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. 2026 Jul 13;32:189. doi: 10.1186/s10020-026-01553-x

Integrated analysis of STRC variants in hereditary hearing impairment using maker-mediated refinement of long-read sequencing with MLPA validation

Cheng-Yu Tsai 1,2, Yue-Sheng Lu 1, Yu-Ting Chiang 2, Ming-Yu Lo 1, Pei-Hsuan Lin 1,3, Shih-Feng Tsai 4,5, Chuan-Jen Hsu 1,6, Pei-Lung Chen 2,3,7,8, Jacob Shu-Jui Hsu 2,✉, Chen-Chi Wu 1,3,9,10,✉
PMCID: PMC13679130  PMID: 42437880

Abstract

Background

Sensorineural hearing impairment (SNHI) is a common disorder with a significant genetic basis. Standard next-generation sequencing (NGS) often fails to accurately identify pathogenic variants in the STRC gene due to its complex genomic structure, including large rearrangements and a highly homologous pseudogene. Long-read sequencing (LRS) offers improved resolution for these complex regions.

Methods

We developed a comprehensive workflow that integrates PacBio-based LRS with marker-mediated refinements and MLPA validations to specifically address pseudogene interference. This methodology was applied to analyze the STRC gene in a cohort of 100 unrelated Taiwanese patients with SNHI of unknown genetic origin after initial NGS screening.

Results

We identified bi-allelic STRC variants in 11 unrelated patients (11% diagnostic yield), including homozygous deletions, compound heterozygous deletions and conversions, and compound heterozygous single nucleotide variants (SNVs) and copy number variants (CNVs). All unrelated cases resolved with bi-allelic STRC variants were affected with mild or moderate SNHI, occupying 15.1% of total 73 mild-to-moderate SNHI patients in this study.

Conclusion

Our results highlight the diagnostic utility of this combined strategy, integrating LRS with marker-mediated refinements that validated by MLPA assays, in detecting complex STRC variants and advance the understanding of the genetic etiology of SNHI that remains unresolved by conventional NGS approaches.

Graphical Abstract

graphic file with name 10020_2026_1553_Figa_HTML.webp

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Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s10020-026-01553-x.

Keywords: Hereditary hearing impairment, Stereocilia, Long-reads sequencing, High-fidelity reads, Copy number variants, Marker-mediated refinements

Introduction

Sensorineural hearing impairment (SNHI) is a common childhood disorder with diverse etiologies. Genetic factors contribute to over 50% of childhood SNHI cases, classified as hereditary hearing impairment (HHI). To date, more than 120 genes have been associated with non-syndromic HHI (Shearer et al. 1999; Tsai et al. 2024). Among these, the stereocilin (STRC) gene, inherited in an autosomal recessive pattern (DFNB16, OMIM #603720), is a significant cause of non-syndromic HHI. The stereocilin protein encoded by STRC is expressed in the hair bundles of outer hair cells (OHCs) in the cochlea, where it connects adjacent stereocilia and interacts with the tectorial membrane (Verpy et al. 2011). Pathogenic STRC variants are associated with mild to moderate SNHI and deafness-infertility syndrome in men, especially when large deletions span the STRC and CATSPER2 region (15q15.3) (Zhang et al. 2007; Markova et al. 2020).

STRC variants are a major contributor to HHI in various populations, with reported prevalence 16.1% of non-DFNB1 Europeans (Del Castillo et al. 2022), 6.1–11.2% in Americans (Mandelker et al. 2014; Amr et al. 2018), and 2.8–10.3% in East Asians (Ito et al. 2019; Kim et al. 2020; Nishio and Usami 2022). Studies have also identified STRC variants in Middle Eastern populations (Sharma et al. 2023), with an overall prevalence of 16.1% in multiethnic studies (Sloan-Heggen et al. 2016). In addition to single nucleotide variants (SNVs), copy number variants (CNVs) or structural variants (SVs), are important genetic causes of STRC-related HHI. CNVs encompass large-scale genomic rearrangements (50 bp to megabases), including deletions, duplications, translocations, and conversions (Ho et al. 2020). These variants have been identified in at least 29 HHI-related genes, with STRC, OTOA, and GJB2/GJB6 being the most frequently implicated (Abbasi et al. 2022). A meta-analysis of 37 studies by Han et al. (2021) reported that 14.4% of patients with mild-to-moderate HHI carry bi-allelic pathogenic STRC variants, with homozygous deletions accounting for approximately 70% of these variants.

A significant diagnostic challenge for STRC variant detection is the presence of the highly homologous pseudo-STRC (STRCP1) located ~ 80 kb upstream of STRC. STRCP1 shares 97% sequence identity with STRC and can lead to gene-to-pseudogene conversion, complicating genetic testing (Shearer et al. 2014). While next generation sequencing (NGS) is effective for identifying HHI-associated variants (Tsai et al. 2024), its short read length (150–300 bp) limits its ability to simultaneously resolve CNVs and pseudogene interference. This limitation hinders the accurate detection of SNVs and CNVs within the STRC “dark region”, which spans the five-prime untranslated region to exon 18 and shows 99.8% identity between STRC and STRCP1 (with less than 30 bp nucleotide differences within an ~ 11 kb region). To address this, various bioinformatic tools and fluorescence-based approaches, such as CNV calling algorithms (Mahmoud et al. 2019; van Belzen et al. 2021), array comparative genomic hybridization (aCGH) (Shearer et al. 2010; Hoppman et al. 2013; Moteki et al. 2016), allelic-specific droplet digital polymerase chain reaction (PCR) (Mandelker et al. 2014; Amr et al. 2018; Alvaro et al. 2025), and multiplex ligation-dependent probe amplification (MLPA) (Os and Schouten 2011; Mochizuki et al. 2019), have been developed to complement NGS for CNV detection. Long-range (LR) PCR/nested PCR (Mandelker et al. 2014; Vona et al. 2015) has also been used to minimize pseudogene interference and improve NGS accuracy. However, these methods often require complex, multi-step workflows with numerous exon- or intron-specific primers for validation. A single, efficient genetic assay capable of resolving the complexities of STRC variant detection would be of great value in both clinical and research settings.

Long-read sequencing (LRS) offers a promising solution by directly sequencing large genomic regions (kilobases to megabases). Platforms such as Pacific Biosciences (PacBio) (Rhoads and Au 2015) and Oxford Nanopore Technologies (ONT) (Wang et al. 2021) provide high-resolution LRS for the detection of SNVs, indels, and CNVs (Oehler et al. 2023). PacBio’s high-fidelity (HiFi) sequencing using circular consensus sequencing (CCS) achieves high accuracy for both SNVs (> 99%) and CNVs (> 95%) (Wenger et al. 2019) and has been successfully applied to direct LRS assays of 193 medically relevant genes, including STRC. In 2025, Laurent et al. (Laurent et al. 2025) reported that the LRS approach successfully identified bi-allelic STRC variants in a multiplex family, resolving ambiguous NGS results and demonstrating the utility of LRS in clinical applications. However, the application of LRS-mediated CNV detection in large-scale clinical studies remains limited.

