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 ofEFHC1 variants in a cohort of patients with GGEs. This
information is likely to provide new information in the assessment of
the clinical utility of EFHC1 testing.
Mutation screening in our cohort revealed that 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 investigatedEFHC1 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 inEFHC1 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 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>A, c.685T>C and
c.881G>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 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 EFHC1variants, 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>A and c.685T>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>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>A and c.896A>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>T, c.475C>G, c.545G>A,
c.1343T>C, c.1855A>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 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>G and
c.1765G>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>C, c.887G>A, and
c.1820A>G are only over 1% in specific subpopulations
(c.685T>C: South Asian; c.887G>A and
c.1820A>G: African; Table S1). The remaining six variants
(c.475C>T, c.475C>G, c.545G>A,
c.662G>A, c.1343T>C, and
c.1855A>C) present allele frequencies higher than 1% in
different populations.
To verify whether the frequency of 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>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; P value: 0.0245). Three variants
(c.896A>G, c.1765G>A, and
c.1820A>G) were not found in our control group or the
Brazilian population databases; thus, the OR value was infinite (∞). For
the remaining variants (c.475C>T, c.475C>G,
c.545G>A, c.662G>A, c.887G>A,
c.1343T>C, and c.1855A>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 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 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, SCN1A, in the context of Dravet
syndrome (Gonsales et al., 2019). Indeed, as opposed to SCN1A ,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). ForSCN1A , 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 in silicoalgorithms to analyze 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>A and
c.685T>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 theEFHC1 variants from our cohort, only the variants
c.662G>A and c.685T>C were classified as
‘pathogenic.’ However, c.662G>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>C can strictly be
classified as ‘pathogenic’ (1/11, 9%).
It is noteworthy that the variant c.662G>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 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>A, c.887G>A, and
c.1820A>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, 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 BRCA1 and 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 EFHC1 for clinical genetic testing.