Skip to content


  • Review
  • Open Access

Genetic architecture of retinal and macular degenerative diseases: the promise and challenges of next-generation sequencing

Genome Medicine20135:84

  • Published:


Inherited retinal degenerative diseases (RDDs) display wide variation in their mode of inheritance, underlying genetic defects, age of onset, and phenotypic severity. Molecular mechanisms have not been delineated for many retinal diseases, and treatment options are limited. In most instances, genotype-phenotype correlations have not been elucidated because of extensive clinical and genetic heterogeneity. Next-generation sequencing (NGS) methods, including exome, genome, transcriptome and epigenome sequencing, provide novel avenues towards achieving comprehensive understanding of the genetic architecture of RDDs. Whole-exome sequencing (WES) has already revealed several new RDD genes, whereas RNA-Seq and ChIP-Seq analyses are expected to uncover novel aspects of gene regulation and biological networks that are involved in retinal development, aging and disease. In this review, we focus on the genetic characterization of retinal and macular degeneration using NGS technology and discuss the basic framework for further investigations. We also examine the challenges of NGS application in clinical diagnosis and management.


  • Retinal Pigment Epithelium
  • Retinitis Pigmentosa
  • Retinal Degeneration
  • Usher Syndrome
  • Macular Dystrophy

Genetics of retinal degenerative diseases

The retina is our window to the outside world and is the site of capture, integration and processing of visual information. The vertebrate retina consists of a retinal pigment epithelium (RPE) monolayer, Müller glial cells, and six major types of neurons (Figure 1a) [1, 2]. In mouse and human retinas, rods constitute over 95% of the photoreceptors and are responsible for night vision; whereas different cone sub-types (red, long wave-length (L); green, medium wave-length (M); and blue, short wave-length (S)) are associated with daylight vision, color perception and high visual acuity [3, 4]. The distribution of rod and cone photoreceptors is not uniform in the retina; for example, in humans, the central part of the retina (fovea) and a 6 mm2 cone-rich area around the fovea (macula) are responsible for high-resolution central vision (Figure 1b) [5]. Rhodopsin is the visual pigment in the rods, whereas the three kinds of cones in humans contain a distinct visual pigment (L-, M- and S-opsin in L, M, and S-cones, respectively) [4]. Development and homeostasis in the retina must be stringently controlled for normal vision [6].
Figure 1
Figure 1

Structure of the retina. (a) Schematic organization of retinal circuits. The vertebrate retina consists of six major types of neurons - rod and cone photoreceptors, horizontal, bipolar, amacrine and ganglion cells. The rod and cone photoreceptors are specialized light-sensing neurons, which capture photons and transduce visual signals to the inner retina. The RPE serves as a barrier between the choroidal capillaries and the neural retina and is crucial for photoreceptor survival. Bipolar cells relay signals to the amacrine and ganglion cells through synapses in the inner plexiform layer. Ganglion cell axons project towards the optic nerve head and carry signals to the brain. (b) Ocular fundus photograph of a healthy retina showing retinal blood vessels, optic disc, macula (5 to 6 mm diameter), and fovea (central pit of the macula). Photograph provided by Dr Emily Chew (National Eye Institute, National Institutes of Health, Bethesda, MD).

The dysfunction or death of retinal photoreceptors is the primary cause of vision loss in the majority of retinal degenerative diseases (RDDs) [7, 8], which are clinically and genetically heterogeneous. In general, loss of photoreceptors in the macular region is termed macular degeneration and results in central vision defects. By contrast, the loss of peripheral vision - for example, in retinitis pigmentosa (RP) - generally starts with rod dysfunction or death that is followed by cone degeneration. RDDs are associated with a diverse spectrum of phenotypes. Some, such as RP, congenital and early-onset retinal degeneration including Leber congenital amaurosis (LCA), and cone-rod dystrophies, are monogenic and non-syndromic. Some syndromic diseases, including Bardet-Biedl syndrome (BBS) and Usher syndrome, exhibit a highly penetrant retinal degeneration phenotype with multiple affected tissues and are also monogenic. Other RDDs, such as age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy, are complex multifactorial entities.

Monogenic RDDs, by definition, are often caused by mutations in a single gene and have a Mendelian pattern of inheritance (dominant, recessive, or X-linked). Genetic-linkage studies in large pedigrees have led to the identification of almost 200 causative genes [9]. Genetic defects in monogenic RDDs are highly penetrant, with little influence of non-genetic factors; nevertheless, clinical severity has often been difficult to correlate to genetic mutation because of variable penetrance, mutations in more than one gene, or modifier variants. The clinical manifestations in complex multifactorial RDDs result from the interplay among multiple susceptibility genes and epigenetic and/or environmental factors; therefore, conventional linkage and positional cloning methods have not been very effective in identifying the underlying genetic cause(s). Candidate gene and genome-wide association studies (GWAS) have been successful in identifying susceptibility loci for complex RDDs. A recent meta-analysis of GWAS discovered at least 19 genes and associated cellular pathways for AMD pathogenesis; these included the complement system (CFH, C2/CFB, C3 and CFI), the high-density lipoprotein pathway (LIPC and CETP), the extracellular or collagen matrix pathway (TIMP3, COL8A1 and COL10A1) and the angiogenesis signaling pathway (VEGFA) [10]. Several loci have also been identified for primary open angle glaucoma [1113] and diabetic retinopathy [14]. The associated alleles do not, however, specify causality, and further genetic and functional dissection of susceptibility loci will be crucial for understanding their roles in disease pathogenesis.

The discovery of new RDD genes helps in the elucidation of the molecular pathways that underlie retinal development and homeostasis, and in delineating the genetic underpinnings of the degenerative process. Several recent reviews have provided excellent synopses of the genetic and biological features of human RDDs [7, 8, 1518]. We therefore focus on recent advances in next-generation sequencing (NGS) methods [19] (Boxes 1 and 2) that have provided an unprecedented opportunity for the unbiased discovery of genes and causal variants, and for the comprehensive dissection of the genetic architecture of RDDs. We also highlight the merits and limitations associated with different NGS methods, and present a framework for integrative analyses to elucidate a more complete, multidimensional view of genomic function in retinal health and diseases. Finally, we discuss the progress and challenges in the application of NGS approaches for diagnosis and management of RDDs.

NGS approaches for gene identification in Mendelian RDDs

Extensive genetic and clinical heterogeneity is observed in RDDs that have Mendelian inheritance. Although almost 200 genes have been identified, the genetic defects in many patients are still unknown. NGS offers a rapid, high-throughput and cost-effective approach to identify mutations underlying monogenic disorders.

Whole-exome sequencing

A majority of Mendelian diseases are caused by highly penetrant mutations that disrupt protein coding or splice site sequences. Conventional methods of gene identification involve linkage analysis followed by sequencing of candidate genes in the critical linked region. However, small pedigrees and simplex cases (Box 1) with uncertain inheritance patterns make it difficult to identify genetic defects using the positional-candidate strategy. Whole-exome sequencing (WES) involves the capture and sequencing of all coding exons (1 to 2% of the whole genome) (Box 1) and has become an ideal choice for Mendelian disease gene discovery with or without prior linkage information [2024] (Figure 2). A majority of commercially available exome capture kits target with high-confidence a subset of genes annotated by the Consensus Coding Sequence (CCDS) project [25] and the RefSeq collection [26]. Ever since it was first described in 2009 as proof-of-concept for Freeman-Sheldon syndrome [21], several studies have successfully employed WES for identifying dozens of genes in inherited diseases. With regard to retinal phenotypes, it was first employed to study an Ashkenazi Jewish family, in which WES of three affected siblings revealed a mutation in a novel gene, DHDDS, as a cause of RP [27]. Table 1 provides a summary of published reports of novel retinal disease genes discovered by WES.
Figure 2
Figure 2

Strategies for the identification of disease-causing variants in Mendelian diseases. Linkage or homozygosity mapping analysis can serve as the starting point in mutation identification by NGS. If linkage is conclusive (LOD score ≥3), linkage can be analyzed using a targeted re-sequencing approach. In cases of multiple suggestive linkage peaks (LOD score <3), whole exome/genome or candidate exome capture will be more suitable. Filtration and prioritization of variants can be customized depending on the availability of genetic information and NGS data. 1000G, 1,000 Genomes; dbSNP, Single Nucleotide Polymorphism database; EVS/EPS, Exome Variant Server (EVS) for the NHLBI Exome Sequencing Project (ESP); SIFT, sorting intolerant from tolerant.

Table 1

List of novel genes identified in retinal and macular degeneration using next-generation sequencing approaches



Gene symbol


Aberration type


Whole-exome sequencing

Retinitis pigmentosa (RP)







Alu insertion




6-bp deletion








Frame-shift, splice-site








Splice-acceptor, missense


Leber congenital amaurosis (LCA)



Missense and truncation










Congenital stationary night blindness (CSNB)



Missense and truncation




Missense and truncation


Ciliopathy with skeleton abnormality



Missense and truncation


High myopia





Bardet-Biedl syndrome (BBS)





Nephronophthisis with retinal degeneration

ZNF423 and CEP164


Missense, truncation, loss of stop codon


Usher syndrome





Benign fleck retina



Missense and truncation


Cone-rod dystrophy













Knobloch syndrome and retinal dystrophy




[46, 47]

Targeted sequencing

RP, cone-rod dystrophy














Usher syndrome








Large insertion


X-linked RP



Intronic mutation


Jobert syndrome





Familial exudative vitreoretinopathy





Candidate exome capture

Retinal- renal ciliopathy



Truncation mutations


cis -Regulatory mapping




Truncation mutation


All of the diseases listed here, except AMD, are monogenic. AMD is a multifactorial and complex disease.

Candidate exome capture

In a slight variation of WES, candidate exome sequencing captures the selected coding regions that are relevant to a specific genetic trait. Consequently, the examination of a smaller genomic region allows better utilization of resources and the analysis of a larger group of patients. This approach has been useful for ciliopathies, where almost 2,500 genes that are implicated in ciliary function can serve as potential candidates [58]. Candidate exome capture of about 13,000 exons from 828 candidate genes recently identified mutations in the SDCCAG8 gene in patients with retinal-renal ciliopathies [56]. Mutations in a single gene can cause overlapping syndromic phenotypes, and so the candidate exome capture strategy helps in refining genotype-phenotype correlations.

Targeted re-sequencing

Mendelian traits have often been mapped using multi-generational families, narrowing the genomic search space for causal gene identification. Owing to the limited number of meiotic crossing-overs, however, linkage intervals can often span several megabases and include tens or hundreds of transcribed sequences. Intelligent guesses to select and sequence candidate genes can lead to labor-intensive and expensive efforts that often provide limited data on the coding regions of annotated genes. NGS can now be used to capture and sequence large genomic regions of interest, expediting the discovery of causal genes (Table 1).

