Skip to main content

GRAde: a long-read sequencing approach to efficiently identifying the CYP11B1/CYP11B2 chimeric form in patients with glucocorticoid-remediable aldosteronism



Glucocorticoid-remediable aldosteronism (GRA) is a form of heritable hypertension caused by a chimeric fusion resulting from unequal crossing over between 11β‐hydroxylase (CYP11B1) and aldosterone synthase (CYP11B2), which are two genes with similar sequences. Different crossover patterns of the CYP11B1 and CYP11B2 chimeric genes may be associated with a variety of clinical presentations. It is therefore necessary to develop an efficient approach for identifying the differences between the hybrid genes of a patient with GRA.


We developed a long-read analysis pipeline named GRAde (GRA deciphering), which utilizes the nonidentical bases in the CYP11B1 and CYP11B2 genomic sequences to identify and visualize the chimeric form. We sequenced the polymerase chain reaction (PCR) products of the CYP11B1/CYP11B2 chimeric gene from 36 patients with GRA using the Nanopore MinION device and analyzed the sequences using GRAde. Crossover events were identified for 30 out of the 36 samples. The crossover sites appeared in the region exhibiting high sequence similarity between CYP11B1 and CYP11B2, and 53.3% of the cases were identified as having a gene conversion in intron 2. More importantly, there were six cases for whom the PCR products indicated a chimeric gene, but the GRAde results revealed no crossover pattern. The crossover regions were further verified by Sanger sequencing analysis.


PCR-based target enrichment followed by long-read sequencing is an efficient and precise approach to dissecting complex genomic regions, such as those involved in GRA mutations, which could be directly applied to clinical diagnosis. The scripts of GRAde are available at


Primary aldosteronism (PA) is the most common and curable form of secondary arterial hypertension. Most diagnosed cases of PA are mainly caused by either aldosterone overproduction by both adrenal glands or unilateral aldosterone-producing adenomas (APA) [1]. However, about 5% of cases are inherited forms of familial hyperaldosteronism (FH) [2]. According to current understanding, there are three well-established forms of FH (FH-I–III), and some germline mutations associated with PA, such as CACN1H and CACN1D, have been identified [3]. However, data from genetic analyses reveal a more complex situation, and more heritable forms of PA may still be undiscovered [2].

Familial hyperaldosteronism type I (FH1), also called glucocorticoid-remediable aldosteronism (GRA), is transmitted as an autosomal-dominant disorder and accounts for 0.5–1.0% of PA cases [4]. GRA is caused by a chimeric gene resulting from a nonhomologous crossing-over event on chromosome 8q24.3, between the 11β‐hydroxylase (CYP11B1) and aldosterone synthase (CYP11B2) genes. This chimeric enzyme contains the promoter of CYP11B1 at the 5′ end and the coding sequences from CYP11B2 at the 3′ end. It can therefore synthesize aldosterone under adrenocorticotropic hormone (ACTH) control (Fig. 1A).

Fig. 1
figure 1

The challenge of CYP11B1/CYP11B2 chimeric form identification. A Scheme of the CYP11B1/CYP11B2 chimeric gene. B Long-range PCR shows the PCR products of the CYP11B1/CYP11B2 chimeric gene and unexpected fragments. Amplification of a chimeric gene is expected to produce a 3.9 kb product. Case A has a clear single band of the expected size of the PCR product, but cases B and C have multiple bands with weak signals of the expected size of the main product. C Dot plot revealing the high degree of similarity between the CYP11B1 and CYP11B2 genomic sequences. The plot was generated by blast2seq using default parameters

Although GRA is a genetic disease, the clinical and biochemical characteristics of patients are highly variable, and even patients from the same familial type may present different symptoms [5, 6]. In addition, different crossover patterns of the chimeric CYP11B1/CYP11B2 gene within a familial type have been described [7]. These findings suggest that variability in clinical presentations might be related to heterogeneity in the hereditary factor—in particular, in the crossover pattern of the hybrid gene. Currently, the genetic diagnosis of GRA is made using Southern blotting or long-range polymerase chain reaction (PCR) techniques [8,9,10,11,12]. However, these methods are unable to precisely determine the crossover pattern. Therefore, it is crucial to develop a more precise method for identifying the specific type of hybrid gene that is carried by a patient.

The detection of pathogenic gene fusion in inherited diseases and oncology is particularly useful. In general, fusion events cause a loss or gain of function in one of the fused partners. However, unlike with oncogenic fusion genes, it is relatively difficult to detect gene conversion in genes with highly similar sequences. New detection strategies for this kind of gene fusion are urgently required to facilitate diagnostic and therapeutic decisions.

