BioMed Research International

BioMed Research International / 2013 / Article
Special Issue

Laboratory Genetic Testing in Clinical Practice

View this Special Issue

Research Article | Open Access

Volume 2013 |Article ID 198089 | 9 pages |

IROme, a New High-Throughput Molecular Tool for the Diagnosis of Inherited Retinal Dystrophies

Academic Editor: Gokce A. Toruner
Received04 Nov 2012
Accepted27 Nov 2012
Published26 Dec 2012


The molecular diagnosis of retinal dystrophies is difficult because of the very important number of genes implicated and is rarely helped by genotype-phenotype correlations. This prompted us to develop IROme, a custom designed in solution-based targeted exon capture assay (SeqCap EZ Choice library, Roche NimbleGen) for 60 retinitis pigmentosa-linked genes and three candidate genes (942 exons). Pyrosequencing was performed on a Roche 454 GS Junior benchtop high-throughput sequencing platform. In total, 23 patients affected by retinitis pigmentosa were analyzed. Per patient, 39.6 Mb were generated, and 1111 sequence variants were detected on average, at a median coverage of 17-fold. After data filtering and sequence variant prioritization, disease-causing mutations were identified in ABCA4, CNGB1, GUCY2D, PROM1, PRPF8, PRPF31, PRPH2, RHO, RP2, and TULP1 for twelve patients (55%), ten mutations having never been reported previously. Potential mutations were identified in 5 additional patients, and in only 6 patients no molecular diagnosis could be established (26%). In conclusion, targeted exon capture and next-generation sequencing are a valuable and efficient approach to identify disease-causing sequence variants in retinal dystrophies.

1. Introduction

Retinitis pigmentosa (RP) (MIM number 268000) is a group of genetically highly heterogeneous-inherited retinal dystrophies [1]. Typically, night blindness starts during adolescence, and patients progressively loose the rod photoreceptor-mediated peripheral vision. At later stages, the cone photoreceptors also become affected, constricting vision over time to the most central fovea and eventually resulting in complete blindness. To date, more than fifty genes have been linked to nonsyndromic RP (RetNet; Inheritance can be autosomal dominant (AD), autosomal recessive (AR) or X-linked, and, rarely, mitochondrial or digenic [2]. Sporadic or simplex cases account for about 30% [3].

The molecular diagnosis of RP is difficult because (i) there is no genotype/phenotype correlation in a vast majority of patients, (ii) a high intra- and interfamilial variability of clinical phenotypes is observed in patients carrying the same causative mutation, (iii) different mutations in a same disease-linked gene cause highly variable clinical phenotypes if not clinically distinct retinal degenerations, and (iv) overlapping clinical phenotypes and disease-linked genes exist with additional retinal degenerations, that is, early-onset Leber congenital amaurosis (LCA), congenital stationary night blindness (CSNB), cone-rod dystrophies (CRD), enhanced S-cone syndrome (ESCS), or syndromic RP in Bardet-Biedl and Usher syndrome [2]. However, identification of RP-linked sequence variants is important for genetic counseling and patient management.

Similar to other Mendelian disorders, mutations in RP patients were identified until recently by linkage mapping and subsequent Sanger sequencing of candidate genes [4]. For molecular diagnosis, the validated RP mutations could be detected by arrayed primer extension (APEX) chip technology [5]. However, a low success rate in detecting mutations by APEX was inherent to the genetic heterogeneity of RP patients, and in a cohort of 272 Spanish families affected by ARRP, causative mutations were identified in only 11% of them [6].

The development of next-generation sequencing (NGS) tools in recent years has allowed the production of an enormous volume of sequencing data at low costs [7]. Whole genome sequencing and downstream data handling remains cost and labor intensive, limiting its use in routine mutation detection [8]. Targeted capture of the about 30 Mb of protein-coding regions in the human genome, the so-called exome, reduced the sequencing and data handling effort by a factor of 100 and allowed the identification of mutations in unrelated patients affected by the same syndrome [9]. Exome sequencing has since been widely used as a tool for Mendelian disease gene discovery [10, 11]. Initially array-based, targeted sequence capture has become easy-to-use, thanks to the development of in-solution capture methods [12]. Finally, benchtop high-throughput sequencers made exome sequencing available to small-size diagnostic laboratories [13].

