nine of the 11
variants (82\%) presented allele frequencies higher than 1\% in at least
one subpopulation, and six of them (6/11, 54\%) had allele frequencies
higher than 5\%. Furthermore, we calculated the OR and statistical
significance (\emph{P} values) of the association between
the\emph{EFHC1} variants found in patients with GGE in our cohort versus
in a group of 100 control subjects of Brazilian origin and in two
population databases of Brazilian individuals (BIPMed: Secolin et al.,
2019; and ABraOM: Naslavsky et al., 2017) using an unconditional exact
test (Z-pooled, one-tailed). The allele frequencies and test results are
shown in Table 3. Moreover, we used 13 computer algorithms to estimate
the deleterious effects of the identified variants (Table S2).

Finally, we classified the 11 variants identified in our cohort
according to the ACMG/AMP guidelines (Table S3). The variants
c.662G\textgreater{}A and c.685T\textgreater{}C were the only ones
classified as `pathogenic.' However, c.662G\textgreater{}A also meets
the criteria for `benign.' Six of the 11 variants were classified as
`benign,' and the remaining variants are considered variants of
uncertain significance (VUS).

\textbf{DISCUSSION}

Variant interpretation is currently one of the most significant
challenges in genomic medicine (Wright et al., 2019). Frequently, the
causal relationship between the genotype, the identified variant, and
the disease phenotype is not evident. This phenomenon results in
ambiguous, erroneous, or incomplete interpretation of the genetic tests.
An uncertain test result will not provide useful information to clarify
the diagnosis, assist in treatment or management, or help in disease
prevention (Richards et al., 2015). With this premise in mind, we
revisited the criteria for interpretation of the clinical impact
of\emph{EFHC1} variants in a cohort of patients with GGEs. This
information is likely to provide new information in the assessment of
the clinical utility of \emph{EFHC1} testing.

Mutation screening in our cohort revealed that \emph{EFHC1} variants
were almost exclusively in JME patients (10/11); only one of them was
also identified in an individual with GTCS on awakening. However, we
acknowledge the limited number of patients with other GGE phenotypes
included in the present study. Few studies have investigated\emph{EFHC1}
variants in GGEs other than JME (Stogmann et al., 2006; Subaran et al.,
2015). These studies also included a limited cohort of non-JME
phenotypes (37 and 23 individuals, respectively), with variants
considered potentially pathogenic reported in only two patients with JAE
and one with unclassified GGE (Stogmann et al., 2006). Thus, the
relatively low frequency of potentially pathogenic variants
in\emph{EFHC1} in subjects with common GGEs suggests that it might not
be the leading cause of these epilepsies.

All variants identified in our study are missense; they were found in
patients with a family history of epilepsy (n = 20) as well as sporadic
patients (n=25), a phenomenon that has also been observed in previous
studies (Annesi et al., 2007; Bai et al., 2009; Jara-Prado et al., 2012;
Ma et al., 2006; Medina et al., 2008; Raju et al., 2017; Subaran et al.,
2015; Stogmann et al., 2006; Suzuki et al., 2004; Thounaojam et al.,
2017; von Podewils et al., 2015). Furthermore, we found that overall,
patients carrying variants in \emph{EFHC1} did not present distinct
clinical characteristics compared to those without it. Previous studies
also did not identify an apparent correlation between molecular findings
and clinical features (Annesi et al., 2007; Thounaojam et al., 2017),
although von Podewils et al. (2015) reported an association of the
variants c.545G\textgreater{}A, c.685T\textgreater{}C and
c.881G\textgreater{}A with early-onset GTCSs, a higher risk of status
epilepticus, and a decreased risk of bilateral myoclonic seizures in
series. Hence, we cannot exclude the possibility that additional studies
in larger cohorts might highlight subtle phenotypic differences in JME
patients with and without specific genetic variants.

We found that the amino acid residue changes that result from the
missense variants are distributed throughout the EFHC1/myoclonin protein
(Figure 3): in the three DM10 domains, in the region between the first
two domains, in the EF-hand motif, and in the C-terminal region.
Therefore, as described previously, we found no preferred region for the
occurrence of \emph{EFHC1} variants in patients with JME and other GGEs
(Bailey et al., 2017). Notably, our study is one of the few to report
variants in the EF region: p.E589K and p.N607S, both of which are novel.

