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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.

Next-generation sequencing (NGS [6]) has enabled simultaneous interrogation of many genes, up to the entire exome or genome, in patients with suspected genetic etiologies, leading to high diagnostic rates in several clinical scenarios (1-8). Along with the high throughput of NGS, improved single-nucleotide variant (SNV) detection accuracy, and continuously dropping cost, several recent bioinformatics algorithms have enabled sensitive analytical identification of other types of variants, mainly copy number variants (CNVs), from NGS read depth or coverage data (9-12). As such, NGS has become the technology of choice in most clinical molecular genetics laboratories, with ongoing improvements to increasingly support accurate SNV and CNV calling.

Nonetheless, genes or genomic regions with high homology, among others, pose a major challenge to those laboratories because they are not easily amenable to sequencing and copy number analysis with available NGS platforms. Specifically, our group recently showed that the analysis of 193 medically relevant genes, including 85 with strong disease associations such as STRC [7] (hearing loss), PMS2 (colon cancer), PKD1 (polycystic kidney disease), and SMN1 (spinal muscular atrophy), was stymied by substantial homology issues (13). The challenge is inherent in the fact that NGS reads generated by most available chemistries are not long enough to distinguish long stretches of highly identical DNA sequences, thus leading to false-positive and false-negative variant calls (14).

While certain approaches, such as long-range PCR, can be used to isolate such genes for specific sequencing and potentially copy number analysis, they are neither very simple nor cost-effective and, most importantly, suffer from the lack of scalability. This hinders the use of specific CNV-detection methods as first-tier tests for highly homologous genes. Alternatively, affordable and scalable nonspecific assays can potentially be used as "screening" tools such that only seemingly positive cases are followed up on. One such screening method would ideally be NGS-based, given its scalability, widespread adoption, and the need to homogenize workflows in molecular genetics laboratories.

Here, we describe a clinical algorithm that uses NGS coverage data to infer copy number states at the relevant high homology target(s), followed by confirmatory testing only in the apparently positive patient samples, thus obviating the need for parallel copy number testing on all patients undergoing NGS testing. Although NGS-based algorithms have been published (9-12), their potential application to CNV analysis for genes with high homology would be a novel application, and one of importance to clinical NGS testing, especially because 193 genes that are medically relevant have homologous pseudogenes or regions. Because of the homology issue, these genes are particularly enriched for structural rearrangements and often have recurrent CNVs. It would be impractical to run gene-specific copy number assays for several or all (in a genome-wide testing setting) of those genes on every patient in the clinical laboratory.

Although other techniques, such as multiplex ligation-dependent probe amplification and competitive PCR, can be used (15), here we chose allele-specific droplet digital PCR (ddPCR) as a confirmatory platform. ddPCR has recently been used for several applications, including the accurate detection of CNVs with an unprecedented dynamic range (16). This is largely due to the extreme partitioning of simple TaqMan, usually duplex, PCR assays into thousands of smaller droplet PCR reactions that can be specifically quantified by use of TaqMan probe fluorophores (16). Therefore, ddPCR combines the simplicity of the PCR design, and potentially its allele-specific modified form (17), with high resolution.

As a proof of concept and to illustrate its feasibility and clinical utility, we applied this approach to copy number detection in STRC, a tandemly duplicated gene wherein biallelic pathogenic variants, mainly deletions, significantly contribute to hearing loss (14, 18-20).

Materials and Methods


Control DNA samples with known STRC copy number status (R-1369, R-1370, R-1371, R-1372) or those genotyped by the Illumina Human 850K Bead Chip (Illumina Inc.) for single-nucleotide polymorphism-based copy number analysis (HL-20, HL-21, HL-22, HL-23) were used. For the latter samples, the data were analyzed by CNV Workshop (21) and vendor-provided analysis software (GenomeStudio). Only unique probes were selected for the analysis of CNVs involving regions that included the STRC and CATSPER2 genes.