These technological advances prompted us to develop a custom designed in solution-based targeted capture assay, called IROme, for the detection of mutations located in the exons, including complete 3′-untranslated regions (UTR), intron-exon boundaries and potential promoter, and 5′-UTR regions of 63 genes on a 454 GS Junior sequencing platform.

2. Material and Methods

2.1. Patients and DNA Samples

These studies were approved by the Swiss Federal Department of Health (authorization number 035.0003-48) and followed the principles of the Declaration of Helsinki. The 23 patients analyzed in this study were of Swiss, Algerian, and Tunisian origin. Blood samples were collected after informed consent. Genomic DNA was extracted from peripheral blood using a Nucleon BACC2 genomic DNA extraction kit (GE Healthcare, Glattbrugg, Switzerland). Four patients had been previously analyzed at Asper Biotech for known RP-linked mutations by APEX technology [5].

2.2. Design of Solution-Based Capture Assay for Retinitis Pigmentosa-Linked Genes

Exons of targeted genes were identified in the reference human genome version hg19 ( (Table 1). For each exon 50 bp were added in both 5′ and 3′ of the exon, including the complete 3′UTR for each gene. Potential alternative transcripts were also considered in the design. To include potential proximal promoters, an additional 1000 bp in 5′ of the first exon of each gene, containing the complete 5′-UTR, were added. The resulting custom-designed SeqCap EZ Choice library (NimbleGen, Roche) was called IROme, version 1.

GeneAliasChrChr locationExonsPathology

ABCA4 RP19, STGD1, CORD3, and ARMD2194458391-94586688 (rs)50ADRP, ARRP, ARCRD, and ARMD
AIPL1 LCA4176327057-6338519 (rs)6ARLCA, ADCRD
BEST1 RP50, BMD, and VMD21161717293-61732987 (fs)11ADRP, ARRP, and ADMD
C2ORF71 RP54229284556-29297127 (rs)2ARRP
CA4 RP171758227302-58236902 (fs)8ADRP
CABP4 CSNB2B1167219877-67226699 (fs)7ARLCA, ARCSNB
CEP290 LCA10, BBS14, and NPHP61288442794-88535993 (rs)53ARLCA, ARBBS
CERKL RP262182401403-182545392 (rs)14ARRP, ARCRD
CLRN1 RP61, USH3A3150643950-150690786 (rs)3ARRP
CNGA1 RP49447937994-48018689 (rs)13ARRP
CNGA2 X150906923-150913776 (fs)6
CNGB1 RP451657917847-58005020 (rs)33ARRP
CRB1 LCA8, RP121197170592-197447585 (fs)12ARRP, ARLCA
CRX LCA7, CORD21948325097-48364769 (fs)4ADRP, ADLCA, ARLCA, and ADCRD