In this study, we employed an integrated approach to address the challenges of STRC variant detection. We utilized amplicon-based LRS technology to enrich STRC genomic segments, generating 10 kb PacBio CCS HiFi reads for SNV and CNV analysis. To overcome pseudogene interference, we developed a refinement pipeline integrating specific markers between STRC and STRCP1, complemented by MLPA validation. This combined workflow provides a robust solution for detecting complex variations in clinical genetics.

Methods

Subjects

This study included 100 unrelated patients with SNHI of unknown genetic cause after first-tier NGS (Lee et al. 2023); those with reduced coverage or uncertain STRC variants in the NGS results were analyzed using our workflow to obtain further evidence for a clinical diagnosis. The cohort consisted of 73 patients with mild-to-moderate SNHI and 27 patients with severe-to-profound SNHI. Three National Institute of Standards and Technology (NIST) reference samples-HG001 (NA12878), HG002 (NA24385), and HG005 (NA24631)-were used for benchmarking (Zook et al. 2016). Lymphoblastoid cell line DNA from these reference samples were obtained from the Coriell Institute for Medical Research (Camden, NJ, USA). Genomic DNAs of patients were extracted using the MagCore® genomic DNA extraction kit (Cat. No. MGB400-03). This study was approved by the National Taiwan University Hospital (approval number 202012083RIND), and informed consent was obtained from all participants or their legal guardians.

Long-read amplification of STRC

Amplicon-based PacBio SMRTbell® libraries were constructed according to the recommended PacBio workflow (PN 101-921-300) for single-molecule real-time (SMRT) sequencing. A two-step PCR procedure was performed for each sample. In the first step, four pairs of 42 bp primers were designed, each consisting of a 25 bp target-specific primer (Table 1) and a 17 bp M13 sequence added at the 5' end (5'-GTAAAACGACGGCCAGT for forward and 5'-CAGGAAACAGCTATGAC for reverse). All the 25 bp target-specific primers were designed using Primer3web (version 4.1.0) (Untergasser et al. 2012). These primers, synthesized by MB Mission Biotech Co., Ltd. (Taipei, Taiwan), were modified with a 5' amino modifier group C6 (5AmMC6) and used with the TaKaRa LA Taq® kit (code no. RR002A) to generate initial PCR amplicons. In the second step, multiple pairs of asymmetric M13-barcoded forward/reverse primers (as specified in the PacBio workflow) and KAPA HiFi HotStart ReadyMix (KK2602) were used to generate M13-barcoded amplicons. These amplicons were purified using AMPure® PB Beads and capillary electrophoresis, followed by SMRTbell library construction using the SMRTbell® Express Template Prep Kit 2.0 (PN 100-938-900). The SMRTbell products were sequenced on the PacBio Sequel IIe platform using the Sequel II Sequencing Kit 2.0 (PN 101-820-200).

Table 1.

Targeted primers for amplifying STRC genomic regions

Primer Loci of targeted primer (hg19) 25-bp targeted primers
LRP1-F chr15:43905486–43905510 CCCTCTGTAGCCCTGCTGTCTAGAT
LRP1-R chr15:43911226–43911250 CCAGTTCTCCATCTCCATGTCTGCC
LRP2-F chr15:43901216–43901240 GCCCAAGAGGACAAGTAGGTGACAG
LRP2-R chr15:43906328–43906352 TGTTCAGAGGGCTTGCTTTAGTGGG
LRP3-F chr15:43896367–43896391 AAGGAGAGTGGGGAGATCTGGACAG
LRP3-R chr15:43901539–43901563 GGGAAGGGGCTCGTCTTTCTTTCTC
LRP4-F chr15:43891467–43891491 ATTAACTCCCCATCGCCAGCTGATG
LRP4-R chr15:43896550–43896574 TTCCTTGATCCCCAGGGTGAGATGA
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LRP long-range PCR primer, F forward, R reverse

Data analysis

Raw data from LRS assays were processed using SMRT® Link (ver. 10.2.1, PacBio), the recommended software for PacBio sequencing data analysis. Reads were mapped to the hg19 reference genome using pbmm2 (ver. 1.9) (PacBio, pbmm2 2023) for direct comparison with any carried variant not definitively determined in our previous NGS assays. Variant calling, normalization, and annotation were performed using DeepVariant (ver. 1.4.0) (Poplin et al. 2018), BCFtools (ver. 1.13) (Li 2011), and ANNOVAR (ver. 2021Oct19) (Wang et al. 2010), respectively. Mapped BAM files were merged and converted using SAMtools (ver. 1.15.1) (Li et al. 2009), and long reads were refined based on specific markers using Bamql (ver. 1.6) (Masella et al. 2016).

Multiple predictive scores were used to assess variant pathogenicity, including SIFT (Ng and Henikoff 2003) and PolyPhen-2 (Adzhubei et al. 2013) (retrieved from dbNSFP, ver4.1) (Liu et al. 2020), CADD (GRCh37-ver. v1.7) (Schubach et al. 2024), and SpliceAI (ver. 1.3.1) (Jaganathan et al. 2019). Allele frequencies were obtained from the population databases gnomAD (Karczewski et al. 2020) (ver. 2.1.1, last accessed March 20, 2025) and Taiwan Biobank (Wei et al. 2021) (last accessed March 20, 2025). Pathogenicity assertions of variants were retrieved from the ClinVar (Azaiez et al. 2018) (last accessed April 15, 2026) and Deafness Variant Database (DVD, ver. 9.1) (Azaiez et al. 2018) disease databases. All the pathogenicity assessments of SNVs were conducted based on the guideline of American College of Medical Genetics and Genomics (ACMG) (Richards et al. 2015) and Varsome webtool (Kopanos et al. 2019).

Multiplex ligation-dependent probe amplification (MLPA) assays

The 150 ng DNA samples of individuals quantified by Qubit method were prepared for CNV validation using MLPA assays. MLPA assays were performed with a commercially available kit (SALSA® MLPA® Probemix P461-B1, MRC Holland, Amsterdam, The Netherlands). This kit contains 45 MLPA probes targeting the chromosome 15q15.3 and 16q12.2 regions, including STRC, CATSPER2, STRCP1, OTOA, and other nearby regions. Seven and four probes in this kit were designed for exons 19–28 of STRC and STRCP1, respectively. The experimental procedure followed the recommended general MLPA® protocol (version-008).

Results

Target enrichment and long-read sequencing of the STRC region

We performed PacBio-mediated LRS on 100 unrelated patients with SNHI, including 73 with mild-to-moderate and 27 with severe-to-profound SNHI. Figure 1A shows the amplicon-based target enrichment strategy for STRC using four pairs of PCR primers (see Methods) to generate 5.1–5.9 kb amplicons. These amplicons, covering a quarter of the STRC region (19.4 kb) and containing hundreds of base pairs of overlapping regions, were successfully amplified in the control samples. The amplicon length was further extended to 10 kb using interlaced primer pairs, generating PCR products Amp-01, Amp-02 and Amp-03 in the control samples (Fig. 1B). This approach also amplified homologous segments within the pseudogene STRCP1. The expected regions of the STRC/STRCP1 amplifications are listed in Table S1.

Fig. 1.

