ArticleJournal of human genetics2024
Exploring unsolved cases of lissencephaly spectrum: integrating exome and genome sequencing for higher diagnostic yield.
Article in Journal of human genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Genetic causes of the lissencephaly spectrum: insights from chromosomal microarray and clinical/whole-exome sequencing.Croatian medical journal · 2026Article
- Genetic landscape and phenotypic correlations of lissencephaly: prenatal and postnatal insights.Brain communications · 2026Article
- Fetal malformations of cortical development: review and clinical guidance.Brain : a journal of neurology · 2025Review
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Lissencephaly is a rare brain malformation characterized by abnormal neuronal migration during cortical development. In this study, we performed a comprehensive genetic analysis using next-generation sequencing in 12 unsolved Japanese lissencephaly patients, in whom PAFAH1B1, DCX, TUBA1A, and ARX variants were excluded using the Sanger method. Exome sequencing (ES) was conducted on these 12 patients, identifying pathogenic variants in CEP85L, DYNC1H1, LAMC3, and DCX in four patients. Next, we performed genome sequencing (GS) on eight unsolved patients, and structural variants in PAFAH1B1, including an inversion and microdeletions involving several exons, were detected in three patients. Notably, these microdeletions in PAFAH1B1 could not to be detected by copy number variation (CNV) detection tools based on the depth of coverage methods using ES data. The density of repeat sequences, including Alu sequences or segmental duplications, which increase the susceptibility to structural variations, is very high in some lissencephaly spectrum genes (PAFAH1B1, TUBA1A, DYNC1H1). These missing CNVs were due to the limitations of detecting repeat sequences in ES-based CNV detection tools. Our study suggests that a combined approach integrating ES with GS can contribute to a higher diagnostic yield and a better understanding of the genetic landscape of the lissencephaly spectrum.
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