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A custom ddPCR method for the detection of copy number variations in the nebulin triplicate region

  • Lydia Sagath ,

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing, (LS); (KK)

    Affiliations Folkhälsan Research Center, Helsinki, Finland, Department of Medical Genetics, Medicum, University of Helsinki, Helsinki, Finland

  • Vilma-Lotta Lehtokari,

    Roles Conceptualization, Project administration, Writing – review & editing

    Affiliations Folkhälsan Research Center, Helsinki, Finland, Department of Medical Genetics, Medicum, University of Helsinki, Helsinki, Finland

  • Carina Wallgren-Pettersson,

    Roles Funding acquisition, Project administration, Writing – review & editing

    Affiliations Folkhälsan Research Center, Helsinki, Finland, Department of Medical Genetics, Medicum, University of Helsinki, Helsinki, Finland

  • Katarina Pelin,

    Roles Funding acquisition, Project administration, Writing – review & editing

    Affiliations Folkhälsan Research Center, Helsinki, Finland, Department of Medical Genetics, Medicum, University of Helsinki, Helsinki, Finland, Molecular and Integrative Biosciences Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland

  • Kirsi Kiiski

    Roles Conceptualization, Project administration, Supervision, Writing – original draft, Writing – review & editing, (LS); (KK)

    Affiliations Folkhälsan Research Center, Helsinki, Finland, Department of Medical Genetics, Medicum, University of Helsinki, Helsinki, Finland


The human genome contains repetitive regions, such as segmental duplications, known to be prone to copy number variation. Segmental duplications are highly identical and homologous sequences, posing a specific challenge for most mutation detection methods. The giant nebulin gene is expressed in skeletal muscle. It harbors a large segmental duplication region composed of eight exons repeated three times, the so-called triplicate region. Mutations in nebulin are known to cause nemaline myopathy and other congenital myopathies. Using our custom targeted Comparative Genomic Hybridization arrays, we have previously shown that copy number variations in the nebulin triplicate region are pathogenic when the copy number of the segmental duplication block deviates two or more copies from the normal number, which is three per allele. To complement our Comparative Genomic Hybridization arrays, we have established a custom Droplet Digital PCR method for the detection of copy number variations within the nebulin triplicate region. The custom Droplet Digital PCR assays allow sensitive, rapid, high-throughput, and cost-effective detection of copy number variations within this region and is ready for implementation a screening method for disease-causing copy number variations of the nebulin triplicate region. We suggest that Droplet Digital PCR may also be used in the study and diagnostics of other segmental duplication regions of the genome.

1. Introduction

Approximately 5% of the human genome is composed of segmental duplications (SD)—sequences of 10–300kb in size, repeated at least two times in the genome. Repetitive regions of the genome are particularly prone to copy number variations (CNVs) [14].

The giant sarcomeric gene nebulin (NEB, MIM ID *161650) is located in the chromosomal region 2q23.3. It spans over 249 kb of genomic region and has 183 exons. Mutations in NEB are known to cause nemaline myopathy (NM; MIM IDs: NEM2 #256030) and other congenital myopathies. NEB-caused NM is inherited mainly in an autosomal recessive fashion, but lately, rare dominant mutations in the form of large deletions have also been described [5, 6]

In its mid-region, NEB harbors a 30 kb SD where eight exons are repeated three times (exons 82–89, 90–97, 98–105). Each 8-exon-block spans 10 kb of genomic region, and the repeats are highly similar between themselves [7]. The ratio of the 8-exon-block compared with the diploid genome is thus 3:1, and the block has been named the NEB TRI region. This region has been shown to harbor normal as well as pathogenic copy number variation. It has been shown that a single allele tolerates the gain or loss of one but not several repeated blocks [8].

Array Comparative Genomic Hybridization (aCGH) still constitutes the gold standard method for CNV analysis, although massively parallel sequencing-based methods are continuously being developed and are rapidly improving in both accuracy and reliability [9]. In CNV detection, SD regions remain a challenge for both aCGH and sequencing-based methods because of their repetitive nature. When composing arrays, it is usually impossible to design unique probes to target these regions, which are often left without probes. Furthermore, their repetitive nature tests both amplification steps and alignment in sequencing-based methods.

