Novel method for combined linkage and genome-wide association analysis finds evidence of distinct genetic architecture for two subtypes of autism

The Autism Genome Project has assembled two large datasets originally designed for linkage analysis and genome-wide association analysis, respectively: 1,069 multiplex families genotyped on the Affymetrix 10 K platform, and 1,129 autism trios genotyped on the Illumina 1 M platform. We set out to exploit this unique pair of resources by analyzing the combined data with a novel statistical method, based on the PPL statistical framework, simultaneously searching for linkage and association to loci involved in autism spectrum disorders (ASD). Our analysis also allowed for potential differences in genetic architecture for ASD in the presence or absence of lower IQ, an important clinical indicator of ASD subtypes. We found strong evidence of multiple linked loci; however, association evidence implicating specific genes was low even under the linkage peaks. Distinct loci were found in the lower IQ families, and these families showed stronger and more numerous linkage peaks, while the normal IQ group yielded the strongest association evidence. It appears that presence/absence of lower IQ (LIQ) demarcates more genetically homogeneous subgroups of ASD patients, with not just different sets of loci acting in the two groups, but possibly distinct genetic architecture between them, such that the LIQ group involves more major gene effects (amenable to linkage mapping), while the normal IQ group potentially involves more common alleles with lower penetrances. The possibility of distinct genetic architecture across subtypes of ASD has implications for further research and perhaps for research approaches to other complex disorders as well

ANS: Aberrant Neurodevelopment of the Social Cognition Network in Adolescents with Autism Spectrum Disorders

Background: Autism spectrum disorders (ASD) are characterized by aberrant neurodevelopment. Although the ASD brain undergoes precocious growth followed by decelerated maturation during early postnatal period of childhood, the neuroimaging approach has not been empirically applied to investigate how the ASD brain develops during adolescence. Methodology/Principal Findings: We enrolled 25 male adolescents with high functioning ASD and 25 typically developing controls for voxel-based morphometric analysis of structural magnetic resonance image. Results indicate that there is an imbalance of regional gray matter volumes and concentrations along with no global brain enlargement in adolescents with high functioning ASD relative to controls. Notably, the right inferior parietal lobule, a role in social cognition, have a significant interaction of age by groups as indicated by absence of an age-related gain of regional gray matter volume and concentration for neurodevelopmental maturation during adolescence. Conclusions/Significance: The findings indicate the neural correlates of social cognition exhibits aberrant neurodevelopment during adolescence in ASD, which may cast some light on the brain growth dysregulation hypothesis. The period of abnormal brain growth during adolescence may be characteristic of ASD. Age effects must be taken into account while measures of structural neuroimaging have been clinically put forward as potential phenotypes for ASD

Teasing apart the heterogeneity of autism: Same behavior, different brains in toddlers with fragile X syndrome and autism

To examine brain volumes in substructures associated with the behavioral features of children with FXS compared to children with idiopathic autism and controls. A cross-sectional study of brain substructures was conducted at the first time-point as part of an ongoing longitudinal MRI study of brain development in FXS. The study included 52 boys between 18–42 months of age with FXS and 118 comparison children (boys with autism-non FXS, developmental-delay, and typical development). Children with FXS and autistic disorder had substantially enlarged caudate volume and smaller amygdala volume; whereas those children with autistic disorder without FXS (i.e., idiopathic autism) had only modest enlargement in their caudate nucleus volumes but more robust enlargement of their amygdala volumes. Although we observed this double dissociation among selected brain volumes, no significant differences in severity of autistic behavior between these groups were observed. This study offers a unique examination of early brain development in two disorders, FXS and idiopathic autism, with overlapping behavioral features, but two distinct patterns of brain morphology. We observed that despite almost a third of our FXS sample meeting criteria for autism, the profile of brain volume differences for children with FXS and autism differed from those with idiopathic autism. These findings underscore the importance of addressing heterogeneity in studies of autistic behavior

A genome-wide scan for common alleles affecting risk for autism.

Although autism spectrum disorders (ASDs) have a substantial genetic basis, most of the known genetic risk has been traced to rare variants, principally copy number variants (CNVs). To identify common risk variation, the Autism Genome Project (AGP) Consortium genotyped 1558 rigorously defined ASD families for 1 million single-nucleotide polymorphisms (SNPs) and analyzed these SNP genotypes for association with ASD. In one of four primary association analyses, the association signal for marker rs4141463, located within MACROD2, crossed the genome-wide association significance threshold of P < 5 × 10(-8). When a smaller replication sample was analyzed, the risk allele at rs4141463 was again over-transmitted; yet, consistent with the winner's curse, its effect size in the replication sample was much smaller; and, for the combined samples, the association signal barely fell below the P < 5 × 10(-8) threshold. Exploratory analyses of phenotypic subtypes yielded no significant associations after correction for multiple testing. They did, however, yield strong signals within several genes, KIAA0564, PLD5, POU6F2, ST8SIA2 and TAF1C