Whole-genome sequencing

WES, candidate exome capture and targeted re-sequencing can examine only a fraction of the genome and present additional challenges (Table 2). A role for variants beyond coding regions has occasionally been reported in Mendelian traits, including in vision disorders; for example, intronic mutations in RP1[59] and OFD1[53] are implicated as causes of RP, and CSPG2 variants may cause congenital vitreoretinopathies [60]. The inability of WES to assess the impact of non-coding, conserved or regulatory regions, and other long-range genomic alterations has encouraged researchers to move towards whole-genome sequencing (WGS), which is becoming more cost-effective. Nevertheless, progress has been slow because of the technological limitations associated with the analysis and handling of large datasets.
Table 2

A comparison of next-generation sequencing methodologies





Whole-exome sequencing or candidate exome capture

Customized, economical compared to WGS, manageable data size

Captures genetic variants only in the coding regions of the genome; inefficient hybridization step; high DNA input; susceptible to capture bias

SNP and indel discovery in coding exons; suitable for the identification of causal genes in high-penetrance Mendelian diseases

Targeted re-sequencing

Customized, economical compared to WGS

Genetic variant discovery is limited by array design; high DNA input; inefficient hybridization step; captures only a small proportion of the genome

SNP and indel discovery; suitable for sequencing linkage intervals and genomic regions at or around associated signals

Whole-genome sequencing

Uncovers genome-wide coding and non-coding variants, no capture bias

Expensive; very large dataset; analysis methods are still evolving

Genome-wide SNP, indel and CNV discovery; suitable for rare variant discovery in Mendelian, complex or sporadic traits


Cost-effective method for evaluating known rare variants (MAF of 1 to 5%)

Does not identify novel variants; limited to coding region; limited representation of intronic and regulatory variants

Genome-wide association analysis with rare variants


Array-independent profiling of the transcriptome

High coverage required for the identification of low-copy transcripts; not applicable for the identification of variants that cause loss of protein; limited by tissue- or cell-type availability

Genome-wide expression profiling; alternative transcript identification; non-coding RNA detection; SNP profiling; eQTL analysis


Genome-wide profiling of epigenetic marks (DNA methylation and histone modifications) and cis-regulatory elements

Dependent on the quality of antibody; requires high input; analysis methods still evolving; high coverage needed for accurate profiling

DNA methylation; histone modifications; tissue-specific enhancer profiling

RNA-Seq or transcriptome sequencing

The transcriptome represents a collection of all transcribed sequences (RNAs), both protein-coding and non-coding, in a cell type or particular tissue and at a specific stage of development or age. While microarray technology and methods for serial analysis of gene expression (SAGE) have been valuable, transcriptome profiling using NGS technology (RNA-Seq) is becoming popular because of its ability to survey the transcriptome in a high-throughput and quantitative manner and at low cost [61]. In addition, RNA-Seq has proven useful for annotating protein-coding genes, discovering novel alternatively spliced transcripts and non-coding RNAs (ncRNAs), single nucleotide polymorphism (SNP) profiling, and the detection of gene fusions or rearrangements [62]. DNA-Seq approaches generate a large number of variants and often secondary filtering is required to prioritize the candidate disease genes. Tissue-specific expression profiles therefore offer a valuable first-level screen to identify relevant disease-causing variants (Figure 2).

Profiling of alternative transcripts

In humans, a majority of genes (>90%) undergo alternative splicing to generate tissue-specific and functionally diverse protein isoforms [63]. The role of novel transcripts in diverse pathways and disease causation is slowly being recognized [64, 65]. Retina-specific isoforms of RPGR and BBS3 have been implicated in X-linked RP and BBS, respectively [66, 67]. In addition, mutations in the spliceosome-component genes (PRPF31, PRPF3, and PRPF8) are associated with RP [68]. Thus, retina-specific RNA or transcriptome profiling provides an excellent opportunity to identify novel functionally relevant transcripts. As novel transcripts may be present at low-copy number, RNA CaptureSeq (Box 1) can provide an alternative approach for enrichment [69].

Non-coding RNA

ncRNAs appear to play prominent and diverse roles in normal development, physiology, and disease. Several specific microRNAs (miRNAs) are expressed in the retina (miR-96, miR-182, and miR-183) [70, 71] and RPE (miR-204/211) [72]. Long antisense (non-coding) transcripts have been associated with eight transcription factors that are involved in eye development [73], and two ncRNAs, TUG1[74] and Six3OS[75], have been linked to retinal differentiation. Inactivation of DICER1, an RNase III endonuclease that is essential for the production and function of mature miRNAs, has been implicated in retinal degeneration [76], and Alu RNA toxicity has been suggested to a play a role in AMD [77]. Thus, further exploration of ncRNAs seems essential, and RNA-Seq offers a starting point for the identification of novel ncRNAs in development and disease.

SNP profiling

Genetic variations within the transcribed (coding or non-coding) regions of the genome can alter the expression or function of the encoded sequence. Therefore, RNA-Seq can provide profiles of genetic variants in both the quasi-complete set of transcribed genes (mRNAs) and ncRNAs in a cost-effective manner and without the need for target-probe hybridization, a necessary but inefficient step in capture-based methods. However, RNA-Seq should be applied with caution, as it is possible to miss variants that result in the loss of a gene product.

ChIP-Seq-based approaches

The genomic location and function of regulatory elements contributes significantly to the development of human diseases. Chromatin immunoprecipitation followed by NGS (ChIP-Seq) can be used to profile cis-regulatory elements (CREs) (Box 1), which include transcription factor binding sites clustered within promoters, enhancers and silencers [78]. ChIP-Seq has been employed to generate genome-wide maps of CREs for two key photoreceptor-specific transcription factors, CRX[79] and NRL[80]. In addition, these data have permitted the prioritization of disease-associated genes or variants for further study. Not surprisingly, several CRX and NRL target genes are associated with RDDs [79, 80], and recently, CRX ChIP-Seq data were used to filter the candidates to identify mutations in MAK (encoding a regulator of ciliary length) as a cause of autosomal recessive RP [57].

ChIP-Seq can also be employed for genome-wide profiling of epigenetic modifications, such as DNA methylation and covalent modifications of histones [81]. Epigenetic variations can influence gene expression and developmental programming [82, 83]. Evidence suggests the involvement of epigenetic mechanism in RDDs; but such modifications are more likely to be associated with complex disorders, such as AMD [84], glaucoma and diabetic retinopathy. Epigenetic profiling of retina, RPE and other eye tissues or cells, in combination with gene profiling, should provide valuable insights into disease mechanisms. The Encyclopedia of DNA Elements (ENCODE) project has systematically integrated gene expression data with information on regulatory elements, transcription factor binding, and epigenetic modifications for as much as 80% of the genome [85]. Expression profiles and other data relevant to retinal and macular diseases have not, however, been incorporated as yet.

NGS approaches for complex traits

The identification of the genetic susceptibility variants underlying complex multifactorial disorders requires extensive efforts including large patient cohorts and cumbersome analytical tools. GWAS have been successful in uncovering associated loci for numerous diseases, but such studies only examine common (tagging) variants in populations and additional investigations are necessary to identify causality. In this section, we provide an overview of the possible applications of NGS, with an emphasis on the study design for complex diseases.

GWAS and meta-analysis

GWAS have begun to unravel the genetic architecture of complex traits. Hundreds of susceptibility loci associated with multifactorial diseases have now been discovered [86]. AMD provided the first example of GWAS success with the identification of CFH susceptibility loci [87]. Multiple GWAS and large-scale meta-analysis studies have to date revealed as many as 19 AMD susceptibility loci [10, 88]. However, the associated variants are not causal and do not explain a substantial fraction of genetic heritability. Rare and structural variants at these associated susceptibility loci might help to explain the causality and missing heritability [89]. NGS approaches have made the identification of rare alleles feasible and have ushered in a new era for a second-generation of association studies in complex diseases (Figure 3).
Figure 3
Figure 3

A general workflow for dissecting genetics of complex traits using different NGS methodologies. Various NGS methods (targeted re-sequencing, exome-chip, whole exome and genome sequencing) can be applied separately or in combination with each other for identification of candidate genes in complex RDDs. Expression and eQTL analysis in retinal tissues can provide the second level of filtering for prioritizing the candidates. Pathway and network analysis can further help in understanding the relationship and interactions among candidates. Ultimately, the functional dissection of candidates would be needed for establishing their roles in disease pathogenesis.

Rare variant identification

The hypothesis that rare variants influencing a complex trait should co-localize with associated common alleles has accelerated targeted re-sequencing of the GWAS loci [90]. Such studies have led to the identification of rare coding variants, R1210C in CFH[49] and G119R in CFI[12], that are associated with AMD. WES studies are also being performed to test the association of rare coding variants with a complex phenotype [91, 92]. However, such studies require a large sample size to achieve the statistical power necessary to detect a significant association that can then justify multiple testing. In the absence of such a dataset, extreme phenotype study design, in which samples from both ends of the phenotype distribution are analyzed, can serve as a suitable alternative (Figure 3) [93]. In addition, inherited macular dystrophies share common clinical characteristics with AMD, and occasionally genes identified in heritable forms, such as TIMP3[94] and ABCA4[95], have been associated with AMD [88, 96]. Thus, WES and WGS in families with macular dystrophies (and occasionally in available AMD families) can uncover rare variants that might contribute to disease pathophysiology.

Copy number variations

Large DNA stretches (>1 kb) that exhibit variable copy number when compared to the reference genome contribute significantly to population dynamics and evolution. Comparative genomic hybridization (CGH) arrays and SNP arrays have been commonly used to detect copy number variations (CNVs) that are implicated in neurodevelopmental (autism, schizophrenia, and intellectual disability) and immune-related diseases (Crohn’s disease, psoriasis, HIV/AIDS, rheumatoid arthritis, and type I diabetes) [97]. The role of CNVs is under investigation in AMD [98100]. An 84-kb deletion spanning CFHR1 and CFHR3 has been associated with protection against the development of AMD [101103]. A recent GWAS has identified CNVs at NPHP1 and EFEMP1 as potential candidates for AMD association [100]. CNVs in additional genes, such as CCR3, CFH, CX3CR1, ERCC6, HTRA1, VEGF, GSTM1, and GSTT1, have also been associated with AMD [104]. Nevertheless, a complete spectrum of CNVs in complex diseases (including AMD) has not been realized yet because of the limited resolution of current methods. NGS-based CNV detection utilizes high-coverage WGS data for unbiased detection of CNVs at much higher resolution than has been available to date, providing information about CNV breakpoints and the location of copy number gains [105, 106]. Nevertheless, methodologies for CNV detection using NGS lack well-defined workflows, protocols, and quality-control measures, imposing substantial computational and bioinformatic challenges [107]. In addition, databases for human structural variation are limited and contain inadequate information on most breakpoints. A validated pipeline for structural variation analysis using NGS data is highly desirable for utilizing the full potential of CNVs for complex trait analysis.