Since the introduction of next-generation sequencing (NGS), tremendous progress has been achieved in all fields of biology. The declining costs and growing availability of NGS have made it the method of choice for genetic analysis and related applications. Nevertheless, NGS suffers from a limited availability of data on several aspects, such as repetitive elements, polymorphic regions, camouflaged genes, and large structural variations (SVs), which prevents full extraction of information associated with the genome [13, 14]. However, recently developed single-molecule sequencing techniques such as single-molecule real-time sequencing (SMRT) and nanopore sequencing provide access to larger variations, because the read lengths are typically several thousands of bases [15, 16]. These techniques provide the opportunity to investigate or diagnose diseases caused by pathogenic structural variations and gene conversion, such as GRA. In this study, we used Oxford nanopore technology (ONT) to sequence the PCR products of chimeric CYP11B1/CYP11B2 genes and developed a long-read analysis pipeline that can efficiently identify and visualize the chimeric forms.


The challenge of GRA chimeric form identification

Previous studies have suggested that a variety of CYP11B1/CYP11B2 chimeric forms are associated with different clinical presentations [5, 6]. Long-range PCR is one of the standard approaches to GRA diagnosis. However, in a few of GRA cases, multiple bands with weak signals for the expected PCR products are obtained (Fig. 1B), making diagnosis difficult. To avoid misdiagnosis of patients with GRA, it is crucial to identify the exact crossover site. The current method for identifying crossover sites is multiplex PCR with Sanger sequencing [12]. However, the genomic sequences of CYP11B1 and CYP11B2 are about 94% identical in the main crossover region, making primer design for the enrichment of regions that are specific to only one of the two genes challenging (Fig. 1C). In addition, this procedure is time-consuming and laborious. To address this issue, we proposed an alternative strategy that combines long-range PCR with nanopore sequencing to confirm the chimeric gene and identify the crossover site, and we developed an analysis pipeline to decipher these long-read sequences.

Overview of GRAde

The analysis workflow of GRAde is illustrated in Fig. 2A. The input for GRAde was FASTQ files, which were generated from the long sequencing reads of PCR products derived from GRA samples. This pipeline consists of two components: quality control for the sequencing reads, and the procedure for determining the crossover regions of the CYP11B1/CYP11B2 chimeric form. High-quality reads were obtained by correcting them using a nonhybrid approach, Canu [17], and then mapping them to the human reference genome. Only the reads that aligned with the loci of CYP11B1 and CYP11B2 were considered for further analysis. To accurately dissect each qualifying read, GRAde used the Smith–Waterman algorithm to align reads to the CYP11B1 and CYP11B2 genomic sequences, respectively, and then analyzed the alignment results to identify the crossover region.

Fig. 2
figure 2

Overview of GRAde. A Analysis workflow of GRAde. B The identification of discriminating and ambiguous bases in the CYP11B1 and CYP11B2 genomic sequences. C The concept of using discriminating and ambiguous bases to recognize the CYP11B1 and CYP11B2 genes. D An example of a fusion plot based on the CYP11B1 genomic sequence. Red and grey points represent the discriminating and ambiguous bases

Due to the high degree of sequence similarity between the CYP11B1 and CYP11B2 genes, we first identified the “discriminating” and “ambiguous” bases by comparing them to the CYP11B1 (chr8:142876120–142879816) and CYP11B2 (chr8:142914143–142917843) sequences (Fig. 2B). We then used the discriminating bases to distinguish the sequences from the genomic source (Fig. 2C). There were 180 and 188 discriminating bases for CYP11B1 and CYP11B2, respectively (Additional file 1). The median of the positional distribution of the neighbor discriminating bases was 11 bp, with a range of 1–221 bp. Ideally, the ambiguous bases should perfectly match both genes, but there are typically some mismatches due to errors in the PCR or sequencing processes and genetic polymorphisms (Fig. 2C). Mismatches of ambiguous bases may affect the interpretation, so the ambiguous bases were discarded if polymorphisms were reported at these positions in the single-nucleotide polymorphism (SNP) database (dbSNP). Based on the alignment results of all reads mapped to CYP11B1 and CYP11B2, we calculated the mismatch rates of each discriminating and ambiguous base and visualized them as a fusion plot (Fig. 2D). Because of the relatively high error rate of nanopore sequencing, if the bases aligned to discriminating sites did not exactly match either CYP11B1 or CYP11B2, we considered them to be sequencing or PCR errors and did not include them in the fusion plot. This fusion plot provides the intuitive and interpretable chimeric form for each sample.