In an attempt to investigate both the NEB TRI region and a similar, yet shorter, repetitive region in another sarcomeric giant, titin (TTN, MIM ID *188840), we have previously designed and published validated custom tiling array designs for the detection of CNVs in nemaline myopathy and other neuromuscular disorders. These include the SD regions also (the NM- and NMD-CGH-arrays) [10, 11]. To date, running more than 300 DNA samples from families with persons affected by neuromuscular disorders on the NM- and NMD-arrays, we have identified CNVs in the NEB TRI region in altogether 13%. In a third of these, the CNVs have been interpreted as being pathogenic.

Although the custom arrays yield a high sensitivity in SD CNV detection, very high copy numbers (CN) of the NEB TRI region are difficult to determine precisely due to the log2-based mathematical model commonly used in aCGH data analysis [12]. This situation may be compared with attempting to estimate the grade of mosaicism of a CNV in a sample. The aCGH method has indeed been used in such studies, even though this is not its original intended use [6, 13, 14].

Furthermore, the custom arrays come at a relatively high cost per sample and a turn-around time of roughly three days. The optimal DNA requirements are high: 1,000 ng of good-quality genomic DNA at a concentration of more than 55 ng/μl is needed per sample per run. Especially in cases where one myopathy-causing mutation in NEB has been identified, and a CNV in the NEB TRI region is suspected to be the other causative mutation, running a custom CGH-array is excessively expensive and time-consuming. Most importantly, setting up a custom ddPCR assay is reasonably straightforward, especially if the required apparatus is already available—which is more likely, as ddPCR is more versatile a method than array-CGH.

Droplet Digital PCR (ddPCR) allows for the precise quantification of specific amplified nucleic acid molecules. Each reaction is partitioned into a theoretical optimum of 20,000 droplets, allowing for independent amplification of target molecules. When combined with target-specific fluorochrome-labeled probes, the fluorescence of each droplet can be read and quantified, and rare DNA target copies can be detected with high sensitivity. Using Poisson statistics and a reference gene of known ploidy, the ratio between target molecules and reference molecules allows for the determination of the CN of the target nucleic acid molecule.

The ddPCR method is helpful for a multitude of purposes, e.g., gene expression studies, mutation and gene edit detection, residual DNA detection, and CNV detection. Digital PCR systems are available from several manufacturers, but the principle of the method remains the same. Designing custom ddPCR assays or acquiring validated assays is easier and more cost-effective than designing or gaining access to custom CGH-arrays. Furthermore, both the running cost and the DNA mass requirement of one sample is roughly 1/100 of a sample run on a custom CGH-array, and the turn-around-time is cut to one-third.

Within the context of neuromuscular disorders, ddPCR has been used in only a few studies. In spinal muscular atrophy patients, the method has been used to determine SMN1 and SMN2 exon 7 copies [15]. In addition, while it appears not to have been used for CN determination of dystrophin in Duchenne muscular dystrophy patients, it has been used in the quantification of exon skipping in the development of antisense oligonucleotide therapy [16]. Furthermore, it has been used in CNV analysis of the BRCA1 gene in high-grade serous ovarian cancer tissue samples and presented as a promising method for diagnostics [17]. To our knowledge, no papers have been published reporting CNV analysis of intragenic segmental duplication regions using ddPCR.

Here, we present our NEB TRI targeted ddPCR-based screening method as a precise, sensitive, high-throughput, and cost-effective method for the CNV analysis of the NEB TRI region and as an example of how ddPCR can be used as an analysis tool for the CN determination of SD regions.

2. Materials and methods

2.1. Samples

Altogether 130 DNA samples were acquired for this study. Of these, 26 were healthy controls from the Fondation Jean Dausset-CEPH, and 20 were healthy controls from the Finnish Red Cross Blood Service. In addition, we used 84 DNA samples from our sample collection of congenital myopathy patients and their family members. The NEB TRI CN of all samples had previously been determined using the NM- or NMD-arrays [10, 11].

The study was approved (approval number 6/E7/05) by the Ethics Committee of the Children’s Hospital, University of Helsinki, Finland. The approval was renewed by the Ethics Review Board of Helsinki University Hospital in 2021. Samples were obtained according to the Declaration of Helsinki of 1975, and written consent was obtained from subjects, or data were analyzed anonymously where appropriate.

The patient samples used were heterogeneous in terms of clinical diagnosis, ethnic background, and age. Furthermore, some DNA samples had been received extracted while some were extracted in our laboratory. The DNA stocks were extracted from leukocytes or saliva, eluted into EDTA, TE-buffer, or water, and stored at -20°C or -80°C. The DNA concentration and quality were measured with DeNovix DS-11 FX+ Spectrophotometer/Fluorometer (DeNovix Inc., Wilmington, DE, USA). Subsequent dilutions for the ddPCR reactions were done in sterile water in appropriate series.

