Suzanne de Bruijn

294 Chapter 6 with proliferative vitreoretinopathy. 73 These examples already illustrate the diverse roles and functionalities of lncRNAs, and that several lncRNA genes could be potential candidate genes for inherited RD or HL. Although the involvement of non-coding RNAs in disease is evident, it is extremely challenging toestablishcausalitybetweenvariants affecting theseelements anddisease. To be able to interpret the effect of a non-coding variant, all connections should be mapped between regulatory genes and their downstream target genes. Unfortunately, our current knowledge of these regulatory connections is far from complete. When a variant is found in close proximity of a disease-associated gene, a potential effect on gene function may seem evident, but for variants located in distantly located enhancer elements or non-coding genes, this association is less straightforward and is likely to remain unrecognized. A cis regulatory element can be located up to 1 Mb away from its target gene, and in some cases even can be located in intronic regions of other coding genes. 74,75 To fully comprehend thegenomic and regulatory landscapeof disease-associatedgenes, the generation of a tissue-specific omics-framework is required. Omics analyses, such as transcriptomics, proteomics and epigenomics, can provide a deeper understanding of genetic variation by studying the consequences of putative variants at multiple levels ( Figure 2 ). An omics-framework would furthermore provide a complete atlas of all active (tissue-specific) coding and non-coding genomic elements and allow the elucidation of potential cis -acting events of variants of unknown significance. The next sections will provide several examples that illustrate how and why the different omics technologies should be adopted in genetic diagnostics. Transcriptomics The integration of RNA studies with genome sequencing data has been most widely explored. Co-expression analyses, functional reporter assays or CRISPR-mediated perturbation studies can be performed to reveal interdependencies between expression of non-coding genes and coding (disease-associated) genes. Additionally, whole transcriptome analysis with total RNA-seq can detect quantitative (up- or downregulation) or qualitative (alternative splicing) abnormalities. Several studies have proven the added value of incorporation of RNA-seq in WES or WGS analyses and showed successful implementation in a clinical setting. RNA-seq was performed on patient-derived blood samples and muscle or skin biopsies. The success rates of RNA- seq analyses reported in these studies range from 7.5%-36% when performed in cases that could not be genetically resolved using WES or WGS. 76-80 By combining genomic and transcriptomic datasets, candidate splice variants could be readily validated or