Experimental Eye Research 116 (2013) 386e394

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Genetic high throughput screening in Retinitis Pigmentosa based on high resolution melting (HRM) analysis Ander Anasagasti a,1, Olatz Barandika a,1, Cristina Irigoyen b, Bruno A. Benitez c, Breanna Cooper c, Carlos Cruchaga c, d, Adolfo López de Munain a, e, f, g, h, Javier Ruiz-Ederra a, * a

Department of Neuroscience, Instituto Biodonostia, Paseo Dr. Begiristain s/n, E-20014 San Sebastián, Spain Department of Ophthalmology, Hospital Universitario Donostia, San Sebastián, Spain Department of Psychiatry, Washington University, St. Louis, MO, USA d Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University, St. Louis, MO, USA e Department of Neurology, Hospital Universitario Donostia, San Sebastián, Spain f CIBERNED, Centro de Investigaciones Biomédicas en Red sobre Enfermedades Neurodegenerativas, Instituto Carlos III, Ministerio de Economía y Competitividad, Spain g Department of Neurosciences, University of the Basque Country UPV-EHU, Spain h Euskampus, University of the Basque Country UPV-EHU, Spain b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 June 2013 Accepted in revised form 14 October 2013 Available online 24 October 2013

Retinitis Pigmentosa (RP) involves a group of genetically determined retinal diseases caused by a large number of mutations that result in rod photoreceptor cell death followed by gradual death of cone cells. Most cases of RP are monogenic, with more than 80 associated genes identified so far. The high number of genes and variants involved in RP, among other factors, is making the molecular characterization of RP a real challenge for many patients. Although HRM has been used for the analysis of isolated variants or single RP genes, as far as we are concerned, this is the first study that uses HRM analysis for a highthroughput screening of several RP genes. Our main goal was to test the suitability of HRM analysis as a genetic screening technique in RP, and to compare its performance with two of the most widely used NGS platforms, Illumina and PGM-Ion Torrent technologies. RP patients (n ¼ 96) were clinically diagnosed at the Ophthalmology Department of Donostia University Hospital, Spain. We analyzed a total of 16 RP genes that meet the following inclusion criteria: 1) size: genes with transcripts of less than 4 kb; 2) number of exons: genes with up to 22 exons; and 3) prevalence: genes reported to account for, at least, 0.4% of total RP cases worldwide. For comparison purposes, RHO gene was also sequenced with Illumina (GAII; Illumina), Ion semiconductor technologies (PGM; Life Technologies) and Sanger sequencing (ABI 3130xl platform; Applied Biosystems). Detected variants were confirmed in all cases by Sanger sequencing and tested for co-segregation in the family of affected probands. We identified a total of 65 genetic variants, 15 of which (23%) were novel, in 49 out of 96 patients. Among them, 14 (4 novel) are probable disease-causing genetic variants in 7 RP genes, affecting 15 patients. Our HRM analysis-based study, proved to be a cost-effective and rapid method that provides an accurate identification of genetic RP variants. This approach is effective for medium sized ( C) point mutation in homozygosis in three samples (g.149301335, in green); a type I (A > G) point mutation on a splice region of the gene found in 2 samples (g.149301194, in dark blue); and a type I (A > G) missense variant in homozygosis, which is predicted as probably damaging in one sample (p.Pro293Leu; g.149301253, in red). Note that samples are in triplicate. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).