To further advance the classification of GGE-related
\emph{EFHC1}variants, we reviewed the ACMG/AMP guidelines, structured
them into a comprehensive classification scheme, and applied them to our
framework. We performed a literature review of variants that had already
been established as either pathogenic or benign. Nine of the 11 variants
found in the present study were previously reported in the literature.
The variants c.662G\textgreater{}A and c.685T\textgreater{}C were
reported as `pathogenic' and found in patients with JME (Annesi et al.,
2007; Bai et al., 2009; Bailey et al., 2017; Ma et al., 2006; Suzuki et
al., 2004; Raju et al., 2017; von Podewils et al., 2015), although
c.685T\textgreater{}C was not considered pathogenic in another report
(Stogmann et al., 2006). Functional assays demonstrated that both
variants reduce the effects of cell death, prevent the apoptosis of
neurons with precarious calcium homeostasis during SNC development
(Suzuki et al., 2004), induce defects in the mitotic spindle, and affect
the radial morphology of glial and migratory neurons (de Nijs et al.,
2009). In addition, both variants were found to co-segregate with
affected individuals in families with JME (Suzuki et al., 2004).

The variants c.887G\textgreater{}A and c.896A\textgreater{}G were
previously reported by Bailey et al. (2017) in patients from Brazil and
classified as VUS, but no further information was provided. The variants
c.475C\textgreater{}T, c.475C\textgreater{}G, c.545G\textgreater{}A,
c.1343T\textgreater{}C, c.1855A\textgreater{}C are widely recognized as
polymorphisms, and thus they are not considered pathogenic (Annesi et
al., 2007; Ma et al., 2006; Stogmann et al., 2006; Subaran et al., 2015;
Suzuki et al., 2004; Thounaojam et al., 2017). Functional studies showed
that they do not affect cell death (de Nijs et al., 2009; Suzuki et al.,
2004).

Moreover, we obtained the allele frequency of the \emph{EFHC1} variants
in different population databases. We observed that the variation in the
allelic frequency is dependent on the ethnic composition of the
investigated population. Only the variants c.896A\textgreater{}G and
c.1765G\textgreater{}A do not have allele frequencies higher than 1\% in
any of the investigated subpopulations (Table S1), and these two
variants were absent from our group of 100 controls as well both
databases of Brazilian individuals (BIPMed and AbraOM; Table 3). The
allele frequencies of c.685T\textgreater{}C, c.887G\textgreater{}A, and
c.1820A\textgreater{}G are only over 1\% in specific subpopulations
(c.685T\textgreater{}C: South Asian; c.887G\textgreater{}A and
c.1820A\textgreater{}G: African; Table S1). The remaining six variants
(c.475C\textgreater{}T, c.475C\textgreater{}G, c.545G\textgreater{}A,
c.662G\textgreater{}A, c.1343T\textgreater{}C, and
c.1855A\textgreater{}C) present allele frequencies higher than 1\% in
different populations.

To verify whether the frequency of \emph{EFHC1} variants in affected
individuals is increased over controls, we screened 100 Brazilian
individuals without a family history of epilepsy and performed an
unconditional exact test. In addition, we performed the same test using
two independent databases with genomic information on Brazilian
individuals (BIPMed e ABraOM) to overcome the possibly insufficient
sample size of our control group. Bailey et al. (2017) employed this
approach with race-matched population groups from the ExAC database,
but, unfortunately, the populations in the ExAC database do not match
the Brazilian population.

We found a statistically significant association with JME for the
variant c.685T\textgreater{}C when comparing the allele frequencies of
our cohort to those in the ABraOM database (OR: 6.17; 95\% confidence
interval: 1.24--30.77; \emph{P} value: 0.0245). Three variants
(c.896A\textgreater{}G, c.1765G\textgreater{}A, and
c.1820A\textgreater{}G) were not found in our control group or the
Brazilian population databases; thus, the OR value was infinite (\selectlanguage{english}[?]). For
the remaining variants (c.475C\textgreater{}T, c.475C\textgreater{}G,
c.545G\textgreater{}A, c.662G\textgreater{}A, c.887G\textgreater{}A,
c.1343T\textgreater{}C, and c.1855A\textgreater{}C), we found no
association between the variant and the risk for GGE.