DHDDS RP59126758773-26797785 (fs)9ARRP
EYS RP25664429876-66417118 (rs)43ARRP
FAM161A RP28262051989-62081278 (rs)6ARRP
FSCN2 RP301779495422-79504156 (fs)5ADRP, ADMD
GUCA1B RP48, GCAP2642152139-42162694 (rs)4ADRP, ADMD
GUCY2D LCA1, CORD6177905988-7923658 (fs)20ARLCA, ADCRD
IMPDH1 LCA11, RP107128032331-128050306 (rs)17ADRP, ADLCA
IMPG2 RP56, sparcan3100945570-101039404 (rs)20ARRP
IQCB1 NPHP53121488610-121553926 (rs)15ARLCA
KLHL7 RP42723145353-23215040 (fs)12ADRP
LCA5 Lebercilin680194708-80247175 (rs)8ARLCA
LPCAT1 AYTL251456595-1524092 (rs)14ARLCA
LRAT LCA144155548097-155674270 (fs)4ARRP, ARLCA
MERTK RP382112656056-112787138 (fs)19ARRP
NR2E3 RP37, PNR1572084977-72110559 (fs)8ADRP, ARRP, and ARESCS
NRL RP271424549316-24584223 (rs)3ADRP, ARRP, and ARESCS
OFD1 RP23X13752832-13787480 (fs)23XRP
OTX2 1457267426-57277197 (rs)5ADLCA
PDE6A RP435149237519-149324356 (rs)22ARRP
PDE6B RP40, CSNBAD24619373-664571 (fs)23ARRP, ADCSNB
PDE6G RP571779617489-79623607 (rs)4ARRP
PRCD RP361774523871-74541458 (fs)5ARRP
PROM1 RP41, STGD4, CORD12, and MCDR2415964699-16086001 (rs)28ARRP, ADCRD, and ADMD
PRPF3 RP181150293925-150325671 (fs)16ADRP
PRPF6 RP602062612488-62664453 (fs)21ADRP
PRPF8 RP13171553923-1588154 (rs)43ADRP
PRPF31 RP111954618837-54635140 (fs)14ADRP
PRPH2 RDS, RP7642664340-42690312 (rs)3ADRP, ADMD, ADCRD, and digenic
RBP3 IRBP1048381487-48390991 (rs)4ARRP
RDH12 LCA13, RP531468168603-68201169 (fs)8ADRP, ARLCA
RGR RP441086004809-86019716 (fs)7ADRP, ARRP, and ADCA
RHO RP4, CSNBAD13129247483-129254012 (fs)5ADRP, ARRP, and ADCSNB
RLBP1 CRALBP1589753098-89764922 (rs)9ARRP
ROM1 1162379194-62382592 (fs)3ADRP, digenic
RP1 855471729-55682531 (fs)4ADRP, ARRP
RP2 X46696375-46741793 (fs)5XRP
RP9 PAP1733134409-33149013 (rs)7ADRP
RPE65 LCA2, RP20168894505-68915642 (rs)14ARRP, ARLCA
RPGR RP3, CORDX1X38128424-38186817 (rs)19XRP, XCRD, XMD
RPGRIP1 LCA6, CORD131421756098-21819460 (fs)24ARLCA, ARCRD
SAG RP47, Arrestin2234216309-234255701 (fs)16ARRP, ARCSNB
SEMA4A RP35, CORD101156117157-156147543 (fs)16ADRP, ARRP, and ADCRD
SNRNP200 RP33296940074-96971297 (rs)45ADRP
SPATA7 LCA31488851268-88936694 (fs)12ARLCA
TOPORS RP31932540542-32552551 (rs)3ADRP
TUB 118040791-8127659 (fs)13
TULP1 LCA15, RP14635465651-35480715 (rs)15ARRP, ARLCA
USH2A RP391215796236-216596738 (rs)73ARRP
ZNF513 RP58227600098-27603657 (rs)4ARRP