2.2. Microarray design, protocol, and data analysis

All samples had previously been run on the NM-CGH-array or the NMD-CGH-array as previously described [10, 11].

To avoid any subtle differences caused by the genome-wide normalization of the analysis software (CytoSure Interpret Software v.4.11.30, Oxford Gene Technology Ltd), the previously acquired aCGH data for NEB was further manually aligned to gain a baseline of zero. The log2 value for the NEB TRI region and large portions of the NEB gene on either side of the NEB TRI were extracted from the aCGH data. The breakpoints used for normalization were Chr2:(152340824_152341131)_(152432955_152433349) and Chr2:(152465657_152465735)_(152592449_152583346). The NEB TRI region used to calculate the normalized CN was Chr2:(152433598_152434494)_(152465223_152465448). The genomic locations are given in the reference genome Hg19/GRCh37. The normalized log2 value of the NEB TRI region was acquired by subtracting the averaged background log2 value from the NEB TRI region. This value was then used to calculate the estimated CN by assuming a normal CN of 6 and compared with the previously published log2 values for different NEB TRI CNs (Table 1 in [8]).

2.3. Digital droplet PCR

The ddPCR assays were designed, performed, and analyzed according to the updated dMIQE guidelines [18, 19]. The dMIQE checklist is available in S1 Table.

2.3.1. Primer and probe design.

Two custom assays to target two different exons of the eight-exon-block of the NEB TRI were designed. The first assay targets the fourth exon of the NEB TRI region, corresponding to NEB exons 85/93/101, referred to from now on as exon IV. The second assay targets the last exon of the NEB TRI region, corresponding to NEB exons 89/97/105, referred to from now on as exon VIII (Fig 1). The target selection was limited by unique sequences, sequence GC% and BsuRI restriction site locations.

Fig 1. A schematic of the exons in the NEB TRI region.

The exons targeted by the ddPCR assays are marked with an asterisk (*).

Primers and probes for the assays were designed using Primer3Plus ( as per the manufacturer’s suggestions. The primers designed had a melting temperature (Tm) of 55.8–57.5°C (calculated by the nearest-neighbor method), a primer concentration of 300 nM, and a salt concentration of 50 nM), a GC content of 50%, and a length of 20 bp. The amplicons were not allowed to contain the BsuRI cut site sequence GGCC. Amplicon lengths were 104 and 169 bp for exons IV and VIII, respectively.

Hydrolysis probes were designed to have a Tm of approximately 65°C and a GC content of 55–57%. Custom probes were labeled with fluorescein amitide (FAM).

The specificity of primers and probes were verified by the Standard Nucleotide BLAST blastn suite (, allowing three hits for both assays. A commercial PrimePCR ddPCR Copy Number Assay for human EIF2C1 labeled with hexachloro-fluorescein (HEX) (Cat. No. 10031243, Bio-Rad Laboratories Inc., Hercules, CA, USA) was used as a reference. EIF2C1 is a diploid gene located on 1p34.3, and also known as Argonaute 1 (AGO1).

The probes were manufactured by Bio-Rad Laboratories Inc. All primer and probe sequences along with amplicon lengths are presented in S2 Table. The custom assays were ordered at a primer-probe ratio of 3.6:1 per assay.

2.3.2. Assay optimization.

The optimal melting temperature for the assays was determined by replacing the annealing step in the ddPCR cycling program with a thermal gradient (temperatures 55.2°C, 58.0°C, 60.0°C, 60.5°C and 62.3°C) for 1 minute. A melting temperature of 60.0°C was chosen, as it allowed equally good separation between droplet clusters in both assays.

The quantity of DNA was scaled down to 10 ng/reaction as per the manufacturer’s suggestion.

2.3.3. No-template controls.

In all runs, individual no-template controls (NTCs) with water were included for both assays in each run. The NTCs were placed in the last wells of each run plate.

2.3.4. Assay protocol.

For each well, a 20 μl reaction was prepared, containing 10 μl 2x Bio-Rad ddPCR Supermix for Probes (No dUTP), 1 μl 20x custom assay, 1 μl PrimePCR ddPCR Copy Number Assay for EIF2C1, 1 μl BsuRI (Thermo Scientific, Waltham, MA, USA) diluted 1:1 in Fast Digest buffer (Thermo Scientific), and 10 ng of DNA template diluted in 7 μl of sterile water. The final concentration of primers and probes was 900 nM and 450 nM, respectively.