A common-variant-based GWAS approach has not explained the complete genetic variance observed in complex traits. Missing heritability might be explained by rare to low frequency variants (minor allele frequency (MAF) of 1 to 5%) [90, 108, 109]. NGS studies, such as the 1,000 Genomes Project [110] and the NHLBI Exome Sequencing Project [111], have identified a large number of such variants, leading to a second-generation genotyping array for testing the association of rare variants in complex traits [112]. Ideally, all common GWAS variants that are associated with distinct phenotypes can be included in such exome-chips. Although any regulatory or novel rare variant is not identified by this approach in RDDs, exome chips offer an economical and rapid platform that can be used to test the hypothesis that certain rare variants are causal alleles in common diseases. Such studies are currently in progress as part of a large AMD consortium and should identify novel variants and genes in the near future.

Expression quantitative trait loci analysis

A vast majority of disease-associated SNPs identified in GWAS are reportedly located in non-coding regions of the genome, and their functional role in causing the disease is generally not understood. Arguably, such variants might regulate gene expression and act as expression quantitative trait loci (eQTL) [113]. Combined analysis of genotyping with RNA-Seq data provides a unique opportunity to correlate genetic variations and expression level at disease-associated loci [114]. Expression profiling is more relevant, however, when implemented in disease-affected tissues or cell types. Although little has been reported on the importance of eQTLs in RDDs, the integration of data on modifier and susceptibility variants with the NGS expression data would facilitate the elucidation of complex regulatory networks that can provide insights into novel intervention strategies.

Making sense of the vast amount of NGS data for disease gene identification

The identification of relevant candidate disease-causing variants from NGS data requires filtering strategies that depend on multiple factors, such as the availability of well-phenotyped patient cohorts, knowledge of the mode of inheritance, and large sample sizes. Computational tools that can predict the impact of a variant on protein function can assist in segregating the deleterious variants from neutral ones [115]. A general guideline for candidate variant identification is provided for Mendelian (Figure 2) and complex RDDs (Figure 3), but each biological question might require a unique approach. For example, homozygosity and linkage data can complement WES or targeted sequencing in Mendelian RDDs [30, 31, 4547]. Similarly, the integration of GWAS data with rare variant, eQTL or pathway-based analyses can yield meaningful results for complex traits such as AMD. Ultimately, validation of genetic causality would require additional investigations using in vitro assays and/or model organisms.

NGS in diagnostics and disease management for RDDs

Over 200 genes have been implicated in RDDs, offering an opportunity to clarify etiology, provide prognosis, and calculate associated risk(s). Nevertheless, molecular diagnosis and counseling are complicated by genetic heterogeneity and extensive phenotypic variability. Mutations in the same gene can cause different phenotypes and similar clinical findings can result from mutations in different genes. For example, RPGR and RP2 are primary causative genes in X-linked RP, but recent studies have reported the prevalence of RP2 and RPGR mutations even in simplex retinal degeneration in males [116] and in pedigrees with ‘apparent’ autosomal dominant inheritance of RP [117]. Thus, the boundaries of distinct clinical entities can be blurred, demanding more comprehensive methods of molecular evaluation [118].

Customized arrays have been developed for screening patients with RP [119, 120] and other retinal dystrophies [121, 122]. The National Eye Institute has established the National Ophthalmic Disease Genotyping and Phenotyping Network (eyeGENE®), which offers molecular diagnosis as a service [123]. The eyeGENE® network currently includes Clinical Laboratory Improvement Amendments (CLIA)-certified diagnostic laboratory partners; over 270 registered clinical organizations with 500 registered users from around the United States and Canada have analyzed over 4,400 samples. eyeGENE® is also working towards setting up high-throughput genotyping and sequencing technologies for improved clinical sequencing. These efforts, however, need continuous validation of reliability, robustness and reproducibility of the technology being employed. Implementation of NGS hardware requires substantial in-house infrastructure and a standardized guideline for NGS protocols. Issues related to the ownership and confidentiality of genomic data and the handling of incidental or secondary findings (Box 1) must also be addressed before NGS technology moves into routine clinical practice.

Understanding the biology of disease

The extreme phenotypic variability in RDDs can be attributed to allelic heterogeneity, modifier loci, and epigenetic and environmental factors, or a combination of these. A modifier gene is predicted to alter the phenotypic outcome of a given genotype by interacting with the primary disease gene or by functioning in the same or a related biological pathway, affecting penetrance, expressivity and pleiotropy [124, 125]. A few examples are warranted. Digenic inheritance has been reported for mutations in ROM1 and peripherin/RDS resulting in RP [126] and for ROM1 and ABCA4 mutations in macular dystrophy [127]. CNOT3 can modify the phenotype of a PRPF31 mutation in RP [128]. The potential involvement of mutations in more than one causative gene has also been described in LCA [129]. Phenotypic differences in ciliopathies are also attributed to modifiers [130, 131]. For example, a common allele in RPGRIP1L, Ala229Thr, is reported to be a modifier of retinal degeneration in ciliopathy patients [132]. AHI1 seems to act as a modifier of CEP290[133] and NPHP1[134], and PDZD7 can modify the phenotype in Usher syndrome patients who have a homozygous USH2A mutation [135]. The search for modifiers has, however, been limited because of their non-Mendelian segregation and restricted exploration within known RDD genes, and because of the abundant normal genetic variations in humans. NGS approaches offer an expanded platform for genome-wide evaluation of modifier variants that would permit a better understanding of phenotypic variability and progression in RDDs.

Mouse models have provided valuable insights into RDD pathogenesis, but complex interactions among retinopathy proteins and additional variants can exacerbate or ameliorate the disease phenotypes. For example, mice that had a combination of Cep290 and Mkks disease alleles had better sensory functions than those with either mutation alone [136]. The development of therapeutic strategies would therefore require a comprehensive understanding of RDD gene interaction networks and cellular pathways. NGS technology should expedite the integration of genetic variants in relevant RDDs and modifier genes with retinal transcriptome and epigenetic profiles.

Conclusions, challenges and future prospects

It is an exciting time in the genetic analysis of RDDs as NGS has led to unprecedented access to various genome-wide datasets. NGS has proven successful in identifying the genetic cause of monogenic RDDs in many patients and families and offers great promise for the genetic dissection of complex RDDs. With the generation and analysis of NGS data becoming more accessible and affordable, a comprehensive catalog of variants for most (if not all) vision-related traits seems a viable prospect. Whole-transcriptome and epigenome analysis in retinal tissues would greatly facilitate the elucidation of important pathways or networks underlying development and disease. In this review, we have highlighted the challenges and the opportunities in applying NGS for gene discovery and clinical diagnosis of RDDs. A major goal lies ahead in developing a unified framework for identifying all disease-relevant variants and genes. Methods for downstream bioinformatic analyses are still evolving and represent a major bottleneck in NGS applications. There is significant room for improvement in mapping and variant-calling methods, especially for small insertions or deletions and CNVs. Better tools are required for combining information from across studies that have used different sequencing platforms or even distinct methods of data analysis. As each kind of NGS data has its own merits, integrated and multidimensional analyses of biological systems with relevant clinical information records would be valuable for intervention and personalized medicine.

Box 1. Glossary

Next-generation sequencing (NGS): also known as massively parallel sequencing or high-throughput sequencing; these recent technologies allow parallel sequencing of multiple samples and produce millions of sequence reads concurrently, thereby reducing the sequencing costs and time considerably.

Mendelian disease: also called monogenic disease; results from single genetic defect(s) that are passed on to the next generation in a Mendelian inheritance pattern (dominant, recessive, or X-linked).

Complex disease: a multifactorial disease that does not exhibit a simple Mendelian inheritance pattern and is caused by complex interplay among genetic, epigenetic and environmental factors.

Simplex case: also known as sporadic cases; those in which only a single individual has a clinical diagnosis and no relatives are affected.

Whole-genome sequencing (WGS): sequencing of the complete DNA of an organism’s genome.

Whole-exome sequencing (WES): capture and sequencing of the coding exons of all genes, comprising roughly 1 to 2% of the genome.

Targeted re-sequencing: sequencing of a defined (targeted) region of the genome; helpful in identifying disease variant(s) when a linkage region has been established.

Transcriptome: the complete set of coding and non-coding transcripts (RNAs) expressed in a specific cell type, tissue or organism.

RNA-Seq: sequencing of transcriptome using NGS; RNA-Seq includes only the transcribed sequences of the genome.

RNA CaptureSeq: use of tiling arrays to capture and sequence selected portions of the transcriptome.

Chromatin immunoprecipitation (ChIP): a method for investigating the genomic DNA region(s) that are interacting with a protein in vivo, by capturing the DNA that is bound to a specific antibody.

ChIP-Seq: combines ChIP with NGS to generate a genome-wide map of DNA-binding proteins; can be used to identify genome-wide epigenetic marks on histones.

cis -Regulatory element (CRE): DNA sequence in or near a gene (in cis) containing binding sites for transcription regulatory proteins that control the spatiotemporal expression of the target gene.

Single-end sequencing: sequencing of a DNA molecule only from one end; as NGS reads can be small, single-end sequencing might not be efficient in mapping low complexity and repetitive regions of the genome.

Paired-end sequencing: sequencing of a DNA molecule from both ends, resulting in a superior alignment across the genome.

Mapping sequence assembly: mapping of NGS reads to a reference genome.

De novo assembly: a method of generating large assemblies of NGS reads in the absence of a reference genome.

Single nucleotide polymorphisms (SNPs): genetic variations that alter a single nucleotide in the genome.

Insertion-deletion variation (indel): any sequence change that leads to the addition or removal of one or more nucleotides compared to the reference genome.

eQTL analysis: analysis of genomic loci that have statistically significant correlation between genotype and gene expression levels, and thus regulate gene expression levels.

Copy number variation (CNV): a segment of DNA (>1 kb) that is present at a variable copy number compared to the reference genome, altering the diploid status at a particular locus.

Deleterious variant: a DNA change that lowers the fitness of the gene by altering the expression or function of the encoded protein or RNA and remains under the selection of various strengths.

Neutral variant: a variant that does not alter the function of a gene or confer any influence on the fitness of a gene.

Conserved variant (or conserved residue): a DNA nucleotide or amino acid residue that shares identity among closely related species.

Private variant: a genetic variant that is confined to a single individual or a family.

Incidental or secondary findings: refers to the identification of any genetic finding that has health or reproductive relevance for the patient, but was not obtained as a part of the original goals of the study.

