In the present work, we have undertaken a proof-of-concept study to determine whether a simple upper-limb movement could be useful to accurately classify low-functioning children with autism spectrum disorder (ASD) aged 2-4. To answer this question, we developed a supervised machine-learning method to correctly discriminate 15 preschool children with ASD from 15 typically developing children by means of kinematic analysis of a simple reach-to-drop task. Our method reached a maximum classification accuracy of 96.7% with seven features related to the goal-oriented part of the movement. These preliminary findings offer insight into a possible motor signature of ASD that may be potentially useful in identifying a well-defined subset of patients, reducing the clinical heterogeneity within the broad behavioral phenotype.

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A substantial genetic contribution in the etiology of developmental dyslexia (DD) has been well documented with independent groups reporting a susceptibility locus on chromosome 15q. After the identification of the DYX1C1 gene as a potential candidate for DD, several independent association studies reported controversial results. We performed a family-based association study to determine whether the DYX1C1 single nucleotide polymorphisms (SNPs) that have been associated with DD before, that is SNPs '23GA' and '1249GT', influence a broader phenotypic definition of DD. A significant linkage disequilibrium was observed with 'Single Letter Backward Span' (SLBS) in both single-marker and haplotype analyses. These results provide further support to the association between DD and DYX1C1 and it suggests that the linkage disequilibrium with DYX1C1 is more saliently explained in Italian dyslexics by short-term memory, as measured by 'SLBS', than by the categorical diagnosis of DD or other related phenotypes.

We applied a family-based association approach to investigate the role of the DYX1C1 gene on chromosome 15q as a candidate gene for developmental dyslexia (DD) to 158 families containing at least one dyslexic child. We directly sequenced exons 2 and 10 of the DYX1C1 gene and found eight single nucleotide polymorphism (SNPs), three of which (À3G4A, 1249 G4T, 1259 C4G) were suitable for the genetic analyses. We performed single-and multimarker association analyses with DD as a categorical trait by FBAT version 1.4 and TRANSMIT version 2.5.4 programs. Our sample had a power of at least 80% to detect an association between the selected phenotypes and the informative polymorphisms at a significance level of 5%. The results of the categorical analyses did not support the involvement of the DYX1C1 gene variants in this sample of dyslexics and their relatives. Quantitative and multimarker analyses, which provide greater power to detect loci with a minor effect, consistently yielded nonsignificant results. While D1X1C1 is a good candidate gene for DD, we were unable to replicate the original findings between DYX1C1 gene and DD, perhaps due to genetic heterogeneity.