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 EFHC1 variants.
We performed a series of analyses for each identified EFHC1variant. 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 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 theEFHC1 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 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).