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Background  While next generation sequencing (NGS) is a useful tool for the identification of genetic variants to aid diagnosis and support therapy decision, high sequencing costs have limited its application within routine clinical care, especially in economically depressed areas. To investigate the utility of a multi-disease NGS based genetic test, we designed a custom sequencing assay targeting over thirty disease-associated areas including cardiac disorders, intellectual disabilities, hearing loss, collagenopathies, muscular dystrophy, Ashkenazi Jewish genetic disorders, and complex Mendelian disorders. We focused on these specific areas based on the interest of our collaborative clinical team, suggesting these diseases being the ones in need for the development of a sequencing-screening assay.    Results  We targeted all coding, untranslated regions (UTR) and flanking intronic regions of 650 known disease-associated genes using the Roche-NimbleGen EZ SeqCapV3 capture system and sequenced on the Illumina HiSeq 2500 Rapid Run platform. Eight controls with known variants and one HapMap sample were first sequenced to assess the performance of the panel. Subsequently, as a proof of principle and to explore the possible utility of our test, we analyzed test disease subjects (n = 16). Eight had known Mendelian disorders and eight had complex pediatric diseases. In addition to assess whether copy number variation may be of utility as a companion assay relative to these specific disease areas, we used the Affymetrix Genome-Wide SNP Array 6.0 to analyze the same samples.    Conclusion  We identified potentially disease-associated variants: 22 missense, 4 nonsense, 1 frameshift, and 1 splice variants (16 previously identified, 12 novel among dbSNP and 15 novel among NHLBI Exome Variant Server). We found multi-disease targeted high-throughput sequencing to be a cost efficient approach in detecting disease-associated variants to aid diagnosis.

Development of a Targeted Multi-Disorder High-Throughput Sequencing Assay for the Effective Identification of Disease-Causing Variants