Summary Identification of complex molecular networks underlying common human phenotypes is a major challenge of modern genetics. In this study we develop a method for NETwork-Based Analysis of Genetic associations (NETBAG). We use NETBAG to identify a large biological network of genes affected by rare de novo CNVs in autism. The genes forming the network are primarily related to synapse development, axon targeting and neuron motility. The identified network is strongly related to genes previously implicated in autism and intellectual disability phenotypes. Our results are also consistent with the hypothesis that significantly stronger functional perturbations are required to trigger the autistic phenotype in females compared to males. Overall, the presented analysis of de novo variants supports the hypothesis that perturbed synaptogenesis is at the heart of autism. More generally, our study provides proof of the principle that networks underlying complex human phenotypes can be identified by a network-based functional analysis of rare genetic variants. Identification of complex molecular networks underlying common human phenotypes is a major challenge of modern genetics. Recent evidence suggests that rare variants, including copy number variations (CNVs), play a significant role in the etiology of autism spectrum disorders (ASD). Although many such variants have been identified, the specific molecular networks associated with this complex disorder remain largely unknown. In this study we develop a method for NETwork-Based Analysis of Genetic associations (NETBAG). We use NETBAG to identify a large biological network of genes affected by rare de novo CNVs in autism. The genes forming the network are primarily related to synapse development, axon targeting and neuron motility. The identified network is strongly related to genes previously implicated in autism and intellectual disability phenotypes. Our results are also consistent with the hypothesis that significantly stronger functional perturbations are required to trigger the autistic phenotype in females compared to males. Overall, the presented analysis of de novo variants discovered through an unbiased genome-wide study supports the hypothesis that perturbed synaptogenesis is at the heart of autism. More generally, our study provides proof of the principle that networks underlying complex human phenotypes can be identified by a network-based functional analysis of rare genetic variants observed in a large collection of affected individuals.

Autism spectrum disorders (ASD) are characterized by both phenotypic and genetic heterogeneity. Our analysis of functional networks perturbed in ASD suggests that both truncating and non-truncating de novo mutations contribute to autism, although there is a strong bias against truncating mutations in early embryonic development. We find that functional mutations are preferentially observed in genes likely to be haploinsufficient. Multiple cell types and brain areas are affected, but the impact of ASD mutations appears to be strongest in the cortical neurons and the medium spiny neurons of the striatum, implicating corticostriatal brain circuits. In females, truncating ASD mutations on average impact genes with 50–100% higher brain expression levels compared to males. Our study also suggests that truncating de novo mutations play a smaller role in the etiology of high-functioning ASD cases. Overall, we find that stronger functional insults usually lead to more severe intellectual, social and behavioral ASD phenotypes.

Despite the successful identification of several relevant genomic loci, the underlying molecular mechanisms of schizophrenia remain largely unclear. We developed a computational approach (NETBAG+) that allows an integrated analysis of diverse disease-related genetic data using a unified statistical framework. The application of this approach to schizophrenia-associated genetic variations, obtained using unbiased whole-genome methods, allowed us to identify several cohesive gene networks related to axon guidance, neuronal cell mobility, synaptic function and chromosomal remodeling. The genes forming the networks are highly expressed in the brain, with higher brain expression during prenatal development. The identified networks are functionally related to genes previously implicated in schizophrenia, autism and intellectual disability. A comparative analysis of copy number variants associated with autism and schizophrenia suggests that although the molecular networks implicated in these distinct disorders may be related, the mutations associated with each disease are likely to lead, at least on average, to different functional consequences.

Autism is a severe neurodevelopmental disorder defined by social and communication deficits and ritualistic-repetitive behaviors that are detectable in early childhood. The etiology of idiopathic autism is strongly genetic, and oligogenic transmission is likely. The first stage of a two-stage genomic screen for autism was carried out by the Collaborative Linkage Study of Autism on individuals affected with autism from 75 families ascertained through an affected sib-pair. The strongest multipoint results were for regions on chromosomes 13 and 7. The highest maximum multipoint heterogeneity LOD (MMLS/het) score is 3.0 at D13S800 (approximately 55 cM from the telomere) under the recessive model, with an estimated 35% of families linked to this locus. The next highest peak is an MMLS/het score of 2.3 at 19 cM, between D13S217 and D13S1229. Our third highest MMLS/het score of 2.2 is on chromosome 7 and is consistent with the International Molecular Genetic Study of Autism Consortium report of a possible susceptibility locus somewhere within 7q31-33. These regions and others will be followed up in the second stage of our study by typing additional markers in both the original and a second set of identically ascertained autism families, which are currently being collected. By comparing results across a number of studies, we expect to be able to narrow our search for autism susceptibility genes to a small number of genomic regions. Am. J. Med. Genet. (Neuropsychiatr. Genet.) 88:609-615, 1999.

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