To estimate the effect of potentially deleterious changes on protein
function, we used the \emph{in silico} prediction algorithms recommended
by the ACMG/AMP (Richards et al., 2015). There was no consensus in these
analyses---we did not observe concordance among the results of the 13
utilized algorithms for the investigated variants. Therefore, we were
unable to use this evidence; the ACMG/AMP guidelines state that all of
the \emph{in silico} programs must agree on the prediction. This finding
is in sharp contrast with what we have recently observed in the analysis
of another epilepsy-related gene, \emph{SCN1A,} in the context of Dravet
syndrome (Gonsales et al., 2019). Indeed, as opposed to \emph{SCN1A}
,\emph{EFHC1} is a gene that is tolerant to variation. This potential
can be inferred by its probability of being loss-of-function intolerant
(pLI), calculated as 0.000, and its Z scores (deviation of observed
counts from the expected number) of 0.46 and 0.14 for synonymous and
missense variants, respectively (http://gnomad.broadinstitute.org).
For\emph{SCN1A} , a gene considered to be intolerant to variation, these
values are much higher: pLI = 1.000, synonymous Z score = 0.88, and
missense Z score = 5.22 (http://gnomad.broadinstitute.org).

The lack of consensus in the prediction analysis can be attributed to
differences in the parameters used by the different algorithms (Sun \&
Yu, 2019; Walters-Sen et al., 2015). Also, we hypothesize that because
the phenotype in most GGE patients is not as severe as in Dravet
syndrome, it is possible that the putative genetic changes do not cause
drastic disruptions in protein function. This factor would lead to
inaccurate predictions. A recent study that evaluated the limitations in
computational methods revealed that prediction models are usually
excessively dependent on the conservation feature of the variants, a
factor that results in predictive errors (Sun \& Yu, 2019). The
overlooked disease susceptibility of genes might also explain the
failures of the computational tools. Interestingly, the limitations in
the predictive power of the currently available \emph{in
silico}algorithms to analyze \emph{EFHC1} variants were seen even for
amino acid substitutions that were previously shown to cause functional
abnormalities in biological assays (Suzuki et al., 2004). Indeed, as
mentioned above, the variants c.662G\textgreater{}A and
c.685T\textgreater{}C, which was found in 1 and 3 of our patients with
JME, respectively, were previously studied by Suzuki et al. (2004) and
found to affect the apoptotic activity of neurons with precarious
calcium homeostasis.

Applying the proposed modified classification guidelines to
the\emph{EFHC1} variants from our cohort, only the variants
c.662G\textgreater{}A and c.685T\textgreater{}C were classified as
`pathogenic.' However, c.662G\textgreater{}A also met the criteria to be
classified as `benign.' In cases when the criteria for benign and
pathogenic variants are contradictory, the ACMG/AMP rules for combining
criteria states that the variant should be classified as VUS (Richards
et al., 2015). Thus, only variant c.685T\textgreater{}C can strictly be
classified as `pathogenic' (1/11, 9\%).

It is noteworthy that the variant c.662G\textgreater{}A only met one
benign criterion---the stand-alone BA1---due to an allele frequency of
5.3\% in one African subpopulation. Indeed, all six variants classified
as `benign' (6/11, 55\%) met the stand-alone criteria BA1 because they
present an allele frequency greater than 5\% in at least one database
subpopulation. Therefore, the evidence-based population frequency
promoted a substantial increase in the classification scores, which in
some cases could have been overly weighted, especially when analyzing
genes of minor effect. This phenomenon would induce an increased
susceptibility rather than a major effect.

Ethnicity might have an important influence in defining which genetic
factors are implicated in diseases with complex inheritance, including
GGEs (Subaran et al., 2015). Thus, different genetic backgrounds would
present distinct epilepsy susceptibility genes. In this scenario, one
recently reported possibility is that \emph{EFHC1} variants might be
pathogenic when they are found in specific genetic backgrounds (Subaran
et al., 2015). Interestingly, we found variants in our patients
originating from the Northeast part of Brazil that are common only in
specific populations: c.662G\textgreater{}A, c.887G\textgreater{}A, and
c.1820A\textgreater{}G, each with a higher allele frequency in
populations of African ancestry. A study that investigated the global
ancestry of Brazilians showed that the genetic composition of the
populations from the specific region where the probands harbor these
variants are originated (the Northeast region) is closer to the
Europeans (Saloum de Neves Manta et al., 2013). It is important to
highlight that the underrepresentation of non-European ancestry groups
in population databases poses an additional challenge to the
interpretation of genetic variants (Petrovski \& Goldstein, 2016). A
better population match would improve the application of population
frequency criteria for underrepresented ethnicities.

