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Molecular Genetics Innovations in Diagnosis of Metabolic Disorders: Integration with Multi-Omics and Computational Approaches (Review Article) | ||
| Al-Zahraa Journal for Health and Medical Sciences | ||
| Volume 4, Issue 1, March 2026, Pages 51-70 PDF (495.72 K) | ||
| Document Type: Review Article | ||
| Author | ||
| Ammar Kadhim Alzubaidi* | ||
| Institute of Genetic Engineering and Biotechnology for postgraduate studies, University of Baghdad, Baghdad, Iraq | ||
| Abstract | ||
| Inherited metabolic disorders (IMDs) are a diverse group of hereditary abnormalities that leads to a defect in metabolic pathway. Its diagnosis has been transformed by the innovations of molecular genetics and computational biology. Conventionally, diagnosis of IMDs is dependent on clinical findings and biochemical tests. Yet, these methods are limited due to a heterogeneity of such disorders and a large number of genes involved. The main objective of this review is to highlight the role of next-generation sequencing (NGS), including targeted gene panels, whole-exome sequencing (WES), and whole-genome sequencing (WGS), in the diagnosis of IMDs and providing reliable information in identifying genetic causes, and to explore the integrated analysis of several molecular layers such as genomics, transcriptomics, proteomics, metabolomics, and epigenetics. Targeted mass spectrometry and untargeted metabolomics methods are essential approaches for screening and identifying the metabolic patterns that act as a diagnosis biomarker to confirm the biochemical phenotypes associated with IMDs. Moreover, a new diagnostic model has been developed from the combination data of transcriptomics and proteomics to determine whether a gene mutation leads to a protein's dysfunction or not. The review concludes that the IMDs diagnosis should be lied in a fully integrated between molecular genetics techniques with multi-omics pipeline enhanced by artificial intelligence (AI) and machine learning (ML), which will provide a more rapid, accurate, and accessible path to diagnosis and, ultimately, more effective treatment. | ||
| Keywords | ||
| NGS; Metabolic patterns; Transcriptomics; Proteomics; metabolomics | ||
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