Recurrent Chromosome 16p13.1 Duplications Are a Risk Factor for Aortic Dissections

Chromosomal deletions or reciprocal duplications of the 16p13.1 region have been implicated in a variety of neuropsychiatric disorders such as autism, schizophrenia, epilepsies, and attention-deficit hyperactivity disorder (ADHD). In this study, we investigated the association of recurrent genomic copy number variants (CNVs) with thoracic aortic aneurysms and dissections (TAAD). By using SNP arrays to screen and comparative genomic hybridization microarrays to validate, we identified 16p13.1 duplications in 8 out of 765 patients of European descent with adult-onset TAAD compared with 4 of 4,569 controls matched for ethnicity (P = 5.0×10−5, OR = 12.2). The findings were replicated in an independent cohort of 467 patients of European descent with TAAD (P = 0.005, OR = 14.7). Patients with 16p13.1 duplications were more likely to harbor a second rare CNV (P = 0.012) and to present with aortic dissections (P = 0.010) than patients without duplications. Duplications of 16p13.1 were identified in 2 of 130 patients with familial TAAD, but the duplications did not segregate with TAAD in the families. MYH11, a gene known to predispose to TAAD, lies in the duplicated region of 16p13.1, and increased MYH11 expression was found in aortic tissues from TAAD patients with 16p13.1 duplications compared with control aortas. These data suggest chromosome 16p13.1 duplications confer a risk for TAAD in addition to the established risk for neuropsychiatric disorders. It also indicates that recurrent CNVs may predispose to disorders involving more than one organ system, an observation critical to the understanding of the role of recurrent CNVs in human disease and a finding that may be common to other recurrent CNVs involving multiple genes

Neuropathology of 16p13.11 Deletion in Epilepsy

16p13.11 genomic copy number variants are implicated in several neuropsychiatric disorders, such as schizophrenia, autism, mental retardation, ADHD and epilepsy. The mechanisms leading to the diverse clinical manifestations of deletions and duplications at this locus are unknown. Most studies favour NDE1 as the leading disease-causing candidate gene at 16p13.11. In epilepsy at least, the deletion does not appear to unmask recessive-acting mutations in NDE1, with haploinsufficiency and genetic modifiers being prime candidate disease mechanisms. NDE1 encodes a protein critical to cell positioning during cortical development. As a first step, it is important to determine whether 16p13.11 copy number change translates to detectable brain structural alteration. We undertook detailed neuropathology on surgically resected brain tissue of two patients with intractable mesial temporal lobe epilepsy (MTLE), who had the same heterozygous NDE1-containing 800 kb 16p13.11 deletion, using routine histological stains and immunohistochemical markers against a range of layer-specific, white matter, neural precursor and migratory cell proteins, and NDE1 itself. Surgical temporal lobectomy samples from a MTLE case known not to have a deletion in NDE1 and three non-epilepsy cases were included as disease controls. We found that apart from a 3 mm hamartia in the temporal cortex of one MTLE case with NDE1 deletion and known hippocampal sclerosis in the other case, cortical lamination and cytoarchitecture were normal, with no differences between cases with deletion and disease controls. How 16p13.11 copy changes lead to a variety of brain diseases remains unclear, but at least in epilepsy, it would not seem to be through structural abnormality or dyslamination as judged by microscopy or immunohistochemistry. The need to integrate additional data with genetic findings to determine their significance will become more pressing as genetic technologies generate increasingly rich datasets. Detailed examination of brain tissue, where available, will be an important part of this process in neurogenetic disease specifically

Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism

Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases

Accurate Distinction of Pathogenic from Benign CNVs in Mental Retardation

Copy number variants (CNVs) have recently been recognized as a common form of genomic variation in humans. Hundreds of CNVs can be detected in any individual genome using genomic microarrays or whole genome sequencing technology, but their phenotypic consequences are still poorly understood. Rare CNVs have been reported as a frequent cause of neurological disorders such as mental retardation (MR), schizophrenia and autism, prompting widespread implementation of CNV screening in diagnostics. In previous studies we have shown that, in contrast to benign CNVs, MR-associated CNVs are significantly enriched in genes whose mouse orthologues, when disrupted, result in a nervous system phenotype. In this study we developed and validated a novel computational method for differentiating between benign and MR-associated CNVs using structural and functional genomic features to annotate each CNV. In total 13 genomic features were included in the final version of a Naïve Bayesian Tree classifier, with LINE density and mouse knock-out phenotypes contributing most to the classifier's accuracy. After demonstrating that our method (called GECCO) perfectly classifies CNVs causing known MR-associated syndromes, we show that it achieves high accuracy (94%) and negative predictive value (99%) on a blinded test set of more than 1,200 CNVs from a large cohort of individuals with MR. These results indicate that this classification method will be of value for objectively prioritizing CNVs in clinical research and diagnostics

Genome-Wide Copy Number Variation in Epilepsy: Novel Susceptibility Loci in Idiopathic Generalized and Focal Epilepsies

Epilepsy is one of the most common neurological disorders in humans with a prevalence of 1% and a lifetime incidence of 3%. Several genes have been identified in rare autosomal dominant and severe sporadic forms of epilepsy, but the genetic cause is unknown in the vast majority of cases. Copy number variants (CNVs) are known to play an important role in the genetic etiology of many neurodevelopmental disorders, including intellectual disability (ID), autism, and schizophrenia. Genome-wide studies of copy number variation in epilepsy have not been performed. We have applied whole-genome oligonucleotide array comparative genomic hybridization to a cohort of 517 individuals with various idiopathic, non-lesional epilepsies. We detected one or more rare genic CNVs in 8.9% of affected individuals that are not present in 2,493 controls; five individuals had two rare CNVs. We identified CNVs in genes previously implicated in other neurodevelopmental disorders, including two deletions in AUTS2 and one deletion in CNTNAP2. Therefore, our findings indicate that rare CNVs are likely to contribute to a broad range of generalized and focal epilepsies. In addition, we find that 2.9% of patients carry deletions at 15q11.2, 15q13.3, or 16p13.11, genomic hotspots previously associated with ID, autism, or schizophrenia. In summary, our findings suggest common etiological factors for seemingly diverse diseases such as ID, autism, schizophrenia, and epilepsy

Association Testing Of Copy Number Variants in Schizophrenia and Autism Spectrum Disorders

Background: Autism spectrum disorders and schizophrenia have been associated with an overlapping set of copynumber variant loci, but the nature and degree of overlap in copy number variants (deletions compared toduplications) between these two disorders remains unclear.Methods: We systematically evaluated three lines of evidence: (1) the statistical bases for associations of autismspectrum disorders and schizophrenia with a set of the primary CNVs thus far investigated, from previous studies;(2) data from case series studies on the occurrence of these CNVs in autism spectrum disorders, especially amongchildren, and (3) data on the extent to which the CNVs were associated with intellectual disability anddevelopmental, speech, or language delays. We also conducted new analyses of existing data on these CNVs inautism by pooling data from seven case control studies.Results: Four of the CNVs considered, dup 1q21.1, dup 15q11-q13, del 16p11.2, and dup 22q11.21, showed clearstatistical evidence as autism risk factors, whereas eight CNVs, del 1q21.1, del 3q29, del 15q11.2, del 15q13.3, dup16p11.2, dup 16p13.1, del 17p12, and del 22q11.21, were strongly statistically supported as risk factors forschizophrenia. Three of the CNVs, dup 1q21.1, dup 16p11.2, and dup 16p13.1, exhibited statistical support as riskfactors for both autism and schizophrenia, although for each of these CNVs statistical significance was nominal fortests involving one of the two disorders. For the CNVs that were statistically associated with schizophrenia but werenot statistically associated with autism, a notable number of children with the CNVs have been diagnosed withautism or ASD; children with these CNVs also demonstrate a high incidence of intellectual disability anddevelopmental, speech, or language delays.Conclusions: These findings suggest that although CNV loci notably overlap between autism and schizophrenia,the degree of strongly statistically supported overlap in specific CNVs at these loci remains limited. These analysesalso suggest that relatively severe premorbidity to CNV-associated schizophrenia in children may sometimes bediagnosed as autism spectrum disorder