The clinical utility of our approach was evaluated by use of DNA from 517 deidentified patients with hearing referred to the Partners Laboratory for Molecular Medicine for OtoGenome testing between April 2015 and November 2016. Patients were clinically consented and samples obtained through physician-ordered genetic testing at the CLIA-accredited partners LMM. The additional follow-up testing and analyses were performed under a waived research consent protocol approved by the Partners Healthcare Institutional Review Board.


ddPCR was performed as previously described (12). To the greatest extent possible, we maximized the number of nucleotides that were divergent between STRC and pSTRC in our assay design, especially toward the 3; end of the forward and/or reverse primer sequences (Fig. 1), where mismatches have been shown to have the greatest impact on primer-template binding and overall PCR specificity (22). Two allele-specific ddPCR assays, one targeting exon 23 (STRC.ex23) and one to intron 25 (STRC.in25), were designed. As controls, we used 2 nonspecific ddPCR assays targeting exons 2 (STRC.ex02) and 10 (STRC.ex10). Because CATSPER2 also has substantial homology issues (see Table 1 in the Data Supplement that accompanies the online version of this article at, and to be able to distinguish the STRC/CATSPER2 contiguous deletion, and to diagnose of deafness-infertility syndrome in males, we also designed a CATSPER2 allelespecific ddPCR assay with a similar approach. Here, we focused on STRC, though the CATSPER2 assay had equivalent performance (data not shown).

The following primers and probes were used in the above assays: STRC.ex02, Forward: CACTGCAATT TCTGCCTCCTTC, Probe: CTGAGAGGTAGCCC CG-MGBNFQ, and Reverse: TCTCAGCCTCCCAA GTAGCTG; STRC.ex10, Forward: TGAGGAGCTG CAGAGCCTAGT, Probe: CCTGAGTGATCCAA CG-MGBNFQ, and Reverse: GTCCCATTGGCTG CACATTC; STRC.ex23, Forward: AGAGATGGA GCTCTCAGACTTTG, Probe: AGGACTGCCTG ACATT-MGBNFQ, and Reverse: TTTGCCCATG GCTGCCCGCAG; STRC.in25, Forward: TGCT GAGGTAAAGGTGGACTTACTG, Probe: AGAAG GATCATGAAGGTC-MGBNFQ, and Reverse: AAG GAAGAGATGGCTTCAAATGA; CATSPER2.ex06., Forward: GGCCCACATCTGACATGAGA, Probe: ATGGCCTCTAGAGCAT-MGBNFQ, and Reverse: TGAGCAGCAACATCAAGAGGAA. In addition, the following assay was used as an internal reference: RPP 30, Forward: GATTTGGACCTGCGAGCG, Probe: CTGACCTGAAGGCTCT-MGBNFQ, and Reverse: GCGGCTGTCTCCACAAGT. All STRC and CASTPER2 target probes were labeled with 6-carboxy fluorescein (FAM) while the reference RPP30 probe was labeled with 4,7,2,-trichloro-7,-phenyl-6-carboxyfluo rescein (VIC). All primers and labeled probes were or dered from Integrated DNA Technologies and Life Technologies, respectively.

Data analysis was performed by the QuantaSoft software (Bio-Rad), and the copy number state at each of the STRC and/or pSTRC targets was computed based on the ratio of the concentration of the target (FAM) over the reference (VIC). Copy number calls were made as follows:

Loss = CNV [less than or equal to] 1.5;

No CNV = 1.5-2.5; and

Gain = CNV > 2.5


The OtoGenomeV3 test, performed at the Partners Healthcare Laboratory for Molecular Medicine, involved oligonucleotide-based target capture (Agilent SureSelect) followed by Illumina HiSeq NGS of the coding regions and splice sites of 87 genes associated with nonsyndromic sensorineural hearing loss or syndromes wherein hearing loss was the major presenting feature. This version of the test was updated to include additional probes (n = 79) in the STRC gene and adjacent regions (see Table 2 and Fig. 1 in the online Data Supplement) to support NGS-based copy number calling in this gene. NGS data analysis, quality control, and variant calling was performed as previously described (12).


Copy number analysis of NGS data was performed by use of a previously validated inhouse algorithm, VisCap (9, 10, 12). Raw coverage data across each target interval, including the STRC gene, was obtained by the Depth Of Coverage functionality of GATK. Then, fractional coverage values for each target or exon were calculated by dividing target or exon-level coverage values by the net coverage across all targets [i.e., target fractional coverage = (target coverage)/(sum of all target coverages)]. Next, we calculated the [log.sub.2] ratio of each fractional-coverage value divided by the median across the batch (10 samples). [Log.sub.2] thresholds for gains (0.4) and losses (-0.55) were established based on the analysis of positive control samples. This algorithm was shown to have very high analytical sensitivity, while its analytical specificity was significantly improved through visual scoring (10).