For droplet generation, 20 μl of reaction mixture and 70 μl of Bio-Rad Droplet Generation Oil for Probes were pipetted onto a DG8 cartridge and covered by a DG8 gasket in a DG8 Cartridge Holder. Droplets were generated using the Droplet Generator QX200 (Bio-Rad Laboratories Inc). The droplets were then transferred to ddPCR 96-well plates (Bio-Rad Laboratories Inc.) and sealed with the PX1 PCR Plate Sealer (Bio-Rad Laboratories Inc.). The PCR reaction was performed using the DNA Engine Tetrad 2 Thermal Cycler (Bio-Rad Laboratories Inc) with the ramp rate set to 2°C/sec. The samples were cycled as follows: 95°C 10 minutes; 40 cycles of 94°C 30 seconds, 60°C 1 minute; 98°C 10 minutes; 4°C hold. On each plate, a no-template control for each assay was included. Droplet fluorescence was measured using the QX200 Droplet Reader (Bio-Rad Laboratories Inc) and the QuantaSoft Analysis v. software.

All reagent and consumable manufacturer information and catalog numbers are presented in S3 Table.

2.4. Data analysis

2.4.1. Analysis of raw data.

The samples were primarily analyzed on the QuantaSoft Analysis v. and QuantaSoft Analysis Pro v. 1.0.596 (Bio-Rad Laboratories Inc.) softwares.

Intensity thresholds for droplets were set manually per plate, assay, and channel. Each droplet was assigned to a group based on their intensities of the two detection channels: FAM positive-HEX positive, FAM positive-HEX negative, FAM negative-HEX positive, and FAM negative-HEX negative. Assuming Poisson distribution, a ratio between the target and reference channel was automatically calculated by the software. The NEB TRI CN was estimated by doubling the thus estimated ratio of assay target to diploid reference.

2.4.2. Filtering of raw data.

Droplet data were extracted from the QuantaSoft Analysis software and imported into Microsoft Excel.

The data were filtered by removing runs in which any single droplet cluster had 100 or fewer accepted droplets and runs in which the total accepted droplet count was 10,000 or less. Runs on whole-genome amplified DNA (n = 4), samples from patients with known mosaicism in NEB (n = 1), samples with other CNVs in NEB (n = 9), samples run successfully in only one of the assays (n = 15), and samples with no successful runs in either assay were excluded. After the filtering steps, results from 98 independent samples remained, with 176 and 162 data rows for NEB IV and NEB VIII, respectively. Of these 98 samples, 39 were controls and 59 were from neuromuscular disorder patients and healthy family members thereof. The filtered data were extracted as comma-separated value (CSV) files. Subsequent statistical analyses were performed in RStudio v.1.3.959.

2.4.3. Assessment of intra-assay and inter-assay variability.

Intra-assay analyses were conducted to assess reproducibility within experiments. The analyses were performed separately for the two custom ddPCR assays (NEB IV n = 8, NEB VIII n = 9) using duplicates run within the same experiment.

Inter-assay analyses were conducted to assess repeatability between experiments. The analyses were performed separately for the two custom ddPCR assays (NEB IV n = 60, NEB VIII n = 42). In cases where a sample had been run more than twice in separate experiments or had an intra-assay duplicate, we used the two runs with the highest accepted droplet counts from two separate experiments.

2.4.4. Statistical analysis.

To determine the concordance between the ddPCR and aCGH results, we used linear regression analysis, the Pearson correlation coefficient, and a weighted κ-analysis [20]. Unweighted κ-analysis was used to estimate the level at which the ddPCR assays were able to distinguish between pathogenic and benign CNVs. Linear regression analysis and the Pearson correlation coefficient were also used to evaluate the concordance between the two ddPCR assays.

For the κ-analysis, we classified the degree of agreement as 0.81–1.0, almost perfect agreement; 0.61–0.80, substantial agreement; 0.41–0.60, moderate agreement; 0.21–0.40, fair agreement; and <0.20, slight agreement.

The weighted κ-analysis was performed for both assays for the concordance between the aCGH and ddPCR results using the assigned aCGH CN and the ddPCR CN estimate rounded up or down to the nearest integer. The range, mean, standard deviation (σ), and coefficient of variation in percent (%CV) were extracted and calculated for each CN group for aCGH and the two ddPCR assays separately.

