Methods to Improve Molecular Diagnosis in Genomic Cold Cases in Pediatric Neurology

Abstract

During the last decade, genetic testing has emerged as an important etiological diagnostic tool for Mendelian diseases, including pediatric neurological conditions. A genetic diagnosis has a considerable impact on disease management and treatment; however, many cases remain undiagnosed after applying standard diagnostic sequencing techniques. This review discusses various methods to improve the molecular diagnostic rates in these genomic cold cases. We discuss extended analysis methods to consider, non-Mendelian inheritance models, mosaicism, dual/multiple diagnoses, periodic re-analysis, artificial intelligence tools, and deep phenotyping, in addition to integrating various omics methods to improve variant prioritization. Last, novel genomic technologies, including long-read sequencing, artificial long-read sequencing, and optical genome mapping are discussed. In conclusion, a more comprehensive molecular analysis and a timely re-analysis of unsolved cases are imperative to improve diagnostic rates. In addition, our current understanding of the human genome is still limited due to restrictions in technologies. Novel technologies are now available that improve upon some of these limitations and can capture all human genomic variation more accurately. Last, we recommend a more routine implementation of high molecular weight DNA extraction methods that is coherent with the ability to use and/or optimally benefit from these novel genomic methods.

Case Report of Suspected Gonadal Mosaicism in FOXP1-Related Neurodevelopmental Disorder

“…The timing of the mutation will define its clinical consequences [ 18 ]. In practice, it is difficult to distinguish between parental gonadal mosaicism, de novo gonadal issues, or early post-zygotic mutation [ 19 ], although the recurrence risk is different in these cases. De novo variants are associated with low, about 1%, recurrence risk.…”

Digenic variant interpretation with hypothesis-driven explainable AI

“…In contrast, in the other digenic mechanisms, two mutations jointly cause a specific phenotype. This distinction is crucial for accurate clinical management, as treatment strategies might differ based on the underlying genetic cause 47 . Moreover, understanding whether it’s a DM or not is essential for genetic counseling, offering insights into inheritance patterns and risks to family members.…”

The Diagnostic Landscape of Adult Neurogenetic Disorders

“…While the discussion about the theoretical superiority of WGS over WES has ramped up over the past several years (e.g., [123]), the translation of the hypothetical advantages of WGS into reality has led to the recognition of some of the limitations of "traditional" shortread sequencing and bioinformatic tools available to interpret genomic data. This has led to the development of superior and complementary technologies and tools to overcome some of the technical barriers to realizing the promise of WGS, including long-read sequencing technology, optical genome mapping, multi-omics approaches, and the development of many new bioinformatic tools to allow for interrogation of CNVs, repeat expansions, and non-coding regions [84,[124][125][126][127][128][129][130][131].…”