In contrast to Mendelian disorders, common or complex inheritance
diseases might not have one single major causative gene. Nevertheless,
multiple genetic variants contribute to a small effect on disease risk.
Hence, reduced penetrance and small effect size are possible
explanations for why healthy individuals might harbor pathogenic
variants (Cooper et al., 2013; Wright et al., 2019). Penetrance refers
to the proportion of individuals in a population with a disease-related
genotype that manifests the disease phenotype, while a small effect size
implies that the variant has a low impact on the multifactorial etiology
of the disease (Cooper et al., 2013). Therefore, some variants are
insufficient to cause disease on their own and require interaction with
other genetic and/or environmental factors to surpass an estimated
threshold into a pathogenic phenotype (Cooper et al., 2013). In this
context, \emph{EFHC1} might be considered a partially penetrant gene.
Even among disorders for which this concept has been well-established,
such as cancer syndromes, the implications of low penetrance or small
effect size when considering a gene suitable for testing in the clinical
setting has been discussed (Ellsworth, Turner, \& Ellsworth, 2019; Wendt
\& Margolin, 2019). For instance, variants found in the highly penetrant
susceptibility genes \emph{BRCA1} and \emph{BRCA2} for breast cancer are
clinically actionable (Ellsworth et al., 2019). However, definitive
clinical recommendations cannot be drawn for lower risk genes (Wendt \&
Margolin, 2019), and effective management and therapeutics strategies
are still required for patients who harbor variants in other genes
(Ellsworth et al., 2019). Although it is known that multiple gene
variants are necessary to produce the GGE phenotype, there are still no
accepted models on a presumed polygenic inheritance regarding the type
of variants and the involved genes (Mullen, Berkovic, \& Commission,
2018). Moreover, the clinical management of the GGEs is not altered by
the result of the genetic test. This fact suggests that it should be
considered only in the research context (Mullen et al., 2018).

Another crucial debate regarding genetic testing is the challenge that
clinicians face when reporting the results to the patients or the
parents, especially when a VUS is found. These variants have undefined
clinical significance, and it is a consensus that they should not be
used in clinical decision-making (Richards et al., 2015). Patients
report anxiety symptoms, worries, and uncertainty in response to a VUS
result (Makhnoon, Shirts, \& Bowen, 2019). Moreover, in the case of
genetic testing performed in children, there is the possibility that the
parents will misinterpret the test results, a phenomenon that would lead
to unnecessary anxiety due to excessive medical attention (Wynn et al.,
2018). Thus, given that almost half of the variants found in our cohort
are VUS (5/11, 45\%), this potential represents a significant concern
when considering \emph{EFHC1} for clinical genetic testing.

\textbf{CONCLUSIONS}

There is still a significant limitation in the medical interpretation of
genetic testing for complex, non-monogenic phenotypes. Our study shows
that almost half of the \emph{EFHC1} variants found in patients with
GGEs remained as VUS after applying the modified classification proposed
in this study using the ACMG/AMP guidelines. In addition, the only
variant that could be classified as `pathogenic' (c.685T\textgreater{}C)
was found in only 3\% of the JME patients in our current study and in
1--5\% in the reported literature (Annesi et al., 2007; Podewils, 2015;
Raju et al., 2017; Stogmann et al., 2006). Our results, together with
previous evidence, indicate that \emph{EFHC1} variants are currently
best classified as a risk factor---not a causal, major gene---for JME
and other GGEs. Given that the interpretation of genetic testing results
for \emph{EFHC1} variants is complex and offers little information for
clinical decision-making, we suggest that the inclusion of
\emph{EFHC1}in gene panels for genetic testing should be limited to
research purposes.

\textbf{Acknowledgments}

We gratefully acknowledge the patients and their families for
participating in this study.

This work was supported by the Fundacao de Amparo a Pesquisa do Estado
de Sao Paulo (FAPESP: 2013/07559-3) and the Conselho Nacional de
Pesquisa (CNPq: 143189/2009-3). Dr. IL-C is supported by the CNPq (grant
numbers: 403299/2016-0 and 309494/2014-1).

\textbf{Conflict of Interest Statement}

All authors declare no conflicts of interest related to the work
reported in this manuscript.

