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Published in final edited form as: Hear Res. 2016 Feb 2;333:179–184. doi: 10.1016/j.heares.2016.01.018

A NEXT-GENERATION SEQUENCING GENE PANEL (MIAMI OTOGENES) FOR COMPREHENSIVE ANALYSIS OF DEAFNESS GENES

Demet Tekin 1,#, Denise Yan 1,#, Guney Bademci 2, Yong Feng 3, Shengru Guo 2, Joseph Foster II 2, Susan Blanton 1,2, Mustafa Tekin 1,2, Xuezhong Liu 1,2,3
PMCID: PMC4798889  NIHMSID: NIHMS756797  PMID: 26850479

Abstract

Extreme genetic heterogeneity along with remarkable variation in the distribution of causative variants across in different ethnicities makes single gene testing inefficient for hearing loss. We developed a custom capture/next-generation sequencing gene panel of 146 known deafness genes with a total target size of approximately 1MB. The genes were identified by searching databases including Hereditary Hearing Loss Homepage, the Human Genome Mutation Database (HGMD), Online Mendelian Inheritance in Man (OMIM) and most recent peer-reviewed publications related to the genetics of deafness. The design covered all coding exons, UTRs and 25 bases of intronic flanking sequences for each exon. To validate our panel, we used 6 positive controls with variants in known deafness genes and 8 unsolved samples from individuals with hearing loss. Mean coverage of the targeted exons was 697X. On average, each sample had 99.8%, 96.2% and 92.7% of the targeted region coverage of 1X, 50X and 100X reads, respectively. Analysis detected all known variants in nuclear genes. These results prove the accuracy and reliability of the custom capture experiment.

Keywords: Deafness, Hearing loss, Gene, Custom capture, Next Generation Sequencing

1. Introduction

Hearing loss (HL) affects at least 30% of the population at some time in their lives. In the U.S., clinically significant HL is present in at least 1.9 per 1,000 infants at birth (Morton and Nance, 2006). 50-60% of these cases have a genetic etiology. 30% of cases of prelingual deafness are classified as syndromic; the remainder are non-syndromic. To date, more than 80 deafness genes, with more than 1200 mutations, have been implicated in HL (http://deafnessvariationdatabase.org), making it one of the most genetically heterogeneous traits. Their identification has dramatically improved the clinical diagnosis and management of deaf and hard-of-hearing families (Angeli et al., 2012).

The extreme heterogeneity of genetic HL can be explained by the complexity of the process of sound transduction in the auditory system, which requires coordination of multiple mechanisms involving the inner ear and nervous system. A defect in any aspect of this complex series of events can lead to HL (Tekin et al., 2001; Angeli et al., 2012). For many decades, linkage analysis has been the most powerful and extensively used strategy to delineate the genetic basis of inherited disorders. However, this approach is time consuming and requires the availability of cohorts of homogeneous and informative large pedigrees; thus the etiology of HL remains unexplained for a large proportion of small poorly characterized families. These obstacles, however, may be overcome by the advent of next generation sequencing (NGS) technologies. Several NGS platforms, including whole genome sequencing (WGS) and whole exome sequencing (WES) offer powerful applications not only to molecular diagnostics but also to identify rare variants and new causative genes (Diaz-Horta et al., 2014; Grati et al., 2015, Bademci et al., 2015). Nonetheless, there are still major interpretative challenges, ranging from the validation of large numbers of genomic changes in a patient to the economic feasibility of these approaches (Shearer et al., 2010; Yan et al., 2013; Tsai and Liu, 2014). Therefore, we undertook a targeting sequencing approach of 146 known and candidate deafness genes for nonsyndromic and syndromic forms of deafness. This study highlights the utility of NGS techniques combined with rare variant analysis tools to provide insight into the genetic etiology of an extremely heterogeneous disorder, such as HL.

2. Methods

2.1. Subjects

This study was approved by the local Institutional Review Board at the University of Miami in the USA. A signed informed consent form was obtained from each participant. Subjects were sequentially accrued probands referred to our laboratory as part of a large study of genetic HL. Diagnosis of sensorineural HL loss was established via standard audiometry in a soundproofed room according to current clinical standards. Clinical evaluation included a thorough physical examination and otoscopy by a geneticist and an otolaryngologist.

