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Updated: Aug 13, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
Published on: September 17, 2019
Exploring Genetic and Neural Risk of Specific Reading Disability within a Nuclear Twin Family Case Study: A
Tina Thomas1, Griffin Litwin2,3, David J Francis2,4
1Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA.
Abstract:
Imaging and genetic studies have characterized biological risk factors contributing to specific reading disability (SRD). The current study aimed to apply this literature to a family of twins discordant for SRD and an older sibling with reading difficulty. Intraclass correlations were used to understand the similarity of imaging phenotypes between pairs. Reading-related genes and brain region phenotypes, including asymmetry indices representing the relative size of left compared to right hemispheric structures, were descriptively examined. SNPs that corresponded between the SRD siblings and not the typically developing (TD) siblings were in genes ZNF385D, LPHN3, CNTNAP2, FGF18, NOP9, CMIP, MYO18B, and RBFOX2. Imaging phenotypes were similar among all sibling pairs for grey matter volume and surface area, but cortical thickness in reading-related regions of interest (ROIs) was more similar among the siblings with SRD, followed by the twins, and then the TD twin and older siblings, suggesting cortical thickness may differentiate risk for this family. The siblings with SRD had more symmetry of cortical thickness in the transverse temporal and superior temporal gyri, while the TD sibling had greater rightward asymmetry. The TD sibling had a greater leftward asymmetry of grey matter volume and cortical surface area in the fusiform, supramarginal, and transverse temporal gyrus. This exploratory study demonstrated that reading-related risk factors appeared to correspond with SRD within this family, suggesting that early examination of biological factors may benefit early identification. Future studies may benefit from the use of polygenic risk scores or machine learning to better understand SRD risk.
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