Statistical analysis was executed in RStudio v.1.3.959 using R v.4.0.4 and Microsoft Excel.

3. Results

3.1. Data overview

Custom ddPCR assays were run on n = 130 samples. After filtering, 98 samples remained for analysis. In these samples, the NEB TRI CN ranged from 5 to 14, lacking samples representing CNs of 12 and 13 due to unavailability. The sample numbers per NEB TRI CN are presented in Table 1.

Table 1. Number and percentage of samples representing different NEB TRI CNs.

The NEB TRI CN of 6 is considered the normal CN, with 5 and 7 being benign CNVs. A deviation of two or more NEB TRI blocks is considered a pathogenic CNV.

Example 2D droplet plots from successful samples and NTCs are provided in S1 Fig.

The mean accepted droplet count was 12,974 (σ 1,701.2, %CV 13.1) for the NEB TRI exon IV assay and 13,285 (σ 1,434.9, %CV 10.8) for the NEB TRI exon VIII assay, indicating a reasonably consistent quality in the reaction set up. The droplet counts for both clusters and all accepted droplets are presented in S4 Table. The mean, σ, and %CV values for copies per partition are presented in S5 Table.

The estimated ddPCR CNs are shown plotted against the aCGH-determined NEB TRI CN in Fig 2.

Fig 2. Boxplots visualizing the CN of the NEB TRI exon IV and VIII assays in relation to the CN assigned by aCGH with linear regression trend line.

The NEB TRI exon IV assay gives a relationship with a lower linear slope than the NEB TRI exon VIII assay, which seems to follow a 1:1 linear relationship adequately. The dashed lines represent the linear regression trend lines.

The minimum, maximum, and mean values for the NEB TRI CN given by the aCGH results and the two ddPCR assays are presented in S6 Table. The average %CV for the NM- and NMD-CGH-arrays was 2.86. For the ddPCR assays, the %CV was 12.14 and 9.22 for the NEB TRI exon IV and NEB TRI exon VIII assays, respectively.

3.2. Intra-assay analysis

The intra-assay duplicate means, standard deviations, and %CV were calculated, along with an average %CV for the complete assay to assess reproducibility. The intra-assay %CV mean for NEB TRI exon IV (n = 8) was 3.8, and the intra-assay %CV mean for NEB TRI exon VIII (n = 9) was 4.1. The intra-assay analysis is presented in S7 Table.

3.3. Inter-assay analysis

The inter-assay duplicate means, standard deviations, and %CV was calculated, along with an average %CV for the complete assay to assess repeatability. The inter-assay %CV mean for NEB TRI exon IV (n = 60) was 19.3, and the inter-assay %CV mean for NEB TRI exon VIII (n = 42) was 5.1. The inter-assay analysis is presented in S8 Table.

3.4. Pearson correlation coefficient

The Pearson coefficient for the NEB TRI exon IV assay against the aCGH-determined CN was 0.898. The corresponding Pearson coefficient for the NEB TRI exon VIII assay was 0.957. Additionally, we calculated a Pearson coefficient to compare the two ddPCR assays with each other, yielding a value of 0.910.

3.5. Linear regression

The linear regression model for the NEB TRI exon IV assay yielded an estimate of 1.004, a multiple R2 value of 0.807, and an adjusted R2 value of 0.805 (p < 0.0001). The linear regression model for NEB TRI exon VIII yielded an estimate of 1.115, a multiple R2 value of 0.917, and an adjusted R2 value of 0.916 (p < 0.0001). The complete linear regression analysis results are presented in S9 Table.

3.6. Kappa analysis for copy number variation detection comparison

The κ-value for the NEB TRI exon IV assay in detection of CN was 0.558, indicating moderate agreement (p < 0.00001) between aCGH and the NEB TRI exon IV assay. The κ-value for the NEB exon VIII assay in detection of CN was 0.778, indicating substantial agreement (p < 0.00001) between aCGH and the NEB TRI exon VIII assay. The Kappa tables are available in S10 Table.

3.7. Kappa analysis for detection of pathogenicity comparison

The κ-value for the NEB TRI exon IV assay in detection of pathogenicity was 0.388, indicating a fair agreement (p < 0.0005) between aCGH and the NEB TRI exon IV assay. The κ-value for the NEB TRI exon VIII assay in detection of pathogenicity was 0.774, indicating a substantial agreement (p < 0.00001). The Kappa tables are available in Table 2, with additional statistics in S11 Table.

