Molecular genetic decoding of malformations of cortical development

Abstract

JGM is likely to be affected. As novel genes and variants associated with MCD are discovered using next-generation sequencing (NGS) technology, an updated classification system for MCD has become more reliant on NGS findings [1,4]. Identification of disease-causing mutations, including inherited or de novo mutation, in MCD has revealed the molecular genetic basis of clinical manifestations [5][6][7][8][9]. As an example, recently identified genes responsible for microcephaly were found to encode DNA repair systems and centrosomal proteins that are crucial for regulating cell division [5,10,11].The current challenge is to validate the increasing number of disease-causing mutations that are being identified by NGS. Malformations of cortical development (MCD) cover a broad spectrum of developmental disorders which cause the various clinical manifestations including epilepsy, developmental delay, and intellectual disability. MCD have been clinically classified based on the disruption of developmental processes such as proliferation, migration, and organization. Molecular genetic studies of MCD have improved our understanding of these disorders at a molecular level beyond the clinical classification. These recent advances are resulted from the development of massive parallel sequencing technology, also known as nextgeneration sequencing (NGS), which has allowed researchers to uncover novel molecular genetic pathways associated with inherited or de novo mutations. Although an increasing number of disease-related genes or genetic variations have been identified, genotype-phenotype correlation is hampered when the biological or pathological functions of identified genetic variations are not fully understood. To elucidate the causality of genetic variations, in vivo disease models that reflect these variations are required. In the current review, we review the use of NGS technology to identify genes involved in MCD, and discuss how the functions of these identified genes can be validated through in vivo disease modeling.