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\textbf{TABLES}

\textbf{Table 1.} Eleven \emph{EFHC1} variants found in our cohort of
125 individuals with genetic generalized epilepsies (GGEs).\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\textbf{DNA change} & \textbf{Amino acid change} & \textbf{Patient ID} &
\textbf{Phenotype} & \textbf{Gender} & \textbf{Seizure onset (years)} &
\textbf{Type of seizures} & \textbf{Family history of
seizures}\tabularnewline
\midrule
\endhead
NM\_018100.3:c.475C\textgreater{}T & NP\_060570.2:p.(Arg159Trp) & 2 &
JME & M & 17 & My, GTC & Yes\tabularnewline
& & 13 & JME & F & 12 & My, A, GTC & Yes\tabularnewline
& & 18 & JME & M & 16 & My, A, GTC & Yes\tabularnewline
& & 22 & JME & F & 14 & My, A, GTC & Yes\tabularnewline
& & 24 & JME & M & 18 & My, GTC & Yes\tabularnewline
& & 26 & JME & M & NI & NI & No\tabularnewline
& & 29 & JME & F & 14 & My, GTC & Yes\tabularnewline
& & 39 & JME & F & NI & My, A, GTC & Yes\tabularnewline
& & 41 & JME & M & NI & NI & No\tabularnewline
& & 42 & JME & F & 5 & My, A, GTC & Yes\tabularnewline
& & 47 & JME & F & 19 & My & Yes\tabularnewline
& & 52 & JME & F & 15 & GTC, My, A & No\tabularnewline
& & 60 & JME & F & 9 & GTC, My & No\tabularnewline
& & 78 & JME & M & 15 & GTC, My, A & No\tabularnewline
& & 82 & JME & F & 14 & GTC, My & No\tabularnewline
& & 93 & JME & F & 12 & GTC, My & No\tabularnewline
& & 97 & JME & F & 20 & GTC, My & No\tabularnewline
NM\_018100.3:c.475C\textgreater{}G & NP\_060570.2:p.(Arg159Gly) & 71 &
JME & M & 12 & GTC, A & No\tabularnewline
NM\_018100.3:c.545G\textgreater{}A & NP\_060570.2:p.(Arg182His) & 9 &
JME & F & NI & NI & Yes\tabularnewline
& & 21 & JME & F & 17 & My, GTC & Yes\tabularnewline
& & 28 & JME & F & 13 & My, A, GTC & Yes\tabularnewline
& & 43 & JME & F & 11 & My, GTC & Yes\tabularnewline
& & 46 & JME & F & 19 & My & Yes\tabularnewline
NM\_018100.3:c.662G\textgreater{}A & NP\_060570.2:p.(Arg221His) & 65 &
JME & M & 9 & GTC, My & No\tabularnewline
NM\_018100.3:c.685T\textgreater{}C & NP\_060570.2:p.(Phe229Leu) & 4 &
JME & M & 7 & My, GTC & Yes\tabularnewline
& & 16 & JME & F & NI & NI & No\tabularnewline
& & 23 & JME & M & NI & NI & No\tabularnewline
NM\_018100.3:c.887G\textgreater{}A & NP\_060570.2:p.(Arg296His) & 56 &
JME & M & 14 & GTC, My, A & No\tabularnewline
& & 58 & JME & M & 14 & GTC, My, A & No\tabularnewline
NM\_018100.3:c.896A\textgreater{}G & NP\_060570.2:p.(Lys299Arg) & 28 &
JME & F & 13 & My, A, GTC & Yes\tabularnewline
NM\_018100.3:c.1343T\textgreater{}C & NP\_060570.2:p.(Met448Thr) & 4 &
JME & M & 7 & My, GTC & Yes\tabularnewline
& & 21 & JME & F & 17 & My, GTC & Yes\tabularnewline
& & 44 & JME & F & NI & NI & No\tabularnewline
& & 62 & JME & M & 14 & GTC, My & No\tabularnewline
& & 66 & JME & F & 7 & GTC, My, A & No\tabularnewline
& & 69 & JME & M & 21 & GTC, My, A & No\tabularnewline
& & 76 & JME & M & 4 & GTC, My, A & No\tabularnewline
& & 99 & JME & F & 16 & GTC, My, A & No\tabularnewline
NM\_018100.3:c.1765G\textgreater{}A* & NP\_060570.2:p.(Glu589Lys) & 87 &
JME & M & 11 & GTC, My, A & No\tabularnewline
NM\_018100.3:c.1820A\textgreater{}G* & NP\_060570.2:p.(Asn607Ser) & 60 &
JME & F & 9 & GTC, My & No\tabularnewline
NM\_018100.3:c.1855A\textgreater{}C & NP\_060570.2:p.(Ile619Leu) & 2 &
JME & M & 17 & My, GTC & Yes\tabularnewline
& & 19 & JME & F & 21 & My, A, GTC & Yes\tabularnewline
& & 32 & JME & F & 14 & My, A, GTC & Yes\tabularnewline
& & 34 & JME & F & 12 & My, A, GTC & Yes\tabularnewline
& & 38 & JME & F & NI & NI & No\tabularnewline
& & 43 & JME & F & 11 & My, GTC & Yes\tabularnewline
& & 48 & JME & M & 14 & My, A, GTC & Yes\tabularnewline
& & 56 & JME & M & 14 & GTC, My, A & No\tabularnewline
& & 58 & JME & M & 14 & GTC, My, A & No\tabularnewline
& & 68 & JME & M & 13 & GTC, My & No\tabularnewline
& & 70 & JME & M & 3 & GTC, My, A & No\tabularnewline
& & 89 & JME & M & NI & GTC, My & No\tabularnewline
& & 104 & GTCSa & F & 11 & GTC & Yes\tabularnewline
\bottomrule
\end{longtable}