coverage necessary to get an optimal positive predictive value for the SNP-calling algorithm (Vallania et al., 2010). All the amplicons had at least 30 coverage per allele and sample. All procedures were performed according to the manufacturer’s specifications. For full details see Cruchaga et al., (2012), Haller et al., (2012), Jin et al., (2012). 2.8. Ion Personal Genome Machine (PGM-Ion Torrent) The Ion Torrent Personal Genome Machine (PGM-Ion Torrent, Life Technologies, Spain) was used for sequencing of the RHO gene associated with dominant RP (http://www.sph.uth.tmc.edu/Retnet/ ) in a total of 35 RP patients who presented a clear dominant inheritance pattern. A total of 10 primer pairs were designed using the Ion AmpliSeq Designer web-hosted software, available at www. ampliseq.com. Ion AmpliSeq Library Preparation Kit (Life Technologies) was used to construct amplicon libraries from genomic target regions for shotgun sequencing following the manufacturer’s instructions. The quality and the quantity of each library were assessed with the Agilent 2100 Bioanalyzer instrument (DNA High Sensitivity Chip, Agilent Technologies, Sunnyvale CA, USA). DNA libraries were amplified clonally by emulsion PCR applying the manufacturer’s instructions. Sequencing of the amplicon libraries was carried out using Ion 316 chips and following the Ion PGM 200 Sequencing Kit protocol. Signal processing and base calling of data generated were performed using Torrent Suite version 3.4.1 software. Genomic variants were detected by aligning generated sequences with GRCh37/hg19 human genome. 2.9. Bioinformatics analysis The sequencing data from the 1000 Genome Project and the Exome Variant Server database (http://evs.gs.washington.edu/EVS/ ) were used to estimate the frequency of novel and rare (minor allele frequency less than 5%) missense, nonsense and splice site variants in samples unselected for studies of RP.

Among the variants found, we selected those potential diseasecausing variants, according to the following established criteria (Bell et al., 2007): 1) non-sense, frameshift and splice site variants; 2) variants reported as pathogenic; 3) variants that did not occur with a frequency greater than 0.5% of the non-syndromic RP patients and 4) variants were consistent with the known pattern of inheritance of the selected RP gene. 2.12. Pathogenicity score In order to identify the phenotypically relevance of known and novel genomic variants detected, functional annotations for genomic variation data were analyzed using the web-based tools Ensembl (http://www.ensembl.org/), NCBI (http://www.ncbi.nlm. nih.gov/) and SNPnexus (http://www.snp-nexus.org/). 2.13. Web sources 1000 Genomes, www.1000genomes.org/ AmpliSeq Designer, www.ampliseq.com e-PCR software http://www.ncbi.nlm.nih.gov/projects/e-pcr/ Ensembl, http://www.ensembl.org/ Exome Variant Server database (EVS) (http://evs.gs.washington. edu/EVS/) In-Silico http://genome.ucsc.edu/cgi-bin/hgPcr International HapMap Project, www.hapmap.org/ NCBI dbSNP, http://www.ncbi.nlm.nih.gov/projects/SNP/ NCBI Variation Viewer, http://www.ncbi.nlm.nih.gov/sites/ varvu RetNet, http://www.sph.uth.tmc.edu/Retnet/ SNPnexus http://www.snp-nexus.org/ The Human Gene Mutation Database (HGMD), www.hgmd.cf.ac. uk/ The Human Genome Variation Society (HGVS), www.hgvs.org/ 3. Results Using an HRM analysis-based genetic screening, we were able to detect genetic variants that differed only in one nucleotide in up to 330 bp amplicons, with all class of SNPs changes detected including class IV (a change between A and T), which produces the smallest shift in Tm within all genetic variations of less than 0.2  C. Apart from single point mutations, we were able to detect small insertions and deletions (up to 6 bp). We included variants that have been previously reported as pathogenic and those novel variants with a potential implication in the pathogenesis of RP, inferred by the change in the nucleotide sequence, using the web-based tools described above. We also included those variants with a potential

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Table 2 List of genetic variants found by HRM analysis. Asterisks (*) indicate novel variants. In bold are those variants likely implicated in the RP pathogenesis. Note that some probands have more than one variant. Note that changes are shown in those cases affecting coding regions. Abbreviations: HapMap: International HapMap Project; HGVS: The Human Genome Variation Society; HGMD: The Human Gene Mutation Database; Id: Identification of the variant, by the rs number when known or by the genomic position when unknown; na: not available; MAF: minor allele frequency; 1000 Genomes: The 1000 Genomes Project. Proband

Gene

Change

Id

Inheritance

Cigosity

MAF

Prediction

Reference

1 4 4 4 4 7 7 7 8 8 8 9 9 9 12 19 20 20 24 24 24 25 27 27 32 32 38 38 39 42 44 45 45 45 45 46 46 46 47 47 47 48 54 54 56 56 57 59 59 59 59 59 61 61 61 61 63 63 63 64 64 64 66 67 68 68 68 68 71 72 73

