Recently, a number of studies have highlighted the causal role genomic CNVs may play in the occurrence of autism and ASD 
. Whereas these studies showed that a large percentage of autism patients carry potentially harmful genomic gains or losses, remarkably few of these aberrations were found to be recurrent. The size of the CNVs and the high degree of genetic heterogeneity among patient cohorts are factors that have hampered identification of susceptibility genes within the autism-associated CNVs. Consequently, candidate gene selection has remained a highly biased process. An additional problem has been the high degree of variability in the clinical features and co-morbidities of individual autism patients used for these studies, which may obscure the identification of risk genes acting in a subgroup of patients.
Taking these matters into account and trying to minimize confounding effects, we set out to perform a high resolution, genome-wide CNV screen in a genetically homogeneous Dutch autism cohort, followed by subsequent candidate gene selection of the genes in these CNVs using a novel bioinformatics tool, Prioritizer. Furthermore, the genes in 13 literature-derived autism susceptibility loci, including linkage regions and cytogenetically relevant regions, were analyzed as cross reference for the gene analysis of our initial CNV data.
First, to obtain a more uniform patient cohort and limiting the variability in clinical features, focusing strictly on autism, we applied a slightly modified version of a clinical checklist to clinical data obtained from our patients (see methods
). This resulted in the formation of two autism patient groups; non-complex-autism and complex-autism. The phenotype of the two groups differed in the number of abnormalities in family history, growth disorders, and dysmorphic features or congenital anomalies. The non-complex-autism phenotype group was characterized by little or none of the aforementioned abnormalities, the complex-autism phenotype group was typified by the presence of multiple abnormalities. Similar to the original assumption by de Vries et al.
, we hypothesized that these phenotypical differences may reflect a difference in the genomic defects underlying the disorder. However, no significant difference in the number or size of CNVs was observed between these patient groups, suggesting that the genes within these regions are responsible for the difference in phenotypes, not the total number of genes that are affected by genomic gain or loss.
Second, we identified a number of plausible novel autism candidate genes from our CNV regions using Prioritizer 
. The Prioritizer analysis of CNVs in the non-complex-autism group has yielded meaningful data, since several of the genes highly ranked have previously been associated with cognitive or neuropsychiatric disorders. Retinoic acid induced 1
), located in an unstable region on chromosome 17p11.2, is involved in Smith-Magenis syndrome (MIM182290) 
, a disorder with cognitive impairment and behavioral abnormalities. Bromodomain-containing protein 1
), at chromosome 22q13, has recently been associated with schizophrenia and bipolar affective disorder 
. The LARGE
gene, at 22q12.3, has recently been implicated in Walker-Warburg syndrome 
, a rare autosomal recessive disorder with mental retardation and muscular dystrophy, and disruption of the LARGE
gene was observed in a patient with schizophrenia 
. Moreover, these neuropsychiatric disorders display several clinical features that overlap with autism. Many other genes with known or putative functions in neuronal development, axon growth, and synaptic function were ranked highly, including neurotrimin
), D4 zinc and double PHD fingers family 1
also called NeuD4
) and S100 calcium binding protein A5
). These genes show highly restricted brain expression patterns (The Allen brain atlas, www.brain-map.org
, specifically in brain regions where morphological alterations in post-mortem brains of autism patients have been identified (e.g. cerebral cortex, cerebellum, and hippocampus). In addition, these observations strengthen findings from structural and functional Magnetic Resonance Imaging studies 
Third, gene-network analysis of susceptibility genes in the non-complex-autism group and in 13 literature-derived autism-susceptibility loci revealed an overrepresentation of genes related to glycobiology, and suggests that dosage alterations in these genes could contribute to the autism phenotype. Congenital disorders of glycosylation (CDGs) are genetic diseases caused by defects in the synthesis, metabolism or functions of glycans, impacting on N- or O-linked protein glycosylation as well as lipid glycosylation 
. Whereas CDGs almost invariably show autosomal recessive inheritance and very severe disease phenotypes, the effects of gene dosage changes, as observed in autism patients, may reflect the expression of less severe dysfunction of the pathways in which these genes operate. Pedigree analysis of the patients carrying gains and losses of the glycobiology-related genes is in good agreement with a recently reported model for the genetics of autism, postulating that autism is mainly caused by either de novo
mutations with high penetrance in males, or by mutations that are inherited from an unaffected mother 
.Three of the seven CNVs occurred de novo
in the patients, and the other four CNVs were inherited from apparently unaffected parents, mostly of maternal origin (3 out of 4). The inheritance pattern shows that the effects of the observed gene dosage changes may not be fully penetrant, and interaction with other factors may be required to produce an autism phenotype.
The seven glycobiology-related genes identified in CNVs in our autism cohort are expressed in developing murine brain regions known to be altered in the human autistic brain. The essential role of protein glycosylation for normal brain development has been demonstrated by the severe brain phenotypes in Walker-Warburg syndrome and Muscle-Eye-Brain disease. These syndromes are caused by protein O-mannosyltransferase deficiencies resulting in truncation of the O-proteoglycan core. Also brain abnormalities resulting from defects in protein N-glycosylation have been found, while ARSA
(arylsulfatase A)-deficiency leads to motor and mental symptoms (see below). In the present study we encountered genomic losses and gains in genes encoding enzymes involved in all of these glycosylation pathways. O-glycosylation was represented by LARGE
(dup) and GALNT9
(del). Interestingly, a schizophrenia patient with a disruption in the LARGE
and the autism patient in the present study both carry a intragenic gain that may result in an internal disruption of the LARGE
gene. N-glycosylation was represented by B3GALT6
(dup) and GCNT2
(del). Deficiencies of beta1,3- and beta1,4-galactosyltransferases, particularly B3GALTL
, lead to neuronal phenotypes: B3GALTL
deficiency, either by bi-allelic truncating mutations or by combination of genomic loss and point mutation, causes Peters Plus syndrome (MIM261540), an autosomal recessive syndrome with multiple symptoms including psychomotor retardation 
. Psychomotor retardation was also observed in a patient with B4GALT1
deficiency caused by a homozygous truncating mutation 
, indicating that galactosyltransferases play a role in development of the brain. GCNT2
gives rise to the developmental I antigen of which some mutations cause cataract (MIM110800). No brain phenotypes are known, but our expression analysis shows that CGNT2
displays a distinct spatiotemporal expression pattern suggestive for a function during brain development. The ARSA
gene encodes the lysosomal enzyme arylsulfatase A, involved in cerebroside metabolism. Homozygous or compound heterozygous ARSA
mutations cause metachromatic leukodystrophy (MLD, MIM250100) that displays early, late and adult forms, all with neurological and neuropsychiatric symptoms. In this study we report a gain of ARSA
that could result in a gain-of-function of ARSA.
The fact that genomic gains as well as losses in these pathways appear to contribute to autism suggests that the ratios of the enzymes encoded by these genes is tightly regulated in the brain, and that changes in stoichiometry may lead to aberrant sugar chains on their protein substrates. Therefore, it will be paramount to identify the protein targets of these glycobiology-related genes in the brain, and to study their function. This will further increase our insight in the mechanisms by which they influence brain development, and how they can lead to neuropsychiatric disorders when functionally impaired. Ultimately, new possibilities for the development of pharmacological intervention strategies in the treatment of autism may emerge.