Genes are listed alphabetically according to their official gene symbol, and, in addition, gene aliases commonly used in ophthalmic research provided. Chromosomal (chr) location is based on the Homo sapiens high-coverage assembly GRCh37, yielding in the UCSC hg19 database (fs: forward strand; rs: reverse strand). For each gene the number of exons is listed. Targeted sequence capture was directed against genes causing autosomal dominant (AD), autosomal recessive (AR) X-linked (X), retinitis pigmentosa (RP), and Leber congenital amaurosis (LCA). Other retinopathies caused by a given gene are also indicated: cone or cone-rod dystrophy (CRD), macular degeneration (MD), congenital stationary night blindness (CSNB), Bardet-Biedl syndrome (BBS), enhanced S-cone syndrome (ARESCS), and chorioretinal atrophy (CA). Heterozygote ROM1 and PRPH2 mutations cause digenic disease. ORF15 of RPGR was not included in the assay.
2.3. GS Junior Sequencing

The workflow for GS Junior sequencing is summarized in Figure 1. DNA concentrations were measured on a NanoDrop spectrophotometer (Thermo Fisher Scientific, Wilmington, DE). 500 ng of gDNA were fragmented by nebulization, and size selected by Agencourt AMPure XP beads (Beckman-Coulter, Beverly, MA) to obtain fragments between 500 and 1200 bp. Adaptors provided in the GS Titanium Rapid Library Preparation Kit (Roche, Basel, Switzerland) were ligated to the fragmented DNA and then quantified by fluorometry (QuantiFluor, Promega, Madison, WI). This library was amplified by ligation-mediated (LM)-PCR using specific 454 primers. Then, 1 μg of the PCR amplification product was dried down with COT-DNA (Roche) and 454-Hybridization Enhancing Primer in a Speedvac. The pellet was resuspended in NimbleGen’s hybridization buffer and hybridized to the custom-designed SeqCap EZ Choice library (NimbleGen, Roche), called IROme v1, for 70 h at 47°C in a thermocycler. The captured DNA was bound to Streptavidin M-270 Beads (Invitrogen Dynal, Oslo, Norway) for 45 min at 47°C and, using a magnet support, washed with the 4 different NimbleGen buffers provided according to the manufacturer’s instructions. The captured DNA-Beads were amplified by LM-PCR using the same specific 454 primers as before. Captured and noncaptured DNA was subjected to quantitative PCR on a Lightcycler480II (Roche, Basel, Switzerland) to measure the relative fold enrichment of the targeted sequences. Postcapture samples with an enrichment higher than 200-fold were further processed. According to the 454 GS Junior protocol (Roche), an emulsion PCR was done on 2 molecules per beads. After PCR, the beads were collected, washed, and bound to the Enrichment Beads. The enriched DNA was then eluted and quantified with the provided bead counter. Sequencing was performed following the 454 GS Junior protocol. Briefly, 500′000 enriched DNA beads were mixed with Packing Beads. Then, the PicoTiterPlate (PTP) was sequentially loaded with Prelayer Beads, DNA-Packing Beads, Postlayer Beads, and PPiase Beads. Finally, the PTP was mounted in the 454 GS Junior Sequencer, and the program was run in full processing for shotgun sequencing.

2.4. Data Analysis

The workflow for data analysis and data validation is summarized in Figure 2. Sequencing data (.sff file) were analyzed with Roche 454 Reference Mapper program. Reference text (ref.txt) for gene annotations and the snp131 version of the single nucleotide polymorphism database (snp131.txt) were downloaded from the Golden Path database ( The sequence variants provided by the 454HCDiffs.txt file were filtered for known SNPs (, type of amino acid changes (, and repetitive sequences. An additional in-house developed program was used to check the remaining SNPs against reference sequences obtained in Ensembl. Sequence variants were further prioritized according to inheritance, if family information was available, and to the percentage of reads containing a given sequence variant (threshold at 20%). To analyze the coverage, scripts were written to extract global coverage data from the 454AlignmentInfo.tsv file (unique depth, column 5) and the quality of coverage at each targeted nucleotide (column 4). Part of the sequencing data was analyzed by Sequence Pilot version 3.5 (JSI Medicals, Kippenheim, Germany).

2.5. Data Validation

Sanger sequencing validated all potential pathogenic sequence variants. Briefly, 20-bp primers flanking the given region and yielding amplicons of 300–600 bp were designed (primer sequences available on request). The polymerase chain reaction (PCR) was performed in a total volume of 20 μL, containing 20 ng genomic DNA, 1 mM of each primer (Eurogentec, Liège, Belgium), and 10 μL FastStart PCR Master Mix (Roche, Basel, Switzerland). Amplification was performed in a GeneAmp 9700 thermal cycler (Applied Biosystems, Carlsbad, CA, USA) with the following conditions: 1 min at 95°C, 35 cycles of 1 min at 94°C, 1 min at 58°C, 1 min at 72°C, and, a final elongation step at 72°C for 10 min. PCR-amplified products were purified with an Invitek MSB Spin PCRapace kit (STRATEC Molecular GmbH, Berlin, Germany). Sanger sequencing was done in a final reaction volume of 10 μL, using BigDye Terminator v3.1 (Applied Biosystems) with forward and reverse primers. Fragments were separated on an ABI PRISM 3100 genetic analyzer (Applied Biosystems). Sequences were analyzed using Chromas 2.23 software (Technelysium, Tewantin, QLD, Australia).