2.2. Target enrichment panel design

We used the SureSelect target capture system (Agilent) to develop a custom capture panel of 146 known and candidate deafness causing genes under the Miami Otogenetic Program (supplementary Table). Using the Agilent SureDesign online tool (https://earray.chem.agilent.com/suredesign/), a SureSelect custom kit with the target size of approximately 1 MB (Agilent, Santa Clara, CA, https://www.agilent.com) was designed to include genes associated with both syndromic and nonsyndromic hereditary HL. The deafness genes were selected from Hereditary Hearing Loss Homepage (http://hereditaryhearingloss.org), RefSeq (http://www.ncbi.nlm.nih.gov/refseq/), Ensembl (http://www.ensembl.org), The Human Genome Mutation Database (http://www.hgmd.org) and Online Mendelian Inheritance in Man (http://www.ncbi.nlm.nih.gov/omim/). Candidate genes from research from our laboratory were also added into the design. A total of 146 deafness genes encompassing 2812 regions were covered as all coding exons, 5′ and 3′ untranslated regions (UTRs) and 25 bases of intronic flanking sequences for each exon by the solution based custom capture preparation. Approximately 39 % and 44 % of the genes included in the panel are associated with syndromic and nonsyndromic HL, respectively, while 7% of them have been reported responsible for both forms. 52% of them have been shown to be responsible for autosomal recessive HL, and 27% for autosomal dominant HL, 7% for both modes of inheritance and 6% for X-linked deafness (Fig. 1).

Fig. 1.

Fig. 1

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Distribution of the 146 targeted genes in the MiamiOtoGenes

2.3. Sequencing

The targeted sequencing was processed at the Hussman Institute for Human Genomics (HIHG) Sequencing core, University of Miami. The Agilent's SureSelect Target Enrichment (Agilent, Santa Clara, CA) of coding exons and flanking intronic sequences in-solutionhybridization capture system was used following the manufacturer's standard protocol. Adapter sequences for the Illumina HiSeq2000 were ligated and the enriched DNA samples were prepared using the standard methods for the HiSeq2000 instrument (Illumina). Paired-end reads of 99 bases length were produced. Conventional capillary sequencing was used to confirm the candidate variants observed in targeted- genes panel. The primers were designed using Primer3, v. 0.4.0 (http://primer3.ut.ee). PCR (polymerase chain reaction) reactions included 40-60 ng of genomic DNA with Taq DNA polymerase (Sigma). PCR products were visualized on agarose gels, purified using Qiagen Qiaquick purification kit according to the manufacturers’ protocols. Sequence analysis was performed with the ABI PRISM Big Dye Terminator Cycle Sequencing V3.1 Ready Reaction Kit and the ABI PRISM 3730 DNA Analyzer (Applied Biosystems). Sequence traces were analyzed using the DNASTAR Lasergene software.

2.4. Bioinformatics

The Illumina CASAVA v1.8 pipeline was used to assemble 99 bp sequence reads. BWA (Li and Durbin, 2010) was used to align sequence reads to the human reference genome (hg19) and variants were called using the GATK software package (McKenna et al., 2010, DePristo et al., 2011). All variants were submitted to SeattleSeq137 to assess functional consequences. Further annotation was obtained using data from dbSNP141, variant frequency data from the NHLBI Exome Sequencing Project (Exome Variant Server, NHLBI Exome Sequencing Project (ESP), Seattle, WA Project (Exome Variant Server, 2012), the HGMD Human Gene Mutation Database (Stenson, et al., 2003) and the OMIM Online Mendelian Inheritance in Man database (OMIM Online Mendelian Inheritance in Man, December, 2012). The variant calls were determined by The Genomes Management Application (GEM.app; https://genomics.med.miami.edu/) (Gonzalez et al., 2013).

3. Results

Our aim was to assess the technical applicability of targeted NGS: (i) the sequencing quality of targeted NGS, in terms of representation and coverage; (ii) the sequence reliability, measured in terms of sensitivity and specificity, as compared to Sanger sequencing; and (iii) the reproducibility of this targeted NGS methodology, using a new gene panel incorporating 146 genes. Solution-based targeted enrichment, Agilent SureSelect custom kit, of 146 known deafness genes (954.67 kbp target region) followed by NGS on an Illumina HiSeq 2000 was performed. After alignment with the human reference genome, the mean coverage of the targeted exons was 697X. On average, each sample had 99.8%, 96.2% and 92.7% of the targeted region coverage of 1X, 50X and 100X reads, respectively (Fig. 2).

Fig. 2.

Fig. 2

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Total Number of reads obtained from each sample (A). Coverage percentages for the 1X, 50X and 100X read depth for each sample (B).