Table 2. The Kappa tables for detection of pathogenicity of assays NEB TRI exon IV and exon VIII.

4. Discussion

Our results show that the ddPCR method is a viable option for the detection of CNVs of the NEB TRI region. To date, only the NMD-CGH-array and other custom CGH-arrays covering the regions have been reliable methods in CNV analysis of the region. Methods based on algorithms applied to massively parallel sequencing data may detect these variations, but exact CN determination cannot be done reliably. The aim of the study was to develop a method complementary to aCGH to be used for NEB TRI CNV screening because, in comparison, the running of a custom-aCGH is both rather costly and time-consuming. As ddPCR is more approachable than aCGH, our custom method allows for anyone with the necessary equipment to adopt the method for NEB TRI CNV screening.

Critical evaluation of the data showed that extreme outliers passing the droplet number cutoffs in the analysis pipeline all had reasonable explanations. Among these were factors such as known mosaicism for a nebulin deletion, the sample having been whole-genome amplified, or the DNA otherwise being of low quality, all reflected in the corresponding aCGH data also.

Our data demonstrate the importance of verification in the development of new ddPCR assays. Although our two assays were designed to function in the same PCR conditions, there was a substantial difference between them in terms of accuracy. The NEB TRI exon IV assay consistently gave lower CN estimates than expected, while the NEB TRI exon VIII assay gave good estimates and showed substantial agreement with aCGH results, both in terms of CN determination and identification of pathogenic CNs. Hence, we believe our assay for NEB TRI exon VIII is representative. We appreciate the fact that the designed assays only cover representative portions of the NEB SD region and that multiple assays throughout the region would increase their accuracy. However, the design of suitable assays is limited by amplicon size and the repetitive nature of the region.

Based on our experience, different DNA extraction methods, the quality of the DNA, and possible inhibitors of the amplification reaction likely affect ddPCR assays. Access lacking to extensive sample collections of DNA extracted locally and stored identically, it is difficult to draw any conclusions as to which factors may affect the results marginally and which may affect it significantly. This further emphasizes the importance of correct assay optimization and adherence to the dMIQE guidelines [18, 19] to the highest possible degree of reproducibility and repeatability.

Furthermore, manual handling such as pipetting differences between performers may also affect the result. Full automation of the ddPCR assay procedure would be necessary to implement the SD-specific method in a diagnostic setting and would most likely improve both repeatability and reproducibility. The ddPCR method is already being implemented in a diagnostic setting, and these automated ddPCR systems are finding their way into service laboratories. We believe that the usage of ddPCR in various types of diagnostics will eventually replace some of the methods currently in use; one such example is ddPCR replacing MLPA in the detection of single-exon deletions of BRCA1 [17].

One of the benefits of ddPCR compared with aCGH is the usage of an internal reference gene instead of a complete reference genome. Selecting a reference genome for aCGH to suit the needs for the CNV analysis of regions in which benign CNVs are common, such as the NEB TRI region, warrants carefulness. Since aCGH is a genome-wide comparative method, a reference with a known CN of any region of interest is always needed for drawing conclusions, and the task becomes even more challenging if one is interested in more than one such region. For this specific problem, ddPCR offers a more clear-cut solution: the CN is determined by comparing the repetitive region with an intragenomic diploid region.

To our knowledge, and based on our aCGH data, no cases in which the NEB SD harbors a CNV of partial repetitive blocks have been recorded. We have, however, identified CNVs spanning one NEB TRI block and adjacent regions of the SD [5, 21]. In addition, we have identified one case of a large mosaic CNV spanning the entire SD region in addition to both upstream and downstream flanking regions, resulting in a short transcript [6]. The ddPCR method presented here is thus intended as a first-tier screening method—any findings should be investigated by another validated method, such as the NM- or NMD-CGH arrays (or equivalent), to exclude the possibility of a larger CNV extending beyond the NEB TRI region. The last introns of each of the NEB TRI region blocks contain Alu and LINE repeats, known to contribute to non-allelic homologous recombination. The hypothesis is that these repeats initially contributed to the emergence of the NEB TRI region in humans [22] and that they, at the same time, contribute to the susceptibility of the region for CNVs. The relatively high proportion of CNVs observed in the NEB TRI region would implicate that these repetitive elements and their intricate replication contribute to the high prevalence of aberrations in NEB. The knowledge of CNVs being prevalent in the NEB TRI region warrants a search for their presence in other intragenic SDs.