To evaluate the extent to which medically relevant genes with homology issues (n = 193) might require copy number analysis for optimal clinical sensitivity, we estimated the contribution of CNVs to the detection rate in each of the 193 genes. We searched Gene Reviews and documented the percent contribution of CNVs, if available, as well as calculated the proportion of CNVs to the total disease mutations in the Human Gene Mutation Database. Sixty genes had deletions and/or duplications previously reported in patients, and in 40 genes CNVs contributed more than 10% of the all disease-mutation alleles (see Table 1 in the online Data Supplement). This gene number is likely to be an underestimate given that studies to date may not have sufficiently assessed the contribution of CNVs to disease.


STRC encodes the stereocilin protein, a structural component of the sensory hair cells in the inner ear, required for mechanoreception. Biallelic loss of function of the STRC gene is a substantial contributor to nonsyndromic deafness (14, 18-20). Furthermore, a contiguous gene deletion including STRC and an adjacent gene involved in sperm motility, CATSPER2, causes deafness-infertility syndrome in men (23).

STRC is tandemly duplicated, leading to a second nonfunctional copy or pseudogene,pSTRC, with 98.9% homology across the entire locus, 99% identity in the coding sequence, and 100% identity in the first half (exons 1-15) of the STRC gene (14). Therefore, we used the few STRC-unique nucleotides in the second half of the gene, as confirmed by long-range PCR and Sanger sequencing (14), to design an allele-specific ddPCR assay for this gene. A similar approach has been shown to distinguish copy number changes between the 2 highly homologous genes, SMN1 and SMN2 (Bio-Rad).

We tested the performance of the STRC-allele-specific ddPCR assays with 8 different samples known to have 0 (homozygous deletion), 1 (heterozygous deletion), 2 (normal), or 3 (duplication) STRC copies (Table 1). As expected, only the specific assays accurately detected all tested copy number states for STRC (Fig. 1 and Table 1). For example, "4" and "2" copies were detected by the nonspecific, STRC.ex02 and STRC.ex10, assays in the STRC normal and homozygous deletion samples, respectively, demonstrating that these assays were nonspecifically quantifying the 2 copies from each STRC and pSTRC (i.e., 4 total copies). On the other hand, the allele-specific ddPCR assays specifically yielded "2" and "0" STRC copies for the normal and homozygous deletion samples, respectively. It is worth noting that the STRC.ex02 and STRC.ex10 nonspecific assays did not accurately detect the expected "3" and "5" copy number states in the tested heterozygous deletion (2 pSTRC + 1 STRC) and duplication (2 pSTRC + 3 STRC) samples. Instead, 4 total copies were detected in both by the nonspecific assays, most likely suggesting that exons 2 and 10 might not have been part of the STRC deletion or duplication in those 2 samples. Indeed, copy number analysis from the NGS data confirmed this observation (Fig. 2, middle 2 panels). Furthermore, no CATSPER2 copy number change was observed in either sample by the allele-specific ddPCR in this gene (data not shown), consistent with the observation that the nearby first half of the STRC gene (exons 1-15) is not impacted.

In addition to their analytical specificity, the STRC allele-specific ddPCR assays demonstrated high precision across all tested copy number states (Table 1).


Using coverage data from NGS, we and others have recently demonstrated the ability to robustly call CNVs (9, 11, 12, 24). We have also validated a highly sensitive algorithm, VisCap, for clinical diagnostic purposes (10). This algorithm uses normalized fractional coverage data at each NGS-targeted region to infer their copy number states (9, 12).

We therefore redesigned our NGS capture for hearing loss genes (25) to include additional probes (n = 79) targeting the STRC gene and its surrounding region (see Fig. 1 and Table 2 in the online Data Supplement). As shown in Fig. 2, all STRC copy number states (0, 1,2, or 3) were appropriately detected by our NGS-based CNV caller and were consistent with the allele-specific ddPCR calls for the same samples (Fig. 1). As expected, calls in the second half of the gene (exons 16-29) were more specific than those in the first half (exons 1-15). This is evident from the copy number calls for the homozygous deletion (Fig. 2, lowest panel), where normalized coverage in exons 1-15 met the threshold for a single copy deletion while in fact this was due to nonspecific sampling of 2 (pSTRC) out of the 4 total copies (STRC + pSTRC). On the other hand, as best demonstrated in the heterozygous deletion and duplication samples (Fig. 2, middle panels), normalized coverage calls in some, but not all, exons between 16-29 showed a specific STRC pattern with 1 copy number deletion (blue) or duplication (red). Nonetheless, some calls in this region were still nonspecific showing, for example, a 3 out of 4 (or 0.75) copy number call, falling below the threshold for a heterozygous deletion (Fig. 2, third panel from top).