To validate the platform as a tool for genetic testing, a total of 6 positive control DNA samples previously characterized by standard Sanger sequencing and 8 samples with unknown genotypes were used for establishing the amplicon resequencing workflow and assessing the analytical sensitivity and specificity of the targeted NGS (Table 1). Positive control samples with mutations at different states (homozygous, heterozygous and compound heterozygous) and types (missense, nonsense, frameshift) used for this study were from patients with mutations in TMC1 (Transmembrane channel-like protein; DFNB7/B11), MYO15A (Myosin 15A; DFNB3), CIB2 (calcium- and integrin-binding protein 2; DFNB48), and GJB2. The analysis of 6 positive control samples confirmed both the expected mutations and the allele state providing a detection rate of 100%. It did not highlight any further unexpected variant, confirming the absence of any unreported variant in the validation set. Of the 8 subjects with idiopathic HL, two (samples 7 and 9) were from families segregating syndromic HL. Sample number 7 was from a patient with mild HL who was shown to be heterozygous for A-to-G transition at the conserved acceptor splice site of USH2A (MIM 608400) in intron 61 (c.12067-2A>G; IVS61 as-2A>G). This mutation is expected to lead to incorrect splicing of USH2A transcripts. He was also found to be heterozygous for the common single base-pair deletion in exon 13 (c.2299delG, p.Glu767Serfs*21) that is predicted to generate a premature termination codon. These variants were validated by Sanger sequencing and were found to co-segregate with the phenotype by genotype analysis of all available family members. Sample number 9 was a subject exhibiting sensorineural HL and a hypopigmented streak on the skin. She was found to be heterozygous for a variant in the MITF (MIM 156845; microphthalmia associated transcription factor) gene on 3p14.p13 (c.1195G>A, p.G399R; rs531830542). This variant has a minor allele frequency ofapproximately 0.0002 in the African population. Two other unknown subjects were found to be compound heterozygous for mutations in GJB2 (MIM 121011). The changes p.L90P (c.269T>C; rs80338945) and p.R143W (c.427C>T; rs80338948) were identified in the first individual with moderate HL (sample 8). In the other proband, who had profound deafness (sample 10), we found heterozygous changes c.35delG (p.G12VfsX; rs1801002) and p.E47X (c.139G>T; rs104894398) (Table 1).

Table 1.

Identified causative variants in 14 samples (4 samples remain unsolved).

Sample ID Sample Status Gene Transcript Nucleotide change Protein Change
1 Positive control TMC1 NM_138691.2 c.[1589_1590delCT];[1589_1590delCT] p.[Ser530*];[Ser530*]
2 Positive control MYO15A NM_016239.3 c.[4652C>A];[4652C>A] p.[Ala1551Asp];[Ala1551Asp]
3 Positive control CIB2 NM_006383.3 c.[330T>A];[330T>A] p.[Tyr110*];[Tyr110*]
4 Positive control TMC1 NM_138691.2 c.[1718T>A];[2130-1delG] p.[Ile573Asn];splice acceptor
5 Positive control GJB2 NM_004004.5 c.[35delG];[35delG] p.[Gly12Valfs*2];.[Gly12Valfs*2]
6 Positive control GJB2 NM_004004.5 c.[35delG];[−23+1G>A] p.[Gly12Valfs*2];splice acceptor
7 Solved USH2A NM_206933.2 c. [2299delG];[12067-2A>G] p.[Glu767Serfs*21];splice acceptor
8 Solved GJB2 NM_004004.5 c.[427C>T];[269T>C] p.[Arg143Try];[Leu90Pro]
9 Solved MITF NM_000248.3 c.[1195G>A];[=] p.[Gly399Arg];[=]
10 Solved GJB2 NM_004004.5 c.[35delG];[139G>T] p.[Gly12Valfs*2];[Glu47*]
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4. Discussion