The size of nebulin has been shown to correlate with thin filament and sarcomere lengths, and there seem to be limits for optimal nebulin size within any given species [23]. According to the Ruler Hypothesis [24, 25], large enough gains in CN in the NEB TRI region may be pathogenic. We have shown that gains of two or more blocks of NEB TRI in one allele segregate with disease and are absent in control samples; therefore, such gains are considered pathogenic [8]. A gain or loss of two or more blocks would significantly lengthen or shorten the nebulin polypeptide, which would impair optimal and energy-efficient force production [23]. While our custom CGH-arrays effectively determine the CN in the NEB TRI region, they are comparatively expensive and laborious compared with the now established ddPCR assay. CNV detection algorithms applied to massively parallel sequencing data can sensitively detect variation in SD regions. However, they may not precisely and effectively determine the exact CN of the repeated blocks. In the case of the NEB TRI, exact CN determination is crucial, as a one copy gain is considered benign, but a two-copy gain pathogenic.

Our custom ddPCR assays can be used to screen large sample numbers for NEB TRI CNVs, especially in samples with one identified mutation in NEB and samples from families in which a NEB TRI CNV has been found. We also suggest that the method may be applicable to other segmental duplications and similar intragenic SDs regions of the genome and approach the subject in our ongoing research.

Supporting information

S2 Table. Primer and probe sequences and amplicon lenghts and details.


S3 Table. Reagent and consumables catalog numbers and manufacturer details.


S5 Table. Molecule copies per partition statistics.


S6 Table. The range, mean, standard deviation (σ), and coefficient of variation in percent (%CV) of values represented by their NEB TRI CN determined by aCGH and the NEB TRI exon IV and exon VIII ddPCR assays.


S10 Table. Cohen’s kappa table and values for detection of copy number comparison between ddPCR and aCGH.


S11 Table. Additional Cohen’s kappa statistics for the detection of pathogenicity comparison between ddPCR and aCGH.


S1 Fig. Example 2D droplet intensity plots of NEB TRI exon IV and exon VIII assays on patient DNA and no-template controls as extracted from the Quantasoft Analysis Pro software.



We thank Mari Ainola, Vesa-Petteri Kouri, and Soili Kytölä for their help and expertise in the setting up the method, and the Musculoskeletal and Inflammation Research Group (TULES-Group) for providing access to the ddPCR apparatus. We thank Marilotta Turunen for excellent technical assistance and Saara Tegelberg for valuable comments on the manuscript.


