Bioinformatics analyses of variants
To characterize the population frequency and the pathogenicity of the
variants, we predicted the population allele frequency of the variants
using 1000 genomes (https://www.internationalgenome.org/), gnomAD
(https://gnomad.broadinstitute.org/) and ExAc allele frequency
(http://exac.broadinstitute.org)
based on the previous mentioned (Gudmundsson et al., 2021; Kobayashi et
al., 2017; Zheng-Bradley & Flicek, 2017).
dbSNP
(https://www.ncbi.nlm.nih.gov/snp/), Clinvar (https://www.
ncbi.nlm.nih.gov/clinvar/) and HGMD (Human Gene Mutation Database,
http://www. hgmd.cf.ac.uk/ac/index.php) were used to predict the
pathogenicity of the variants (Landrum et al., 2018; Sherry et al.,
2001; Stenson et al., 2014). Pathogenicity of both variants was analyzed
according to the
American College of Medical Genetics and Genomics
guidelines (Richards et al., 2015). MetaDome
(https://stuart.radboudumc.nl/metadome/) (Wiel et al., 2019) combined
with NCBI-BLAST (https:// blast.ncbi.nlm.nih.gov/Blast.cgi) to analyze
the variant tolerance of TMEM126B exon 2 as well as exon 3 and
amino acid conservation among mammals.
Alternative splice site predictor
(http://wangcomputing.com/assp/) (Wang & Marín, 2006) and exonic
splicing regulation (http://krainer01.cshl.edu/tools/ESE2/)
(Cartegni et al., 2003) to predict the effect of two variants onTMEM126B mRNA splicing.