Box 2. Next-generation sequencing methods

Next-generation sequencing: also known as massively parallel sequencing or high-throughput sequencing, this process enables the sequencing of a large number of DNA sequences in a single reaction; NGS has revolutionized the genomics field by producing fast, inexpensive and accurate sequencing data.

Platforms: several NGS platforms are commercially available, including Roche/454, Illumina/Solexa, ABI SOLiD, Life/APG, Helicos BioSciences and Pacific Biosciences; Illumina is a widely used platform because it provides large throughput at low cost.

Template preparation: starting material (genomic DNA, cDNA or immuno-precipitated DNA) is sheered to small-size fragments of 30 to 400 base pairs and ligated to a universal adapter. DNA can then be amplified using emulsion PCR or bridging amplification, followed by sequencing, or in the case of single molecule templates, sequenced directly.

Sequencing reaction: NGS chemistry methods (cyclic reversible termination, sequencing by ligation, pyrosequencing and real-time sequencing) vary among different platforms; sequencing by synthesis (SBS) is a commonly used approach that includes the use of DNA polymerase or DNA ligase for stepwise nucleotide addition.

Output: high throughput is a key feature of NGS protocols, with a single run producing several gigabases (Gb) of sequence data. Output varies among platforms; for example, Illumina HiSeq 2000 produces 95 to 600 Gb and SoLiD/ABI 5500 produces 90 to 300 Gb in a single run.

Assembly and mapping: once sequencing is accomplished, the initial analysis of base calling is performed by proprietary software on the sequencing platform, followed by aligning the data to a known reference genome if available or by de novo assembly; short read lengths can make mapping in the repetitive regions challenging.

Analysis: downstream analysis varies depending on the biological question; DNA-Seq data are subjected to variant and CNV detection, whereas RNA-Seq data are used to characterize the transcriptome and ChIP-Seq is used for large-scale analysis of chromatin features.



Age-related macular degeneration


Bardet-Biedl syndrome


Chromatin immunoprecipitation


Copy number variation


cis-regulatory element


Expression quantitative trait loci


Genome-wide association studies


Long wave-length


Leber congenital amaurosis


Medium wave-length


Minor allele frequency




Non-coding RNAs


Next-generation sequencing


Retinal degenerative diseases


Retinitis pigmentosa


Retinal pigment epithelium


Short wave-length


Single nucleotide polymorphism


Whole-exome sequencing


Whole-genome sequencing.



We thank Dr Emily Chew (National Eye Institute, National Institutes of Health, Bethesda, MD) for providing the fundus photograph of the human retina. This work is supported by the Intramural Research Program of the National Eye Institute.