ID: identification; JME: juvenile myoclonic epilepsy; GTCSa: generalized
tonic-clonic seizures on awakening; M: male; F: female; My: myoclonic
seizures; GTC: generalized tonic-clonic; A: absence; NI: not informed.
*novel variants.

\textbf{Table 2.} Guidelines for combining different criteria to
classify \emph{EFHC1} variants into 'pathogenic,' 'likely pathogenic,'
benign,' 'likely benign,' or variants of uncertain significance (VUS).\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Classification} & \textbf{Evidence of pathogenicity or benign
impact}\tabularnewline
\midrule
\endhead
Pathogenic & 1 Very Strong + 1 to 4 Strong\tabularnewline
& 1 Very Strong + 2 to 4 Moderate\tabularnewline
& 1 Very Strong + 1 Moderate + 1 Supporting\tabularnewline
& 1 Very Strong + 2 or 3 Supporting\tabularnewline
& 2 to 4 Strong\tabularnewline
& 1 Strong + 3 or 4 Moderate\tabularnewline
& 1 Strong + 2 Moderate + 2 or 3 Supporting\tabularnewline
Likely pathogenic & 1 Very Strong + 1 Moderate\tabularnewline
& 1 Strong + 1 or 2 Moderate\tabularnewline
& 1 Strong + 2 or 3 Supporting\tabularnewline
& 3 or 4 Moderate\tabularnewline
& 2 Moderate + 2 or 3 Supporting\tabularnewline
Benign & 1 Stand-alone\tabularnewline
& 2 or 3 Strong\tabularnewline
Likely benign & 1 Strong + 1 Supporting\tabularnewline
& 3 to 5 Supporting\tabularnewline
VUS & Other criteria shown above are not met\tabularnewline
& Contradictory criteria for pathogenic and benign\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3.} Allele frequencies of \emph{EFHC1} variants found in
our cohort of genetic generalized epilepsy (GGE) patients in 100 control
subjects and population databases of Brazilian individuals.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllll@{}}
\toprule
\begin{minipage}[b]{0.09\columnwidth}\raggedright\strut
\textbf{Nucleotide change}\strut
\end{minipage} & \begin{minipage}[b]{0.09\columnwidth}\raggedright\strut
\textbf{AF in relation to phenotype}\strut
\end{minipage} & \begin{minipage}[b]{0.09\columnwidth}\raggedright\strut
\textbf{Control subjects} \textbf{(n = 100)}\strut
\end{minipage} & \begin{minipage}[b]{0.09\columnwidth}\raggedright\strut
\textbf{Control subjects} \textbf{(n = 100)}\strut
\end{minipage} & \begin{minipage}[b]{0.09\columnwidth}\raggedright\strut
\textbf{Control subjects} \textbf{(n = 100)}\strut
\end{minipage} & \begin{minipage}[b]{0.09\columnwidth}\raggedright\strut
\textbf{BIPMed} \textbf{(n = 258)}\strut
\end{minipage} & \begin{minipage}[b]{0.09\columnwidth}\raggedright\strut
\textbf{BIPMed} \textbf{(n = 258)}\strut
\end{minipage} & \begin{minipage}[b]{0.09\columnwidth}\raggedright\strut
\textbf{BIPMed} \textbf{(n = 258)}\strut
\end{minipage} & \begin{minipage}[b]{0.09\columnwidth}\raggedright\strut
\textbf{AbraOM} \textbf{(n = 609)}\strut
\end{minipage} & \begin{minipage}[b]{0.09\columnwidth}\raggedright\strut
\textbf{AbraOM} \textbf{(n = 609)}\strut
\end{minipage} & \begin{minipage}[b]{0.09\columnwidth}\raggedright\strut
\textbf{AbraOM} \textbf{(n = 609)}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{AF}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{OR}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{P} \textbf{value}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{AF}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{OR}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{P}\textbf{-value}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{AF}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{OR}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\emph{P} \textbf{value}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
NM\_018100.3:c.475C\textgreater{}T\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
8.5\% (JME)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
7.5\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.15\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.3966\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
11.4\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.72\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.1450\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
14.5\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.55\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0615\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
NM\_018100.3:c.475C\textgreater{}G\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.5\% (JME)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.5\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.00\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.6\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.86\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.5243\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.2\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.41\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.2455\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
NM\_018100.3:c.545G\textgreater{}A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.5\% (JME)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
4.0\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.62\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.2645\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
4.3\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.58\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.1452\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
4.2\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.59\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.1536\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
NM\_018100.3:c.662G\textgreater{}A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.5\% (JME)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.5\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.00\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.6\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.86\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.5243\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.2\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.04\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.3895\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
NM\_018100.3:c.685T\textgreater{}C\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.5\% (JME)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.5\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
3.03\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.1883\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.4\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
3.91\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.1219\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.2\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
6.17\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0245*\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
NM\_018100.3:c.887G\textgreater{}A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.0\% (JME)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
[?]\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.1058\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.2\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5.19\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.1422\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.4\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.45\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.1554\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
NM\_018100.3:c.896A\textgreater{}G\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.5\% (JME)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
[?]\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.2645\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
[?]\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.1205\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
[?]\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0603\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
NM\_018100.3:c.1343T\textgreater{}C\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
4.0\% (JME)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
4.0\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.00\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5.8\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.68\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.1976\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
7.8\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.49\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0639\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
NM\_018100.3:c.1765G\textgreater{}A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.5\% (JME)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
[?]\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.2645\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
[?]\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.1205\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
[?]\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0603\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
NM\_018100.3:c.1820A\textgreater{}G\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.5\% (JME)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
[?]\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.2645\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
[?]\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.1205\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0\%\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
[?]\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0603\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
NM\_018100.3:c.1855A\textgreater{}C\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
6.0\% (JME) 7.0\% (GTCSa)\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
4.5\%\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
1.35 3.50\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
0.2657 0.1888\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
5.4\%\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
1.11 2.90\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
0.4557 0.2935\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
4.1\%\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
1.49 3.89\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
0.1367\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