Variety of CYP11B1/CYP11B2 chimeric forms in GRA samples

We collected 36 samples from patients who were diagnosed with GRA based on clinical practice guidelines, which was confirmed by using the long-range PCR technique to reveal the chimeric genes. The chimeric genes were amplified using the long-range PCR technique and then subjected to nanopore sequencing. We also amplified CYP11B2 genes from six other patients with PA as negative controls. The results of the GRAde analysis of the 36 GRA samples are summarized in Table 1, and the fusion forms are shown in Fig. 3A and Additional file 2. Sixteen of the 36 cases had fusion sites located at intron 2, and the crossover region of the other cases was within the ranges of exon 3–intron 3 (seven cases), exon 4–intron 4 (five cases) and exon 5–intron 5 (two cases). There were no fusion patterns in the fusion plots for any of the negative control samples of normal CYP11B2 genes (Fig. 3B). Notably, there were also six GRA cases for whom no fusion patterns were apparent (Fig. 3C and Additional file 2), suggesting that their diagnoses might be based on false-positive test results.

Table 1 GRAde analysis results for GRA samples
Fig. 3
figure 3

A variety of cross-over regions could be identified using GRAde. Fusion plots for A representative fusion forms, B negative controls (normal CYP11B2 gene), and C false-positive cases that were diagnosed with GRA based on PCR but without any fusion observable in the sequences. The pink triangle denotes the possible cross-over region

Validating the crossover site via multiplex PCR with Sanger sequencing

To demonstrate the validity of our approach and GRAde, we selected a case for whom the PCR produced multiple products and the signal for the chimeric gene was relatively weak (Fig. 4A). After sequencing the PCR products using the nanopore technology and analyzing the reads using GRAde, a clear fusion pattern was revealed in the fusion plot, although the background noise was high at the 3′ end of the chimeric gene (Fig. 4B). Using the fusion plot, we designed primers to amplify the identified crossover region and sequenced the amplified products via Sanger sequencing. Nucleotide sequence analysis showed that the gene-conversion site was in the middle of intron 2, which was consistent with the GRAde result.

Fig. 4
figure 4

A case in which the cross-over region was validated by Sanger sequence analysis. A Long-range PCR revealed PCR products of the CYP11B1/CYP11B2 chimeric gene in sample #17. Amplification with a chimeric gene is expected to produce a 3.9 kb product. B The fusion plot of sample #17 reveals a possible cross-over region located in intron 2, with high background noise. C Sequencing of the chimeric PCR product demonstrated that the crossover site was located in intron 2. The nucleotides that differed between CYP11B1, CYP11B2, and the chimeric gene are highlighted in different colors

Runtime and robustness of GRAde

We evaluated the runtime and robustness of GRAde for use as a diagnostic tool. We generated testing sets that were randomly sampled from case No. 34, with a varying number of reads (100, 200, 500, 1000, 2000, or 3000). As shown in Additional file 3, analysis of input with 3000 reads could be completed within six minutes, and the most time-consuming step was performing the Canu algorithm for hybridization-based sequence correction. We were able to achieve stable results using the sample with 200 reads, so we consider that to be the minimum number of reads required for analysis (Additional file 3).


The emerging long-read sequencing technologies offer improvements in the characterization of genetic variation and regions that are difficult to assess using short reads. These techniques have been used to investigate genetic disorders with previously known or strongly suspected disease loci [18] and are considered a diagnostic tool. The general strategy of long-read–based diagnosis is to enrich the locus of causality and then perform long-read sequencing. Long-range PCR is a technique currently used for the detection of GRA [8,9,10,11,12], so we chose it as the enrichment method. However, GRA is a special case because the locus of causality is formed by two genes with a high degree of sequence similarity (Fig. 1C). This results in challenging primer design and poor PCR products for evaluation, due to issues like the presence of multiple bands and weak signals for the expected chimeric products (Fig. 1B). Also, the presence of the expected band in the PCR product does not necessarily mean it is a chimeric gene, which can lead to incorrect diagnoses based on false-positive test results (Fig. 3C). Therefore, long-range PCR in addition to long-read sequencing is a more efficient method of chimeric gene detection than traditional Sanger sequencing and may reduce the false-positive rate of traditional PCR testing.

However, PCR-based enrichment still has several limitations. PCR amplification can be influenced by improper PCR conditions [19, 20], thus producing false negatives or incorporating PCR errors. Analysis of sequencing reads from PCR products with multiple bands may also be hampered by a high level of background noise and an unclear fusion pattern (Fig. 3A). To avoid this, the development of alternative target-enrichment methods for GRA chimeric genes is required, such as capture-based [21, 22] and CRISPR-based enrichment methods [23, 24].