Authors’ Affiliations

Neurobiology-Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA


  1. Dowling JE: The Retina: an Approachable Part of the Brain. 1987, Cambridge, MA: Belknap PressGoogle Scholar
  2. Masland RH: The fundamental plan of the retina. Nat Neurosci. 2001, 4: 877-886.PubMedGoogle Scholar
  3. Lamb TD, Collin SP, Pugh EN: Evolution of the vertebrate eye: opsins, photoreceptors, retina and eye cup. Nat Rev Neurosci. 2007, 8: 960-976.PubMedPubMed CentralGoogle Scholar
  4. Nathans J, Thomas D, Hogness DS: Molecular genetics of human color vision: the genes encoding blue, green, and red pigments. Science. 1986, 232: 193-202.PubMedGoogle Scholar
  5. Curcio CA, Sloan KR, Kalina RE, Hendrickson AE: Human photoreceptor topography. J Comp Neurol. 1990, 292: 497-523.PubMedGoogle Scholar
  6. Swaroop A, Kim D, Forrest D: Transcriptional regulation of photoreceptor development and homeostasis in the mammalian retina. Nat Rev Neurosci. 2010, 11: 563-576.PubMedGoogle Scholar
  7. Bramall AN, Wright AF, Jacobson SG, McInnes RR: The genomic, biochemical, and cellular responses of the retina in inherited photoreceptor degenerations and prospects for the treatment of these disorders. Annu Rev Neurosci. 2010, 33: 441-472.PubMedGoogle Scholar
  8. Wright AF, Chakarova CF, Abd El-Aziz MM, Bhattacharya SS: Photoreceptor degeneration: genetic and mechanistic dissection of a complex trait. Nat Rev Genet. 2010, 11: 273-284.PubMedGoogle Scholar
  9. RetNet. [], []
  10. Fritsche LG, Chen W, Schu M, Yaspan BL, Yu Y, Thorleifsson G, Zack DJ, Arakawa S, Cipriani V, Ripke S, Igo RP, Buitendijk GH, Sim X, Weeks DE, Guymer RH, Merriam JE, Francis PJ, Hannum G, Agarwal A, Armbrecht AM, Audo I, Aung T, Barile GR, Benchaboune M, Bird AC, Bishop PN, Branham KE, Brooks M, Brucker AJ, Cade WH: Seven new loci associated with age-related macular degeneration. Nat Genet. 2013, 45: 433-439.PubMedGoogle Scholar
  11. Thorleifsson G, Walters GB, Hewitt AW, Masson G, Helgason A, DeWan A, Sigurdsson A, Jonasdottir A, Gudjonsson SA, Magnusson KP, Stefansson H, Lam DS, Tam PO, Gudmundsdottir GJ, Southgate L, Burdon KP, Gottfredsdottir MS, Aldred MA, Mitchell P, St Clair D, Collier DA, Tang N, Sveinsson O, Macgregor S, Martin NG, Cree AJ, Gibson J, Macleod A, Jacob A, Ennis S: Common variants near CAV1 and CAV2 are associated with primary open-angle glaucoma. Nat Genet. 2010, 42: 906-909.PubMedPubMed CentralGoogle Scholar
  12. van Koolwijk LM, Ramdas WD, Ikram MK, Jansonius NM, Pasutto F, Hysi PG, Macgregor S, Janssen SF, Hewitt AW, Viswanathan AC, ten Brink JB, Hosseini SM, Amin N, Despriet DD, Willemse-Assink JJ, Kramer R, Rivadeneira F, Struchalin M, Aulchenko YS, Weisschuh N, Zenkel M, Mardin CY, Gramer E, Welge-Lüssen U, Montgomery GW, Carbonaro F, Young TL, Bellenguez C, McGuffin P, : Common genetic determinants of intraocular pressure and primary open-angle glaucoma. PLoS Genet. 2012, 8: e1002611-PubMedPubMed CentralGoogle Scholar
  13. Wiggs JL, Yaspan BL, Hauser MA, Kang JH, Allingham RR, Olson LM, Abdrabou W, Fan BJ, Wang DY, Brodeur W, Budenz DL, Caprioli J, Crenshaw A, Crooks K, Delbono E, Doheny KF, Friedman DS, Gaasterland D, Gaasterland T, Laurie C, Lee RK, Lichter PR, Loomis S, Liu Y, Medeiros FA, McCarty C, Mirel D, Moroi SE, Musch DC, Realini A: Common variants at 9p21 and 8q22 are associated with increased susceptibility to optic nerve degeneration in glaucoma. PLoS Genet. 2012, 8: e1002654-PubMedPubMed CentralGoogle Scholar
  14. Grassi MA, Tikhomirov A, Ramalingam S, Below JE, Cox NJ, Nicolae DL: Genome-wide meta-analysis for severe diabetic retinopathy. Hum Mol Genet. 2011, 20: 2472-2481.PubMedPubMed CentralGoogle Scholar
  15. Priya RR, Chew EY, Swaroop A: Genetic studies of age-related macular degeneration: lessons, challenges, and opportunities for disease management. Ophthalmology. 2012, 119: 2526-2536.PubMedGoogle Scholar
  16. Swaroop A, Chew EY, Rickman CB, Abecasis GR: Unraveling a multifactorial late-onset disease: from genetic susceptibility to disease mechanisms for age-related macular degeneration. Annu Rev Genomics Hum Genet. 2009, 10: 19-43.PubMedPubMed CentralGoogle Scholar
  17. Pacione LR, Szego MJ, Ikeda S, Nishina PM, McInnes RR: Progress toward understanding the genetic and biochemical mechanisms of inherited photoreceptor degenerations. Annu Rev Neurosci. 2003, 26: 657-700.PubMedGoogle Scholar
  18. Berger W, Kloeckener-Gruissem B, Neidhardt J: The molecular basis of human retinal and vitreoretinal diseases. Prog Retin Eye Res. 2010, 29: 335-375.PubMedGoogle Scholar
  19. Metzker ML: Sequencing technologies - the next generation. Nat Rev Genet. 2010, 11: 31-46.PubMedGoogle Scholar
  20. Bamshad MJ, Ng SB, Bigham AW, Tabor HK, Emond MJ, Nickerson DA, Shendure J: Exome sequencing as a tool for Mendelian disease gene discovery. Nat Rev Genet. 2011, 12: 745-755.PubMedGoogle Scholar
  21. Ng SB, Turner EH, Robertson PD, Flygare SD, Bigham AW, Lee C, Shaffer T, Wong M, Bhattacharjee A, Eichler EE, Bamshad M, Nickerson DA, Shendure J: Targeted capture and massively parallel sequencing of 12 human exomes. Nature. 2009, 461: 272-276.PubMedPubMed CentralGoogle Scholar
  22. Choi M, Scholl UI, Ji W, Liu T, Tikhonova IR, Zumbo P, Nayir A, Bakkaloglu A, Ozen S, Sanjad S, Nelson-Williams C, Farhi A, Mane S, Lifton RP: Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. Proc Natl Acad Sci U S A. 2009, 106: 19096-19101.PubMedPubMed CentralGoogle Scholar
  23. Gnirke A, Melnikov A, Maguire J, Rogov P, LeProust EM, Brockman W, Fennell T, Giannoukos G, Fisher S, Russ C, Gabriel S, Jaffe DB, Lander ES, Nusbaum C: Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat Biotechnol. 2009, 27: 182-189.PubMedPubMed CentralGoogle Scholar
  24. Priya RR, Rajasimha HK, Brooks MJ, Swaroop A: Exome sequencing: capture and sequencing of all human coding regions for disease gene discovery. Methods Mol Biol. 2012, 884: 335-351.PubMedGoogle Scholar
  25. CCDS database. [], []
  26. RefSeq. [], []
  27. Zuchner S, Dallman J, Wen R, Beecham G, Naj A, Farooq A, Kohli MA, Whitehead PL, Hulme W, Konidari I, Edwards YJ, Cai G, Peter I, Seo D, Buxbaum JD, Haines JL, Blanton S, Young J, Alfonso E, Vance JM, Lam BL, Peričak-Vance MA: Whole-exome sequencing links a variant in DHDDS to retinitis pigmentosa. Am J Hum Genet. 2011, 88: 201-206.PubMedPubMed CentralGoogle Scholar
  28. Tucker BA, Scheetz TE, Mullins RF, DeLuca AP, Hoffmann JM, Johnston RM, Jacobson SG, Sheffield VC, Stone EM: Exome sequencing and analysis of induced pluripotent stem cells identify the cilia-related gene male germ cell-associated kinase (MAK) as a cause of retinitis pigmentosa. Proc Natl Acad Sci U S A. 2011, 108: E569-E576.PubMedPubMed CentralGoogle Scholar
  29. Schrader KA, Heravi-Moussavi A, Waters PJ, Senz J, Whelan J, Ha G, Eydoux P, Nielsen T, Gallagher B, Oloumi A, Boyd N, Fernandez BA, Young TL, Jones SJ, Hirst M, Shah SP, Marra MA, Green J, Huntsman DG: Using next-generation sequencing for the diagnosis of rare disorders: a family with retinitis pigmentosa and skeletal abnormalities. J Pathol. 2011, 225: 12-18.PubMedGoogle Scholar
  30. Abu-Safieh L, Alrashed M, Anazi S, Alkuraya H, Khan AO, Al-Owain M, Al-Zahrani J, Al-Abdi L, Hashem M, Al-Tarimi S, Sebai MA, Shamia A, Ray-Zack MD, Nassan M, Al-Hassnan ZN, Rahbeeni Z, Waheeb S, Alkharashi A, Abboud E, Al-Hazzaa SA, Alkuraya FS: Autozygome-guided exome sequencing in retinal dystrophy patients reveals pathogenetic mutations and novel candidate disease genes. Genome Res. 2013, 23: 236-247.PubMedPubMed CentralGoogle Scholar
  31. Davidson AE, Schwarz N, Zelinger L, Stern-Schneider G, Shoemark A, Spitzbarth B, Gross M, Laxer U, Sosna J, Sergouniotis PI, Waseem NH, Wilson R, Kahn RA, Plagnol V, Wolfrum U, Banin E, Hardcastle AJ, Cheetham ME, Sharon D, Webster AR: Mutations in ARL2BP, encoding ADP-ribosylation-factor-like 2 binding protein, cause autosomal-recessive retinitis pigmentosa. Am J Hum Genet. 2013, doi: 10.1016/j.ajhg.2013.06.003Google Scholar
  32. Perrault I, Hanein S, Zanlonghi X, Serre V, Nicouleau M, Defoort-Delhemmes S, Delphin N, Fares-Taie L, Gerber S, Xerri O, Edelson C, Goldenberg A, Duncombe A, Le Meur G, Hamel C, Silva E, Nitschke P, Calvas P, Munnich A, Roche O, Dollfus H, Kaplan J, Rozet JM: Mutations in NMNAT1 cause Leber congenital amaurosis with early-onset severe macular and optic atrophy. Nat Genet. 2012, 44: 975-977.PubMedGoogle Scholar
  33. Koenekoop RK, Wang H, Majewski J, Wang X, Lopez I, Ren H, Chen Y, Li Y, Fishman GA, Genead M, Schwartzentruber J, Solanki N, Traboulsi EI, Cheng J, Logan CV, McKibbin M, Hayward BE, Parry DA, Johnson CA, Nageeb M, Poulter JA, Mohamed MD, Jafri H, Rashid Y, Taylor GR, Keser V, Mardon G, Xu H, Inglehearn CF, : Mutations in NMNAT1 cause Leber congenital amaurosis and identify a new disease pathway for retinal degeneration. Nat Genet. 2012, 44: 1035-1039.PubMedPubMed CentralGoogle Scholar
  34. Falk MJ, Zhang Q, Nakamaru-Ogiso E, Kannabiran C, Fonseca-Kelly Z, Chakarova C, Audo I, Mackay DS, Zeitz C, Borman AD, Staniszewska M, Shukla R, Palavalli L, Mohand-Said S, Waseem NH, Jalali S, Perin JC, Place E, Ostrovsky J, Xiao R, Bhattacharya SS, Consugar M, Webster AR, Sahel JA, Moore AT, Berson EL, Liu Q, Gai X, Pierce EA: NMNAT1 mutations cause Leber congenital amaurosis. Nat Genet. 2012, 44: 1040-1045.PubMedPubMed CentralGoogle Scholar
  35. Chiang PW, Wang J, Chen Y, Fu Q, Zhong J, Chen Y, Yi X, Wu R, Gan H, Shi Y, Chen Y, Barnett C, Wheaton D, Day M, Sutherland J, Heon E, Weleber RG, Gabriel LA, Cong P, Chuang K, Ye S, Sallum JM, Qi M: Exome sequencing identifies NMNAT1 mutations as a cause of Leber congenital amaurosis. Nat Genet. 2012, 44: 972-974.PubMedGoogle Scholar