Fig. 1

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Long-read sequencing (LRS) workflow for STRC amplicon-based target enrichment and HiFi sequencing. A Electrophoretic detection of expected 5 kb STRC amplicons (STRC-LRP-5K-01 to -04). B Electrophoretic detection of expected 10 kb STRC amplicons (STRC-LRP-10K-01 to -03, or Amp-01 to -03). C Schematic of the two-step PCR for 10 kb amplicon generation, SMRTbell library construction, and circular consensus sequencing (CCS) of HiFi reads

SMRTbell libraries were constructed using these 10 kb amplicons as circular templates for the PacBio CCS approach, generating HiFi sequencing data with 98–99% accuracy (Fig. 1C). All samples, including three NIST reference samples (HG001, HG002, HG005), were mapped to the hg19 reference genome using pbmm2, followed by variant calling and annotation.

Benchmarking and refinement of STRC/STRCP1 copy number ratios using NIST reference samples

To establish benchmarks for our LRS assay, we first analyzed three NIST reference samples (HG001, HG002, and HG005). In addition to our LRS data, we incorporated publicly available sequencing data generated using the PacBio CCS pipeline (11 kb), obtained from the open-access Genome in a Bottle (GIAB) repository (Genome in a Bottle (GIAB) 2024). Figure 2A illustrates the mapping coverage of these reference samples across the STRC and STRCP1 regions. The distribution of mapped reads from the external GIAB data (top panel) appeared symmetrical between STRC and STRCP1 in HG001 and HG002, indicating similar copy numbers. However, HG005 exhibited an asymmetrical distribution, suggesting a difference in copy number between the two regions. In contrast, our initial LRS assay produced an asymmetric distribution for HG001 and HG002, which was inconsistent with the external GIAB data (Fig. 2A, bottom panel). This discrepancy suggested potential effect caused by pseudogene co-amplification in our LRS assay, leading to inaccurate copy number estimation.

Fig. 2.

Fig. 2

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Refinement and validation of STRC/STRCP1 copy number ratios in NIST reference samples. A Mapped LRS reads from external (GIAB) data and this study. B Illustration of the refined copy number (rCN) calculation process. C rCN plots for reference samples HG001, HG002, and HG005 from external data and this study. D Multiplex ligation dependent probe amplification (MLPA) plots for HG005. Green arrows: STRC signal peaks; red arrows: STRCP1 signal peaks; y-axis: relative copy number; blue dashed line: normal copy number (1.00). Asterisks: peaks at exon 28 of STRC/STRCP1. (NIST: National Institute of Standards and Technology)

To address this, we developed a refinement process using a set of divergent markers between STRC and STRCP1, spanning from intron 26 to intron 15 (Table 2 and Figure S1). These markers were grouped into 14 clusters for STRC (M1-M14) and STRCP1 (pM1-pM14), respectively, based on their genomic locations. To ensure accurate filtering of mis-mapped reads resulting from pseudogene interference, we selected STRC markers that exhibited significant differences from STRCP1 and were confidently absent from the population genome (gnomAD database), as indicated by adequate coverage documentation within the database. Figure 2B illustrates the filtering process, which generated four distinct groups of filtered reads. Reads where all markers within a cluster at both STRC and STRCP1 matched the reference nucleotide (Ref) were categorized as group (1) for STRC and group (3) for STRCP1. Conversely, reads where all markers within a cluster at both STRC and STRCP1 matched the alternative nucleotide (Alt) were classified as group (2) for STRC and group (4) for STRCP1. The refined copy number (rCN) ratios for STRC and STRCP1 were then calculated as follows: for STRC, the sum of read counts in the combined groups (1 + 4) was divided by the sum of read counts in the combined groups (1 + 2 + 3 + 4); for STRCP1, the sum of read counts in the combined groups (2 + 3) was divided by the sum of read counts in the combined groups (1 + 2 + 3 + 4) (Fig. 2B, bottom panel).

Table 2.

The 14 STRC/STRCP1 divergent marker clusters

Loci of marker
(STRC)		 Ref Alt Exon/Intron Allele frequency (Coverage) Loci of marker
(STRCP1)		 Ref Alt Allele frequency (Coverage) Amplicon
M1-1 chr15-43892640 C A Intron 26 N/A (> 30) pM1-1 chr15-43992458 A C N/A (> 30) Amp 01
M1-2 chr15-43892646 T G Intron 26 N/A (> 30) pM1-2 chr15-43992464 G T N/A (> 30) Amp 01
M1-3 chr15-43893008 T C Intron 26 N/A (> 30) pM1-3 chr15-43992826 C T N/A (> 30) Amp 01
M2-1 chr15-43893462 G A Intron 24 N/A (> 30) pM2-1 chr15-43993281 A G N/A (> 30) Amp 01
M2-2 chr15-43893466 C T Intron 24 N/A (> 30) pM2-2 chr15-43993285 T C AC = 2 (> 30) Amp 01
M2-3 chr15-43893476 T C Intron 24 N/A (> 30) pM2-3 chr15-43993295 C T AC = 1 (> 30) Amp 01
M3-1 chr15-43893830 C A Intron 23 N/A (> 30) pM3-1 chr15-43993648 A C AC = 3 (> 30) Amp 01
M3-2 chr15-43893859 T C Intron 23 N/A (> 30) pM3-2 chr15-43993677 C T N/A (> 30) Amp 01
M3-3 chr15-43894034 A G Intron 23 N/A (~ 30) pM3-3 chr15-43993854 G A AC = 1 (~ 30) Amp 01
M4-1 chr15-43895366 C T Intron 23 N/A (> 30) pM4-1 chr15-43994876 T C N/A (> 30) Amp 01
M4-2 chr15-43895375 T C Intron 23 N/A (> 30) pM4-2 chr15-43994885 C T N/A (> 30) Amp 01
M4-3 chr15-43895417 C T Intron 23 N/A (> 100) pM4-3 chr15-43994927 T C N/A (> 30) Amp 01
M5-1 chr15-43895520 C T Exon 23 N/A (~ 120) pM5-1 chr15-43995030 T C N/A (> 30) Amp 01
M5-2 chr15-43895542 T A Exon 23 N/A (~ 120) pM5-2 chr15-43995052 A T N/A (> 30) Amp 01
M6-1 chr15-43895679 A G Intron 22 N/A (> 30) pM6-1 chr15-43995158 G A N/A (> 30) Amp 01
M6-2 chr15-43895682 G A Intron 22 N/A (> 30) pM6-2 chr15-43995161 A G N/A (> 30) Amp 01
M6-3 chr15-43895686 G C Intron 22 N/A (> 30) pM6-3 chr15-43995165 C G N/A (> 30) Amp 01
M7-1 chr15-43896031 A G Intron 22 N/A (> 30) pM7-1 chr15-43995511 G A N/A (> 30) Amp 01
M7-2 chr15-43896042 C T Intron 22 N/A (> 30) pM7-2 chr15-43995522 T C N/A (> 30) Amp 01
M7-3 chr15-43896057 G A Intron 22 N/A (> 30) pM7-3 chr15-43995537 A G N/A (> 30) Amp 01
M8-1 chr15-43896750 G A Intron 20 N/A (> 30) pM8-1 chr15-43996230 A G N/A (~ 30) Amp 01、02
M8-2 chr15-43896760 A G Intron 20 N/A (> 30) pM8-2 chr15-43996240 G A N/A (~ 30) Amp 01、02
M9-1 chr15-43897383 G A Intron 19 N/A (> 30) pM9-1 chr15-43996861 A G N/A (~ 30) Amp 01、02
M9-2 chr15-43897621 C T Intron 18 N/A (> 30) pM9-2 chr15-43997099 T C N/A (> 25) Amp 01、02
M9-3 chr15-43897623 G T Intron 18 N/A (> 30) pM9-3 chr15-43997101 T G N/A (> 25) Amp 01、02
M9-4 chr15-43897624 G C Intron 18 N/A (> 30) pM9-4 chr15-43997102 C G N/A (> 25) Amp 01、02
M10-1 chr15-43899232 G A Intron 18 N/A (~ 25) pM10-1 chr15-43998706 A G N/A (> 25) Amp 01、02
M10-2 chr15-43899690 T C Intron 18 N/A (~ 30) pM10-2 chr15-43999164 C T N/A (~ 30) Amp 01、02
M11-1 chr15-43900188 T A Intron 17 N/A (> 120) pM11-1 chr15-43999662 A T N/A (~ 30) Amp 01、02
M11-2 chr15-43900267 C T Intron 17 N/A (> 120) pM11-2 chr15-43999741 T C N/A (~ 30) Amp 01、02
M12-1 chr15-43900975 G A Intron 16 N/A (~ 25) pM12-1 chr15-44000447 A G N/A (~ 25) Amp 01、02
M13-1 chr15-43901397 C T Intron 16 N/A (~ 30) pM13-1 chr15-44000869 T C N/A (~ 30) Amp 01、02、03
M13-2 chr15-43901476 C T Exon 16 N/A (> 120) pM13-2 chr15-44000948 T C N/A (> 30) Amp 01、02、03
M14-1 chr15-43901755 A G Intron 15 N/A (~ 30) pM14-1 chr15-44001227 G A N/A (> 25) Amp 02、03
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Applying this rCN calculation (Fig. 2C), HG001 and HG002 demonstrated a similar trend of equal proportions per cluster between STRC and STRCP1 in both the external GIAB data and our refined LRS data, supporting the feasibility and accuracy of our refinement strategy. Notably, HG005 showed a higher proportion of STRC in both datasets, suggesting a duplication of STRC in this sample. To confirm this finding, we performed MLPA analysis. As shown in Fig. 2D, HG005 exhibited a 1.5-fold increase in MLPA signals at STRC and a 0.5-fold decrease in signals at STRCP1, consistent with the refined rCN ratios. This indicated a one-copy gain of STRC and a one-copy loss of STRCP1 in HG005. Both HG001 and HG002 showed symmetrical proportions of STRC/STRCP1 in both the refined rCN ratios (Fig. 2C) and MLPA results (Figure S2), further validating the reliability and accuracy of our marker-mediated refinement strategy.

