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Integration of multigene panels for the diagnosis of hereditary retinal disorders using Next Generation Sequencing and bioinformatics approaches.

In recent years, Next-Generation Sequencing (NGS) opened a new way for the study of pathogenic mechanisms and for molecular diagnosis of inherited disorders. In the present work, we focused our attention on the inherited retinal dystrophies (IRDs), a group of specific disorders of the retina, displaying a very high clinical and genetic heterogeneity, whose genetic diagnosis is not easily feasible. It represents a paradigmatic example for the integration of clinical and molecular examination toward precision medicine. In this paper, we discuss the use of targeted NGS resequencing of selected gene panels in a cohort of patients affected by IRDs. We tested the hypothesis to apply a selective approach based on a careful clinical examination. By this approach we reached a 66% overall detection rate for pathogenic variants, with a 52% diagnostic yield. Reduction of the efforts for validation and classification of variants is a clear advantage for the management of genetic testing in a clinical setting.

In this paper, we discuss the use of targeted NGS resequencing of selected gene panels in a cohort of patients affected by IRDs. We tested the hypothesis to apply a selective approach based on a careful clinical examination. By this approach we reached a 66% overall detection rate for pathogenic variants, with a 52% diagnostic yield. Reduction of the efforts for Di Resta, Spiga, Presi, Merella, Battista Pipitone, Manitto, Querques, Battaglia Parodi, Ferrari, Carrera Integration of multigene panels for the diagnosis of hereditary retinal disorders validation and classification of variants is a clear advantage for the management of genetic testing in a clinical setting.

INTRODUCTION
Inherited retinal dystrophies (IRDs) are a group of rare diseases due to a progressive degeneration of retinal photoreceptors, that can lead to vision loss [1,2]. IRDs comprise several different disorders characterized by clinical and genetic heterogeneity, often displaying a phenotypic overlap [3]. Many IRDs are characterized by progressive degeneration of both cone and rod photoreceptors, making the clinical differential diagnosis difficult, especially in the advanced stages [4].
Additionally, there are also syndromic forms in which retina is not the only affected tissue and additional organs can be involved, such as the Usher Syndrome (USH) and the Bardet-Biedl Syndrome (BBS). Furthermore, clinical symptoms can be progressive with variable onset and intra-familial variability, due to an incomplete penetrance and variable expressivity, making the clinical picture more complex [1]. All these factors often complicate or delay a precise diagnosis [1,2,5].
By a genetic point of view, IRDs displays locus and allelic heterogeneity [6], with more than 200 causative genes, that make the genetic characterization very complex. The advent of nextgeneration sequencing (NGS) has opened new frontiers in genetic diagnostics of IRDs, exploiting the high-throughput parallel sequencing and the simultaneous analysis of several samples. Indeed, the overall mutation detection rate for IRDs is variable [6], ranging from 36% to 60%, leaving many cases still genetically unsolved. More than 4000 pathogenic variants have been identified in causative genes, that can converge to the same phenotype [6] or can show different symptoms [1], complicating the molecular diagnosis. Lastly, since some IRDs causative genes are associated to specific inheritance traits (AR, AD, X-linked), a targeted genetic analysis could be more effective, although sometimes establishing the inheritance mode in an affected family is difficult [1].
Considering all the above, it is often complex to determine a priori which genes are to be analyzed and a "non-hypothesis-driven" approach has been applied in large NGS studies [7][8][9].
In the diagnostic laboratory, such an approach increases the risk to identify variants of uncertain significance, complicating the interpretation and implying a big effort in classification.
In this paper, we describe the strategy adopted by our multidisciplinary team to optimize the integration of clinical data and NGS targeted resequencing for the diagnostics of the different forms of IRDs. Our approach for the molecular diagnosis of IRDs, including genes that fit with the phenotype, allowed us to obtain a 66% overall mutation detection rate, consistent with the best rates obtained with the "non-hypothesisdriven" approach.

Clinical diagnosis and sample collection
This investigation conformed to principles outlined in the Declaration of Helsinki. Genomic DNA (gDNA) was extracted from peripheral blood using the automated extractor Maxwell16 (Promega, Milano, Italy); the concentration and gDNA quality were determined using Qubit® Fluorometer (Thermo Fisher Scientific).

Library enrichment and sequencing
Sample enrichment and paired-end libraries preparation were performed using the commercial kit TruSight One (Illumina, San Diego, CA, USA), starting from 50ng gDNA, following the manufacturer's instructions (Document #1000000006694 v00).