  36. Sergouniotis PI, Davidson AE, Mackay DS, Li Z, Yang X, Plagnol V, Moore AT, Webster AR: Recessive mutations in KCNJ13, encoding an inwardly rectifying potassium channel subunit, cause leber congenital amaurosis. Am J Hum Genet. 2011, 89: 183-190.PubMedPubMed CentralGoogle Scholar
  37. Zeitz C, Jacobson SG, Hamel CP, Bujakowska K, Neuillé M, Orhan E, Zanlonghi X, Lancelot ME, Michiels C, Schwartz SB, Bocquet B, Antonio A, Audier C, Letexier M, Saraiva JP, Luu TD, Sennlaub F, Nguyen H, Poch O, Dollfus H, Lecompte O, Kohl S, Sahel JA, Bhattacharya SS, Audo I, : Whole-exome sequencing identifies LRIT3 mutations as a cause of autosomal-recessive complete congenital stationary night blindness. Am J Hum Genet. 2013, 92: 67-75.PubMedPubMed CentralGoogle Scholar
  38. Audo I, Bujakowska K, Orhan E, Poloschek CM, Defoort-Dhellemmes S, Drumare I, Kohl S, Luu TD, Lecompte O, Zrenner E, Lancelot ME, Antonio A, Germain A, Michiels C, Audier C, Letexier M, Saraiva JP, Leroy BP, Munier FL, Mohand-Saïd S, Lorenz B, Friedburg C, Preising M, Kellner U, Renner AB, Moskova-Doumanova V, Berger W, Wissinger B, Hamel CP, Schorderet DF: Whole-exome sequencing identifies mutations in GPR179 leading to autosomal-recessive complete congenital stationary night blindness. Am J Hum Genet. 2012, 90: 321-330.PubMedPubMed CentralGoogle Scholar
  39. Bredrup C, Saunier S, Oud MM, Fiskerstrand T, Hoischen A, Brackman D, Leh SM, Midtbø M, Filhol E, Bole-Feysot C, Nitschké P, Gilissen C, Haugen OH, Sanders JS, Stolte-Dijkstra I, Mans DA, Steenbergen EJ, Hamel BC, Matignon M, Pfundt R, Jeanpierre C, Boman H, Rødahl E, Veltman JA, Knappskog PM, Knoers NV, Roepman R, Arts HH: Ciliopathies with skeletal anomalies and renal insufficiency due to mutations in the IFT-A gene WDR19. Am J Hum Genet. 2011, 89: 634-643.PubMedPubMed CentralGoogle Scholar
  40. Shi Y, Li Y, Zhang D, Zhang H, Li Y, Lu F, Liu X, He F, Gong B, Cai L, Li R, Liao S, Ma S, Lin H, Cheng J, Zheng H, Shan Y, Chen B, Hu J, Jin X, Zhao P, Chen Y, Zhang Y, Lin Y, Li X, Fan Y, Yang H, Wang J, Yang Z: Exome sequencing identifies ZNF644 mutations in high myopia. PLoS Genet. 2011, 7: e1002084-PubMedPubMed CentralGoogle Scholar
  41. Marion V, Stutzmann F, Gérard M, De Melo C, Schaefer E, Claussmann A, Hellé S, Delague V, Souied E, Barrey C, Verloes A, Stoetzel C, Dollfus H: Exome sequencing identifies mutations in LZTFL1, a BBSome and smoothened trafficking regulator, in a family with Bardet-Biedl syndrome with situs inversus and insertional polydactyly. J Med Genet. 2012, 49: 317-321.PubMedGoogle Scholar
  42. Chaki M, Airik R, Ghosh AK, Giles RH, Chen R, Slaats GG, Wang H, Hurd TW, Zhou W, Cluckey A, Gee HY, Ramaswami G, Hong CJ, Hamilton BA, Cervenka I, Ganji RS, Bryja V, Arts HH, van Reeuwijk J, Oud MM, Letteboer SJ, Roepman R, Husson H, Ibraghimov-Beskrovnaya O, Yasunaga T, Walz G, Eley L, Sayer JA, Schermer B, Liebau MC: Exome capture reveals ZNF423 and CEP164 mutations, linking renal ciliopathies to DNA damage response signaling. Cell. 2012, 150: 533-548.PubMedPubMed CentralGoogle Scholar
  43. Puffenberger EG, Jinks RN, Sougnez C, Cibulskis K, Willert RA, Achilly NP, Cassidy RP, Fiorentini CJ, Heiken KF, Lawrence JJ, Mahoney MH, Miller CJ, Nair DT, Politi KA, Worcester KN, Setton RA, Dipiazza R, Sherman EA, Eastman JT, Francklyn C, Robey-Bond S, Rider NL, Gabriel S, Morton DH, Strauss KA: Genetic mapping and exome sequencing identify variants associated with five novel diseases. PLoS One. 2012, 7: e28936-PubMedPubMed CentralGoogle Scholar
  44. Sergouniotis PI, Davidson AE, Mackay DS, Lenassi E, Li Z, Robson AG, Yang X, Kam JH, Isaacs TW, Holder GE, Jeffery G, Beck JA, Moore AT, Plagnol V, Webster AR: Biallelic mutations in PLA2G5, encoding group V phospholipase A2, cause benign fleck retina. Am J Hum Genet. 2011, 89: 782-791.PubMedPubMed CentralGoogle Scholar
  45. Roosing S, Rohrschneider K, Beryozkin A, Sharon D, Weisschuh N, Staller J, Kohl S, Zelinger L, Peters TA, Neveling K, Strom TM, van den Born LI, Hoyng CB, Klaver CC, Roepman R, Wissinger B, Banin E, Cremers FP, den Hollander AI, : Mutations in RAB28, encoding a farnesylated small GTPase, are associated with autosomal-recessive cone-rod dystrophy. Am J Hum Genet. 2013, 93: 110-117.PubMedPubMed CentralGoogle Scholar
  46. Aldahmesh MA, Khan AO, Mohamed JY, Alkuraya H, Ahmed H, Bobis S, Al-Mesfer S, Alkuraya FS: Identification of ADAMTS18 as a gene mutated in Knobloch syndrome. J Med Genet. 2011, 48: 597-601.PubMedGoogle Scholar
  47. Peluso I, Conte I, Testa F, Dharmalingam G, Pizzo M, Collin RW, Meola N, Barbato S, Mutarelli M, Ziviello C, Barbarulo AM, Nigro V, Melone MA, Simonelli F, Banfi S, : The ADAMTS18 gene is responsible for autosomal recessive early onset severe retinal dystrophy. Orphanet J Rare Dis. 2013, 8: 16-PubMedPubMed CentralGoogle Scholar
  48. Estrada-Cuzcano A, Neveling K, Kohl S, Banin E, Rotenstreich Y, Sharon D, Falik-Zaccai TC, Hipp S, Roepman R, Wissinger B, Letteboer SJ, Mans DA, Blokland EA, Kwint MP, Gijsen SJ, van Huet RA, Collin RW, Scheffer H, Veltman JA, Zrenner E, den Hollander AI, Klevering BJ, Cremers FP, : Mutations in C8orf37, encoding a ciliary protein, are associated with autosomal-recessive retinal dystrophies with early macular involvement. Am J Hum Genet. 2012, 90: 102-109.PubMedPubMed CentralGoogle Scholar
  49. Raychaudhuri S, Iartchouk O, Chin K, Tan PL, Tai AK, Ripke S, Gowrisankar S, Vemuri S, Montgomery K, Yu Y, Reynolds R, Zack DJ, Campochiaro B, Campochiaro P, Katsanis N, Daly MJ, Seddon JM: A rare penetrant mutation in CFH confers high risk of age-related macular degeneration. Nat Genet. 2011, 43: 1232-1236.PubMedPubMed CentralGoogle Scholar
  50. van de Ven JP, Nilsson SC, Tan PL, Buitendijk GH, Ristau T, Mohlin FC, Nabuurs SB, Schoenmaker-Koller FE, Smailhodzic D, Campochiaro PA, Zack DJ, Duvvari MR, Bakker B, Paun CC, Boon CJ, Uitterlinden AG, Liakopoulos S, Klevering BJ, Fauser S, Daha MR, Katsanis N, Klaver CC, Blom AM, Hoyng CB, den Hollander AI: A functional variant in the CFI gene confers a high risk of age-related macular degeneration. Nat Genet. 2013, 45: 813-817.PubMedGoogle Scholar
  51. Eisenberger T, Slim R, Mansour A, Nauck M, Nurnberg G, Nurnberg P, Decker C, Dafinger C, Ebermann I, Bergmann C, Bolz HJ: Targeted next-generation sequencing identifies a homozygous nonsense mutation in ABHD12, the gene underlying PHARC, in a family clinically diagnosed with Usher syndrome type 3. Orphanet J Rare Dis. 2012, 7: 59-PubMedPubMed CentralGoogle Scholar
  52. Peachey NS, Ray TA, Florijn R, Rowe LB, Sjoerdsma T, Contreras-Alcantara S, Baba K, Tosini G, Pozdeyev N, Iuvone PM, Bojang P, Pearring JN, Simonsz HJ, van Genderen M, Birch DG, Traboulsi EI, Dorfman A, Lopez I, Ren H, Goldberg AF, Nishina PM, Lachapelle P, McCall MA, Koenekoop RK, Bergen AA, Kamermans M, Gregg RG: GPR179 is required for depolarizing bipolar cell function and is mutated in autosomal-recessive complete congenital stationary night blindness. Am J Hum Genet. 2012, 90: 331-339.PubMedPubMed CentralGoogle Scholar
  53. Webb TR, Parfitt DA, Gardner JC, Martinez A, Bevilacqua D, Davidson AE, Zito I, Thiselton DL, Ressa JH, Apergi M, Schwarz N, Kanuga N, Michaelides M, Cheetham ME, Gorin MB, Hardcastle AJ: Deep intronic mutation in OFD1, identified by targeted genomic next-generation sequencing, causes a severe form of X-linked retinitis pigmentosa (RP23). Hum Mol Genet. 2012, 21: 3647-3654.PubMedPubMed CentralGoogle Scholar
  54. Huang L, Szymanska K, Jensen VL, Janecke AR, Innes AM, Davis EE, Frosk P, Li C, Willer JR, Chodirker BN, Greenberg CR, McLeod DR, Bernier FP, Chudley AE, Müller T, Shboul M, Logan CV, Loucks CM, Beaulieu CL, Bowie RV, Bell SM, Adkins J, Zuniga FI, Ross KD, Wang J, Ban MR, Becker C, Nürnberg P, Douglas S, Craft CM: TMEM237 is mutated in individuals with a Joubert syndrome related disorder and expands the role of the TMEM family at the ciliary transition zone. Am J Hum Genet. 2011, 89: 713-730.PubMedPubMed CentralGoogle Scholar
  55. Nikopoulos K, Gilissen C, Hoischen A, van Nouhuys CE, Boonstra FN, Blokland EA, Arts P, Wieskamp N, Strom TM, Ayuso C, Tilanus MA, Bouwhuis S, Mukhopadhyay A, Scheffer H, Hoefsloot LH, Veltman JA, Cremers FP, Collin RW: Next-generation sequencing of a 40 Mb linkage interval reveals TSPAN12 mutations in patients with familial exudative vitreoretinopathy. Am J Hum Genet. 2010, 86: 240-247.PubMedPubMed CentralGoogle Scholar
  56. Otto EA, Hurd TW, Airik R, Chaki M, Zhou W, Stoetzel C, Patil SB, Levy S, Ghosh AK, Murga-Zamalloa CA, van Reeuwijk J, Letteboer SJ, Sang L, Giles RH, Liu Q, Coene KL, Estrada-Cuzcano A, Collin RW, McLaughlin HM, Held S, Kasanuki JM, Ramaswami G, Conte J, Lopez I, Washburn J, Macdonald J, Hu J, Yamashita Y, Maher ER, Guay-Woodford LM: Candidate exome capture identifies mutation of SDCCAG8 as the cause of a retinal-renal ciliopathy. Nat Genet. 2010, 42: 840-850.PubMedPubMed CentralGoogle Scholar
  57. Ozgul RK, Siemiatkowska AM, Yucel D, Myers CA, Collin RW, Zonneveld MN, Beryozkin A, Banin E, Hoyng CB, van den Born LI, Bose R, Shen W, Sharon D, Cremers FP, Klevering BJ, den Hollander AI, Corbo JC, : Exome sequencing and cis-regulatory mapping identify mutations in MAK, a gene encoding a regulator of ciliary length, as a cause of retinitis pigmentosa. Am J Hum Genet. 2011, 89: 253-264.PubMedPubMed CentralGoogle Scholar
  58. Lee JE, Gleeson JG: A systems-biology approach to understanding the ciliopathy disorders. Genome Med. 2011, 3: 59-PubMedPubMed CentralGoogle Scholar
  59. Audo I, Mohand-Said S, Dhaenens CM, Germain A, Orhan E, Antonio A, Hamel C, Sahel JA, Bhattacharya SS, Zeitz C: RP1 and autosomal dominant rod-cone dystrophy: novel mutations, a review of published variants, and genotype-phenotype correlation. Hum Mutat. 2012, 33: 73-80.PubMedGoogle Scholar
  60. Edwards AO: Clinical features of the congenital vitreoretinopathies. Eye (Lond). 2008, 22: 1233-1242.Google Scholar