Marker-mediated refinement for SNV genotyping of STRC/STRCP1 in reference samples

In Fig. 2D, the MLPA signal peaks at exon 28 of both STRC and STRCP1 (arrows with asterisks) showed an inconsistent copy number compared to the other signal peaks. This observation suggested the presence of an SNV within this region of STRC in HG005, which influenced the expected one-copy gain in the MLPA results. This SNV was identified as c.5125A > G (hg19:chr15:43892272-T-C, NM_153700.2:p.T1709A), as it represents the only divergent nucleotide within exon 28 between STRC (chr15:43892272-T) and STRCP1 (chr15:43992088-C) (Figure S3). This SNV is documented as “likely pathogenic” in both ClinVar (Accession ID: VCV000505381.12) and DVD (variant searching for “chr15:43892272” in GRCh37) databases and was confirmed in the NIST external source data for HG005, but not for HG001 and HG002 (Fig. 3A).

Fig. 3.

Fig. 3

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Marker-mediated refinement of single nucleotide variation (SNV) in reference samples. A Mapped reads from external NIST data. B Refinement process for mapped reads resulting in refined SNV (rSNV) calls. C Schematic of marker-mediated refinement for accurate STRC/STRCP1 SNV genotyping. D Predicted STRC/STRCP1 copy number and genotype status for HG005

However, our initial LRS assay mapping results indicated a heterozygous genotype for c.5125A > G (p.T1709A) in all three reference samples (top panel of Fig. 3B), which was inconsistent with the NIST external source data. This discrepancy was likely due to pseudogene co-amplification. To address this, we applied a refinement process using the STRC/STRCP1 divergent markers (Fig. 3C), specifically using cluster M1, which is closest to the c.5125A > G (p.T1709A) variant, to filter out mis-mapped reads. This refinement significantly improved the mapping results for HG001 and HG002, while the result for HG005 remained unchanged (lower panel of Fig. 3B). These refined results were consistent with the NIST external source data, demonstrating the effectiveness of our marker-mediated refinement for accurate SNV genotyping.

Furthermore, the allele ratio of c.5125A > G (p.T1709A) in HG005 (NIST external source data) was 35% (Ref:Alt = 40:22) rather than 50% (Fig. 3A), reflecting the one-copy gain of STRC. Based on these results, we concluded that reference sample HG005 harbors a one-copy gain of STRC with a heterozygous SNV c.5125A > G (p.T1709A) on STRC (Fig. 3D). Therefore, we established robust benchmarks using NIST reference samples and applied marker-mediated refinement to our LRS results to ensure consistency with external source data.

Identification of STRC variants (CNVs or SNVs) in SNHI patients

Based on the marker-mediated refinement established from reference sample benchmarks, we calculated both rCN ratios and refined single nucleotide variant (rSNV) ratios of STRC for each of the SNHI patients included in this study. CNV analysis was performed using the following rCN ratio thresholds: two-copy loss (serial rCN ratios < 5% or mapping reads < 10 within the STRC region), one-copy loss (serial rCN ratios < 30%), one-copy gain (serial rCN ratios > 70%). Samples with potential CNVs were further validated using MLPA assays.

For SNV detection, rSNV ratios were obtained by marker-mediated refinement using the closest marker cluster to each annotated variant. SNV types were defined as follows: homozygote (rSNV ratio > 80%), heterozygote (rSNV ratio 40–60%), and heterozygote with one-copy gain (rSNV ratio 30–35%).

Our analysis demonstrated total 11 unrelated cases resolved with bi-allelic STRC variants (Table 3), including homozygous deletions (n = 7), compound heterozygosity of deletion and conversion (n = 2), and compound heterozygosity of pathogenic SNV and deletion/conversion (n = 2). In addition, 16 cases had mono-allelic STRC variants (Fig. 4), including heterozygous deletions (n = 2), conversions (n = 2), and SNVs (n = 12). Analysis of allele frequencies showed that STRC deletions (n = 19, 9.5% frequency in the entire cohort) were predominantly found in the mild-to-moderate SNHI group (13%). Similarly, STRC conversions (n = 5, 2.5% frequency) were also predominantly observed in the mild-to-moderate SNHI group (2.7%). In contrast, STRC SNVs were detected with similar allele counts in both mild-to-moderate (n = 8) and severe-to-profound (n = 6) groups.

Table 3.