3. Results and Discussion

3.1. IROme: Design and Validation of the Assay

The vast genetic heterogeneity of RP prompted us to develop a custom-designed hybridization-based targeted exon capture assay, called IROme. Enrichment was targeted towards a total of 63 genes (942 exons), of which 60 genes were linked to RP, LCA, and related retinal dystrophies (Table 1). The exon ORF15 of RPGR was not included in the assay because of the presence of repetitive sequences. Two RP- or LCA-linked genes, IDH3B and RD3, had been reported only in a single family so far and were not included in this version of IROme. Conversely, two candidate genes that were linked to retinal degeneration in mice, but not humans, were added to the assay (TUB and LPCAT1). A third candidate gene located on chromosome X, CNGA2, was included because of its homology to CNGA1. The total of targeted regions spans 394′758 bp.

Of note, after the design of IROme was completed, TTC8 (BBS8/RP51), C8ORF37, and MAK were linked to RP, and KCNJ13 and NMNAT1 to LCA. These latter genes, as well as IDH3B and RD3, will be included in a future version of IROme.

Patients 1–4 had previously been investigated by APEX technology for known RP-linked mutations [5]. All nucleotides tested by APEX were correctly detected by IROme, with a 98.9% accuracy of the sequence reads for nucleotides at a homozygous state (Table 2). A p.USH2A-V2562A mutation had been detected by APEX in patient 2 in a heterozygous state, and this was correctly validated by IROme (46.8% of the sequence reads at 47-fold coverage).

nt tested
nt detected
by IROme
% reads


The nucleotides (nt) tested by APEX represent validated RP-linked mutations or variants. The mean coverage (cvg) refers to the average of the coverage of all exons where the mutations are located. The percentage of sequence reads generated by IROme and correctly calling the nucleotides at homozygous state are indicated.

As an additional control, the IROme assay was tested on genomic DNA of a previously described family of Algerian origin, affected by LCA or early onset retinal degeneration [14]. The causative 6-base in-frame duplication c. TULP1-1593_1598dupTTCGCC was readily detected in exon 15 (Table 3, patient 5).


M het norm
F het norm
1432.644315.31205934231/1p.CRX-Q105X1758.8M het norm

For each patient, the total number of Mb (106 bp) sequenced on the Roche 454 GS Junior (total seq Mb) and the average read length (read length bp) are indicated. The median fold coverage (cvg) was extracted from the unique depth information. From all the sequence variants (total seq var), first only the sequence variants located in coding sequences were analyzed (cds seq var), with filtering (filt seq var) and prioritizing (prio seq var) according to Figure 2. The sequence variants eventually tested and validated by Sanger sequencing (test/val seq var) are also indicated. For each potential mutation, the coverage (cvg pot mut) and the percentage of sequence reads reporting the potential mutation (mut reads %) are indicated. For cosegregation analysis, “?” indicates absence of available family members and/or simplex cases. For patients 1 and 14, the mother (M) and/or the father (F) are healthy heterozygous carriers (het norm).
3.2. IROme: Variant Detection, Coverage, and Data Filtering

A total of 23 RP patients were analyzed by IROme (Table 3). Pyrosequencing generated an average of  Mb per patient, with an average read length of  bp. These long read lengths are comparable to published analyses, where the Roche 454 GS Junior generated the longest read lengths, in comparison to the other benchtop high-throughput sequencing platforms, MiSeq (Illumina) and Ion Torrent PGM (Life Technologies) [13].

On average per patient, sequence variants were found (range: 736–1′826). Among these, were located in coding sequences, and a further were changing the amino acid sequence. By considering all patients, the median coverage was 17-fold, with a maximal 112-fold coverage in one exon of patient 16 (Figure 3). No coverage was observed for four exons (0.3%): exons 1 of RP9, IMPDH1, and LPCAT1 and an alternative exon 2 of CNGA2. These exons contained GC-rich and/or repetitive sequences impeding efficient probe design and targeting [15]. Another 15 exons were not covered in all patients (1.6%). Because these exons were not restricted to the 5′ regions, absence of coverage was attributed to technical limitations or, as observed for patient 9, to a deletion (see below).

For patients 20 and 21, two potential heterozygote mutations had been detected at 22.6% (53-fold coverage) and 21.3% (61-fold coverage), respectively. However, these two sequence variants could not be validated by Sanger sequencing. For further patient analyses, a more stringent threshold up to 35% of sequence reads might be used for prioritization of sequence variants. Alternatively, a dynamic threshold could be implemented, starting at a high stringency and going down until one or two mutations are identified.