Of note, although NGS-based copy number calling in the first half of the STRC gene (exons 1-15) was not specific, it was still extremely useful in assessing the copy number state in this region where no specific ddPCR assays could be designed due to 100% identity with pSTRC. For example, a heterozygous deletion or duplication in STRC exons 1-15 might lead to 3/4 (0.75) or 5/4 (1.25) copy number states, respectively, while a homozygous deletion in this region will show a 2/4 (0.5) seemingly heterozygous call (Fig. 2, lowest panel). Such calls in those exons would at least trigger additional confirmatory testing, such as gel-based analysis, if clinically warranted.

Finally, additional samples (n = 85) were retrospectively and prospectively tested by VisCap and were concordant with the STRC allele-specific ddPCR assays (Table 2 and data not shown).


With a few exceptions, VisCap calls from the STRC-baited regions are not highly specific, rendering those calls unreliable if from a standalone test. To balance this with the potentially prohibitive cost of performing allele-specific ddPCR on all clinical samples, we devised a new STRC copy number testing algorithm. For all patients with hearing loss tested by our NGS panel, copy number analysis in the STRC gene was performed by VisCap. Follow-up testing with the STRC allele-specific ddPCR was then performed for confirmation of positive findings or for cases in which VisCap fails due to coverage issues (Fig. 3). This approach enabled the highly sensitive analytical detection and confirmation of CNVs in the STRC gene and eliminated the need to run the STRC allele-specific ddPCR on the many negative cases in which neither STRC nor pSTRC had a copy number variant (Fig. 2, top panel).

Contribution of STRC Copy Number Analysis to Hearing Loss Genetic Testing Clinical Sensitivity. Using this new algorithm (Fig. 3), we prospectively tested 517 patients with hearing loss referred to our hearing loss NGS diagnostic panel. In total, 122 of 517 were positive, for an overall diagnostic yield of 23.6%. Of those 122 cases, 31 (25.4%) were positive due to biallelic pathogenic variants, mostly deletions, in the STRC gene (Table 2). In other words, STRC copy number detection increased the overall diagnostic yield by 6%, from 17.6% to 23.6% (Fig. 4).

Most of the STRC-positive cases (30 out of 31, or 97%) had at least 1 deletion, with 75% (23 out of 31) carrying 2 deletions (either homozygous or compound heterozygous). Further, 22% (7/31) of cases had 1 deletion and 1 pathogenic sequencing variant, while only 1 case (3%) was positive due to a homozygous pathogenic sequence variant in the STRC gene (Fig. 4). Finally, of the 31 STRC-positive cases, 27 (87%) had mild or moderate hearing loss, which is characteristic for STRC-related deafness (18). Hearing loss severity was not known in the remaining 4 (13%) cases.

Although VisCap has previously been shown to have high analytical sensitivity, its analytical specificity was improved through visual scoring (10). Interestingly, no false-positive calls in the STRC gene were made in our cohort, and only the 30 STRC true-deletion calls were made (Table 2). To further assess analytical sensitivity at the STRC locus, we randomly chose 50 cases with normal VisCap calls in this gene and tested them with our allele-specific ddPCR assays. All 50 cases were confirmed as true negatives.

In addition to the improved positive rate, our testing approach reduced the need for a laborious and costly parallel copy number analysis method by allele-specific ddPCR or any other method in the 486 STRC-negative cases.


CNVs represent a major component of the pathogenic variant spectrum in several disease genes whose sequences share high homology with other genomic regions (see Table 1 in the online Data Supplement). Although essential for diagnostic yield, copy number analysis in those genes is technically challenging and is often not performed in diagnostic laboratories. On the one hand, despite their potential cost-effectiveness, nonspecific approaches are not reliable, need to have wide dynamic range to detect varied (up to 4 or more) copy number states in the homologous region(s), and require follow-up confirmatory testing. On the other hand, parallel methods are highly desirable but are often complex, costly, and, therefore, not easily scalable. To address this, we developed an affordable, highly accurate, and (most importantly) scalable approach for diagnostic copy number analysis in homologous disease genes.