PDE6A BBS1 MKKS MKKS MKKS PDE6A RHO RHO PDE6A PDE6A PRPF31 RHO RHO PRPF31 PDE6A PRPH2 PDE6A PRPF31 PDE6A PRPF31 PRPH2 CRB1 PDE6A LRAT PDE6A PDE6A BBS12 BBS4 USH3A PRPF3 RHO PDE6A RHO RHO PRPH2 BBS1 BBS10 BBS9 PDE6A PDE6A PRPF31 PRPF3 PDE6A PRPF3 PDE6A PDE6A PRPF3 CRB1 ROM1 RHO PDE6A PDE6A MKKS MKKS MKKS BBS10 PDE6A PDE6A PDE6A BBS4 BBS4 BBS9 PDE6A PRPF31 ROM1 PRPH2 PRPF3 PRPF3 RHO PDE6A PRPF31

c.859-63A>G p.Met390Arg p.Gly532Val p.Arg517Cys

rs11167487 rs113624356 rs1545 rs1547 g.10393205T>A* rs75282782 rs2071093 rs2071092 rs10045293 rs11167487 rs73062632 CS941542 rs2071093 rs35315983 rs147010346 rs62645935 rs113137904 rs73062632 g.149278949G>A* rs4806711 rs361524 rs28939720 rs17711594 g.155670099G>A* g.149247226T>C* rs72830272 rs138036823 rs2277598 rs201534956 rs150312839 CM930649 rs14898083 rs29001566 rs2071093 rs361524 rs113624356 rs35676114 rs11773504 rs11167487 rs78775072 rs145505952 g.150316774G>C* rs17711594 g.150325383A>G* rs121909835 rs114973968 g.150325383A>G* rs73071678 g.62382123C>_* g.129252475A>C* rs75319698 rs115072370 rs1545 rs1547 g.10393205T>A* rs73383520 rs12109444 rs11167487 rs6864267 rs2277598 rs75295839 g.33303848T>G* rs10045293 rs4806711 g.62382123C>_* rs114062933 rs116427288 rs77626125 g.129247835G>C* rs34656337 rs192573566

ar ar ar ar ar ar ad ad ar ar ad ad ad ad ar ad ar ad ar ad ad ar ar ar ar ar ar ar ar ad ad ar ad ad ad ar ar ar ar ar ad ad ar ad ar ar ad ar ad ad ar ar ar ar ar ar ar ar ar ar ar ar ar ad ad ad ad ad ad ar ad

hom hom het het het het het het het hom het het het het het het het het het het het het het het het het het het het het het het het het het hom het het hom het het het het het hom hom het het het het het het het het het het hom het het het het het het hom het het het het het hom het

0.330 T c.322þ115T>G p.Tre418Met c.-39þ14A>G c.*13C>T p.Thr126Met p.Gln492His

c.2200-73G>A p.Ile39Thr p.Ile354Thr c.*1038G>A c.2877þ25008T>C p.Gln28Hist p.Ala262Thr p.Pro347Gln c.*43C>A c.*13C>T Met390Arg p.Pro539Leu p.Ala455Thr c.859-63A>G p.Hist655Tyr p.Met65Lys p.Gln492His p.Val685Met p.Pro293Leu c.820-8C>T del2123C HET p.Ile321Leu c.998þ11C>T c.1066-57A>G p.Gly532Val p.Arg517Cys c.*769G>C c.933þ4C>T c.859-63A>G c.2136-17G>T p.Ile354Thr p.Lys46Arg c.2507-37T>A c.-39þ14A>G del2123C HET c.-11A>C c.1760-12C>T c.*42C>T p.Val87Leu delCAGAGA c.*231G>A