The list of patients identified with bi-allelic STRC variants

Case No Family # Allele 1 Allele 2 Phenotype §
PS-008 1 Deletion (Whole) Deletion (Whole) Mild SNHI
PS-011 2 Deletion (Whole) Conversion (Whole) Mild SNHI
PS-028 3 Deletion (Whole) Deletion (Whole) Mild SNHI
PS-032 4 (II-1) c.4143G > A (p.W1381X) Deletion (Whole) Moderate SNHI
PS-036 4 (II-2) c.4143G > A (p.W1381X) Deletion (Whole) Moderate SNHI
PS-046 5 Deletion (Whole) Deletion (Whole) Mild SNHI
PS-047 6 Deletion (Whole) Deletion (Partial) Moderate SNHI
PS-054 7 Deletion (Whole) Deletion (Whole) Moderate SNHI
PS-056 8 Deletion (Whole) Deletion (Whole) Moderate SNHI
PS-061 9 Deletion (Whole) Deletion (Whole) Mild SNHI
PS-065 10 Deletion (Whole) Conversion (Partial) Moderate SNHI
PS-071 11 c.4402C > T (p.R1468X) Conversion (Partial) Moderate SNHI
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#The counts of unrelated patients, where the PS-032 (proband) and PS-036 (sibling) are annotated with their pedigree numbering (in parentheses) in the multiplex family

§The severities of sensorineural hearing impairment (SNHI) are defined to be mild (25–40 dB) and moderate (40–70 dB), respectively

Fig. 4.

Fig. 4

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Statistical summary of STRC variants identified by LRS, encompassing patients with bi-allelic (n = 11) and mono-allelic (n = 16) STRC variants composed of deletions, conversions, or single nucleotide variations (SNVs)

Our LRS assays identified both two-copy and one-copy losses of STRC, as illustrated by the representative rCN ratio plots and their corresponding detailed read-level visualizations (Fig. 5). More cases of these CNVs are provided in Figure S4. These results, combined with MLPA validations, revealed several bi-allelic and mono-allelic CNVs, including deletions and gene conversions. Based on our LRS results, these STRC deletions and conversions spanned from 3' end to intron 18 (markers M1 to M9) or to intron 15 (markers M1 to M14).

Fig. 5.

Fig. 5

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Representative rCN ratio plots and the integrated detection of STRC CNVs. The refined copy number (rCN) ratios via the LRS mapping results distinguish between the STRC gene (blue bars) and the STRCP1 pseudogene (orange bars) based on 14 divergent marker clusters. A Homozygous deletion (PS-008). B Compound heterozygous deletion and conversion (PS-065). C Heterozygous deletion (PS-052). D Heterozygous conversion (PS-100). These CNVs findings are validated using MLPA and are supported by direct read-level visualization of PacBio HiFi mapping tracks. More cases are provided in Supplementary Figure S4

In addition, we identified four types of pathogenic SNVs in STRC (Table 4). All SNVs were heterozygous and displayed sufficient rSNV ratios for confident identification; direct visualizations of these reads are presented in Figure S5. These included c.5125A > G (p.T1709A, n = 9), c.4622G > A (p.R1541Q, n = 3), c.4402C > T (p.R1468X, n = 1), and c.4143G > A (p.W1381X, n = 1). Notably, c.5125A > G (p.T1709A) represents the only divergent nucleotide between STRC and its pseudogene STRCP1 reference transcript in exon 28 (i.e., the Alt nucleotide in STRC being as Ref nucleotide in STRCP1; see Figure S3). This variant is likely pathogenic based on predictive scores and ClinVar assertions (rs1336307815). The c.4622G > A (p.R1541Q) also shows likely pathogenicity according to predictive scores. In addition, we identified a previously reported pathogenic variant, c.4402C > T (p.R1468X), and a novel pathogenic variant, c.4143G > A (p.W1381X), both predicted to result in protein truncation.

Table 4.

Pathogenic SNVs of STRC confirmed in this study

Variant loci (GRCh37)
HGVS (NM_153700.2)
dbSNP ID		 Exon SIFT POL2 CADD # SpliceAI † ClinVar DVD Grpmax_AF §
(gnomAD)		 MAF *
(TWB)		 Marker cluster Cases Ref Pathogenicity assertion ₸

c.5125A > G (p.T1709A)

15–43892272-T-C

(rs1336307815)

 28/29

0.02

(D)

0.994

(D)

 23.9

0.08

(DS-AL)

 LP LP

0.0006067

(EAS)

 N/A M1 9 (Vona et al. 2015)

VUS

(PM2, PP3, PP5)

c.4622G > A (p.R1541Q)

15–43893673-C-T

(rs77493867)

 24/29

0.17

(T)

0.876

(PD)

 22.2

0.02

(DS-AL)

 N/A P

0.00251

(EAS)

 0.0047 M1 3 (Mandelker et al. 2014)

VUS

(PM2, PP5)

c.4143G > A (p.W1381X)

15–43896634-C-T

(No RSID)

 21/29 N/A N/A 41

0.06

(DS-AL)

 N/A N/A N/A N/A M8 1 Novel

Pathogenic

(PVS1, PM2, PM3)

c.4402C > T (p.R1468X)

15–43895583-G-A

(rs377480477)

 23/29 N/A N/A 39

0.06

(DS-AG)

 P P

0.000140

(EUP)

 N/A M8 1 (Vona et al. 2015)

Pathogenic

(PVS1, PM2, PM3, PP5)
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Abbreviations: HGVS Human Genome Variation Society, (SIFT and PolyPhen-2) D damaging, PD possibly damaging, T tolerant, VUS variant of uncertain significance

#CADD PHRED score of the targeted variant (CADD ver.1.7)

†SpliceAI maximal DS score of the targeted variant (parameter of maximum distance: 500 bp). DS (delta score): probability that targeted variant causes altered splicing events at the predicted position (score scale: 0–1 with recommended threshold 0.5); AL (Acceptor Loss); AG (Acceptor Gain)

§Grpmax_AF: The maximal sub-population allele frequency in the gnomAD database (Karczewski et al. 2020) (ver. 2.1.1, last accessed on Oct 10, 2024). EAS: East Asian; EUP: European

*MAF: The minor allele frequency (database: whole genome sequence) in the Taiwan Biobank (Wei et al. 2021) (https://taiwanview.twbiobank.org.tw/variant.php, last accessed Oct 19, 2024)

Marker cluster: The selected cluster of diverse markers documented in Table 2 for marker-mediated SNV refinements

₸The pathogenicity were assessed based on ACMG (American College of Medical Genetics and Genomics) guideline (Richards et al. 2015) and Varsome (Kopanos et al. 2019)

Compound heterozygosity of SNVs and CNVs

Among the patients with confirmed STRC variants, we identified two unrelated individuals, PS-071 and PS-032, harboring compound heterozygous variants consisting of a SNV and a CNV. In patient PS-071, we observed homozygosity of the protein truncating variant c.4402C > T (p.R1468X) (Fig. 6A). In addition, LRS and MLPA analysis revealed gene conversion spanning marker clusters from M1 to M12 in the LRS rCN ratio plot (Fig. 6B). These findings confirm the STRC compound heterozygosity with c.4402C > T (p.R1468X) and conversion in PS-071.

Fig. 6.