This approach uses coverage data from NGS to infer copy number states at each targeted region, including the STRC gene used as a proof of concept in this study. This is the first application of a clinical NGS algorithm for copy number detection in a pseudogene. Although not highly specific, this method has very high analytical sensitivity (10, this study) and is cost-effective because it only requires running a bioinformatics script on readily generated sequencing data. A disadvantage of the NGS-based analysis is its potentially limited dynamic range in assessing the copy number state of genes/regions that have multiple copies (more than 2) of highly identical sequences. For STRC, we showed that our NGS algo rithm could detect copy number changes in the 0-4 range for the nonspecific probes (Fig. 2). We have not tested situations in which more than 4 copies might exist; however, with appropriately optimized algorithms, a wide dynamic range can be achieved (26). In addition, using a few allele-specific NGS capture probes, we were able to get specific copy number changes in the second half of the STRC gene (Fig. 2). Most NGS algorithms rely on coverage information, which can have inter- and intra-assay variation, thus leading to failed or equivocal outcomes. In our experience, this can be avoided by automating the NGS library preparation and increasing the reference cohort size. Finally, coverage normalization within a batch (n = 10 in our algorithm) might be affected if multiple cases within that run had similar deletions (for STRC in our case), potentially leading to false-positive or false-negative calls. However, use of the median (and not the mean) makes our method more robust and resistant to multiple occurrences of STRC deletions. In addition, given that STRC contributes approximately 6%-10% of the overall diagnostic yield, it will be rare to have more than 1 patient with STRC findings, and even then, it would be very unlikely that 2 patients with STRC findings would have the exact same exon(s) impacted in 1 run.

The second component of our approach relies on ddPCR for confirmation of NGS CNV calls. We took advantage of the very limited unique STRC nucleotides to develop a novel allele-specific ddPCR assay that we showed to be highly specific and reproducible (Fig. 1 and Table 1). Our choice of the ddPCR method as a targeted confirmatory platform was based on its well-documented high analytical sensitivity and wide dynamic range in detecting CNVs (16) and its relatively easier work flow than other methods. In addition, ddPCR employs the TaqMan chemistry, which uses unique nucleotide mismatches, that is amenable to an allele-specific copy number detection form, as we have shown here. However, other methodologies, such as gel-based assays, multiplex ligation-dependent probe amplification, or competitive PCR (15), can also be used if such techniques are more established in a given laboratory.

A potential disadvantage of the allele-specific ddPCR format is its reliance on unique nucleotides to distinguish the true copy number state in the relevant gene. Some regions, such as STRC exons 1-15, might be 100% identical to genomic sequences elsewhere, obviating the design of specific ddPCR assays therein. Different approaches might be required for specific CNV detection in those regions. Alternatively, allele-specific ddPCR assays in flanking regions might be used. One advantage of our approach is its ability to assess, although nonspecifically, copy number changes in high-identity regions through the NGS-based component (Fig. 2).

In summary, we have developed a reliable, scalable, and affordable approach for accurate copy number detection in medically relevant genes with substantial homology issues. Using the STRC gene, we have shown that this approach balances its high analytical sensitivity with its cost-effectiveness and can markedly improve the diagnostic yield of NGS panels that include highly homologous genes.

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contribution to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:

Employment or Leadership: S.S. Amr, Partners Personalized Medicine Laboratory of Molecular Medicine; H.L. Rehm, Brigham & Women's Hospital.

Consultant or Advisory Role: None declared.

Stock Ownership: None declared.

Honoraria: None declared.

Research Funding: None declared.

Expert Testimony: None declared.

Patents: None declared.

Role of Sponsor: No sponsor was declared.

Acknowledgment: The authors would like to thank all members of the Partners Laboratory for Molecular Medicine.


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Sami S. Amr, [1,2] Elissa Murphy, [1] Elizabeth Duffy, [1] Rojeen Niazi, [4] Jorune Balciuniene, [4] Minjie Luo, [4,5] Heidi L. Rehm, [1,2,3] and Ahmad N. Abou Tayoun [4,5]*

[1] Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, MA; [2] Department of Pathology, Brigham & Women's Hospital and Harvard Medical School, Boston, MA; [3] The Broad Institute of MIT and Harvard, Cambridge, MA; [4] Division of Genomic Diagnostics, The Children's Hospital of Philadelphia, Philadelphia, PA; [5] The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.