HGVS HGVS 1000Genomes 1000Genomes;HapMap 1000Genomes;HapMap 1000Genomes Bell et al., 1994 HGVS HGVS 1000Genomes Sohocki et al., 2001 HGVS HGVS SNPnexus Rio Frio et al., 2009 HGVS Neveling et al., 2012 1000Genomes;HapMap

dbSNP 1000Genomes Hichri et al., 2005 HGVS HGVS Bunge et al., 1993 HGVS Tam et al, 2004 HGVS HGVS Mykytyn et al., 2002 dbSNP HGVS 1000Genomes;HapMap HGVS HGVS 1000Genomes;HapMap Corton et al., 2010 1000Genomes HGVS SNPnexus SNPnexus HGVS HGVS 1000Genomes;HapMap HGVS dbSNP HGVS 1000Genomes;HapMap HGVS Hichri et al., 2005 HGVS 1000Genomes;HapMap Rio Frio et al., 2009 SNPnexus HGVS HGVS HGVS SNPnexus HGVS HGVS

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391

Table 2 (continued ) Proband

Gene

Change

Id

Inheritance

Cigosity

MAF

Prediction

Reference

76 76 77 79 80 80 83 85 89 92 92 94 96 103 103 103 103 105 111 113

BBS4 BBS4 PRPF31 PDE6A CRB1 USH3A PDE6A RHO PRPF31 PDE6A PDE6A USH3A PRPF3 BBS12 BBS4 BBS4 BBS2 PDE6A PDE6A PRPH2

p.Ile354Thr c.*608G>T g.54619163G>T c.2507-37T>A p.Ile211Phe

rs2277598 rs12898814 ad rs10045293 g.197396943A>T* g.150690575->GACG* rs10045293 rs192412661 CS044091 rs12109444 rs11167487 rs55842922 g.150325252->T* g.123664663G>A* rs2277598 rs117852179 rs11373 rs113137904 rs17711594 rs361524

ar ar het ar ar ar ar ad ad ar ar ar ad ar ar ar ar ar ar ad

het het na het het het het het het het het het het hom hom het het het het het

0.450 0.120 UTR 50 0.100 0.330 na 0.100 T c.422-1G>A c.933þ4C>T c.859-63A>G c.-289G>A p.Gly539Asp p.Ile354Thr c.1249-35G>C p.Ile123Val c.-42C>T p.Gln492His c.*13C>T

effect on the regulation of gene expression (UTRs). In order to validate our methodology, RHO gene was sequenced using Sanger in those RP patients with a clinical diagnosis compatible with adRP. We found that all genetic variants found by direct sequencing were also found detected by our HRM-based procedure. Furthermore, a comparative analysis in the capacity on variant detection between the HRM analysis-based approach and NGS platforms (PGM-Ion Torrent and Illumina) revealed a comparable performance among the three technologies, since all RHO variants found by HRM analysis (shown in Table 2), were also detected by PGM-Ion Torrent and Illumina. We detected a total of 65 genetic variants in 16 RP genes, present in 49 patients (Table 2). Out of the 65 variants, 14 were found in 7 RP genes in 15 patients, and were selected for follow-up, since they met the criteria for potential disease-causing variants, described in Methods. 8 out of 14 variants were found in heterozygosis in 8 patients in adRP genes, and 6 out of 14 variants were found in 7 patients in homozygosis in arRP genes (these 14 variants are highlighted in Table 2). Out of the 65 variants, 15 (23%) were novel. We found a total of 4 indels (3 of which are novel): two novel insertions, consisting on a single and a 4-nt insertion; and 2 deletions, consisting on a novel point deletion (Fig. 2) and a 6-nt deletion (rs34656337). In all cases, segregation analysis revealed that all variants cosegregated with affected family members of probands, and were not present in non-affected first-grade relatives. 4. Discussion In the present paper, we validate the suitability of a high throughput genetic screening method for mutation discovery in RP, based on HRM analysis, which is a fast and highly sensitive postPCR method that provides a rapid identification of genetic variations. Out of more than 80 genes linked so far to syndromic and non-syndromic forms of RP (http://www.sph.uth.tmc.edu/Retnet/), at least 67 RP genes (over 80% of the total) fulfill the criteria used in the present work for HRM-based screening (see Tables 1 and S1). Moreover, this list could be expanded to those genes involved in retinopathies with similar clinical outcome, such as cone-rod dystrophy, or even to other genetic ocular diseases. Although the use of HRM analysis has been reported for genotyping or for mutation screening purposes in RP, these studies were focused either in few