AF: allele frequency; OR: odds ratio; JME: juvenile myoclonic epilepsy;
GTCSa: generalized tonic-clonic seizures on awakening. \emph{P-} values
were calculated using an unconditional exact test (Z-pooled,
one-tailed). Population databases: of Brazilian individuals
(http://bipmed.org/), AbraOM (http://abraom.ib.usp.br/).
*\emph{P}\textless{} 0.05.

\textbf{FIGURE LEGENDS}

\textbf{Figure 1.} Pathogenic classification scheme applied to the
genetic generalized epilepsy (GGE)-related \emph{EFHC1} variants.

\textbf{Figure 2.} Benign classification scheme applied to the genetic
generalized epilepsy (GGE)-related \emph{EFHC1} variants.

\textbf{Figure 3.} Predicted location of variants found in the
EFHC1/myoclonin protein in patients with juvenile myoclonic epilepsy
(JME) and other genetic generalized epilepsies (GGEs). DM: Domain; EFH:
EF-Hand.

\textbf{SUPPLEMENTARY MATERIAL}

\textbf{Table S1.} Population allele frequency of the genetic
generalized epilepsy (GGE)-related missense \emph{EFHC1} variants found
in our cohort.

\textbf{Table S2.} Results of the 13 computer algorithms used to predict
the effects of the \emph{EFHC1} missense variants found in our cohort of
genetic generalized epilepsy (GGE) patients.

\textbf{Table S3.} Classification of the genetic generalized epilepsy
(GGE)-related missense \emph{EFHC1} variants found in our cohort.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure1/Figure1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
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