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Rare copy-number variation (CNV) is an important source of risk for autism spectrum disorders (ASDs). We analyzed 2,446 ASD-affected families and confirmed an excess of genic deletions and duplications in affected versus control groups (1.41-fold, p = 1.0 × 10(-5)) and an increase in affected subjects carrying exonic pathogenic CNVs overlapping known loci associated with dominant or X-linked ASD and intellectual disability (odds ratio = 12.62, p = 2.7 × 10(-15), ∼3% of ASD subjects). Pathogenic CNVs, often showing variable expressivity, included rare de novo and inherited events at 36 loci, implicating ASD-associated genes (CHD2, HDAC4, and GDI1) previously linked to other neurodevelopmental disorders, as well as other genes such as SETD5, MIR137, and HDAC9. Consistent with hypothesized gender-specific modulators, females with ASD were more likely to have highly penetrant CNVs (p = 0.017) and were also overrepresented among subjects with fragile X syndrome protein targets (p = 0.02). Genes affected by de novo CNVs and/or loss-of-function single-nucleotide variants converged on networks related to neuronal signaling and development, synapse function, and chromatin regulation.
Autism spectrum disorder (ASD) is a highly heritable disorder of complex and heterogeneous aetiology. It is primarily characterized by altered cognitive ability including impaired language and communication skills and fundamental deficits in social reciprocity. Despite some notable successes in neuropsychiatric genetics, overall, the high heritability of ASD (~90%) remains poorly explained by common genetic risk variants. However, recent studies suggest that rare genomic variation, in particular copy number variation, may account for a significant proportion of the genetic basis of ASD. We present a large scale analysis to identify candidate genes which may contain low-frequency recessive variation contributing to ASD while taking into account the potential contribution of population differences to the genetic heterogeneity of ASD. Our strategy, homozygous haplotype (HH) mapping, aims to detect homozygous segments of identical haplotype structure that are shared at a higher frequency amongst ASD patients compared to parental controls. The analysis was performed on 1,402 Autism Genome Project trios genotyped for 1 million single nucleotide polymorphisms (SNPs). We identified 25 known and 1,218 novel ASD candidate genes in the discovery analysis including CADM2, ABHD14A, CHRFAM7A, GRIK2, GRM3, EPHA3, FGF10, KCND2, PDZK1, IMMP2L and FOXP2. Furthermore, 10 of the previously reported ASD genes and 300 of the novel candidates identified in the discovery analysis were replicated in an independent sample of 1,182 trios. Our results demonstrate that regions of HH are significantly enriched for previously reported ASD candidate genes and the observed association is independent of gene size (odds ratio 2.10). Our findings highlight the applicability of HH mapping in complex disorders such as ASD and offer an alternative approach to the analysis of genome-wide association data.
While it is apparent that rare variation can play an important role in the genetic architecture of autism spectrum disorders (ASDs), the contribution of common variation to the risk of developing ASD is less clear. To produce a more comprehensive picture, we report Stage 2 of the Autism Genome Project genome-wide association study, adding 1301 ASD families and bringing the total to 2705 families analysed (Stages 1 and 2). In addition to evaluating the association of individual single nucleotide polymorphisms (SNPs), we also sought evidence that common variants, en masse, might affect the risk. Despite genotyping over a million SNPs covering the genome, no single SNP shows significant association with ASD or selected phenotypes at a genome-wide level. The SNP that achieves the smallest P-value from secondary analyses is rs1718101. It falls in CNTNAP2, a gene previously implicated in susceptibility for ASD. This SNP also shows modest association with age of word/phrase acquisition in ASD subjects, of interest because features of language development are also associated with other variation in CNTNAP2. In contrast, allele scores derived from the transmission of common alleles to Stage 1 cases significantly predict case status in the independent Stage 2 sample. Despite being significant, the variance explained by these allele scores was small (Vm< 1%). Based on results from individual SNPs and their en masse effect on risk, as inferred from the allele score results, it is reasonable to conclude that common variants affect the risk for ASD but their individual effects are modest.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder with onset in early childhood. While highly heterogeneous, the core manifestations always include persistent difficulties in social interaction and communication, as well as a pattern of restricted interests, repetitive behaviours, and abnormal sensory processing [1]. In addition, psychiatric comorbidity is high [2], and there are genetic risk overlaps with some other mental and neurodevelopmental disorders. In the vast majority of cases, the condition persists into adulthood [3], albeit with various behavioural features and variable mental and somatic comorbidity over a given lifespan. ASD is associated with high societal, educational, and health care costs, and, in many cases, a dramatic impact on the quality of life of patients and their families. ASDs are highly heritable [4], and a multitude of genetic studies have been published. In addition, more recent reviews also emphasize the role of genetic and environmental factors in the pathophysiology of ASD [5,6], which are mediated by lasting epigenetic changes. The genetic architecture of ASD comprises common and rare variations as well as cytogenetic disturbances, such as copy number variations, translocations, inversions, and numerical chromosomal aberrations [7]. Based on the genes affected and the respective functional effects, the idea of personalised medicine is to eventually use that information for the development of targeted treatments or towards the ability to predict the response to a specific intervention, mainly pharmacological but also psychosocial, given the individual’s genetic and environmental risk constellation. The current Special Issue aims to highlight some core aspects regarding basic and applied science approaches in advancing this field of science.
Currently, psychopharmacological treatment in ASD can improve many comorbid neurodevelopmental disorders, such as attention-deficit/hyperactivity disorder or aggressive behaviour, and the core symptoms of restricted and repetitive behaviours [8,9]. No pharmacological options targeting social interaction and communication are available. Social communication and other strongly relevant targets of intervention in ASD [10], such as adaptive behaviour, cognitive and language development, or quality of life may be improved by early behavioural intervention [11]. Still, individual outcomes are highly variable, even with the same kind of psychosocial intervention approach. A better understanding of the pathophysiological mechanisms underlying this broad range of symptoms and abilities, as well as their longitudinal course, is a crucial first step towards the development of personalised treatments.
Given the heterogeneity regarding the ASD phenotype and its underlying etiology, such as diverse genetic variation and additional environmental risks with the related neurobiological mechanisms, discovering new pharmacological treatments for the condition is a huge challenge. This challenge is at the heart of this Special Issue. Here, we have collected a set of contributions providing state-of-the-art coverage, ranging from the theoretical framework, linking genetics to human behaviour and therapy, to initial practical examples of how genetics can provide valuable insights into the personalized clinical management of autistic individuals. To introduce the papers of this Special Issue, a broad summary of the many challenges related to the development of personalised medicine in ASD is given here. In the final statement from the editors, the specific contributions of the articles included in this Special Issue will be summarised.