1000Genomes;HapMap HGVS HGMD HGVS 1000Genomes;HapMap HGVS SNPnexus Hichri et al., 2005 HGVS HGVS HGVS 1000Genomes;HapMap HGVS

genetic variants or in one RP or LCA gene (Aguirre-Lamban et al., 2010; Cui et al., 2013; Duno et al., 2013; Sergouniotis et al., 2011; Xu et al., 2011). To our best knowledge, this is the first paper that uses HRM analysis technique for a high-throughput screening of several RP genes that was able to characterize a number of likely pathogenic variants of several genes involved in RP and BBS in an accurate and cost-effective manner. Using our approach, we were able to detect up to 14 probable disease-causing genetic variants in 7 genes, among 15 RP patients, that were validated by Sanger sequencing and segregated with their first-degree affected relatives. These genetic variants were present either in homozygosis in arRP genes or in heterozygosis in adRP analyzed genes. Among the variants detected, we found 4 microindels, consisting on 2 insertions and 2 deletions, two of which are likely pathogenic (see Table 2). This is of relevance, since these kinds of variants may have a low detection rate by some of the NGS platforms. However, we could not compare the sensitivity of indels detection between HRM analysis and the two NGS platforms used, since no indels were found within the RHO gene. With the exception of USH2A; RHO and RPGR with an estimated prevalence of about 8e10% each, which in aggregate account for about 30% of all cases of RP (Bowne et al., 2008, 2011; Hartong et al., 2006), most RP genes are responsible for a small proportion of cases, with prevalence ranging from less than 0.1e5%. Thus, the contribution to RP of the genes we included in the present study is relatively high considering this scenario: 10 genes that in aggregate account for about 17% of all cases of RP (experimental group I) and 7 BBS genes responsible of up to 70% of all cases of BardeteBiedl syndrome (experimental group II) (Hartong et al., 2006; Mockel et al., 2011). Therefore, overall diagnosis rate is relatively high (w15%, 15/96), considering that we analyzed RP genes responsible for about 20% of total cases worldwide. This approach has proven effective for the genetic screening of those RP genes that meet the criteria described here. Using our approach, we were able to screen 16 medium-sized RP genes within 7280 working days, including sample preparation; HRM analysis; Sanger sequencing and analysis of candidate variants, with an estimated costs of 15.000 V for a total of 38 kb analyzed in 96 samples. This resulted in 4.1 V/Kb/patient, which is less than half of the cost/Kb/patient involved in PGM-Ion Torrent sequencing. This technique was especially cost-effective for the screening of those genes involved in certain types of syndromic RP, with a reduced

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Fig. 2. HRM difference plots of likely pathogenic variants detected in 4 RP genes. A. ROM1: a novel point deletion in a 281 bp fragment in 2 samples (del 2123C; in blue); B. PRPF31: a novel point mutation compatible with a splice variant in a 248 bp fragment in 1 sample (g.54626832G > A, in blue). C. BBS10: a missense variant in a 299 bp fragment in 2 samples (Phe539Leu, in blue) D. RHO: 3 different genetic variants were detected in a 233 bp fragment: a missense variant in 1 sample (p.Ile321Leu, in green); a point mutation in a splice region of the gene in 1 sample (g.129252450, in red); and a missense variant in 1 sample (p.Pro347Gln, in dark blue). All variants were confirmed by direct sequencing. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).