In our GRA cohort, there were six patients who had PCR products for sequencing in which we could identify no fusion pattern. The possible reasons for this include poor integrity and low purity of the genomic DNA, which could have resulted from the state of storage and the process of DNA extraction. Other possible factors include poor specificity of the primers, inappropriate DNA input, and nucleic-acid contamination. It is difficult to design extremely specific primers for the crossover region of chimeric genes, so this pipeline can assist in excluding nonspecific PCR products and distinguishing the correct signal.

In addition to the CYP11B1/CYP11B2 chimeric gene, there are some SNPs at the CYP11B1 and CYP11B2 loci that are also associated with hypertension [25]. Theoretically, these single nucleotide variants (SNV) can be detected in the current long-read sequence data. To provide precise diagnosis and treatment of GRA, however, it is critical to report all possible variants in genetic testing. Although GRAde was developed for the identification of chimeric forms, therefore, we will incorporate the function of variant-calling to identify SNVs and small insertions and deletions (indels).

Although GRAde is specifically designed for the identification of crossover sites between CYP11B1 and CYP11B2 in GRA patients, the analysis strategy used in GRAde could also be applied to other diseases that are caused by dysfunctional proteins due to unequal crossover events. For example, chronic granulomatous disease (CGD) is caused by the chimeric form of NCF1 (neutrophil cytosolic factor 1) and its pseudogenes NCF1B (neutrophil cytosolic factor 1B pseudogene) and NCF1C (neutrophil cytosolic factor 1C pseudogene) [26]. Because these two pseudogenes are on either side of NCF1 and have 99% sequence identity to NCF1, distinguishing NCF1 from its pseudogenes in CGD patients relies on a set of SNPs [27] and an analysis similar in concept to that of GRAde. In addition to CGD, several other diseases are caused by the chimeric products of the crossover between a gene and its pseudogenes, such as congenital adrenal hyperplasia, caused by the chimeric genes CYP21A1P/CYP21A2 [28], and Gaucher disease, caused by a fusion gene formed from GBA (glucosylceramidase beta) and its pseudogene GBAP1 (glucosylceramidase beta pseudogene 1) [29]. GRAde could easily be modified and used for crossover-site identification in other diseases. Besides the detection of specific fusion genes, our strategy could be also applied to genome-wide detection of this kind of gene fusion by systematically identifying discriminating bases in homologous genes.


In this study, we proposed the strategy of combining long-range PCR with long-read sequencing techniques to identify gene conversions, such as the one that causes GRA. This approach is not only more efficient than general multiplex PCR followed by Sanger sequencing, but also reduces the false-positive rate for PCR-based genetic testing. This analysis procedure could be applied to the diagnosis of other diseases caused by unequal crossover between two genes with highly similar sequences.



This study was approved by the institutional review board of the National Taiwan University Hospital, Taipei, Taiwan (No. 200611031R) ( number NCT00746070). All participants provided written informed consent before inclusion in the study. The Taiwan Primary Aldosteronism Investigation (TAIPAI) group enrolled possible PA patients who first had their aldosterone-to-renin ratio (ARR) screened for PA detection and were then followed-up. Screening, confirmation, and subtype identification of the PA were performed in hypertensive patients according to the standard TAIPAI protocol and aldosteronism consensus [30,31,32,33]. Fulfillment of the following three conditions confirmed a diagnosis of PA: (1) autonomous excess aldosterone production evidenced by an ARR > 35; (2) a TAIPAI score [34] of > 60%; (3) seated post-saline loading plasma aldosterone concentration (PAC) > 16 ng/dL [35], or PAC/plasma renin activity (PRA) > 35 (ng/dL)/(ng/mL/h) in a post-captopril/losartan test [30].

Sample preparation

For the detection of the chimeric gene, PCR was performed using the method described by MacConnachie et al. [12] with some modifications. We used the following primer sets to amplify the normal CYP11B2 gene and the chimeric CYP11B1/CYP11B2 gene with PfuUltra II Fusion HS DNA Polymerase (Agilent): forward: 5′CAGGTCCAGAGCCAGTTCTCCCAT/reverse: 5′ACCCTCCTTCTCCTTGTACACCCA forward: 5′CAGTTCTCCCATGACGTGATCCCT/reverse: 5′ACCCTCCTTCTCCTTGTACACCCA.

The touchdown PCR process was as follows: 95 °C for 2 min; 38 cycles of denaturation at 95 °C for 1 min; annealing at 70–61 °C for 1 min; and extension at 72 °C for 5 min and 72 °C for 3 min. The annealing temperature began at 70 °C and was lowered by 1 °C every two cycles until it reached 61 °C; this annealing temperature was maintained until the end of the cycling process. The PCR amplicons were evaluated in a 0.8% agarose gel, cleaned up with the 0.45X Agencourt AMPure XP beads (Beckman Coulter), and quantified using a Qubit fluorometer (Life Technologies).