NGS data analysis
The read alignment and variant calling were performed with BaseSpace Onsite Sequence Hub. For each case, the analysis of variants was focused on one or more gene panels based on the different clinical phenotypes. The variants were then annotated using Illumina VariantStudio data analysis software. For the identification of possible causative variants, filters were applied taking into account: 1) the quality parameter, 2) the MAF (Minor Allele Frequency) >2% in the 1000Genomes and ExAC database, 3) the localization of the variants, considering only the exonic and intronic regions at ± 20 bp from the coding regions, to identify possible splice-site variations.
In order to optimize the data analysis process and to focus on the identification of causative variants, we created panels of disease genes associated to the different forms of IRDs, as reported in Table 1. Particularly, we set panels for nonsyndromic forms (Achromatopsia (ACHM); Best vitelliform macular dystrophy; Congenital stationary night blindness (CSNB); Choroideremia; Stargardt disease; Retinitis pigmentosa) and for syndromic forms (Bardet-Biedl S., Refsum disorder, Cohen S., Stickler S., Usher S.). We chose causative genes for each disease panel based on public databases, such as OMIM (http://www. ncbi.nlm.nih.gov/omim) or RetNet™ (https:// sph.uth.edu/retnet/) and from the literature [4,6,[10][11][12]. After primary analysis, the search for causative variants started by considering the panel of genes associated to the clinical suspicion. If the suspicion was less focused, more than one panel is analyzed.

Parameters of NGS raw data
All the 35 patients have been sequenced for 4813 genes, included in TruSight One panel (Illumina) using Illumina NextSeq500.
Runs had a mean cluster density equal to 217 k/ mm2. We obtained a mean read enrichment of 59% and target aligned read of 99%. The mean coverage for the analyzed genes associated to the different forms of IRDs was 300X.

Analysis and classification of detected variants
In our cohort, excluding common variants, we detected a total of 57 variants in 29 genes; 30 were novel and 27 were already reported in dbSNP as rare variants. In three patients no variants were found (9%), while the others (91%) presented with different variants with the exception of two pathogenic variants in ABCA4 (NM_000350.2: c.5882G>A; NM_000350.2: c.5018+2T>C), identified in four different unrelated patients.

Evaluation of the diagnostic yield and genotype-phenotype correlation
We found pathogenic or likely pathogenic variants in 23/35 (66%) patients and consistent with the subject clinical presentation. Among these, we were able to reach the genetic diagnosis in 18/35 (52%) patients while in 5/35 (14%) patients we obtained only a partial diagnosis because of the detection of only one causative recessive variant. In 9/35 (26%) patients we identified heterozygous variants with unknown significance (VUS) in diseasegenes but in 5 of them the genotype did not fit to the disease inheritance manner and the genetic diagnosis remained incomplete. Finally, 3 patients were wild-type in analyzed causative genes. In these cases, a multidisciplinary rediscussion would be suggested in an attempt to define further testing or the potential for a research approach.
The majority of patients not reaching the genetic diagnosis had non-syndromic phenotypes, in particular two of the patients with no variants had a  In Table 2, we reported the obtained diagnostic yield for each disease.
In Table 3 are listed all the genes with variants identified in the present work in association with different diseases. It is possible to appreciate that the larger genetic overlap is between the retinal dystrophies and RP phenotypes ( Table 3, the shaded lines).
In the present work, we applied a targeted NGS resequencing for genetic testing of IRDs; selection of gene panels was done based on the clinical suspicion (Table 1) allowing us to reduce the number of genes tested. We reached a diagnosis in a proportion of patients that was consistent with the results from other studies, where wider panels were used. Based on these findings, this approach, reducing the efforts needed for classification and validation of variants, seems to be more suited in the diagnostic field.  Table 2 The percentance of complete, partial and total diagnostic yield obtained using our multigene panel approach for each disease Di Resta, Spiga, Presi, Merella, Battista Pipitone, Manitto, Querques, Battaglia Parodi, Ferrari, Carrera Integration of multigene panels for the diagnosis of hereditary retinal disorders

Figure 2 Different types of identified variants
We identified 57 variants in 29 genes in our cohort and in the pie chart the percentage of each type of detected variant is reported.

Figure 3 Seven genes are multivariated in our cohort
Graph represents the number of detected variants (x-axis) for each gene (y-axis).

CONCLUSION AND GENERAL REMARKS
Thanks to NGS, genetic testing costs are reducing rapidly with the potential for a broader access in the frame of health care systems. As NGS allows parallel analysis, it currently realizes a real improvement for personalized medicine, shortening the time needed to reach a diagnosis, nevertheless we still have to face a number of criticisms [15]. This report, showing an overall mutation detection rate for IRDs of approximately 60%, addresses the challenges ahead,  Table 3 The genetic overlapping between retinal dystrophies and RP phenotypes