  61. Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009, 10: 57-63.PubMedPubMed CentralGoogle Scholar
  62. Morozova O, Hirst M, Marra MA: Applications of new sequencing technologies for transcriptome analysis. Annu Rev Genomics Hum Genet. 2009, 10: 135-151.PubMedGoogle Scholar
  63. Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore SF, Schroth GP, Burge CB: Alternative isoform regulation in human tissue transcriptomes. Nature. 2008, 456: 470-476.PubMedPubMed CentralGoogle Scholar
  64. Licatalosi DD, Darnell RB: RNA processing and its regulation: global insights into biological networks. Nat Rev Genet. 2010, 11: 75-87.PubMedPubMed CentralGoogle Scholar
  65. Wang GS, Cooper TA: Splicing in disease: disruption of the splicing code and the decoding machinery. Nat Rev Genet. 2007, 8: 749-761.PubMedGoogle Scholar
  66. Kirschner R, Rosenberg T, Schultz-Heienbrok R, Lenzner S, Feil S, Roepman R, Cremers FP, Ropers HH, Berger W: RPGR transcription studies in mouse and human tissues reveal a retina-specific isoform that is disrupted in a patient with X-linked retinitis pigmentosa. Hum Mol Genet. 1999, 8: 1571-1578.PubMedGoogle Scholar
  67. Pretorius PR, Baye LM, Nishimura DY, Searby CC, Bugge K, Yang B, Mullins RF, Stone EM, Sheffield VC, Slusarski DC: Identification and functional analysis of the vision-specific BBS3 (ARL6) long isoform. PLoS Genet. 2010, 6: e1000884-PubMedPubMed CentralGoogle Scholar
  68. Mordes D, Luo X, Kar A, Kuo D, Xu L, Fushimi K, Yu G, Sternberg P, Wu JY: Pre-mRNA splicing and retinitis pigmentosa. Mol Vis. 2006, 12: 1259-1271.PubMedPubMed CentralGoogle Scholar
  69. Mercer TR, Gerhardt DJ, Dinger ME, Crawford J, Trapnell C, Jeddeloh JA, Mattick JS, Rinn JL: Targeted RNA sequencing reveals the deep complexity of the human transcriptome. Nat Biotechnol. 2012, 30: 99-104.Google Scholar
  70. Deo M, Yu JY, Chung KH, Tippens M, Turner DL: Detection of mammalian microRNA expression by in situ hybridization with RNA oligonucleotides. Dev Dyn. 2006, 235: 2538-2548.PubMedGoogle Scholar
  71. Xu S, Witmer PD, Lumayag S, Kovacs B, Valle D: MicroRNA (miRNA) transcriptome of mouse retina and identification of a sensory organ-specific miRNA cluster. J Biol Chem. 2007, 282: 25053-25066.PubMedGoogle Scholar
  72. Wang FE, Zhang C, Maminishkis A, Dong L, Zhi C, Li R, Zhao J, Majerciak V, Gaur AB, Chen S, Miller SS: MicroRNA-204/211 alters epithelial physiology. FASEB J. 2010, 24: 1552-1571.PubMedPubMed CentralGoogle Scholar
  73. Alfano G, Vitiello C, Caccioppoli C, Caramico T, Carola A, Szego MJ, McInnes RR, Auricchio A, Banfi S: Natural antisense transcripts associated with genes involved in eye development. Hum Mol Genet. 2005, 14: 913-923.PubMedGoogle Scholar
  74. Young TL, Matsuda T, Cepko CL: The noncoding RNA taurine upregulated gene 1 is required for differentiation of the murine retina. Curr Biol. 2005, 15: 501-512.PubMedGoogle Scholar
  75. Rapicavoli NA, Poth EM, Zhu H, Blackshaw S: The long noncoding RNA Six3OS acts in trans to regulate retinal development by modulating Six3 activity. Neural Dev. 2011, 6: 32-PubMedPubMed CentralGoogle Scholar
  76. Damiani D, Alexander JJ, O’Rourke JR, McManus M, Jadhav AP, Cepko CL, Hauswirth WW, Harfe BD, Strettoi E: Dicer inactivation leads to progressive functional and structural degeneration of the mouse retina. J Neurosci. 2008, 28: 4878-4887.PubMedPubMed CentralGoogle Scholar
  77. Kaneko H, Dridi S, Tarallo V, Gelfand BD, Fowler BJ, Cho WG, Kleinman ME, Ponicsan SL, Hauswirth WW, Chiodo VA, Karikó K, Yoo JW, Lee DK, Hadziahmetovic M, Song Y, Misra S, Chaudhuri G, Buaas FW, Braun RE, Hinton DR, Zhang Q, Grossniklaus HE, Provis JM, Madigan MC, Milam AH, Justice NL, Albuquerque RJ, Blandford AD, Bogdanovich S, Hirano Y: DICER1 deficit induces Alu RNA toxicity in age-related macular degeneration. Nature. 2011, 471: 325-330.PubMedPubMed CentralGoogle Scholar
  78. Wittkopp PJ, Kalay G: Cis-regulatory elements: molecular mechanisms and evolutionary processes underlying divergence. Nat Rev Genet. 2012, 13: 59-69. 10.1038/nri3362.Google Scholar
  79. Corbo JC, Lawrence KA, Karlstetter M, Myers CA, Abdelaziz M, Dirkes W, Weigelt K, Seifert M, Benes V, Fritsche LG, Weber BH, Langmann T: CRX ChIP-seq reveals the cis-regulatory architecture of mouse photoreceptors. Genome Res. 2010, 20: 1512-1525.PubMedPubMed CentralGoogle Scholar
  80. Hao H, Kim DS, Klocke B, Johnson KR, Cui K, Gotoh N, Zang C, Gregorski J, Gieser L, Peng W, Fann Y, Seifert M, Zhao K, Swaroop A: Transcriptional regulation of rod photoreceptor homeostasis revealed by in vivo NRL targetome analysis. PLoS Genet. 2012, 8: e1002649-PubMedPubMed CentralGoogle Scholar
  81. Schones DE, Zhao K: Genome-wide approaches to studying chromatin modifications. Nat Rev Genet. 2008, 9: 179-191.PubMedGoogle Scholar
  82. Davis DM, Dyer MA: Retinal progenitor cells, differentiation, and barriers to cell cycle reentry. Curr Top Dev Biol. 2010, 93: 175-188.PubMedGoogle Scholar
  83. Merbs SL, Khan MA, Hackler L, Oliver VF, Wan J, Qian J, Zack DJ: Cell-specific DNA methylation patterns of retina-specific genes. PLoS One. 2012, 7: e32602-PubMedPubMed CentralGoogle Scholar
  84. Hunter A, Spechler PA, Cwanger A, Song Y, Zhang Z, Ying GS, Hunter AK, Dezoeten E, Dunaief JL: DNA methylation is associated with altered gene expression in AMD. Invest Ophthalmol Vis Sci. 2012, 53: 2089-2105.PubMedPubMed CentralGoogle Scholar
  85. Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, Snyder M, : An integrated encyclopedia of DNA elements in the human genome. Nature. 2012, 489: 57-74.Google Scholar
  86. National Human Genome Research Institute Catalog of Published Genome-Wide Association Studies. [], []
  87. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST, Bracken MB, Ferris FL, Ott J, Barnstable C, Hoh J: Complement factor H polymorphism in age-related macular degeneration. Science. 2005, 308: 385-389.PubMedPubMed CentralGoogle Scholar
  88. Chen W, Stambolian D, Edwards AO, Branham KE, Othman M, Jakobsdottir J, Tosakulwong N, Pericak-Vance MA, Campochiaro PA, Klein ML, Tan PL, Conley YP, Kanda A, Kopplin L, Li Y, Augustaitis KJ, Karoukis AJ, Scott WK, Agarwal A, Kovach JL, Schwartz SG, Postel EA, Brooks M, Baratz KH, Brown WL, Brucker AJ, Orlin A, Brown G, Ho A, : Genetic variants near TIMP3 and high-density lipoprotein-associated loci influence susceptibility to age-related macular degeneration. Proc Natl Acad Sci U S A. 2010, 107: 7401-7406.PubMedPubMed CentralGoogle Scholar
  89. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TF, McCarroll SA, Visscher PM: Finding the missing heritability of complex diseases. Nature. 2009, 461: 747-753.PubMedPubMed CentralGoogle Scholar
  90. Gibson G: Rare and common variants: twenty arguments. Nat Rev Genet. 2011, 13: 135-145.Google Scholar
  91. Do R, Kathiresan S, Abecasis GR: Exome sequencing and complex disease: practical aspects of rare variant association studies. Hum Mol Genet. 2012, 21: R1-R9.PubMedPubMed CentralGoogle Scholar
  92. Kiezun A, Garimella K, Do R, Stitziel NO, Neale BM, McLaren PJ, Gupta N, Sklar P, Sullivan PF, Moran JL, Hultman CM, Lichtenstein P, Magnusson P, Lehner T, Shugart YY, Price AL, de Bakker PI, Purcell SM, Sunyaev SR: Exome sequencing and the genetic basis of complex traits. Nat Genet. 2012, 44: 623-630.PubMedPubMed CentralGoogle Scholar
  93. DeAngelis MM, Lane AM, Shah CP, Ott J, Dryja TP, Miller JW: Extremely discordant sib-pair study design to determine risk factors for neovascular age-related macular degeneration. Arch Ophthalmol. 2004, 122: 575-580.PubMedGoogle Scholar
  94. Weber BH, Vogt G, Pruett RC, Stohr H, Felbor U: Mutations in the tissue inhibitor of metalloproteinases-3 (TIMP3) in patients with Sorsby’s fundus dystrophy. Nat Genet. 1994, 8: 352-356.PubMedGoogle Scholar
  95. Allikmets R, Singh N, Sun H, Shroyer NF, Hutchinson A, Chidambaram A, Gerrard B, Baird L, Stauffer D, Peiffer A, Rattner A, Smallwood P, Li Y, Anderson KL, Lewis RA, Nathans J, Leppert M, Dean M, Lupski JR: A photoreceptor cell-specific ATP-binding transporter gene (ABCR) is mutated in recessive Stargardt macular dystrophy. Nat Genet. 1997, 15: 236-246.PubMedGoogle Scholar
  96. Allikmets R: Further evidence for an association of ABCR alleles with age-related macular degeneration. The International ABCR Screening Consortium. Am J Hum Genet. 2000, 67: 487-491.PubMedPubMed CentralGoogle Scholar
  97. Girirajan S, Campbell CD, Eichler EE: Human copy number variation and complex genetic disease. Annu Rev Genet. 2011, 45: 203-226.PubMedGoogle Scholar
  98. Liu MM, Chan CC, Tuo J: Genetic mechanisms and age-related macular degeneration: common variants, rare variants, copy number variations, epigenetics, and mitochondrial genetics. Hum Genomics. 2012, 6: 13-PubMedPubMed CentralGoogle Scholar
  99. Sivakumaran TA, Igo RP, Kidd JM, Itsara A, Kopplin LJ, Chen W, Hagstrom SA, Peachey NS, Francis PJ, Klein ML, Chew EY, Ramprasad VL, Tay WT, Mitchell P, Seielstad M, Stambolian DE, Edwards AO, Lee KE, Leontiev DV, Jun G, Wang Y, Tian L, Qiu F, Henning AK, LaFramboise T, Sen P, Aarthi M, George R, Raman R, Das MK: A 32 kb critical region excluding Y402H in CFH mediates risk for age-related macular degeneration. PLoS One. 2011, 6: e25598-PubMedPubMed CentralGoogle Scholar
  100. Meyer KJ, Davis LK, Schindler EI, Beck JS, Rudd DS, Grundstad AJ, Scheetz TE, Braun TA, Fingert JH, Alward WL, Kwon YH, Folk JC, Russell SR, Wassink TH, Stone EM, Sheffield VC: Genome-wide analysis of copy number variants in age-related macular degeneration. Hum Genet. 2011, 129: 91-100.PubMedGoogle Scholar
  101. Hageman GS, Hancox LS, Taiber AJ, Gehrs KM, Anderson DH, Johnson LV, Radeke MJ, Kavanagh D, Richards A, Atkinson J, Meri S, Bergeron J, Zernant J, Merriam J, Gold B, Allikmets R, Dean M, : Extended haplotypes in the complement factor H (CFH) and CFH-related (CFHR) family of genes protect against age-related macular degeneration: characterization, ethnic distribution and evolutionary implications. Ann Med. 2006, 38: 592-604.PubMedPubMed CentralGoogle Scholar