Sample barcoding

For simultaneous detection of multiple samples, we used the ligation method to tag the amplified sample with the native barcoding adaptor (Oxford Nanopore Technologies), which allows up to 24 different libraries (barcodes 1–24) to be combined and loaded onto a single flow cell at the same time. The amplified fragment end was repaired and dA-tailed using the End Repair/dA-tailing Module (KAPA Roche). The end-repaired product was purified using 1X Agencourt AMPure XP beads. Next, a unique dT-tailed barcode adaptor was ligated on the dA-tailed template using ligation Master Mix (KAPA Roche). The barcoded samples were then purified with 0.45X Agencourt AMPure XP beads. The quality and quantity of each sample were evaluated using Nanodrop and a Qubit fluorometer, respectively. The barcoded samples were equally pooled for the sequencing library preparation.

Library preparation and sequencing

For the construction of the sequencing library, we used the KAPA hyper prep kit (Roche). First, the amplified and barcoded samples were pooled together, and end repair and dA-tailing were performed using the End Repair/dA-tailing Module. The end-repaired product was purified using 1X Agencourt AMPure XP beads. Next, adapter ligation and tethering were carried out with sequencing adapter (Oxford Nanopore Technologies) and ligation Master Mix. The sequencing-adapter–ligated DNA library was then purified with 0.5X Agencourt AMPure XP beads, Adapter Bead binding buffer (Oxford Nanopore Technologies) was added, and the samples were eluted in the Elution Buffer (Oxford Nanopore Technologies).

Before sequencing, the sequencing-adapter–ligated DNA library was mixed with Library Loading beads, and SpotON Flow Cells (R9.4) (FLO-MIN106D) were primed with running buffer. The samples were run on a MinION sequencing device for approximately 24 h, and the sequencing runs were operated by the MinKNOW software. Base-calling from the electrical data generated by the sequencer was performed using Guppy (v3.0.3) and the resulting raw sequence data were demultiplexed using qcat (v1.1.0).

Implementation of GRAde

Reads with lengths of 3000–5000 bp were considered for downstream analysis. Reads were corrected using a nonhybrid-based approach Canu (v1.4) [17] with the default parameters altered only as follows: genomeSize = 5 k, overlapper = mhap, utgReAlign = true, and stopOnReadQuality = false. Corrected reads were compared to the human reference genome (GRCh38) using ngmlr (v0.2.7) using default parameters [36]. The reads that aligned on the loci of CYP11B1 and CYP11B2 were defined as on-target reads. Each read was aligned to the conserved regions of CYP11B1 and CYP11B2 using the Smith–Waterman algorithm, implemented by the SSW library [37]. The discriminating and ambiguous bases were extracted from the alignment between CYP11B1 and CYP11B2 by the Smith–Waterman algorithm. The mismatch rate of each discriminating and ambiguous base was calculated by parsing the alignment results of reads aligned to CYP11B1 and CYP11B2. The background noise was defined as the 99th percentile of the mismatch rate of all ambiguous bases, which is represented by the lower dashed line in the fusion plot. The foreground, represented by the upper dashed line in the fusion plot, was the average of the mismatch rates of the discriminating bases that had mismatch rates higher than the background. In addition, the mismatch rates of the discriminating bases were fitted using a sigmoid function to identify the crossover site, using the drm function implemented in the drc R package. All scripts were implemented in Python (v2.7) and R (v3.5.1).

Availability of data and materials

The scripts used for the analyses have been deposited at GitHub and are available at



Adrenocorticotropic hormone


Aldosterone-producing adenomas


Aldosterone-to-renin ratio


Chronic granulomatous disease


Clustered regularly interspaced short palindromic repeat


Familial hyperaldosteronism


Glucocorticoid-remediable aldosteronism


Insertion and deletion


Next-generation sequencing


Oxford Nanopore Technology


Primary aldosteronism


Plasma aldosterone concentration


Polymerase chain reaction


Plasma renin activity


Single-molecule real-time sequencing


Single-nucleotide polymorphism


Single nucleotide variant


Structural variation


The Taiwan Primary Aldosteronism Investigation


  1. 1.

    Farrugia FA, Zavras N, Martikos G, Tzanetis P, Charalampopoulos A, Misiakos EP, Sotiropoulos D, Koliakos N. A short review of primary aldosteronism in a question and answer fashion. Endocr Regul. 2018;52(1):27–40.

    Article  Google Scholar 

  2. 2.

