1. Ballan MS. {{Parental Perspectives of Communication about Sexuality in Families of Children with Autism Spectrum Disorders}}. {J Autism Dev Disord};2011 (Jun 17)
To explore the content of communication about sexuality between parents and children with autism spectrum disorders, semi-structured interviews were conducted with 18 parents of children ages 6-13. Content analysis and ethnographic summary were used to interpret the data. Findings suggest that parent’s perceptions of a child’s behaviors and comprehension are associated with the likelihood that communication occurs. However, parents recognize the risks their children experience, with the greatest fears being sexual victimization and misperceptions related to the intent of their child’s behaviors. This study provides information on the nature of communication about sexuality in families of children with autism spectrum disorders and can help tailor interventions aimed at assisting parents to communicate sexuality information effectively.
2. Ben-David E, Granot-Hershkovitz E, Monderer-Rothkoff G, Lerer E, Levi S, Yaari M, Ebstein RP, Yirmiya N, Shifman S. {{Identification of a functional rare variant in autism using genome-wide screen for monoallelic expression}}. {Hum Mol Genet};2011 (Jun 16)
Recent work has led to the identification of several susceptibility genes for autism spectrum disorder (ASD) and an increased appreciation of the importance of rare and de novo mutations. Some of the mutations may be very hard to detect using current strategies, especially if they are located in regulatory regions. We present a new approach to identify functional mutations that exploits the fact that many rare mutations disrupt the expression of genes from a single parental chromosome. The method incorporates measurement of the relative expression of the two copies of a gene across the genome using single nucleotide polymorphism (SNP) arrays. Allelic expression has been successfully used to study common regulatory polymorphisms; however, it has not been implemented as a screening tool for rare mutation. We tested the potential of this approach by screening for monoallelic expression in lymphoblastoid cell lines (LCLs) derived from a small ASD cohort. After filtering regions shared across multiple samples, we identified genes showing monoallelic expression in specific ASD samples. Validation by quantitative sequencing demonstrated that the genes (or only part of them) are monoallelic expressed. The genes included both previously suspected risk factors for ASD and novel candidates. In one gene, named autism susceptibility candidate 2 (AUTS2), we identified a rare duplication that is likely to be the cause of monoallelic expression. Our results demonstrate the ability to identify rare regulatory mutations using genome-wide allelic expression screens, capabilities that could be expanded to other diseases, especially those with suspected involvement of rare dominantly-acting mutations.
3. Casci T. {{Disease genetics: Converging models for autism}}. {Nat Rev Genet};2011;12(7):456-457.
4. Martineau J, Hernandez N, Hiebel L, Roche L, Metzger A, Bonnet-Brilhault F. {{Can pupil size and pupil responses during visual scanning contribute to the diagnosis of autism spectrum disorder in children?}}. {J Psychiatr Res};2011 (Jun 14)
The purpose of this study was to determine whether baseline pupil size and pupil responses during visual scanning with eye-tracking technology could discriminate children with Autism Spectrum Disorder (ASD) from mental age-matched and chronological age-matched controls. To this end, we used stimuli consisting in still color photographs presented centrally to the participant’s midline on a stimulus monitor. Each child was presented with a series of neutral faces, virtual faces (avatars) and different objects, separated by black slides. We recorded the mean pupil size and pupil size changes over time in each of the three categories of stimuli and during exposure to the black slides. Fifty-seven children participated in study (19 ASD, mean age 118 months; 19 mental age-matched controls, mean age 87 months; and 19 chronological age-matched controls, mean age 118 months). We compared the baseline pupil size and pupil responses during visual scanning among the three diagnostic groups. During the presentation of slides, the mean pupil size in the ASD group was clearly smaller than in the MA-matched and CA-matched groups. Discriminate analysis of pupil size during the presentation of black slides and slides with visual stimuli successfully predicted group membership in 72% of the participants. Group membership was correctly classified in 89% of the participants in the ASD group, in 63% in the MA-matched group and in 63% in the CA-matched group. These potential biomarkers may contribute to our understanding of the differences in neurological development in the brain in autism and could prove useful as indicators of ASD.
5. Roelfsema MT, Hoekstra RA, Allison C, Wheelwright S, Brayne C, Matthews FE, Baron-Cohen S. {{Are Autism Spectrum Conditions More Prevalent in an Information-Technology Region? A School-Based Study of Three Regions in the Netherlands}}. {J Autism Dev Disord};2011 (Jun 17)
We tested for differences in the prevalence of autism spectrum conditions (ASC) in school-aged children in three geographical regions in the Netherlands. Schools were asked to provide the number of children enrolled, the number having a clinical diagnosis of ASC and/or two control neurodevelopmental conditions. Prevalence was evaluated by negative binomial regression and adjustments were made for non-response and size of the schools. The prevalence estimates of ASC in Eindhoven was 229 per 10,000, significantly higher than in Haarlem (84 per 10,000) and Utrecht (57 per 10,000), whilst the prevalence for the control conditions were similar in all regions. Phase two is planned to validate school-reported cases using standardized diagnostic methods and to explore the possible causes for these differences.
