Journal
GENES & GENOMICS
Volume 45, Issue 1, Pages 13-21Publisher
SPRINGER
DOI: 10.1007/s13258-022-01344-8
Keywords
Microcephaly; Intellectual disability; Exome sequencing (ES); Parent of origin effect; WDR62; TRIO; SOX11
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This study aimed to dissect the clinical and genetic features in five distinct IDM cases, and identified biallelic loss of function variants in WDR62 and AP4M1 genes in three families, as well as de novo missense variants in SOX11 and TRIO genes in two families. Underline the importance of using multiple inheritance models in NGS data analysis.
Background Syndromic intellectual disability (ID) with accompanying primary microcephaly is a group of rare neurodevelopmental disorders exhibiting extreme genetic and clinical heterogeneity. This layered heterogeneity can partially be resolved by unbiased genetic approaches targeting the genome with next generation sequencing (NGS) technologies, including exome sequencing (ES). Objective This study was performed to dissect the clinical and genetic features in five distinct IDM cases. Methods Singleton or trio ES approach followed by in-depth variant analysis using alternative inheritance models was performed. Results We have identified biallelic loss of function variants in genes WDR62 and AP4M1 in three families, together with de novo missense variants in genes SOX11 and TRIO in two families. ES based haplotype analysis in two cases upon identification of an identical WDR62 variant in the homozygous state in two cases was suggestive of a small shared haplotype of 0.1 Mb. Additionally, we have shown a paternal origin for the de novo variant in TRIO via a polymorphic tag SNP, which enlightens the mutational mechanism for this variant. Conclusion In populations with high parental consanguinity, an autosomal recessive inheritance pattern for data analysis is usually the most obvious choice. Therefore, heterozygous variants may be overlooked in standard NGS analyses in consanguineous families. Our findings underlie the importance of using multiple inheritance models in NGS data analysis.
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