    Perez-Rivas LG, Williams TA, Reincke M. Inherited forms of primary hyperaldosteronism: new genes, new phenotypes and proposition of a new classification. Exp Clin Endocrinol Diabetes. 2019;127(2–03):93–9.

    CAS  PubMed  Google Scholar 

  3. 3.

    Zennaro MC, Boulkroun S, Fernandes-Rosa F. An update on novel mechanisms of primary aldosteronism. J Endocrinol. 2015;224(2):R63-77.

    CAS  Article  Google Scholar 

  4. 4.

    Mulatero P, Tizzani D, Viola A, Bertello C, Monticone S, Mengozzi G, Schiavone D, Williams TA, Einaudi S, La Grotta A, et al. Prevalence and characteristics of familial hyperaldosteronism: the PATOGEN study (Primary Aldosteronism in TOrino-GENetic forms). Hypertension. 2011;58(5):797–803.

    CAS  Article  Google Scholar 

  5. 5.

    Aglony M, Martinez-Aguayo A, Carvajal CA, Campino C, Garcia H, Bancalari R, Bolte L, Avalos C, Loureiro C, Trejo P, et al. Frequency of familial hyperaldosteronism type 1 in a hypertensive pediatric population: clinical and biochemical presentation. Hypertension. 2011;57(6):1117–21.

    CAS  Article  Google Scholar 

  6. 6.

    Lin YF, Peng KY, Chang CH, Hu YH, Wu VC, Chueh JS, Wu KD. Adrenalectomy completely cured hypertension in patients with familial hyperaldosteronism type I who had somatic KCNJ5 mutation. J Clin Endocrinol Metab. 2019;104(11):5462–6.

    Article  Google Scholar 

  7. 7.

    Carvajal CA, Campino C, Martinez-Aguayo A, Tichauer JE, Bancalari R, Valdivia C, Trejo P, Aglony M, Baudrand R, Lagos CF, et al. A new presentation of the chimeric CYP11B1/CYP11B2 gene with low prevalence of primary aldosteronism and atypical gene segregation pattern. Hypertension. 2012;59(1):85–91.

    CAS  Article  Google Scholar 

  8. 8.

    Malagon-Rogers M. Non-glucocorticoid-remediable aldosteronism in an infant with low-renin hypertension. Pediatr Nephrol. 2004;19(2):235–6.

    Article  Google Scholar 

  9. 9.

    Vonend O, Altenhenne C, Büchner NJ, Dekomien G, Maser-Gluth C, Weiner SM, Sellin L, Hofebauer S, Epplen JT, Rump LC. A German family with glucocorticoid-remediable aldosteronism. Nephrol Dial Transplant Off Publ Eur Dial Transpl Assoc Eur Ren Assoc. 2007;22(4):1123–30.

    CAS  Google Scholar 

  10. 10.

    Fallo F, Pilon C, Williams TA, Sonino N, Morra Di Cella S, Veglio F, De Iasio R, Montanari P, Mulatero P. Coexistence of different phenotypes in a family with glucocorticoid-remediable aldosteronism. J Hum Hypertens. 2004;18(1):47–51.

    CAS  Article  Google Scholar 

  11. 11.

    Fardella CE, Pinto M, Mosso L, Gómez-Sánchez C, Jalil J, Montero J. Genetic study of patients with dexamethasone-suppressible aldosteronism without the chimeric CYP11B1/CYP11B2 gene. J Clin Endocrinol Metab. 2001;86(10):4805–7.

    CAS  Article  Google Scholar 

  12. 12.

    MacConnachie AA, Kelly KF, McNamara A, Loughlin S, Gates LJ, Inglis GC, Jamieson A, Connell JM, Haites NE. Rapid diagnosis and identification of cross-over sites in patients with glucocorticoid remediable aldosteronism. J Clin Endocrinol Metab. 1998;83(12):4328–31.

    CAS  Article  Google Scholar 

  13. 13.

    Treangen TJ, Salzberg SL. Repetitive DNA and next-generation sequencing: computational challenges and solutions. Nat Rev Genet. 2011;13(1):36–46.

    Article  Google Scholar 

  14. 14.

    Ebbert MTW, Jensen TD, Jansen-West K, Sens JP, Reddy JS, Ridge PG, Kauwe JSK, Belzil V, Pregent L, Carrasquillo MM, et al. Systematic analysis of dark and camouflaged genes reveals disease-relevant genes hiding in plain sight. Genome Biol. 2019;20(1):97.

    Article  Google Scholar 

  15. 15.

    Huddleston J, Chaisson MJP, Steinberg KM, Warren W, Hoekzema K, Gordon D, Graves-Lindsay TA, Munson KM, Kronenberg ZN, Vives L, et al. Discovery and genotyping of structural variation from long-read haploid genome sequence data. Genome Res. 2017;27(5):677–85.