Fig. 6

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Compound heterozygous genotypes in two unrelated families with truncating variants and copy number variants. A c.4402C > T (p.R1468X) in patient PS-071. B Gene conversion in PS-071 resulting in a single copy loss of STRC. The combination of (A) and (B) results in a compound heterozygous genotype. C c.4143G > A (p.W1381X) in patients PS-032 and PS-036, inherited paternally. D A large STRC deletion in PS-032 and PS-036, inherited maternally. The combination of (C) and (D) forms a compound heterozygous genotype, confirming autosomal recessive inheritance

Patient PS-032 was recruited from a multiplex family with an affected sibling (PS-036), an unaffected father (PS-037), and an unaffected mother (PS-038). A novel protein-truncating variant, c.4143G > A (p.W1381X), was identified with homozygosity in both affected siblings and heterozygosity in the father (Fig. 6C). Subsequent LRS and MLPA analysis revealed a heterozygous deletion spanning marker clusters from M1 to M9 in the LRS rCN ratio plot. This deletion, present in both affected siblings, was inherited from the maternal allele (Figs. 6D and S6). These results confirmed the STRC compound heterozygosity with c.4143G > A (p.W1381X) and deletion in both PS-032 and PS-036, indicating autosomal recessive inheritance in this multiplex family.

However, in the unaffected mother (PS-038), LRS analysis revealed missing rCN ratios for STRC from M10 to M14 (intron 15 to 18), a region beyond the detection range of the MLPA validation (Figure S1). Given the mother’s normal hearing phenotype, we concluded that she carries a benign partial gene conversion. Analysis of divergent nucleotides by STRC/STRCP1 pairwise alignment (Figure S7) revealed only three exonic variants within the region spanning exons 1 to 18. All three are synonymous variants, where the STRC sequence is directly replaced by the homologous STRCP1 segment (Table S2), suggesting that this partial pseudogene conversion is likely harmless.

Discussion

We implemented a combined strategy—integrating amplicon-based LRS assays with marker-mediated refinement that validated by MLPA assays—to investigate the genetic etiology of STRC-related SNHI in 100 unrelated patients who remained undiagnosed after conventional NGS. By leveraging this integrative approach, we accurately identified pathogenic STRC variants, including complex CNVs and SNVs, that are typically obscured by high sequence homology. Our LRS assay detected bi-allelic STRC variants in 11 unrelated patients, yielding a diagnostic rate of 11% for DFNB16 in this cohort. Considering both bi-allelic and mono-allelic variants, we observed a high allele frequency (19%) of STRC variants in 27 patients. Notably, 81.6% of these variants were identified in patients with mild-to-moderate SNHI. This is the first study to report the genetic epidemiology of STRC-related SNHI using LRS, which demonstrated a prevalence of DFNB16 in the Taiwanese cohort comparable to other East Asian populations (Kim et al. 2020; Yokota et al. 2019).

Previous studies across various populations have emphasized the significant contribution of pathogenic STRC variants to HHI (Han et al. 2021). NGS-based studies in Asian populations, supplemented by CNV validation methods, have reported a high frequency of CNVs in patients with mild-to-moderate SNHI (Kim et al. 2020; Nishio and Usami 2022). While NGS effectively detects homozygous SNVs and deletions within the highly divergent region of STRC (exons 18–29) (Schrauwen et al. 2013; Kannan-Sundhari et al. 2020; Morgan et al. 2020), the presence of the homologous pseudogene STRCP1 and potential gene-to-pseudogene conversion events can complicate variant interpretation (Shearer et al. 2014). These challenges can lead to reduced coverage and hinder accurate variant annotation and analysis (Imizcoz et al. 2023; Downie et al. 2020). Although MLPA is widely used for CNV detection (Nishio and Usami 2022; Morgan et al. 2020; Shearer et al. 2013), it may not effectively capture CNVs within the highly homologous STRC/STRCP1 region spanning exons 1 to 18 (Nishio and Usami 2022; Downie et al. 2020).

LRS technologies, capable of sequencing long genomic regions (kilobases to megabases) (Amarasinghe et al. 2020), offer a potential solution for comprehensive detection of both SNVs and CNVs (Oehler et al. 2023; Logsdon et al. 2020). While earlier LRS methods suffered from high error rates (~ 5–20% base calling errors) (Xiao and Zhou 2020), recent advances in the PacBio and ONT platforms have significantly improved accuracy, with error rates now below 1% (Wenger et al. 2019) and 5% (Jain et al. 2018), respectively. This enhanced accuracy makes LRS suitable for clinical applications.

In this study, we used a PacBio-based LRS approach, following the recommended amplicon-based workflow, to identify hidden STRC variants in a Taiwanese SNHI cohort. Although pseudogene co-amplification in our LRS assays initially hindered variant interpretation, we developed a marker-mediated refinement strategy based on STRC/STRCP1 divergent marker clusters to address it. This approach leverages the concept of genetic linkage (Pulst 1999), where proximal genetic loci are more likely to occur on the same haplotype. The performance of our LRS-based refinements was validated using NIST reference samples, with results consistent with external sources and our own experiments.

For CNV detection, our rCN ratios derived from the divergent marker clusters (M1 to M14) effectively mitigated the effects of pseudogene co-amplification and provided normalized results integrated with STRCP1 screening data. Our rCN ratios also covered a broader range (intron 15 to exon 29) than conventional MLPA assays (exon 19 to exon 28). For SNV detection, the confirmed rSNVs in our study (Table 3) represent high-confidence genotyping in STRC. We also identified false-positive pathogenic variants resulting from pseudogene co-amplification in our initial LRS assays due to low-confidence allele ratios of alternative nucleotides (Table S3). These variants are characterized by opposite REF/ALT nucleotides between STRC and STRCP1. Compared to the short reads of NGS, LRS offers the advantage of long-range sequence information, allowing the incorporation of more divergent markers near the targeted SNV for refinement. The combination of STRC/STRCP1 divergent markers and our marker-mediated refinement pipeline provide a useful solution for accurately detecting SNVs and CNVs in STRC, thereby reducing false-positive variants from conventional NGS assays. This strategy combined with the LRS approach will facilitate resolving any challenges hindered by highly homologous pseudogenes (e.g., GBA/GBAP1 relevant for Parkinson’s disease (Pachchek et al. 2023; Zhang et al. 2024) and PMS2/PMS2CL related to Lynch syndrome (Li et al. 2015; Bouras et al. 2024)).

To our knowledge, this is the first study to apply LRS technology to detect genetic causes in a large SNHI cohort. Our study demonstrates the power of LRS in resolving challenging variants in STRC and provides valuable insights into the genetic etiology of HHI. However, several limitations of this study deserve discussion. First, the constraints imposed by long-range PCR and the optimized read length for PacBio HiFi sequencing limited our amplicons to 10 kb. This prevented full, overlapping coverage of the entire STRC region, thereby hindering breakpoint-level resolution for the identified CNVs. While this amplicon-based approach is highly effective for clinical screening, a non-amplicon or whole-genome approach would be required to achieve precise structural definition at the nucleotide level. Second, a major limitation of the current workflow is the lack of divergent markers within the highly homologous region of exons 1 to 15, which exhibits nearly 100% identity between STRC and STRCP1 (Mandelker et al. 2014). The absence of informative markers in this region remains a significant obstacle to the refinement process, posing a substantial risk of false-negative results for pathogenic variations located within these exons. To enhance clinical performance in future studies, the long-range PCR workflow targeting the STRC/STRCP1 divergence sites (Mandelker et al. 2014; Vona et al. 2015) could be integrated with amplicon-based LRS assays to minimize the effect of pseudogene co-amplification.

