An integrated multiomic approach as an excellent tool for the diagnosis of metabolic diseases: our first 3720 patients
Lígia S. Almeida, Catarina Pereira, Ruxandra Aanicai, Sabine Schröder, Tomasz Bochinski, Anett Kaune, Alice Urzì, Tania C. L. S. Spohr, Nikenza Viceconte, Sebastian Oppermann, Mohammed Alasel, Saeedeh Ebadat, Sana Iftikhar, Eresha Jasinge, Solaf M. Elsayed, Hoda Tomoum, Iman Marzouk, Anil Jalan, Agnė Čerkauskaitė, Rimantė Čerkauskienė, Tinatin Tkemaladze, Anjum Muhammad Nadeem, Iman G. Mahmoud, Fawzia Amer Mossad, Mona Kamel, Laila Selim, Huma Arshad Cheema, Omid Paknia, Claudia Cozma, Carlos Juaristi-Manrique, Pilar Guatibonza-Moreno, Tobias Böttcher, Florian Vogel, Jorge Pinto‐Basto, Aida M. Bertoli‐Avella, Peter Bauer
Abstract
To present our experience using a multiomic approach, which integrates genetic and biochemical testing as a first-line diagnostic tool for patients with inherited metabolic disorders (IMDs). A cohort of 3720 patients from 62 countries was tested using a panel including 206 genes with single nucleotide and copy number variant (SNV/CNV) detection, followed by semi-automatic variant filtering and reflex biochemical testing (25 assays). In 1389 patients (37%), a genetic diagnosis was achieved. Within this cohort, the highest diagnostic yield was obtained for patients from Asia (57.5%, mainly from Pakistan). Overall, 701 pathogenic/likely pathogenic unique SNVs and 40 CNVs were identified. In 620 patients, the result of the biochemical tests guided variant classification and reporting. Top five diagnosed diseases were: Gaucher disease, Niemann-Pick disease type A/B, phenylketonuria, mucopolysaccharidosis type I, and Wilson disease. We show that integrated genetic and biochemical testing facilitated the decision on clinical relevance of the variants and led to a high diagnostic yield (37%), which is comparable to exome/genome sequencing. More importantly, up to 43% of these patients (n = 610) could benefit from medical treatments (e.g., enzyme replacement therapy). This multiomic approach constitutes a unique and highly effective tool for the genetic diagnosis of IMDs.