    CAS  Article  Google Scholar 

  16. 16.

    Norris AL, Workman RE, Fan Y, Eshleman JR, Timp W. Nanopore sequencing detects structural variants in cancer. Cancer Biol Ther. 2016;17(3):246–53.

    CAS  Article  Google Scholar 

  17. 17.

    Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 2017;27(5):722–36.

    CAS  Article  Google Scholar 

  18. 18.

    Mantere T, Kersten S, Hoischen A. Long-read sequencing emerging in medical genetics. Front Genet. 2019;10:426.

    CAS  Article  Google Scholar 

  19. 19.

    Grunenwald H. Optimization of polymerase chain reactions. In: Bartlett JMS, Stirling D, editors. PCR protocols. Totowa: Humana Press; 2003. p. 89–99.

    Google Scholar 

  20. 20.

    Lorenz TC. Polymerase chain reaction: basic protocol plus troubleshooting and optimization strategies. J Vis Exp. 2012;63:e3998.

    Google Scholar 

  21. 21.

    Peng Z, Paudel D, Wang L, Luo Z, You Q, Wang J. Methods for target enrichment sequencing via probe capture in legumes. Methods Mol Biol. 2020;2107:199–231.

    CAS  Article  Google Scholar 

  22. 22.

    Eckert S, Chan J, Houniet D, Breuer J, Speight G. Enrichment by hybridisation of long DNA fragments for Nanopore sequencing. Microbial Genomics. 2016;2:e000087.

    Article  Google Scholar 

  23. 23.

    Kang S-H, Lee W, An J-H, Lee J-H, Kim Y-H, Kim H, Oh Y, Park Y-H, Jin YB, Jun B-H, et al. Prediction-based highly sensitive CRISPR off-target validation using target-specific DNA enrichment. Nat Commun. 2020;11(1):3596.

    CAS  Article  Google Scholar 

  24. 24.

    Gilpatrick T, Lee I, Graham JE, Raimondeau E, Bowen R, Heron A, Downs B, Sukumar S, Sedlazeck FJ, Timp W. Targeted nanopore sequencing with Cas9-guided adapter ligation. Nat Biotechnol. 2020;38(4):433–8.

    CAS  Article  Google Scholar 

  25. 25.

    Alvarez-Madrazo S, Mackenzie SM, Davies E, Fraser R, Lee WK, Brown M, Caulfield MJ, Dominiczak AF, Farrall M, Lathrop M, et al. Common polymorphisms in the CYP11B1 and CYP11B2 genes: evidence for a digenic influence on hypertension. Hypertension. 2013;61(1):232–9.

    CAS  Article  Google Scholar 

  26. 26.

    Hayrapetyan A, Dencher PC, van Leeuwen K, de Boer M, Roos D. Different unequal cross-over events between NCF1 and its pseudogenes in autosomal p47(phox)-deficient chronic granulomatous disease. Biochim Biophys Acta. 2013;1832(10):1662–72.

    CAS  Article  Google Scholar 

  27. 27.

    Chanock SJ, Roesler J, Zhan S, Hopkins P, Lee P, Barrett DT, Christensen BL, Curnutte JT, Gorlach A. Genomic structure of the human p47-phox (NCF1) gene. Blood Cells Mol Dis. 2000;26(1):37–46.

    CAS  Article  Google Scholar 

  28. 28.

    Coeli FB, Soardi FC, Bernardi RD, de Araujo M, Paulino LC, Lau IF, Petroli RJ, de Lemos-Marini SH, Baptista MT, Guerra-Junior G, et al. Novel deletion alleles carrying CYP21A1P/A2 chimeric genes in Brazilian patients with 21-hydroxylase deficiency. BMC Med Genet. 2010;11:104.

    Article  Google Scholar 

  29. 29.

    Cormand B, Diaz A, Grinberg D, Chabas A, Vilageliu L. A new gene-pseudogene fusion allele due to a recombination in intron 2 of the glucocerebrosidase gene causes Gaucher disease. Blood Cells Mol Dis. 2000;26(5):409–16.

    CAS  Article  Google Scholar 

  30. 30.

    Wu VC, Hu YH, Er LK, Yen RF, Chang CH, Chang YL, Lu CC, Chang CC, Lin JH, Lin YH, et al. Case detection and diagnosis of primary aldosteronism: the consensus of Taiwan Society of Aldosteronism. J Formos Med Assoc. 2017;116(12):993–1005.

    Article  Google Scholar 

  31. 31.

