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      A recurrent 16p12.1 microdeletion suggests a two-hit model for severe developmental delay

      1 , 2 , 1 ,   1 , 1 , 1 , 1 , 3 , 4 , 1 , 1 , 1 , 5 , 5 , 1 , 6 , 7 , 2 , 8 , 8 , 9 , 9 , 9 , 10 , 10 , 11 , 11 , 12 , 12 , 12 , 12 , 13 , 14 , 14 , 15 , 15 , 16 , 16 , 17 , 18 , 18 , 19 , 19 , 20 , 21 , 22 , 22 , 22 , 22 , 22 , 23 , 24 , 25 , 26 , 26 , 24 , 25 , 27 , 28 , 29 , 1 , 3 , 2 , 1 , 30

      Nature genetics

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          Abstract

          We report the identification of a recurrent 520-kbp 16p12.1 microdeletion significantly associated with childhood developmental delay. The microdeletion was detected in 20/11,873 cases vs. 2/8,540 controls ( p=0.0009, OR=7.2) and replicated in a second series of 22/9,254 cases vs. 6/6,299 controls ( p=0.028, OR=2.5). Most deletions were inherited with carrier parents likely to manifest neuropsychiatric phenotypes ( p=0.037, OR=6). Probands were more likely to carry an additional large CNV when compared to matched controls (10/42 cases, p=5.7×10 -5, OR=6.65). Clinical features of cases with two mutations were distinct from and/or more severe than clinical features of patients carrying only the co-occurring mutation. Our data suggest a two-hit model in which the 16p12.1 microdeletion both predisposes to neuropsychiatric phenotypes as a single event and exacerbates neurodevelopmental phenotypes in association with other large deletions or duplications. Analysis of other microdeletions with variable expressivity suggests that this two-hit model may be more generally applicable to neuropsychiatric disease.

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          Most cited references 45

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          Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.

          There is increasing evidence that genome-wide association (GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study (using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined approximately 2,000 individuals for each of 7 major diseases and a shared set of approximately 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 x 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals (including 58 loci with single-point P values between 10(-5) and 5 x 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.
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            Strong association of de novo copy number mutations with autism.

            We tested the hypothesis that de novo copy number variation (CNV) is associated with autism spectrum disorders (ASDs). We performed comparative genomic hybridization (CGH) on the genomic DNA of patients and unaffected subjects to detect copy number variants not present in their respective parents. Candidate genomic regions were validated by higher-resolution CGH, paternity testing, cytogenetics, fluorescence in situ hybridization, and microsatellite genotyping. Confirmed de novo CNVs were significantly associated with autism (P = 0.0005). Such CNVs were identified in 12 out of 118 (10%) of patients with sporadic autism, in 2 out of 77 (3%) of patients with an affected first-degree relative, and in 2 out of 196 (1%) of controls. Most de novo CNVs were smaller than microscopic resolution. Affected genomic regions were highly heterogeneous and included mutations of single genes. These findings establish de novo germline mutation as a more significant risk factor for ASD than previously recognized.
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              Large recurrent microdeletions associated with schizophrenia.