In conclusion, the design of IROme resulted in an over 98% coverage of the targeted exons. The variant detection workflow could be improved by further increasing the quality of the sequencing data, that is, by using a benchtop sequencer less prone to homopolymer-associated insertion/deletion errors (e.g., MiSeq, Illumina) [13] and high-fidelity DNA polymerases [16].

3.3. IROme: Molecular Diagnosis on RP Patients

IROme analysis yielded in definite diagnosis for 55% of the RP patients, that is, 12 out of 23 patients (Patients 4, 5, 8, 9, 10, 11, 12, 13, 16, 17, 19, and 23). This was in line with the approximately 60% success rate reported for exome capture strategies to identify Mendelian disease genes [4], but represented a 5-fold increase in mutation detection as compared to the APEX assay [6]. A solution-based targeted exon capture assay similar to IROme had also identified disease-causing mutations in 11 out of 17 families affected by various retinal degenerations (65%) [17]. In contrast, in a cohort of 100 RP patients, array-based targeted exon capture resulted in the identification of pathogenic mutations in 36 individuals (36%) [15]. Amplicon-based approaches identified potential mutations in 24% of patients affected by retinal degenerations (5/21) [18], in 79% of ADRP patients (15/19) [19], and 24% of LCA patients (4/17) [20].

In addition to the control (patient 5), only the p.PROM1-R373C mutation identified in patient 10 had been previously described [21], further underscoring the importance of screening RP-linked genes for the presence of new mutations.

The workflow for variant detection was not immediately successful for two patients. For patient 9, a deletion of exons 45–47 in ABCA4 was only found by analyzing the coverage data. For patient 16, the 33 bp insertion in PRPF31 was detected by Sequence Pilot, but not Reference Mapper software.

Potential mutations were found in three patients (13%). Patient 1 inherited from her healthy mother a heterozygous p.C2ORF71-R571delRTVVPP mutation and from her healthy father a heterozygous p.FSCN2-P231S mutation. Digenic RP has been linked so far to heterozygous PRPH2 and ROM1 mutations [2], and further analyses will be necessary to validate this molecular diagnostic. Patient 2 and 20 had, respectively, two and one potential mutation, but no family members were available to confirm the result.

Results were questionable for two additional patients. Patient 6 carried a p.RHO-R252P mutation that had been previously reported [22]. However, unaffected family members were not available to confirm this dominant mutation. Also, a heterozygous p.CRX-Q105X sequence variant was detected in patient 14, but his healthy mother was also carrying it.

Finally, no molecular diagnostic could be established for six patients (26%): in patients 18 and 21 no potential mutations were found by IROme analysis, in patients 7 and 15 the potential mutation did not segregate with disease in the family, and in patients 3 and 22 heterozygous mutations were found in genes only reported for recessive inheritance (CLRN1, EYS).

Of note, all these patients carry novel sequence variants in noncoding regions. To prioritize for potential disease-causing sequence variants in these regions, systematic annotation should not only cover splicing sites, 5′- and 3′-UTRs, but also implement detailed information about transcription factor binding sites and regulatory elements located in the potential proximal promoter regions. Promoter sequence variants could then be tested by reporter transactivation assays (e.g., luciferase reporter assays), but this time-consuming approach cannot be implemented in a routine molecular diagnostic lab.

4. Conclusions

The custom designed in solution-based targeted exon capture assay IROme efficiently detected disease-causing mutations in 55% of RP patients (12/23). A 99.7% coverage of the targeted regions was obtained. The first translated exon often contains sequences with a high GC content in its 5′-UTR that hinders an efficient capture [23]. Remarkably, more than 95% of exons 1 (60/63) were successfully enriched by IROme. In comparison, a pilot study carried out in our laboratory on 25 patients using whole exome sequencing (SureSelect, Agilent) resulted in no coverage of promoter regions, highly variable coverage of 3′-UTRs, and several genes had their first translated exon very poorly covered. For instance, the first exons of the following RP-linked genes could not be correctly analyzed: C2ORF71, CA4, CABP4, CERKL, CNGA1, FAM161A, FSCN2, GUCY2D, IMPDH1, LPCAT1, MERTK, RDH12, RP9, and RPGR (D. F. Schorderet, unpublished results). It is tempting to speculate that the additional sequences upstream of exon 1 included in IROme further enhanced the performance of the NimbleGen exome capture technology, that reportedly has more specific targeting and a higher percentage of on-target reads than competing products [23, 24]. However, because the costs for whole exome sequencing have dramatically decreased to about 1000 $ per patient, this method may in the future replace target enrichment and resequencing, providing that a new line of “whole exome” kits covering effectively all exons, including the first one, of all genes, will become commercially available [24].

