PracticeUpdate Conference Series - SSIEM 2018

these biomarkers, however, are not always high. Inborn errors of metabolism are, generally, severe diseases, and accurate identification of the molecular basis of these diseases is important for appropriate treatment and genetic counselling. The establishment of a diagnosis of an inborn error of metabolism is supported by clinical suspicion and biochemical investigations. The classic molecular studies previously conducted in clinical laboratories consisted of time-consuming and expensive genetic studies. Some genetic diseases are complex. For example, one gene can be associated with different phenotypes. Similar phenotypes may be caused by mutations in different genes. In addition, patterns of inheritance of some of these diseases are not necessarily characterized completely. NGS technology has arisen as an essential tool for rapid and effective diagnosis prior to complex functional studies (that is, cellular enzyme activity). To date, published NGS diagnostic applications have focused on specific disorders and overlapping phenotypes. The appearance of a clinical exome strategy, however, has facilitated simultaneous assessment of different phenotypes. With respect to inborn errors of metabolism, studies of hyperphenylalaninemia, phenylketonuria, cerebral creatine deficiency, glycogen storage diseases, and mitochondrial diseases have generally yielded good results.

Most studies have utilized whole exome sequencing, but customized NGS approaches are being implemented in clinical practice. Additionally, comprehensive genetic testing of Mendelian childhood diseases through NGS is cost-saving. Biochemical diagnosis must remain a key part of the diagnostic algorithm to ensure that selected genetic variants are the real cause of the observed phenotype, because most of these workflows are still biased by personal experience and lack of specificity. Unsolved cases can be explained by various causes. First, the causative genemight not be included in the panel design. Genes may encode proteins involved in altering a biochemical marker that are currently unknown or not related with human disease. Second, metabolic diseases are highly heterogeneous. Overlapping phenotypes may confuse the clinical orientation. In these cases, the disease-causative gene could be involved in another pathway while yielding a similar phenotype. Dr Perez Gonzales concluded that NGS is capable of detecting genomic deletions in metabolic disorders.

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SSIEM 2018 • PRACTICEUPDATE CONFERENCE SERIES