              Reduced fecundity, associated with severe mental disorders, places negative selection pressure on risk alleles and may explain, in part, why common variants have not been found that confer risk of disorders such as autism, schizophrenia and mental retardation. Thus, rare variants may account for a larger fraction of the overall genetic risk than previously assumed. In contrast to rare single nucleotide mutations, rare copy number variations (CNVs) can be detected using genome-wide single nucleotide polymorphism arrays. This has led to the identification of CNVs associated with mental retardation and autism. In a genome-wide search for CNVs associating with schizophrenia, we used a population-based sample to identify de novo CNVs by analysing 9,878 transmissions from parents to offspring. The 66 de novo CNVs identified were tested for association in a sample of 1,433 schizophrenia cases and 33,250 controls. Three deletions at 1q21.1, 15q11.2 and 15q13.3 showing nominal association with schizophrenia in the first sample (phase I) were followed up in a second sample of 3,285 cases and 7,951 controls (phase II). All three deletions significantly associate with schizophrenia and related psychoses in the combined sample. The identification of these rare, recurrent risk variants, having occurred independently in multiple founders and being subject to negative selection, is important in itself. CNV analysis may also point the way to the identification of additional and more prevalent risk variants in genes and pathways involved in schizophrenia.
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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nature genetics
                1061-4036
                1546-1718
                9 March 2010
                14 February 2010
                March 2010
                1 September 2010
                : 42
                : 3
                : 203-209
                Affiliations
                [1 ] Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
                [2 ] Signature Genomic Laboratories, Spokane, WA, USA
                [3 ] Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA
                [4 ] Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
                [5 ] Department of Statistics, Faculty of Science, The University of Auckland, Auckland, New Zealand
                [6 ] Psychology Research Laboratory, McLean Hospital, Belmont, MA, USA
                [7 ] Department of Psychiatry, Harvard Medical School, Boston, MA, USA
                [8 ] Division of Child Neurology, Department of Neurology, University of Louisville, School of Medicine, Louisville, KY, USA
                [9 ] Weisskopf Child Evaluation Center, Department of Pediatrics, University of Louisville, Louisville, KY, USA
                [10 ] Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN, USA
                [11 ] Department of Neurology, Division of Pediatric Neurology, School of Medicine, Indiana University, Indianapolis, IN, USA
                [12 ] Division of Genetics, Maine Medical Partners Pediatric Specialty Care, Maine Medical Center, Portland, ME, USA
                [13 ] Division of Medical Genetics, Duke University Medical Center, Durham, NC, USA
                [14 ] Department of Pediatrics, University of Toledo Medical College and NW Ohio Regional Genetics Center, Toledo, OH, USA
                [15 ] Medical Genetics, Shodair Children's Hospital, Helena, MT, USA
                [16 ] Division of Clinical Genetics, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
                [17 ] Magee-Womens Hospital of UPMC, Pittsburgh, PA, USA
                [18 ] Medical Genetics and Neurodevelopmental Center, St. Vincent Children's Hospital, Indianapolis, IN, USA
                [19 ] Division of Medical Genetics, University of Missouri, Columbia, MO, USA
                [20 ] Geisinger Medical Center, Danville, PA, USA
                [21 ] Division of Genetics, Department of Pediatrics, Louisiana State University Health Sciences Center and Children's Hospital, New Orleans, LA, USA
                [22 ] Department of Pediatrics and Genetics, University of Pennsylvania, and the Children's Hospital of Philadelphia, Philadelphia, PA, USA
                [23 ] South Australian Clinical Genetics Service, SA Pathology at Women's and Children's Hospital, Adelaide, Australia
                [24 ] Department of Paediatrics, The University of Adelaide, Adelaide, Australia
                [25 ] Genetics and Molecular Pathology, and SA Pathology at Women's and Children's Hospital, Adelaide, Australia
                [26 ] Oasi Institute for Research and Care in Mental Retardation and Brain Aging, Troina, Italy
                [27 ] Department of Psychiatry, Harvard Medical School, Boston, MA, USA
                [28 ] VA Boston Healthcare System, Brockton, MA, USA
                [29 ] Departments of Psychiatry and Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
                [30 ] Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
                Author notes
                Corresponding author: Evan E. Eichler, PhD, Department of Genome Sciences, University of Washington School of Medicine, Howard Hughes Medical Institute, Box 355065, Foege S413C, 1705 NE Pacific St., Seattle, WA 98195, eee@ 123456gs.washington.edu
                [*]

                These authors contributed equally to this work

                Article
                nihpa172353
                10.1038/ng.534
                2847896
                20154674

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                Funding
                Funded by: National Institute of Child Health & Human Development : NICHD
                Funded by: Howard Hughes Medical Institute
                Award ID: R01 HD065285-01 ||HD
                Funded by: National Institute of Child Health & Human Development : NICHD
                Funded by: Howard Hughes Medical Institute
                Award ID: ||HHMI_
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