NGS technology has the appeal of reducing the time and cost of testing, especially when the sequencing involves a larger number of genes to be analyzed as in the case of nonsyndromic HL. There are currently three NGS approaches to improve genetic testing for heterogeneous diseases: 1) whole-exome sequencing (WES), 2) whole genome sequencing (WGS), and 3) targeted enrichment of a set of genes (gene panel). Implementation of genetic testing is often evaluated on the basis of outcome parameters such as sensitivity (false-negative rate [FNR]) and specificity (false-positive rate [FPR]). The FPR is not a major problem in NGS, since findings are often validated by Sanger sequencing. However, the FNR is the critical parameter when a disorder is mainly caused by a few, highly penetrant genes, for example, the case of hereditary breast cancer. But, in the context of a heterogeneous disease such as HL, it may be acceptable to balance a low number of false-negative results with a high number of diagnoses that would otherwise not have been possible. When comparing the three options, it is inarguable that theoretically WGS is the most comprehensive approach as it will provide the most complete data set on an individual's genome, offers more coverage of the exome and allows interrogation of single-nucleotide variants (SNVs), indels, structural variants (SVs) and copy number variants (CNVs) in both the ~1% part of the genome that encodes protein sequences and the ~99% of remaining non-coding sequences. Furthermore, sequencing read length isn't a limitation with WGS and doesn't suffer from reference bias. In contrast, WES is targeted to protein coding regions, so reads represent less than 2% of the genome but omits regulatory regions such as promoters and enhancers. This reduces the cost to sequence a targeted region at a high depth and reduces storage and analysis costs. However, coverage uniformity with WES is inferior to WGS; regions of the genome with low sequence complexity restrict the ability to design useful WES capture baits, resulting in off target capture effects and most target probes for exome-seqencing are designed to be less than 120 nucleotides long, making it meaningless to sequence using a greater read length. To address the genetic heterogeneity of HL and reduce the labor and cost of gene-by-gene Sanger sequencing, we developed a platform, MiamiOtogene, that combines targeted genomic enrichment (TGE) and massively parallel sequencing (MPS) to capture and sequence all exons of 146 deafness-causative genes. It represents an alternative and bring down considerably the cost of sequencing, but will only succeed if the disease-causing gene is included in the panel. Reduced costs make it feasible to increase the number of samples to be sequenced, enabling large population based comparisons.

An advantage is that restricted targeting reduces the possibility of incidental findings and allows higher coverage at lower cost than genome-wide approaches. An additional advantage is that novel, relevant genes can simply be added to the panel.

Genetic deafness is a classic example of multilocus genetic heterogeneity. This extreme heterogeneity of human deafness has often hampered genetic studies because many different genetic forms of HL give rise to similar clinical phenotypes, and, conversely, mutations in the same gene can result in a variety of clinical phenotypes. Despite this heterogeneity, HL associated with the DFNB1 locus on chromosome 13q11 is the most common cause in many populations of the world. Mutations in the connexin26 (Cx26, GJB2) (Kelsell et al., 1997), localized to this chromosomal region are responsible for up to 50% of cases in populations of European descent (Gasparini et al., 2000). The major mutations in GJB2 have been seen to be population specific, resulting from founder effects. These include c.35delG, the most common variant in individuals of northern European descent (Van Laer et al., 2001), with a carrier rate of 2% to 4% (Estivill et al., 1998, Green et al., 1999). The c.235delC is most common in East Asian populations (carrier rate: 1% to 2%) (Abe et al., 2000, Kudo et al 2000; Yan et al., 2003); c.167delT is most common in the Ashkenazi Jewish population (carrier rate: 7.5%) (Morell et al., 1998); W24X, W77X and Q124X have been detected commonly in families from different parts of the Indian subcontinent (Richard 2001; Yan et al., 2015). In the Middle East, as in most other regions in the world, the most common gene involved in HL is GJB2, responsible for 27% of congenital HL among Israeli Jews (Brownstein and Avraham 2009) and 14% among Palestinian Arabs (Shahin et al., 2002). Worldwide, the other most frequent causative genes for HL are SLC26A4, MYO15A, OTOF, CDH23, and TMC1, most of which contain several exons with many deafness-causing mutations in each. Because of the size of the genes and the cost of Sanger sequencing, until recently, only the known mutations were screened in clinics, based on relevance according to ethnic origin and HL phenotype. The advent of the $1000 genome has the potential to revolutionize the identification of genes and their mutations underlying genetic disorders. Low-cost high-throughput genomic sequencing technologies and computing power have enabled comprehensive analysis of human genomes in recent years (Tsai and Liu, 2014). The rapid development of the NGS technologies has had a clear consequence on the speed and proficiency of NSHL genes discovery. Some of the causative genes have been discovered in previously reported loci, other are found in unsuspected regions of the genome. In addition, screening of small families not amenable to linkage analysis, has been facilitated through the use of NGS powered methods such as WES. The application of these approaches has proven to be a powerful method resulting in the identification of approximately 21 NSHL genes (between 2010 and early 2015) (Yan et al., 2013; Liu et al 2013; Vona et al., 2015; http:hereditaryhearingloss.org) since the first report describing targeted enrichment and massively parallel sequencing of the DFNB79 locus (MIM: *613354; TPRN) (Rehman et al., 2010) and WES in conjunction with homozygosity mapping of DFNB82 (MIM: *609245; GPSM2) in 2010 (Walsh et al., 2010). These recent breakthroughs are also translating genetic research findings into accurate and sensitive clinical molecular diagnostic tests, which will in turn improve care for patients with hearing loss and related disorders (Shearer et al., 2011; Lin et al., 2012; Yan et al., 2013). Identification of the precise genetic cause of hearing loss is prerequisite to applying precision medicine to the treatment.