Conclusion

This study demonstrates the significant diagnostic potential of our combined strategy—integrating amplicon-based LRS assays and marker-mediated refinement that validated by MLPA assays— for identifying complex STRC variants and elucidating the genetic causes of HHI that remain undetected by conventional NGS. The high allele frequency (19%) of STRC variants observed in our cohort underscores the importance of comprehensive STRC screening, particularly in patients with mild-to-moderate SNHI. Future clinical studies are warranted to further evaluate the diagnostic utility of LRS-based STRC screening in routine genetic testing.

Supplementary Information

Supplementary Material 1. (4.2MB, pdf)

Acknowledgements

We sincerely thank the A1 Laboratory of Genetic Testing of National Taiwan University Hospital (NTUH), GenePhile Bioscience Laboratory of Ko's Obstetrics and Gynecology Clinic, Blossom Biotechnologies, Inc. (Pacific Biosciences distributor in Taiwan), and Taiwan Genome Industry Alliance Inc. (Taipei, Taiwan) for their invaluable experimental resources and technical support. We also thank all the subjects and their families for their generous contributions to this study.

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) used Gemini in order to improve the readability. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

Authors’ contributions

Conceptualization: Cheng-Yu Tsai, Pei-Lung Chen, Jacob Shu-Jui Hsu, and Chen-Chi Wu. Investigation: Cheng-Yu Tsai and Yu-Ting Chiang. Validation: Cheng-Yu Tsai and Yue-Sheng Lu. Formal analysis: Cheng-Yu Tsai. Visualization: Cheng-Yu Tsai. Resources: Pei-Hsuan Lin, Chuan-Jen Hsu, and Pei-Lung Chen. Project administration: Cheng-Yu Tsai, Yue-Sheng Lu, and Ming-Yu Lo. Funding Acquisition: Shih-Feng Tsai, Jacob Shu-Jui Hsu, and Chen-Chi Wu. Supervision: Jacob Shu-Jui Hsu and Chen-Chi Wu. Writing (Original Draft): Cheng-Yu Tsai and Chen-Chi Wu. Writing (Review/Editing): Cheng-Yu Tsai, Pei-Lung Chen, Jacob Shu-Jui Hsu, and Chen-Chi Wu.

Funding

This study was supported by research grants from the National Science and Technology Council of the Executive Yuan of Taiwan (NSTC 110-2314-B-002-189-MY3, Chen-Chi Wu), National Health Research Institutes (NHRI-EX111-10914PI, Chen-Chi Wu), National Taiwan University Hospital and National Taiwan University Joint Program (111-UN0048, Chen-Chi Wu and Jacob Shu-Jui Hsu), and National Taiwan University Hospital Hsin-Chu Branch and National Health Research Institutes Joint Program (NHRI-113-B02, Chen-Chi Wu and Shih-Feng Tsai).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

All patients and/or their guardians signed an informed consent form before participating in the study. All procedures used in the study were approved by the Research Ethics Committee of the National Taiwan University Hospital (201104025RC).

Consent for publication

All patients and/or their guardians have provided written informed consent.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Jacob Shu-Jui Hsu, Email: jacobhsu@ntu.edu.tw.

Chen-Chi Wu, Email: chenchiwu@ntuh.gov.tw.