Meanwhile, custom-designed target enrichment and subsequent next-generation sequencing are a cost-efficient approach for the molecular diagnosis of retinal dystrophies, also with respect to the relative ease of data handling and analysis [25]. Finally, the median global coverage of 17-fold observed with the IROme assay also indicated the possibility to include additional retinal degeneration-linked genes, newly discovered ones or candidate genes.


The authors thank Etienne Bagnoud for technical support in informatics. This paper is supported by Swiss National Science Foundation Grants 31003A-122269 (to P. Escher and D. F. Schorderet) and 31003A_138492 (to P. Escher).


  1. D. T. Hartong, E. L. Berson, and T. P. Dryja, “Retinitis pigmentosa,” The Lancet, vol. 368, no. 9549, pp. 1795–1809, 2006. View at: Publisher Site | Google Scholar
  2. W. Berger, B. Kloeckener-Gruissem, and J. Neidhardt, “The molecular basis of human retinal and vitreoretinal diseases,” Progress in Retinal and Eye Research, vol. 29, no. 5, pp. 335–375, 2010. View at: Publisher Site | Google Scholar
  3. S. Ferrari, E. Di Iorio, V. Barbaro, D. Ponzin, F. S. Sorrentino, and F. Parmeggiani, “Retinitis pigmentosa: genes and disease mechanisms,” Current Genomics, vol. 12, no. 4, pp. 238–249, 2011. View at: Publisher Site | Google Scholar
  4. C. Gilissen, A. Hoischen, H. G. Brunner, and J. A. Veltman, “Disease gene identification strategies for exome sequencing,” European Journal of Human Genetics, vol. 20, no. 5, pp. 490–497, 2012. View at: Google Scholar
  5. J. Zernant, M. Külm, S. Dharmaraj et al., “Genotyping microarray (disease chip) for leber congenital amaurosis: detection of modifier alleles,” Investigative Ophthalmology and Visual Science, vol. 46, no. 9, pp. 3052–3059, 2005. View at: Publisher Site | Google Scholar
  6. A. Ávila-Fernández, D. Cantalapiedra, E. Aller et al., “Mutation analysis of 272 Spanish families affected by autosomal recessive retinitis pigmentosa using a genotyping microarray,” Molecular Vision, vol. 16, pp. 2550–2558, 2010. View at: Google Scholar
  7. M. L. Metzker, “Sequencing technologies—the next generation,” Nature Reviews Genetics, vol. 11, no. 1, pp. 31–46, 2010. View at: Publisher Site | Google Scholar
  8. L. G. Biesecker, “Exome sequencing makes medical genomics a reality,” Nature Genetics, vol. 42, no. 1, pp. 13–14, 2010. View at: Publisher Site | Google Scholar
  9. S. B. Ng, E. H. Turner, P. D. Robertson et al., “Targeted capture and massively parallel sequencing of 12 human exomes,” Nature, vol. 461, no. 7261, pp. 272–276, 2009. View at: Publisher Site | Google Scholar
  10. S. B. Ng, K. J. Buckingham, C. Lee et al., “Exome sequencing identifies the cause of a mendelian disorder,” Nature Genetics, vol. 42, no. 1, pp. 30–35, 2010. View at: Publisher Site | Google Scholar
  11. M. J. Bamshad, S. B. Ng, A. W. Bigham et al., “Exome sequencing as a tool for Mendelian disease gene discovery,” Nature Reviews Genetics, vol. 12, no. 11, pp. 745–755, 2011. View at: Publisher Site | Google Scholar
  12. L. Mamanova, A. J. Coffey, C. E. Scott et al., “Target-enrichment strategies for next-generation sequencing,” Nature Methods, vol. 7, no. 2, pp. 111–118, 2010. View at: Publisher Site | Google Scholar
  13. N. J. Loman, R. V. Misra, T. J. Dallman et al., “Performance comparison of benchtop high-throughput sequencing platforms,” Nature Biotechnology, vol. 30, no. 5, pp. 434–439, 2012. View at: Publisher Site | Google Scholar