    Wu VC, Chang HW, Liu KL, Lin YH, Chueh SC, Lin WC, Ho YL, Huang JW, Chiang CK, Yang SY, et al. Primary aldosteronism: diagnostic accuracy of the losartan and captopril tests. Am J Hypertens. 2009;22(8):821–7.

    CAS  Article  Google Scholar 

  32. 32.

    Kuo CC, Wu VC, Huang KH, Wang SM, Chang CC, Lu CC, Yang WS, Tsai CW, Lai CF, Lee TY, et al. Verification and evaluation of aldosteronism demographics in the Taiwan Primary Aldosteronism Investigation Group (TAIPAI Group). J Renin Angiotensin Aldosterone Syst. 2011;12(3):348–57.

    CAS  Article  Google Scholar 

  33. 33.

    Wu VC, Kuo CC, Wang SM, Liu KL, Huang KH, Lin YH, Chu TS, Chang HW, Lin CY, Tsai CT, et al. Primary aldosteronism: changes in cystatin C-based kidney filtration, proteinuria, and renal duplex indices with treatment. J Hypertens. 2011;29(9):1778–86.

    CAS  Article  Google Scholar 

  34. 34.

    Wu VC, Yang SY, Lin JW, Cheng BW, Kuo CC, Tsai CT, Chu TS, Huang KH, Wang SM, Lin YH, et al. Kidney impairment in primary aldosteronism. Clin Chim Acta. 2011;412(15–16):1319–25.

    CAS  Article  Google Scholar 

  35. 35.

    Wu CH, Wu VC, Yang YW, Lin YH, Yang SY, Lin PC, Chang CC, Tsai YC, Wang SM, T group. Plasma aldosterone after seated saline infusion test outperforms captopril test at predicting clinical outcomes after adrenalectomy for primary aldosteronism. Am J Hypertens. 2019;32(11):1066–74.

    CAS  Article  Google Scholar 

  36. 36.

    Sedlazeck FJ, Rescheneder P, Smolka M, Fang H, Nattestad M, von Haeseler A, Schatz MC. Accurate detection of complex structural variations using single-molecule sequencing. Nat Methods. 2018;15(6):461–8.

    CAS  Article  Google Scholar 

  37. 37.

    Zhao M, Lee WP, Garrison EP, Marth GT. SSW library: an SIMD Smith–Waterman C/C++ library for use in genomic applications. PLoS ONE. 2013;8(12):e82138.

    Article  Google Scholar 

Download references


The authors thank the National Center for High-performance Computing for computer time and facilities. We also thank the Second Core Lab and Sequencing Core, Department of Medical Research, National Taiwan University Hospital for providing laboratory facilities. We would like to thank Uni-edit ( for editing and proofreading this manuscript.

About this supplement

This article has been published as part of BMC Bioinformatics Volume 22 Supplement 10 2021: Selected articles from the 19th Asia Pacific Bioinformatics Conference (APBC 2021): bioinformatics. The full contents of the supplement are available at


This work was supported by the Ministry of Science and Technology, Taiwan (MOST 107–2314-B-002-254-MY3, MOST110-2221-E-002-129-MY3 to CLH) and National Taiwan University Hospital, Taipei, Taiwan (108-N4206 to YCW; 109-N4491 to CIC; 110-S4909 and 110-O01 to CLH). The funders did not play any role in the design of the study, the collection, analysis, and interpretation of data, or in writing of the manuscript. Publication costs are funded by National Taiwan University Hospital (110-S4909 and 110-O01).

Author information





YCW, JJTW, and CLH designed the study. YCW, CIC, PYC, CHK, and KYP performed the experiments. YHH and CLH implemented the tool and performed the analysis. VCW provided sample materials and clinical information. YCW, KYP, VCW, JJTW, and CLH interpreted the data. YCW and CLH drafted the manuscript. All authors read, revised, and approved the manuscript.

Corresponding authors

Correspondence to Jyy-Jih Tsai-Wu or Chia-Lang Hsu.

Ethics declarations

Ethics approval and consent to participate

This study was conducted in accordance with the mandates of the Helsinki Declaration and the guidelines of the ethics committee of National Taiwan University Hospital. All participants provided written informed consent before inclusion in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

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

Supplementary Information

Additional file 1:

Nucleotides that differed between CYP11B1 and CYP11B2.

Additional file 2:

Fusion plots for all GRA patients.

Additional file 3:

Runtime and robustness of GRAde.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wu, YC., Chen, CI., Chen, PY. et al. GRAde: a long-read sequencing approach to efficiently identifying the CYP11B1/CYP11B2 chimeric form in patients with glucocorticoid-remediable aldosteronism. BMC Bioinformatics 22, 613 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Glucocorticoid-remediable aldosteronism
  • Chimeric genes
  • Long-read sequencing