* Address correspondence to this author at: 3615 Civic Center Blvd., Abramson Research Building, 716E, Philadelphia, PA 19104. Fax 215-590-2156; e-mail aboutayoua@

Received August 12,2017; accepted December 8,2017.

Previously published online at DOI: 10.1373/clinchem.2017.280685

[6] Nonstandard abbreviations: NGS, next-generation sequencing; SNVs, single-nucleotide variants; CNVs, copy number variants; ddPCR, droplet digital PCR.

[7] Human genes: STRC, stereocilin; PMS2, PMS1 homolog 2, mismatch repair system component; PKD1, polycystin 1, transient receptor potential channel interacting; SMN1, survival of motor neuron 1, telomeric; CATSPER2, cation channel sperm associated 2; SMN1, survival of motor neuron 1, telomeric; SMN2, survival of motor neuron 2, centromeric.

Caption: Fig. 1. STRC allele-specific droplet digital PCR assay design and performance.

Top, an STRC gene schematic showing all exons (1-29) as vertical rectangles. Light pink rectangles represent exons 1-15 with 100% identity to the pseudogene pSTRC, whereas exons 16-29 (light green) contain very few bases that are divergent from pSTRC. Filled pink and green circles represent nonspecific (STRC.ex02 and STRC.ex10) and STRC gene-specific (STRC.ex23 and STRC.in25) probes, respectively. Dashed areas highlight regions with divergent bases (red bars) where the specific probes/primers (middle) were designed. The STRC-specific bases within the probe/primer sequences are shown in red in the middle table. Bottom, unlike the nonspecific assays (light pink), the STRC.ex23 and STRCin25 (green) assays accurately detected all tested copy number states (homozygous deletion or "0" copies, heterozygous deletion or "1" copy, Normalor "2" copies, and duplication or"3" copies) in STRC. Copy number states are calculated based on the ratio of the concentration of the target over the reference in each sample (see Methods). Each bar graph represents copy number average [+ or -] SE (n = 2-8 runs). See also Table 1.

Caption: Fig.2. NGS-based copy number analysis in STRC.

Filled circles represent relative copy number changes, as inferred from [log.sub.2] ratios of the fractional coverage value for each baited region divided by the median for that region across the batch (see Methods). Blue circles represent deletions while red circles represent copy number gains. As in Fig. 1, the 4 samples with the following STRC genotypes were tested (from top to bottom): Normal, duplication, heterozygous deletion, and homozygous deletion. Each row represents data from 1 sample with copy number changes across all chromosomes shown on the left, while a zoomed-in view of the STRC region on chromosome 15 (dashed area) is shown on the right. The x axis displays chromosome numbers (left) or STRC-baited region/exon numbers (right).

Caption: Fig. 3. STRC copy numberanalysis algorithm.

See text for additional information.

Caption: Fig. 4. Contribution of STRC copy number analysis to the clinical sensitivity of hearing loss genetic testing.

Left, breakdown of the overall testing results in a cohort of 517 hearing loss patients. The inconclusive category refers to reports with variants of uncertain significance or one heterozygous pathogenic variant in a recessive gene. Only benign or likely benign variants were detected in the "negative" category. "Positive - Other" refers to reports with positive findings in hearing loss genes other than STRC. "Positive - STRC' refers to positive reports due to biallelic pathogenic variants in STRC. Right, of the total 31 positive cases due to STRC, 15 had homozygous deletions, 8 were compound heterozygous for 2 different deletions, 7 were compound heterozygous for a deletion and an SNV, and only 1 had a homozygous pathogenic SNV. See Table 2 for additional genotype information.
Table 1. Concordance and reproducibility of the STRC.ex23 and
STRC.in25 allele-specific ddPCR STRC assays.