References

  1. Abbasi W, French CE, Rockowitz S, Kenna MA, & Eliot Shearer A. Evaluation of copy number variants for genetic hearing loss: a review of current approaches and recent findings. Human Genetics. 2022;141(3):387–400. [DOI] [PubMed]
  2. Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen‐2. Curr Protoc Hum Genet. 2013;76(1):7.20. 1-7.20. 41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Alvaro S, Castillo D, Genovés J, Prados ED, Levorato M, Albertí A, Martorell L. Refining the detection of complex rearrangements in 15q15. 3 region involving the STRC gene in hereditary hearing loss patients. J Human Genet. 2025;70(8)395-403. [DOI] [PubMed]
  4. Amarasinghe SL, et al. Opportunities and challenges in long-read sequencing data analysis. Genome Biol. 2020;21(1):1–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Amr SS, et al. Allele-specific droplet digital PCR combined with a next-generation sequencing-based algorithm for diagnostic copy number analysis in genes with high homology: proof of concept using stereocilin. Clin Chem. 2018;64(4):705–14. [DOI] [PubMed] [Google Scholar]
  6. Azaiez H, et al. Genomic landscape and mutational signatures of deafness-associated genes. Am J Hum Genet. 2018;103(4):484–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bouras A, et al. PMS2 or PMS2CL? Characterization of variants detected in the 3′ of the PMS2 gene. Genes Chromosomes Cancer. 2024;63(1):e23193. [DOI] [PubMed] [Google Scholar]
  8. Del Castillo I, et al. Genetic etiology of non-syndromic hearing loss in Europe. Hum Genet. 2022;141(3):683–96. [DOI] [PubMed] [Google Scholar]
  9. Downie L, et al. Exome sequencing in infants with congenital hearing impairment: a population-based cohort study. Eur J Hum Genet. 2020;28(5):587–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Genome in a Bottle (GIAB). National Institute of Standards and Technology (NIST). https://www.nist.gov/programs-projects/genome-bottle. Last accessed 16 Oct 2024.
  11. Han S, et al. Prevalence and characteristics of STRC gene mutations (DFNB16): a systematic review and meta-analysis. Front Genet. 2021;12:707845. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Ho SS, Urban AE, Mills RE. Structural variation in the sequencing era. Nat Rev Genet. 2020;21(3):171–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Hoppman N, et al. Genetic testing for hearing loss in the United States should include deletion/duplication analysis for the deafness/infertility locus at 15q15. 3. Mol Cytogenet. 2013;6(1):1–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Imizcoz T, et al. Next-generation sequencing improves precision medicine in hearing loss. Front Genet. 2023;14:1264899. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Ito T, et al. Rapid screening of copy number variations in STRC by droplet digital PCR in patients with mild-to-moderate hearing loss. Hum Genome var. 2019;6(1):1–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Jaganathan K, et al. Predicting splicing from primary sequence with deep learning. Cell. 2019;176(3):535-548. e24. [DOI] [PubMed] [Google Scholar]
  17. Jain M, et al. Nanopore sequencing and assembly of a human genome with ultra-long reads. Nat Biotechnol. 2018;36(4):338–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kannan-Sundhari A, et al. Screening consanguineous families for hearing loss using the miamiotogenes panel. Genet Test Mol Biomarkers. 2020;24(10):674–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Karczewski KJ, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581(7809):434–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Kim BJ, et al. Significant Mendelian genetic contribution to pediatric mild-to-moderate hearing loss and its comprehensive diagnostic approach. Genet Med. 2020;22(6):1119–28. [DOI] [PubMed] [Google Scholar]
  21. Kopanos C, et al. VarSome: the human genomic variant search engine. Bioinformatics. 2019;35(11):1978–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Laurent S, et al. Improved variant detection using long-read sequencing and optical mapping: illustration in STRC-related hearing loss. Eur J Med Genet. 2025;73:104986. [DOI] [PubMed] [Google Scholar]
  23. Lee YH, et al. Revisiting genetic epidemiology with a refined targeted gene panel for hereditary hearing impairment in the Taiwanese population. Genes (Basel). 2023;14(4):880. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011;27(21):2987–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Li H, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Li J, et al. A comprehensive strategy for accurate mutation detection of the highly homologous PMS2. J Mol Diagn. 2015;17(5):545–53. [DOI] [PubMed] [Google Scholar]
  27. Liu X, et al. dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs. Genome Med. 2020;12:1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Logsdon GA, Vollger MR, Eichler EE. Long-read human genome sequencing and its applications. Nat Rev Genet. 2020;21(10):597–614. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Mahmoud M, et al. Structural variant calling: the long and the short of it. Genome Biol. 2019;20(1):1–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Mandelker D, et al. Comprehensive diagnostic testing for stereocilin: an approach for analyzing medically important genes with high homology. J Mol Diagn. 2014;16(6):639–47. [DOI] [PubMed] [Google Scholar]
  31. Markova T, et al. Clinical features of hearing loss caused by STRC gene deletions/mutations in Russian population. Int J Pediatr Otorhinolaryngol. 2020;138:110247. [DOI] [PubMed] [Google Scholar]
  32. Masella AP, et al. BAMQL: a query language for extracting reads from BAM files. BMC Bioinformatics. 2016;17:1–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Mochizuki T, et al. Mutation analyses by next-generation sequencing and multiplex ligation-dependent probe amplification in Japanese autosomal dominant polycystic kidney disease patients. Clin Exp Nephrol. 2019;23:1022–30. [DOI] [PubMed] [Google Scholar]
  34. Morgan A, et al. Lights and shadows in the genetics of syndromic and non-syndromic hearing loss in the Italian population. Genes (Basel). 2020;11(11):1237. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Moteki H, et al. Detection and confirmation of deafness-causing copy number variations in the STRC gene by massively parallel sequencing and comparative genomic hybridization. Ann Otol Rhinol Laryngol. 2016;125(11):918–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Ng PC, Henikoff S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003;31(13):3812–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Nishio SY, Usami SI. Frequency of the STRC-CATSPER2 deletion in STRC-associated hearing loss patients. Sci Rep. 2022;12(1):634. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Oehler JB, et al. The application of long-read sequencing in clinical settings. Hum Genomics. 2023;17(1):73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Os PGEV, Schouten JP. Multiplex ligation-dependent probe amplification (MLPA®) for the detection of copy number variation in genomic sequences. In: PCR mutation detection protocols. 2011. p. 97–126. [DOI] [PubMed] [Google Scholar]
  40. PacBio, pbmm2. 2023. https://github.com/PacificBiosciences/pbmm2. ver 1.13.
  41. Pachchek S, et al. Accurate long-read sequencing identified GBA1 as major risk factor in the Luxembourgish Parkinson’s study. npj Parkinsons Dis. 2023;9(1):156. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Poplin R, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018;36(10):983–7. [DOI] [PubMed] [Google Scholar]
  43. Pulst SM. Genetic linkage analysis. Arch Neurol. 1999;56(6):667–72. [DOI] [PubMed] [Google Scholar]
  44. Rhoads A, Au KF. PacBio sequencing and its applications. Genomics Proteomics Bioinformatics. 2015;13(5):278–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Richards S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Schrauwen I, et al. A sensitive and specific diagnostic test for hearing loss using a microdroplet PCR-based approach and next generation sequencing. Am J Med Genet A. 2013;161(1):145–52. [DOI] [PubMed] [Google Scholar]
  47. Schubach M, et al. CADD v1. 7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions. Nucleic Acids Res. 2024;52(D1):D1143–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Sharma N, et al. A systematic review of the monogenic causes of non-syndromic hearing loss (NSHL) and discussion of current diagnosis and treatment options. Clin Genet. 2023;103(1):16–34. [DOI] [PubMed] [Google Scholar]
  49. Shearer AE, et al. Genetic hearing loss overview. In: Margaret PA, et al., editors. GeneReviews®. Seattle: University of Washington; 1999. Available from: https://www.ncbi.nlm.nih.gov/books/NBK1434/. [PubMed] [Google Scholar]
  50. Shearer AE, et al. Comprehensive genetic testing for hereditary hearing loss using massively parallel sequencing. Proc Natl Acad Sci U S A. 2010;107(49):21104–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Shearer AE, et al. Advancing genetic testing for deafness with genomic technology. J Med Genet. 2013;50(9):627–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Shearer AE, et al. Copy number variants are a common cause of non-syndromic hearing loss. Genome Med. 2014;6(5):1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Sloan-Heggen CM, et al. Comprehensive genetic testing in the clinical evaluation of 1119 patients with hearing loss. Hum Genet. 2016;135(4):441–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Tsai CY, Hsu JSJ, Chen PL, Wu CC. Implementing next-generation sequencing for diagnosis and management of hereditary hearing impairment: a comprehensive review. Expert Rev Mol Diagn. 2024;24(9)753–65. [DOI] [PubMed]
  55. Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, Rozen SG. Primer3—new capabilities and interfaces. Nucleic Acids Res. 2012;40(15):e115. 10.1093/nar/gks596. [DOI] [PMC free article] [PubMed]
  56. van Belzen IA, et al. Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology. NPJ Precis Oncol. 2021;5(1):1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Verpy E, et al. Stereocilin connects outer hair cell stereocilia to one another and to the tectorial membrane. J Comp Neurol. 2011;519(2):194–210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Vona B, et al. DFNB16 is a frequent cause of congenital hearing impairment: implementation of STRC mutation analysis in routine diagnostics. Clin Genet. 2015;87(1):49–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164–e164. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Wang Y, et al. Nanopore sequencing technology, bioinformatics and applications. Nat Biotechnol. 2021;39(11):1348–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Wei CY, et al. Genetic profiles of 103,106 individuals in the Taiwan Biobank provide insights into the health and history of Han Chinese. NPJ Genom Med. 2021;6(1):1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Wenger AM, et al. Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome. Nat Biotechnol. 2019;37(10):1155–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Xiao T, Zhou W. The third generation sequencing: the advanced approach to genetic diseases. Transl Pediatr. 2020;9(2):163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Yokota Y, et al. Frequency and clinical features of hearing loss caused by STRC deletions. Sci Rep. 2019;9(1):4408. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Zhang Y, et al. Sensorineural deafness and male infertility: a contiguous gene deletion syndrome. J Med Genet. 2007;44(4):233–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Zhang X, et al. Clinical, mechanistic, biomarker, and therapeutic advances in GBA1-associated Parkinson’s disease. Transl Neurodegener. 2024;13(1):48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Zook JM, et al. Extensive sequencing of seven human genomes to characterize benchmark reference materials. Sci Data. 2016;3(1):1–26. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (4.2MB, pdf)

Data Availability Statement

No datasets were generated or analysed during the current study.

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