number of patients and/or repertoire of causal genes. Using this approach, we interrogated the 7 most prevalent BBS genes in 7 working days and were able to detect 2 putative pathogenic variants in 75% of the cases (3 out of 4 BBS patients), one of which is novel (see Table 2). The main limitation of our strategy is that HRM analysis becomes work-intensive when analyzing large RP genes, and NGS seems to be the best strategy currently available for analyzing those large genes involved in RP with up to 19 kb such as USH2A or GPR98. However, only a relative small number of RP genes do not meet the criteria we established for HRM analysis (about 15 out of 82), supporting the validity of the strategy used here for most RP genes, which can be combined with the use of NGS of the large RP genes. The development of NGS has lead to a quantum shift in sequencing capacity, which will likely have a remarkable impact in the molecular characterization of complex monogenic diseases in a near future, where RP is a good example. Indeed, NGS has been used to re-analyze those patients with no previous mutations detected in “common” RP genes, and to analyze the suitability of NGS in the diagnostic context (Bowne et al., 2011; Fu et al., 2013; Glockle et al., 2013; Neveling et al., 2012; O’Sullivan et al., 2012; Redin et al., 2012; Shanks et al., 2013). One of the limitations of NGS is related with the great amount of variants detected that need a filtering process. Despite the fact that NGS technology allows the simultaneous analysis of several RP genes, they detect a myriad of variants that need further processing. For instance, (Neveling et al., 2012), analyzing 111 RP genes by NGS detected 128.000 variants. In another study that analyzed 30 RP genes, they obtained about 9000 variants (Redin et al., 2012), comparing with 65 in our case. Another goal of the present work was to compare the power of genetic variant detection of our HRM-based methodology, with two of the most widely used NGS platforms. For that purpose we analyzed RHO gene, one of the most prevalent RP genes, responsible of over 25% of adRP and 1% of arRP (Bowne et al., 2008, 2011; Hartong et al., 2006). Using our HRM-based method, we were able

to detect the same number of likely pathogenic variants in RHO, than those detected with Illumina or PGM-Ion Torrent sequencing methods. However, despite no differences in mutation discovery power were observed, we must take into account that this comparison was made at a single gene level. Therefore, a more extensive comparison involving several genes would be necessary, in order to make a more precise comparison between these different methods. Taking into account the extreme cost-effective and fast performance obtained within the set of RP genes tested, HRM can be an affordable diagnostic tool for most of RP genes in a large cohort of patients, provided that our results can be extrapolated to the rest of RP genes that fulfill the inclusion criteria for HRM analysis (shown in Table S1). Furthermore our approach can also be used as a complementary tool for NGS. On one hand, it is expected that novel RP genes will be discovered by NGS technology. Thus, HRM analysis could be applied in a cost-effective way to interrogate those emerging RP genes in patients without a clear molecular diagnosis for RP in previous conventional genetic analysis, since the use of NGS is not cost-effective when analyzing a small group of genes. On the other hand, current algorithms employed for multiplex synthesis of primers in NGS generate amplicons, which typically do not cover 100% of all regions in some of the target genes. Thus, regions with an incomplete coverage could be screened by HRM analysis, faster and cheaper, comparing with Sanger sequencing (Guedes et al., 2013). With respect to the use of APEX genotyping microarrays, an advantage of our HRM-based screening approach is the possibility of finding novel variants, which in our study represented 23% of the variants we identified. This strategy can also be applied to analyze regulatory loci (promoters or UTR regions), which have been excluded in diagnostic studies based on APEX microarrays. This is of relevance since it is becoming evident that translational regulation of gene expression is key for normal cell function and that its dysfunction is linked to the pathophysiology of several diseases (reviewed in Chatterjee and Pal (2009), Scheper et al. (2007)). It is