                                          Copy number--Run 1

Sample (a)   Previous genotype            STRC.ex23    STRC.in25

R-1371-RP1   Normal (CN = 2)                 1.80         2.00
R-1371-RP2                                   1.90         1.90
             Mean ([+ or -] SE)             1.850         1.95
                                            (0.05)       (0.05)
R-1369-RP1   Homozygous loss (CN =0)         0.0          0.00
R-1369-RP2                                   0.03         0.00
             Mean ([+ or -] SE)             0.002          0
                                           (0.002)       (0.00)
R-1370-RP1   Heterozygous loss (CN = 1)      1.02         0.99
R-1370-RP2                                   0.97         0.98
             Mean ([+ or -] SE)              0.99         0.99
                                           (0.035)      (0.005)
R-1372-RP1   Duplication (CN = 3)            3.10         3.00
R-1372-RP2                                   2.90         3.00
             Mean ([+ or -] SE)              3.00         3.00
                                            (0.10)       (0.00)
HL20-RP1     Normal (CN = 2)                 1.86         2.03
HL20-RP2     1.8                             1.99         2.08
             Mean ([+ or -] SE)              1.83         2.01
                                            (0.03)       (0.02)
HL21-RP1     Homozygous loss (CN =0)        0.002        0.001
HL21-RP2                                    0.0007       0.0007
             Mean ([+ or -] SE)             0.005         0.00
                                            (0.00)       (0.00)
HL22-RP1     Heterozygous loss (CN = 1)      0.95         0.9
HL22-RP2                                     0.98         0.86
             Mean ([+ or -] SE)              0.97         0.88
                                            (0.01)       (0.02)
HL23-RP1     Duplication (CN =3)             3.18         2.92
HL23-RP2                                     2.91         2.72
             Mean ([+ or -] SE)              3.05         2.82
                                            (0.13)       (0.10)

                                          Copy number--Run 2

Sample (a)   Previous genotype            STRC.ex23    STRC.in25

R-1371-RP1   Normal (CN = 2)                 1.90         2.00
R-1371-RP2                                   2.00         2.10
             Mean ([+ or -] SE)              1.95         2.05
                                            (0.05)       (0.05)
R-1369-RP1   Homozygous loss (CN =0)         0.00         0.00
R-1369-RP2                                   0.00         0.00
             Mean ([+ or -] SE)              0.00         0.00
                                            (0.00)       (0.00)
R-1370-RP1   Heterozygous loss (CN = 1)      0.92         0.95
R-1370-RP2                                   0.94         0.94
             Mean ([+ or -] SE)              0.93         0.95
                                            (0.01)      (0.005)
R-1372-RP1   Duplication (CN = 3)            3.00         3.10
R-1372-RP2                                   2.90         3.00
             Mean ([+ or -] SE)              2.95         3.05
                                            (0.05)       (0.05)
HL20-RP1     Normal (CN = 2)                  2            2
HL20-RP2     1.8                             2.03         2.01
             Mean ([+ or -] SE)              2.04         2.02
                                            (0.04)       (0.01)
HL21-RP1     Homozygous loss (CN =0)          0            0
HL21-RP2                                      0            0
             Mean ([+ or -] SE)               0            0
                                            (0.00)       (0.00)
HL22-RP1     Heterozygous loss (CN = 1)      0.93          1
HL22-RP2                                     0.96         0.97
             Mean ([+ or -] SE)              0.95         0.99
                                            (0.01)       (0.01)
HL23-RP1     Duplication (CN =3)             3.01         3.09
HL23-RP2                                      3           2.93
             Mean ([+ or -] SE)              3.01         3.01
                                            (0.00)       (0.08)

                                          Copy number--Run 3

Sample (a)   Previous genotype            STRC.ex23    STRC.in25

R-1371-RP1   Normal (CN = 2)                 2.10         1.90
R-1371-RP2                                   1.90         2.00
             Mean ([+ or -] SE)              2.00         1.95
                                            (0.10)       (0.05)
R-1369-RP1   Homozygous loss (CN =0)         0.00         0.00
R-1369-RP2                                   0.00         0.00
             Mean ([+ or -] SE)              0.00         0.00
                                            (0.00)       (0.00)
R-1370-RP1   Heterozygous loss (CN = 1)      0.96         0.94
R-1370-RP2                                   0.98         0.96
             Mean ([+ or -] SE)              0.97         0.95
                                            (0.01)       (0.01)
R-1372-RP1   Duplication (CN = 3)            3.00         3.00
R-1372-RP2                                   3.10         3.00
             Mean ([+ or -] SE)              3.05         3.00
                                            (0.05)       (0.00)
HL20-RP1     Normal (CN = 2)                 2.07         2.03
HL20-RP2     1.8                             2.02
             Mean ([+ or -] SE)              2.04         2.03
                                            (0.03)       (0.01)
HL21-RP1     Homozygous loss (CN =0)          0           0.01
HL21-RP2                                      0            0
             Mean ([+ or -] SE)               0          0.001
                                            (0.00)       (0.00)
HL22-RP1     Heterozygous loss (CN = 1)      0.94         0.93
HL22-RP2                                     0.93         0.93
             Mean ([+ or -] SE)              0.93         0.93
                                            (0.00)       (0.00)
HL23-RP1     Duplication (CN =3)             3.37         2.82
HL23-RP2                                     3.12         3.09
             Mean ([+ or -] SE)              3.25         2.95
                                            (0.12)       (0.13)

