Autism is a highly heritable neurodevelopmental disorder, yet the genetic underpinnings of the disorder are largely unknown. Aberrant brain overgrowth is a well-replicated observation in the autism literature; but association, linkage, and expression studies have not identified genetic factors that explain this trajectory. Few studies have had sufficient statistical power to investigate whole-genome gene expression and genotypic variation in the autistic brain, especially in regions that display the greatest growth abnormality. Previous functional genomic studies have identified possible alterations in transcript levels of genes related to neurodevelopment and immune function. Thus, there is a need for genetic studies involving key brain regions to replicate these findings and solidify the role of particular functional pathways in autism pathogenesis. We therefore sought to identify abnormal brain gene expression patterns via whole-genome analysis of mRNA levels and copy number variations (CNVs) in autistic and control postmortem brain samples. We focused on prefrontal cortex tissue where excess neuron numbers and cortical overgrowth are pronounced in the majority of autism cases. We found evidence for dysregulation in pathways governing cell number, cortical patterning, and differentiation in young autistic prefrontal cortex. In contrast, adult autistic prefrontal cortex showed dysregulation of signaling and repair pathways. Genes regulating cell cycle also exhibited autism-specific CNVs in DNA derived from prefrontal cortex, and these genes were significantly associated with autism in genome-wide association study datasets. Our results suggest that CNVs and age-dependent gene expression changes in autism may reflect distinct pathological processes in the developing versus the mature autistic prefrontal cortex. Our results raise the hypothesis that genetic dysregulation in the developing brain leads to abnormal regional patterning, excess prefrontal neurons, cortical overgrowth, and neural dysfunction in autism.
Pubmed ID: 22457638 RIS Download
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Open source whole genome association analysis toolset, designed to perform range of basic, large scale analyses in computationally efficient manner. Used for analysis of genotype/phenotype data. Through integration with gPLINK and Haploview, there is some support for subsequent visualization, annotation and storage of results. PLINK 1.9 is improved and second generation of the software.
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View all literature mentionsAdjusting batch effects in microarray expression data using Empirical Bayes methods.
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