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thus plausible that point mutations and truncations affecting these regions, could explain, at least in part, the missing fraction of the heritable risk for RP, which remains in about 40% of total RP cases world-wide (Daiger et al., 2007; Hartong et al., 2006). In summary, we have developed a fast HTP-genetic screening strategy based on HRM analysis, extremely cost-effective in the context of a complex genetic disorder such as RP, where most of the genes involved fit the selection criteria we used. Acknowledgments JR-E is a Miguel Servet Fellow, National Institute of Health Carlos III (Instituto de Salud Carlos III/ISCIII). OB is supported by funds from Fundación ILUNDAIN de Estudios Neurológicos. AA is supported by grants from Fundación Jesús de Gangoiti Barrera and from the Departamento de Industria, Basque Government (SAIOTEK-PE11BN002/PC12BN001). This work was supported by grants from the National Institute of Health Carlos III (Instituto de Salud Carlos III/ISCIII), Ministerio de Ciencia e Innovación (CP10/00572) to JRE; from the Departamento de Industria, Basque Government (SAIOTEK: SAIO11-PE11BN002; and SAIO12-PC12BN001) to JRE; from Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación (FIS PS09-00660) to ALdM, a grant from the Foundation of Patients of Retinitis Pigmentosa of Gipuzkoa (BEGISARE), and from the USNational Institutes of Health (P30-NS069329-01). The authors thank Maribel Gómez Osua and Naiara Telletxea for her technical support during DNA isolation, and Ana Gorostidi and Olaia Zuriarrain for technical assistance and David Otaegui for helpful advice with PGM-Ion Torrent and Sanger sequencing. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.exer.2013.10.011. References Aguirre-Lamban, J., Riveiro-Alvarez, R., Garcia-Hoyos, M., Cantalapiedra, D., AvilaFernandez, A., Villaverde-Montero, C., Trujillo-Tiebas, M.J., Ramos, C., Ayuso, C., 2010. Comparison of high-resolution melting analysis with denaturing highperformance liquid chromatography for mutation scanning in the ABCA4 gene. Investig. Ophthalmol. Vis. Sci. 51, 2615e2619. Anasagasti, A., Irigoyen, C., Barandika, O., Lopez de Munain, A., Ruiz-Ederra, J., 2012. Current mutation discovery approaches in Retinitis Pigmentosa. Vis. Res. 75, 117e129. Bell, J., Bodmer, D., Sistermans, E., Ramsden, S.D., 2007. Practice Guidelines for the Interpretation and Reporting of Unclassified Variants (UVs) in Clinical Molecular Genetics. Clinical Molecular Genetics Society, Leeds. Bell, C., Converse, C.A., Hammer, H.M., Osborne, A., Haites, N.E., 1994. Rhodopsin mutations in a Scottish retinitis pigmentosa population, including a novel splice site mutation in intron four. Br. J. Ophthalmol. 78, 933e938. Blanco-Kelly, F., Garcia-Hoyos, M., Corton, M., Avila-Fernandez, A., RiveiroAlvarez, R., Gimenez, A., Hernan, I., Carballo, M., Ayuso, C., 2012. Genotyping microarray: mutation screening in Spanish families with autosomal dominant Retinitis Pigmentosa. Mol. Vis. 18, 1478e1483. Bowne, S.J., Sullivan, L.S., Gire, A.I., Birch, D.G., Hughbanks-Wheaton, D., Heckenlively, J.R., Daiger, S.P., 2008. Mutations in the TOPORS gene cause 1% of autosomal dominant Retinitis Pigmentosa. Mol. Vis. 14, 922e927. Bowne, S.J., Sullivan, L.S., Koboldt, D.C., Ding, L., Fulton, R., Abbott, R.M., Sodergren, E.J., Birch, D.G., Wheaton, D.H., Heckenlively, J.R., Liu, Q., Pierce, E.A., Weinstock, G.M., Daiger, S.P., 2011. Identification of disease-causing mutations in autosomal dominant Retinitis Pigmentosa (adRP) using next-generation DNA sequencing. Investig. Ophthalmol. Vis. Sci. 52, 494e503. Bragg, L.M., Stone, G., Butler, M.K., Hugenholtz, P., Tyson, G.W., 2013. Shining a light on dark sequencing: characterising errors in Ion Torrent PGM data. PLoS Comput. Biol. 9, e1003031. Bunge, S., Wedemann, H., David, D., Terwilliger, D.J., van den Born, L.I., AulehlaScholz, C., Samanns, C., Horn, M., Ott, J., Schwinger, E., et al., 1993. Molecular analysis and genetic mapping of the rhodopsin gene in families with autosomal dominant retinitis pigmentosa. Genomics 17, 230e233. Chang, S., Vaccarella, L., Olatunji, S., Cebulla, C., Christoforidis, J., 2011. Diagnostic challenges in Retinitis Pigmentosa: genotypic multiplicity and phenotypic variability. Curr. Genomics 12, 267e275.

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