The prominent finding in this study was the confirmation of precision and reliability of the custom capture deafness panel (MiamiOtoGenes) designed by the Miami Otogenetic Program. The abundance and relative affordability of NGS techniques has launched a new era in the study of human disease. It is becoming routine to pinpoint the disease causing mutation using this new technology. This gene hunting approach is more straightforward in homogenous diseases or in cases where large families are available. However, for a heterogeneous disorder like hereditary deafness, more creative approaches are required. We utilized a custom capture NGS panel to rapidly sequence a set of 146 candidate genes. The deafness-associated genes included and the methods used in multi-gene panels vary by laboratory. The obvious difference in the number of genes results from criteria and weighting system to prioritize genes for inclusion. In the design stage, we limited ourselves to genes that have a strong association with nonsyndromic and syndromic forms of deafness to facilitate interpretation of the findings because of sufficiency of evidence. The genes were selected by searching databases including Hereditary Hearing Loss Homepage, RefSeq, Ensembl, The Human Genome Mutation Database (HGMD), Online Mendelian Inheritance in Man (OMIM) and most recent peer-reviewed publications related to the genetics of deafness. We have also included a few in house newly discovered candidate genes for validation. This approach has both benefits and drawbacks. By focusing on a list of genes, those genes could be sequenced more rapidly and cost effectively than whole exome or genome sequencing. For instance, in this study, only one thirtieth of the whole exome is captured and sequenced. However, even when using a whole exome or genome sequencing approach with a limited number of patients, the first pass analysis focuses on biologically relevant candidate genes, which are responsible for approximately 60% of cases with congenital hearing loss. Therefore, ruling out the causative mutations in known deafness genes as a first step with a candidate gene based panel by utilizing target enrichment NGS strategy may be a more streamlined method of assessing heterogeneous diseases to integrate genomic analysis into clinical translation. This approach is certainly faster and cheaper than the current practice of iteratively performing dozens of traditional Sanger sequencing tests. While there is an inherent bias in this method since we are only testing the genes we chose to sequence, and we could very well be missing important variants in genes outside of our gene set, given that most patients will have a mutation in a known gene, it is a reasonable first step (Atik, et al., 2015).

In summary, with an ever-increasing number of options for genetic testing, the decision of whether to go for a panel, WES, or WGS becomes complicated. There have been many recent, successful applications of WES in establishing the etiology of suspected Mendelian disorders (Rabbani et al., 2014). In contrast to WES, WGS captures genomic variations within and outside of the exome. Thus, the most obvious feature of WGS is that it substantially increases the volume of detected variants per individual. The current cost of clinical-grade WES is high, typically >US$4500 and the continuing and rapid advances in DNA sequencing technologies have now reached the point at which a genome can be sequenced at very reasonable costs, with a current cost of research-grade around $7666 (Wetterstrand 2012). However, the price of sequencing a genome is misleading because it fails to consider the exponentially higher analyzing costs and translating the outputs into meaningful information that benefits patients. The turnaround time for both WES and WGS is three months on average. These drawbacks are mainly due to the interpretation challenge. WGS is a tremendously powerful test specially for complex disorders and has diverse clinical utility. Nonetheless, there are multiple challenges preventing clinical implementation of WGS, such as high costs beyond sequencing, the accuracy of NGS platforms, analysis algorithms, the ability to meaningfully interpret data and the need to address ethical issues raised by incidental and secondary findings.

The turnaround time for our panel is about 3-4 weeks compared to typically months for clinical exome tests, and the cost per panel is approximately between $212 and $305. Overall, our panel sequencing detected causal variants in approximately 50 percent of cases, a diagnostic yield much higher than the 25 percent or so reported in several large clinical exome sequencing studies (Yang et., 2013; 2014; Lee et al., 2014)). One reason for this might be the interpretative challenge of exome sequencing.

Supplementary Material

Highlights.

A custom capture/next-generation sequencing gene panel for deafness was developed 6 controls and 8 unsolved hearing loss samples were used for validation.

Following custom capture and NGS, bioinformatic analayses detected all known variants.

These results prove the accuracy and reliability of the custom capture experiment.

Acknowledgements

This study was supported by R01 DC05575, R01 DC01246, 2P50DC000422-Sub-Project 6432, and R01 DC012115 from the National Institutes of Health/National Institute on Deafness and Other Communication Disorders to Xuezhong Liu and R01DC09645 and R01DC012836 to Mustafa Tekin.

Footnotes

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