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      Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology

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          Abstract

          Autism spectrum disorder (ASD) is a common, highly heritable neuro-developmental condition characterized by marked genetic heterogeneity 13 . Thus, a fundamental question is whether autism represents an etiologically heterogeneous disorder in which the myriad genetic or environmental risk factors perturb common underlying molecular pathways in the brain 4 . Here, we demonstrate consistent differences in transcriptome organization between autistic and normal brain by gene co-expression network analysis. Remarkably, regional patterns of gene expression that typically distinguish frontal and temporal cortex are significantly attenuated in the ASD brain, suggesting abnormalities in cortical patterning. We further identify discrete modules of co-expressed genes associated with autism: a neuronal module enriched for known autism susceptibility genes, including the neuronal specific splicing factor A2BP1/FOX1, and a module enriched for immune genes and glial markers. Using high-throughput RNA-sequencing we demonstrate dysregulated splicing of A2BP1-dependent alternative exons in ASD brain. Moreover, using a published autism GWAS dataset, we show that the neuronal module is enriched for genetically associated variants, providing independent support for the causal involvement of these genes in autism. In contrast, the immune-glial module showed no enrichment for autism GWAS signals, indicating a non-genetic etiology for this process. Collectively, our results provide strong evidence for convergent molecular abnormalities in ASD, and implicate transcriptional and splicing dysregulation as underlying mechanisms of neuronal dysfunction in this disorder.

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          Most cited references 19

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          Advances in autism genetics: on the threshold of a new neurobiology.

          Autism is a heterogeneous syndrome defined by impairments in three core domains: social interaction, language and range of interests. Recent work has led to the identification of several autism susceptibility genes and an increased appreciation of the contribution of de novo and inherited copy number variation. Promising strategies are also being applied to identify common genetic risk variants. Systems biology approaches, including array-based expression profiling, are poised to provide additional insights into this group of disorders, in which heterogeneity, both genetic and phenotypic, is emerging as a dominant theme.
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            Common genetic variants on 5p14.1 associate with autism spectrum disorders.

            Autism spectrum disorders (ASDs) represent a group of childhood neurodevelopmental and neuropsychiatric disorders characterized by deficits in verbal communication, impairment of social interaction, and restricted and repetitive patterns of interests and behaviour. To identify common genetic risk factors underlying ASDs, here we present the results of genome-wide association studies on a cohort of 780 families (3,101 subjects) with affected children, and a second cohort of 1,204 affected subjects and 6,491 control subjects, all of whom were of European ancestry. Six single nucleotide polymorphisms between cadherin 10 (CDH10) and cadherin 9 (CDH9)-two genes encoding neuronal cell-adhesion molecules-revealed strong association signals, with the most significant SNP being rs4307059 (P = 3.4 x 10(-8), odds ratio = 1.19). These signals were replicated in two independent cohorts, with combined P values ranging from 7.4 x 10(-8) to 2.1 x 10(-10). Our results implicate neuronal cell-adhesion molecules in the pathogenesis of ASDs, and represent, to our knowledge, the first demonstration of genome-wide significant association of common variants with susceptibility to ASDs.
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              Variations in DNA elucidate molecular networks that cause disease.

              Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase beta (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                16 August 2011
                25 May 2011
                25 March 2013
                : 474
                : 7351
                : 380-384
                Affiliations
                [1 ]Program in Neurogenetics and Neurobehavioral Genetics, Department of Neurology and Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, CA 90095-1769
                [2 ]Banting and Best Department of Medical Research, Donnelly Centre, University of Toronto, Toronto, ON, Canada
                [3 ]Institute of Psychiatry, King's College London
                [4 ]Department of Human Genetics, University of California Los Angeles, Los Angeles, California 90095, USA
                Author notes
                [5 ]To whom correspondence should be addressed. dhg@ 123456mednet.ucla.edu
                Article
                NIHMS313080
                10.1038/nature10110
                3607626
                21614001

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                Funding
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R37 MH060233-12 || MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R37 MH060233-11 || MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R37 MH060233 || MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH081754-05 || MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH081754-04 || MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH081754-03 || MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH081754-02 || MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH081754-01 || MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH081754 || MH
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