Sample (a)   Previous genotype            Concordant

R-1371-RP1   Normal (CN = 2)                 Yes
             Mean ([+ or -] SE)

R-1369-RP1   Homozygous loss (CN =0)         Yes
             Mean ([+ or -] SE)

R-1370-RP1   Heterozygous loss (CN = 1)      Yes
             Mean ([+ or -] SE)

R-1372-RP1   Duplication (CN = 3)            Yes
             Mean ([+ or -] SE)

HL20-RP1     Normal (CN = 2)                 Yes
HL20-RP2     1.8
             Mean ([+ or -] SE)

HL21-RP1     Homozygous loss (CN =0)         Yes
             Mean ([+ or -] SE)

HL22-RP1     Heterozygous loss (CN = 1)      Yes
             Mean ([+ or -] SE)

HL23-RP1     Duplication (CN =3)             Yes
             Mean ([+ or -] SE)

(a) In each run, all samples were run as duplicates.

RP = repeat

Table 2. STRC pathogenic alleles in hearing-loss-positive cases.

Allele 1 (a)                               Allele 2 (a)

STRC (ex1-26) and CATSPER2 (ex1-7) del     STRC (ex1-26) and CATSPER2
                                             (ex1-7) del
STRC (ex1-29) del                          STRC (ex1-29) del
STRC (ex16-29) del                         STRC (ex16-29) del
STRC (ex1-26) and CATSPER2 (ex1-7) del     STRC (ex16-29) del
STRC (ex16-29) del                         STRC (ex16-25) del
STRC (ex16-29) del                         STRC (ex1-29) and CATSPER2
                                             (ex1-13) del
STRC (ex1-26) and CATSPER2 (ex1-7) del     c.5188C>T; p.Arg1730*
STRC (ex1-26) and CATSPER2 (ex1-7) del     c.4027C>T; p.Gln1343*
STRC (ex1-26) and CATSPER2 (ex1-7) del     c.3670C>T; p.Arg1224*
STRC (ex1-26) and CATSPER2 (ex1-7) del     c.1086C>A; p.Tyr362*
STRC (ex1-26) and CATSPER2 (ex1-7) del     c.3217C>T; p.Arg1073*
STRC (ex16-29) del                         c.379C>T; p.Arg127*
STRC (ex16-29) del                         c.4701 + 1G>A; p.?
c.3493C>T; p.Gln1165*                      c.3493C>T; p.Gln1165*

Allele 1 (a)                               Number of patients

STRC (ex1-26) and CATSPER2 (ex1-7) del             11

STRC (ex1-29) del                                  1
STRC (ex16-29) del                                 3
STRC (ex1-26) and CATSPER2 (ex1-7) del             6
STRC (ex16-29) del                                 1
STRC (ex16-29) del                                 1

STRC (ex1-26) and CATSPER2 (ex1-7) del             1
STRC (ex1-26) and CATSPER2 (ex1-7) del             1
STRC (ex1-26) and CATSPER2 (ex1-7) del             1
STRC (ex1-26) and CATSPER2 (ex1-7) del             1
STRC (ex1-26) and CATSPER2 (ex1-7) del             1
STRC (ex16-29) del                                 1
STRC (ex16-29) del                                 1
c.3493C>T; p.Gln1165 *                              1

(a) Minimum CNV breakpoints are displayed.
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Title Annotation:Molecular Diagnostics and Genetics
Author:Amr, Sami S.; Murphy, Elissa; Duffy, Elizabeth; Niazi, Rojeen; Balciuniene, Jorune; Luo, Minjie; Reh
Publication:Clinical Chemistry
Date:Apr 1, 2018
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