  14. A. Mataftsi, D. F. Schorderet, L. Chachoua et al., “Novel TULP1 mutation causing leber congenital amaurosis or early onset retinal degeneration,” Investigative Ophthalmology and Visual Science, vol. 48, no. 11, pp. 5160–5167, 2007. View at: Publisher Site | Google Scholar
  15. K. Neveling, R. W. Collin, C. Gilissen et al., “Next-generation genetic testing for retinitis pigmentosa,” Human Mutation, vol. 33, no. 6, pp. 963–972, 2012. View at: Publisher Site | Google Scholar
  16. I. Vandenbroucke, H. Van Marck, P. Verhasselt et al., “Minor variant detection in amplicons using 454 massive parallel pyrosequencing: experiences and considerations for successful applications,” Biotechniques, vol. 51, no. 3, pp. 167–177, 2011. View at: Google Scholar
  17. I. Audo, K. M. Bujakowska, T. Leveillard et al., “Development and application of a next-generation-sequencing (NGS) approach to detect known and novel gene defects underlying retinal diseases,” Orphanet Journal of Rare Diseases, vol. 7, no. 1, article 8, 2012. View at: Publisher Site | Google Scholar
  18. S. J. Bowne, L. S. Sullivan, D. C. Koboldt et al., “Identification of disease-causing mutations in autosomal dominant retinitis pigmentosa (adRP) using next-generation DNA sequencing,” Investigative Ophthalmology and Visual Science, vol. 52, no. 1, pp. 494–503, 2011. View at: Publisher Site | Google Scholar
  19. J. Song, N. Smaoui, R. Ayyagari et al., “High-throughput retina-array for screening 93 genes involved in inherited retinal dystrophy,” Investigative Ophthalmology and Visual Science, vol. 52, no. 12, pp. 9053–9060, 2011. View at: Publisher Site | Google Scholar
  20. F. Coppieters, B. de Wilde, S. Lefever et al., “Massively parallel sequencing for early molecular diagnosis in Leber congenital amaurosis,” Genetics in Medicine, vol. 14, no. 6, pp. 576–585, 2012. View at: Publisher Site | Google Scholar
  21. Z. Yang, Y. Chen, C. Lillo et al., “Mutant prominin 1 found in patients with macular degeneration disrupts photoreceptor disk morphogenesis in mice,” Journal of Clinical Investigation, vol. 118, no. 8, pp. 2908–2916, 2008. View at: Publisher Site | Google Scholar
  22. E. R. Grinberg, L. I. Dzhemileva, and E. K. Khusnutdinova, “The novel R252P Mutation of the RHO gene in patients with retinitis pigmentosa from Bashkortostan,” Molecular Biology, vol. 41, no. 4, pp. 746–748, 2007. View at: Publisher Site | Google Scholar
  23. P. Frommolt, A. T. Abdallah, J. Altmüller et al., “Assessing the enrichment performance in targeted resequencing experiments,” Human Mutation, vol. 33, no. 4, pp. 635–641, 2012. View at: Publisher Site | Google Scholar
  24. A. M. Sulonen, P. Ellonen, H. Almusa et al., “Comparison of solution-based exome capture methods for next generation sequencing,” Genome Biology, vol. 12, no. 9, article R94, 2011. View at: Publisher Site | Google Scholar
  25. M. Fromer, J. L. Moran, K. Chambert et al., “nd statistical genotyping of copy-number variation from whole-exome sequencing depth,” The American Journal of Human Genetics, vol. 91, no. 4, pp. 597–607, 2012. View at: Publisher Site | Google Scholar

Copyright © 2013 Daniel F. Schorderet et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1610 Views | 667 Downloads | 20 Citations
 PDF  Download Citation  Citation
 Download other formatsMore
 Order printed copiesOrder
 Sign up for content alertsSign up

You are browsing a BETA version of Click here to switch back to the original design.