  102. Hughes AE, Orr N, Esfandiary H, Diaz-Torres M, Goodship T, Chakravarthy U: A common CFH haplotype, with deletion of CFHR1 and CFHR3, is associated with lower risk of age-related macular degeneration. Nat Genet. 2006, 38: 1173-1177.PubMedGoogle Scholar
  103. Spencer KL, Hauser MA, Olson LM, Schmidt S, Scott WK, Gallins P, Agarwal A, Postel EA, Pericak-Vance MA, Haines JL: Deletion of CFHR3 and CFHR1 genes in age-related macular degeneration. Hum Mol Genet. 2008, 17: 971-977.PubMedGoogle Scholar
  104. Liu MM, Agron E, Chew E, Meyerle C, Ferris FL, Chan CC, Tuo J: Copy number variations in candidate genes in neovascular age-related macular degeneration. Invest Ophthalmol Vis Sci. 2011, 52: 3129-3135.PubMedPubMed CentralGoogle Scholar
  105. Korbel JO, Urban AE, Affourtit JP, Godwin B, Grubert F, Simons JF, Kim PM, Palejev D, Carriero NJ, Du L, Taillon BE, Chen Z, Tanzer A, Saunders AC, Chi J, Yang F, Carter NP, Hurles ME, Weissman SM, Harkins TT, Gerstein MB, Egholm M, Snyder M: Paired-end mapping reveals extensive structural variation in the human genome. Science. 2007, 318: 420-426.PubMedPubMed CentralGoogle Scholar
  106. Tuzun E, Sharp AJ, Bailey JA, Kaul R, Morrison VA, Pertz LM, Haugen E, Hayden H, Albertson D, Pinkel D, Olson MV, Eichler EE: Fine-scale structural variation of the human genome. Nat Genet. 2005, 37: 727-732.PubMedGoogle Scholar
  107. Alkan C, Coe BP, Eichler EE: Genome structural variation discovery and genotyping. Nat Rev Genet. 2011, 12: 363-376.PubMedPubMed CentralGoogle Scholar
  108. Bodmer W, Bonilla C: Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet. 2008, 40: 695-701.PubMedPubMed CentralGoogle Scholar
  109. Pritchard JK: Are rare variants responsible for susceptibility to complex diseases?. Am J Hum Genet. 2001, 69: 124-137.PubMedPubMed CentralGoogle Scholar
  110. Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, Kang HM, Marth GT, McVean GA, : An integrated map of genetic variation from 1, 092 human genomes. Nature. 2012, 491: 56-65.PubMedGoogle Scholar
  111. Fu W, O’Connor TD, Jun G, Kang HM, Abecasis G, Leal SM, Gabriel S, Rieder MJ, Altshuler D, Shendure J, Nickerson DA, Bamshad MJ, Akey JM, : Analysis of 6, 515 exomes reveals the recent origin of most human protein-coding variants. Nature. 2013, 493: 216-220.PubMedGoogle Scholar
  112. Exome Chip Design. [], []
  113. Cheung VG, Spielman RS, Ewens KG, Weber TM, Morley M, Burdick JT: Mapping determinants of human gene expression by regional and genome-wide association. Nature. 2005, 437: 1365-1369.PubMedPubMed CentralGoogle Scholar
  114. Majewski J, Pastinen T: The study of eQTL variations by RNA-seq: from SNPs to phenotypes. Trends Genet. 2011, 27: 72-79.PubMedGoogle Scholar
  115. Cooper GM, Shendure J: Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nat Rev Genet. 2011, 12: 628-640.PubMedGoogle Scholar
  116. Branham K, Othman M, Brumm M, Karoukis AJ, Atmaca-Sonmez P, Yashar BM, Schwartz SB, Stover NB, Trzupek K, Wheaton D, Jennings B, Ciccarelli ML, Jayasundera KT, Lewis RA, Birch D, Bennett J, Sieving PA, Andreasson S, Duncan JL, Fishman GA, Iannaccone A, Weleber RG, Jacobson SG, Heckenlively JR, Swaroop A: Mutations in RPGR and RP2 account for 15% of males with simplex retinal degenerative disease. Invest Ophthalmol Vis Sci. 2012, 53: 8232-8237.PubMedPubMed CentralGoogle Scholar
  117. Churchill JD, Bowne SJ, Sullivan LS, Lewis RA, Wheaton DK, Birch DG, Branham KE, Heckenlively JR, Daiger SP: Mutations in the X-linked retinitis pigmentosa genes RPGR and RP2 found in 8.5% of families with a provisional diagnosis of autosomal dominant retinitis pigmentosa. Invest Ophthalmol Vis Sci. 2013, 54: 1411-1416.PubMedPubMed CentralGoogle Scholar
  118. Swaroop A: What’s in a name? RPGR mutations redefine the genetic and phenotypic landscape in retinal degenerative diseases. Invest Ophthalmol Vis Sci. 2013, 54: 1417-PubMedPubMed CentralGoogle Scholar
  119. Bowne SJ, Sullivan LS, Koboldt DC, Ding L, Fulton R, Abbott RM, Sodergren EJ, Birch DG, Wheaton DH, Heckenlively JR, Liu Q, Pierce EA, Weinstock GM, Daiger SP: Identification of disease-causing mutations in autosomal dominant retinitis pigmentosa (adRP) using next-generation DNA sequencing. Invest Ophthalmol Vis Sci. 2011, 52: 494-503.PubMedPubMed CentralGoogle Scholar
  120. Neveling K, Collin RW, Gilissen C, van Huet RA, Visser L, Kwint MP, Gijsen SJ, Zonneveld MN, Wieskamp N, de Ligt J, Siemiatkowska AM, Hoefsloot LH, Buckley MF, Kellner U, Branham KE, den Hollander AI, Hoischen A, Hoyng C, Klevering BJ, van den Born LI, Veltman JA, Cremers FP, Scheffer H: Next-generation genetic testing for retinitis pigmentosa. Hum Mutat. 2012, 33: 963-972.PubMedPubMed CentralGoogle Scholar
  121. Booij JC, Bakker A, Kulumbetova J, Moutaoukil Y, Smeets B, Verheij J, Kroes HY, Klaver CC, van Schooneveld M, Bergen AA, Florijn RJ: Simultaneous mutation detection in 90 retinal disease genes in multiple patients using a custom-designed 300-kb retinal resequencing chip. Ophthalmology. 2011, 118: 160-167. e1-3PubMedGoogle Scholar
  122. Song J, Smaoui N, Ayyagari R, Stiles D, Benhamed S, MacDonald IM, Daiger SP, Tumminia SJ, Hejtmancik F, Wang X: High-throughput retina-array for screening 93 genes involved in inherited retinal dystrophy. Invest Ophthalmol Vis Sci. 2011, 52: 9053-9060.PubMedPubMed CentralGoogle Scholar
  123. Blain D, Goetz KE, Ayyagari R, Tumminia SJ: eyeGENE(R): a vision community resource facilitating patient care and paving the path for research through molecular diagnostic testing. Clin Genet. 2013, 84: 190-197.PubMedGoogle Scholar
  124. Haider NB, Ikeda A, Naggert JK, Nishina PM: Genetic modifiers of vision and hearing. Hum Mol Genet. 2002, 11: 1195-1206.PubMedGoogle Scholar
  125. Nadeau JH: Modifier genes in mice and humans. Nat Rev Genet. 2001, 2: 165-174.PubMedGoogle Scholar
  126. Kajiwara K, Berson EL, Dryja TP: Digenic retinitis pigmentosa due to mutations at the unlinked peripherin/RDS and ROM1 loci. Science. 1994, 264: 1604-1608.PubMedGoogle Scholar
  127. Poloschek CM, Bach M, Lagreze WA, Glaus E, Lemke JR, Berger W, Neidhardt J: ABCA4 and ROM1: implications for modification of the PRPH2-associated macular dystrophy phenotype. Invest Ophthalmol Vis Sci. 2010, 51: 4253-4265.PubMedGoogle Scholar
  128. Venturini G, Rose AM, Shah AZ, Bhattacharya SS, Rivolta C: CNOT3 is a modifier of PRPF31 mutations in retinitis pigmentosa with incomplete penetrance. PLoS Genet. 2012, 8: e1003040-PubMedPubMed CentralGoogle Scholar
  129. Wiszniewski W, Lewis RA, Stockton DW, Peng J, Mardon G, Chen R, Lupski JR: Potential involvement of more than one locus in trait manifestation for individuals with Leber congenital amaurosis. Hum Genet. 2011, 129: 319-327.PubMedGoogle Scholar
  130. Hildebrandt F, Benzing T, Katsanis N: Ciliopathies. N Engl J Med. 2011, 364: 1533-1543. 10.1056/NEJMra1010172.PubMedGoogle Scholar
  131. Rachel RA, Li T, Swaroop A: Photoreceptor sensory cilia and ciliopathies: focus on CEP290, RPGR and their interacting proteins. Cilia. 2012, 1: 22-PubMedPubMed CentralGoogle Scholar
  132. Khanna H, Davis EE, Murga-Zamalloa CA, Estrada-Cuzcano A, Lopez I, den Hollander AI, Zonneveld MN, Othman MI, Waseem N, Chakarova CF, Maubaret C, Diaz-Font A, MacDonald I, Muzny DM, Wheeler DA, Morgan M, Lewis LR, Logan CV, Tan PL, Beer MA, Inglehearn CF, Lewis RA, Jacobson SG, Bergmann C, Beales PL, Attié-Bitach T, Johnson CA, Otto EA, Bhattacharya SS, Hildebrandt F: A common allele in RPGRIP1L is a modifier of retinal degeneration in ciliopathies. Nat Genet. 2009, 41: 739-745.PubMedPubMed CentralGoogle Scholar
  133. Coppieters F, Casteels I, Meire F, De Jaegere S, Hooghe S, van Regemorter N, Van Esch H, Matuleviciene A, Nunes L, Meersschaut V, Walraedt S, Standaert L, Coucke P, Hoeben H, Kroes HY, Vande Walle J, de Ravel T, Leroy BP, De Baere E: Genetic screening of LCA in Belgium: predominance of CEP290 and identification of potential modifier alleles in AHI1 of CEP290-related phenotypes. Hum Mutat. 2010, 31: E1709-E1766.PubMedPubMed CentralGoogle Scholar
  134. Louie CM, Caridi G, Lopes VS, Brancati F, Kispert A, Lancaster MA, Schlossman AM, Otto EA, Leitges M, Gröne HJ, Lopez I, Gudiseva HV, O’Toole JF, Vallespin E, Ayyagari R, Ayuso C, Cremers FP, den Hollander AI, Koenekoop RK, Dallapiccola B, Ghiggeri GM, Hildebrandt F, Valente EM, Williams DS, Gleeson JG: AHI1 is required for photoreceptor outer segment development and is a modifier for retinal degeneration in nephronophthisis. Nat Genet. 2010, 42: 175-180.PubMedPubMed CentralGoogle Scholar
  135. Ebermann I, Phillips JB, Liebau MC, Koenekoop RK, Schermer B, Lopez I, Schafer E, Roux AF, Dafinger C, Bernd A, Zrenner E, Claustres M, Blanco B, Nürnberg G, Nürnberg P, Ruland R, Westerfield M, Benzing T, Bolz HJ: PDZD7 is a modifier of retinal disease and a contributor to digenic Usher syndrome. J Clin Invest. 2010, 120: 1812-1823.PubMedPubMed CentralGoogle Scholar
  136. Rachel RA, May-Simera HL, Veleri S, Gotoh N, Choi BY, Murga-Zamalloa C, McIntyre JC, Marek J, Lopez I, Hackett AN, Zhang J, Brooks M, den Hollander AI, Beales PL, Li T, Jacobson SG, Sood R, Martens JR, Liu P, Friedman TB, Khanna H, Koenekoop RK, Kelley MW, Swaroop A: Combining Cep290 and Mkks ciliopathy alleles in mice rescues sensory defects and restores ciliogenesis. J Clin Invest. 2012, 122: 1233-1245.PubMedPubMed CentralGoogle Scholar


© The article is a work of the United States Government; Title 17 U.S.C 105 provides that copyright protection is not available for any work of the United States government in the United States; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd.