Computational protocol: The Analysis of A Frequent TMPRSS3 Allele Containing P.V116M and P.V291L in A Cis Configuration among Deaf South Koreans

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Protocol publication

[…] After having recruited our previous cohort published in 2014 regarding TMPRSS3 [], we collected 88 hearing-impaired patients who underwent cochlear implantation and agreed to participate in molecular genetic testing. These patients were ascertained from two tertiary referral hospitals—Seoul National University Hospital (SNUH) and Seoul National University Bundang Hospital (SNUBH)—between June 2013 and October 2014. Among these 88 patients, we focused on families that segregated postlingual-onset progressive and severe autosomal recessive non-syndromic SNHL that mandated cochlear implantation. Subjects with either GJB2 or SLC26A4 variants were excluded. Congenital severe-to-profound SNHL cases were not included. Subjects whose hearing loss was related to trauma or infection were also excluded. Furthermore, subjects for whom the onset of hearing loss was at or over the 6th decade were not included in the present study in order to focus more on genetic SNHL cases rather than age related ones. After all exclusions, our cohort was comprised of 31 probands manifesting early onset postlingual progressive and severe hearing loss. Either of the two deafness gene panels for targeted resequencing (TRS) was utilized for molecular genetic testing; one is TRS-204 containing 204 known deafness genes by SGI (Samsung genomic institute, http://www.samsunghospital.com/dept/main/index.do?DP_CODE=BP7) and the other is TRS-129 containing 129 known deafness genes by Otogenetics Corporation (http://www.otogenetics.com/).Raw data from the TRS technology was mapped and filtered sequentially in accordance with the exclusion criteria and the in-house flow chart of hierarchical molecular genetic tests, as we previously reported [,]. The obtained reads were aligned with the UCSC hg19 reference genome (https://genome.ucsc.edu/), which is an interactive website offering access to genome sequence data from a variety of vertebrate and invertebrate species, including major model organisms, integrated with a large collection of aligned annotations, with narrowed down variants. In brief, the data were filtered to select candidate variants in autosomal recessive genes regarding non-syndromic SNHL through the following bioinformatics analysis. In a basic filtering step, non-synonymous Single Nucleotide Polymorphisms (SNPs) with Q call >10 and read depths >20 were selected; the selected SNPs were compared and tagged using the Single Nucleotide Polymorphism database (dbSNP build 138) and an in-house database. In the end, only novel SNPs and SNPs causing known diseases remained. Next, we checked the inheritance patterns in each family, and excluded variants that did not co-segregate with the SNHL phenotype. More specifically, we first excluded variants that did not fit for the autosomal recessive inheritance pattern. For example, detection of only one novel variant with unknown pathogenic potential in a known recessive deafness gene indicates that the variant is likely to be a fortuitously detected SNP.Next, we excluded variants of the autosomal dominant gene that were shared by the proband and one of the parents with normal hearing from the two families. Finally, the remaining SNPs were validated by Sanger sequencing (a,b). The SNPs were also checked against an additional 426 unrelated Korean control chromosomes. Pathogenicity of the splicing variant was predicted using ESE finder (http://krainer01.cshl.edu/cgi-bin/tools/ESE3/esefinder.cgi?process=home) and BDGP (Berkeley Drosophila Genome Project; http://www.fruitfly.org/seq_tools/splice.html). Missense variants were predicted using SIFT (http://sift.jcvi.org/) and Polyphen-2 (http://genetics.bwh.harvard.edu/pph2/). For estimation of the evolutionary conservation of the amino acid sequences, we referred to the GERP++ score from UCSC Genome Browser (http://genome.ucsc.edu/). These filtering processes and subsequent segregation studies led us to detect two kinds of interesting TMPRSS3 mutant alleles—one is the allele with two equal candidate variants in a cis configuration and the other is a novel splice site variant of TMPRSS3. […]

Pipeline specifications

Software tools ESEfinder, PolyPhen, GERP
Databases dbSNP BDGP UCSC Genome Browser
Diseases Deafness