A HOLISTIC APPROACH TO REACH A DIAGNOSIS
Copy Number Variation (CNV) Analysis using NGS Cloud (Chromosome Level)
The artificial intelligence-supported genetic data analysis platform NGS Cloud has been diagnosed due to its holistic CNV detection approach in a deceased neonate.
The diagnostic yield of whole-genome sequencing (WGS) tests remains 25-40%, although there is a vast improvement in the last decade (1). Moreover, it takes 6 to 8 years for rare diseases to get an accurate diagnosis (2).
We are living in an era where almost every day, clinical scientists establish a new gene-phenotype relation. It is crucial to keep all the utilised tools up-to-date, incorporating the latest and current literature knowledge and perform periodic reanalysis to increase the diagnostic yield of genetic data analyses. Moreover, this case study demonstrated that CNV variants, such as deletions and duplications, should be a standard part of the pipeline (3).
The physicians considered WGS as the best diagnostic test option for the family at the clinical assessment. They obtained the intracardiac blood of the deceased newborn with microphthalmia, a single nostril, cleft palate, holoprosencephaly, patent ductus arteriosus, pulmonary hypertension.
The Methods and the Findings:
The BAM file was uploaded to the NGS Cloud using PECULIAR (PairEnd Client UpLoad & Integrated Analysis Raw data). The clinical findings were entered as HPO (Human Phenotype Ontology) Terms (4). The HOPE (Harmonization of Ontologies by PairEnd) prioritised the variants strongly associated with the given phenotype. In the last step, trisomy 13 was detected within minutes by the GENIUS algorithm (GENe Interactions Under Scope) (Figure 1).
Figure 1. The display of the CNV Result in NGS Cloud
Nowadays, most cases (60-75%) remain unresolved after the first genetic analysis, and they can be diagnosed with a reanalysis (5). To overcome this challenge, we have built the modern data infrastructure of NGS Cloud that integrates advanced and up-to-date bioinformatics pipelines, automatic update of new publications, variants and other data points regarding gene-phenotype relationships with literature mining. Thus, it decreases the analysis time from hours to minutes, reducing manual data processing and, most importantly, more accurate clinical interpretation.