  1. 1. Bailey JA, Yavor AM, Massa HF, Trask BJ, Eichler EE. Segmental duplications: organization and impact within the current human genome project assembly. Genome Res. 2001;11(6):1005–17. pmid:11381028
  2. 2. Eichler EE. Recent duplication, domain accretion and the dynamic mutation of the human genome. Trends Genet. 2001;17(11):661–9. pmid:11672867
  3. 3. Pu L, Lin Y, Pevzner PA. Detection and analysis of ancient segmental duplications in mammalian genomes. Genome res. 2018;28(6):901–9. pmid:29735604
  4. 4. Bailey JA, Gu Z, Clark RA, Reinert K, Samonte RV, Schwartz S, et al. Recent segmental duplications in the human genome. Science. 2002;297(5583):1003–7. pmid:12169732
  5. 5. Kiiski KJ, Lehtokari VL, Vihola AK, Laitila JM, Huovinen S, Sagath LJ, et al. Dominantly inherited distal nemaline/cap myopathy caused by a large deletion in the nebulin gene. Neuromuscul Disord. 2019;29(2):97–107. pmid:30679003
  6. 6. Sagath L, Lehtokari VL, Välipakka S, Vihola AK, Gardberg M, Hackman P, et al. Congenital asymmetric distal myopathy with hemifacial weakness caused by a heterozygous large de novo mosaic deletion in nebulin. Neuromuscular disorders: NMD. 2021. pmid:33933294
  7. 7. Donner K, Sandbacka M, Lehtokari VL, Wallgren-Pettersson C, Pelin K. Complete genomic structure of the human nebulin gene and identification of alternatively spliced transcripts. Eur J Hum Genet. 2004;12(9):744–51. pmid:15266303
  8. 8. Kiiski K, Lehtokari VL, Löytynoja A, Ahlstén L, Laitila J, Wallgren-Pettersson C, et al. A recurrent copy number variation of the NEB triplicate region: only revealed by the targeted nemaline myopathy CGH array. Eur J Hum Genet. 2016;24(4):574–80. pmid:26197980
  9. 9. Välipakka S, Savarese M, Johari M, Sagath L, Arumilli M, Kiiski K, et al. Copy number variation analysis increases the diagnostic yield in muscle diseases. Neurol Genet. 2017;3(6):e204. pmid:30238059
  10. 10. Kiiski K, Laari L, Lehtokari VL, Lunkka-Hytönen M, Angelini C, Petty R, et al. Targeted array comparative genomic hybridization–a new diagnostic tool for the detection of large copy number variations in nemaline myopathy-causing genes. Neuromuscul Disord. 2013;23(1):56–65. pmid:23010307
  11. 11. Sagath L, Lehtokari VL, Välipakka S, Udd B, Wallgren-Pettersson C, Pelin K, et al. An Extended Targeted Copy Number Variation Detection Array Including 187 Genes for the Diagnostics of Neuromuscular Disorders. J Neuromuscul Dis. 2018;5(3):307–14. pmid:30040739
  12. 12. Yin XL, Li J. Detecting copy number variations from array CGH data based on a conditional random field model. J Bioinform Comput Biol. 2010;8(2):295–314. pmid:20401947
  13. 13. Cheung SW, Shaw CA, Scott DA, Patel A, Sahoo T, Bacino CA, et al. Microarray-based CGH detects chromosomal mosaicism not revealed by conventional cytogenetics. Am J Med Genet. 2007;143a(15):1679–86. pmid:17607705
  14. 14. Pham J, Shaw C, Pursley A, Hixson P, Sampath S, Roney E, et al. Somatic mosaicism detected by exon-targeted, high-resolution aCGH in 10,362 consecutive cases. Eur J Hum Genet. 2014;22(8):969–78. pmid:24398791
  15. 15. Park S, Lee H, Shin S, Lee ST, Lee KA, Choi JR. Analytical validation of the droplet digital PCR assay for diagnosis of spinal muscular atrophy. Clin Chim Acta. 2020;510:787–9. pmid:32956702
  16. 16. Verheul RC, van Deutekom JC, Datson NA. Digital Droplet PCR for the Absolute Quantification of Exon Skipping Induced by Antisense Oligonucleotides in (Pre-)Clinical Development for Duchenne Muscular Dystrophy. PLoS ONE. 2016;11(9):e0162467. pmid:27612288
  17. 17. De Paolis E, De Bonis M, Concolino P, Piermattei A, Fagotti A, Urbani A, et al. Droplet digital PCR for large genomic rearrangements detection: A promising strategy in tissue BRCA1 testing. Clin Chim Acta. 2021;513:17–24. pmid:33301768
  18. 18. The dMIQE Group, Huggett JF. The Digital MIQE Guidelines Update: Minimum Information for Publication of Quantitative Digital PCR Experiments for 2020. Clin Chem. 2020;66(8):1012–29.
  19. 19. Huggett JF, Foy CA, Benes V, Emslie K, Garson JA, Haynes R, et al. The digital MIQE guidelines: Minimum Information for Publication of Quantitative Digital PCR Experiments. Clin chem. 2013;59(6):892–902. pmid:23570709
  20. 20. Cohen J. Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bull. 1968;70(4):213–20. pmid:19673146
  21. 21. Lehtokari VL, Kiiski K, Sandaradura SA, Laporte J, Repo P, Frey JA, et al. Mutation update: the spectra of nebulin variants and associated myopathies. Human mutat. 2014;35(12):1418–26. pmid:25205138
  22. 22. Björklund AK, Light S, Sagit R, Elofsson A. Nebulin: a study of protein repeat evolution. J Mol Biol. 2010;402(1):38–51. pmid:20643138
  23. 23. Gohlke J, Tonino P, Lindqvist J, Smith JE, Granzier H. The number of Z-repeats and super-repeats in nebulin greatly varies across vertebrates and scales with animal size. J Gen Physiol. 2021;153(3). pmid:33337482
  24. 24. Wang K. Titin/connectin and nebulin: giant protein rulers of muscle structure and function. Adv Biophys. 1996;33:123–34. pmid:8922107
  25. 25. Tskhovrebova L, Trinick J. Titin and Nebulin in Thick and Thin Filament Length Regulation. Subcell Biochem. 2017;82:285–318. pmid:28101866