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      Paroxysmal Kinesigenic Dyskinesia Caused by 16p11.2 Microdeletion

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          Abstract

          Background

          Four cases of paroxysmal kinesigenic dyskinesia (PKD) have been reported in individuals with proximal 16p11.2 microdeletions that include PRRT2.

          Case Report

          We describe a fifth patient with PKD, features of Asperger’s syndrome, and mild language delays. Sanger sequencing of the PRRT2 gene did not identify any mutations implicated in PKD. However, microarray-based comparative genomic hybridization (aCGH) detected a 533.9-kb deletion on chromosome 16, encompassing over 20 genes and transcripts.

          Discussion

          This case underscores the importance of aCGH testing for individuals with PKD who do not have PRRT2 mutations, particularly when developmental delays, speech problems, intellectual disability, and/or autism spectrum disorder are present.

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          Most cited references30

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          Structural variation of chromosomes in autism spectrum disorder.

          Structural variation (copy number variation [CNV] including deletion and duplication, translocation, inversion) of chromosomes has been identified in some individuals with autism spectrum disorder (ASD), but the full etiologic role is unknown. We performed genome-wide assessment for structural abnormalities in 427 unrelated ASD cases via single-nucleotide polymorphism microarrays and karyotyping. With microarrays, we discovered 277 unbalanced CNVs in 44% of ASD families not present in 500 controls (and re-examined in another 1152 controls). Karyotyping detected additional balanced changes. Although most variants were inherited, we found a total of 27 cases with de novo alterations, and in three (11%) of these individuals, two or more new variants were observed. De novo CNVs were found in approximately 7% and approximately 2% of idiopathic families having one child, or two or more ASD siblings, respectively. We also detected 13 loci with recurrent/overlapping CNV in unrelated cases, and at these sites, deletions and duplications affecting the same gene(s) in different individuals and sometimes in asymptomatic carriers were also found. Notwithstanding complexities, our results further implicate the SHANK3-NLGN4-NRXN1 postsynaptic density genes and also identify novel loci at DPP6-DPP10-PCDH9 (synapse complex), ANKRD11, DPYD, PTCHD1, 15q24, among others, for a role in ASD susceptibility. Our most compelling result discovered CNV at 16p11.2 (p = 0.002) (with characteristics of a genomic disorder) at approximately 1% frequency. Some of the ASD regions were also common to mental retardation loci. Structural variants were found in sufficiently high frequency influencing ASD to suggest that cytogenetic and microarray analyses be considered in routine clinical workup.
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            Association between microdeletion and microduplication at 16p11.2 and autism.

            Autism spectrum disorder is a heritable developmental disorder in which chromosomal abnormalities are thought to play a role. As a first component of a genomewide association study of families from the Autism Genetic Resource Exchange (AGRE), we used two novel algorithms to search for recurrent copy-number variations in genotype data from 751 multiplex families with autism. Specific recurrent de novo events were further evaluated in clinical-testing data from Children's Hospital Boston and in a large population study in Iceland. Among the AGRE families, we observed five instances of a de novo deletion of 593 kb on chromosome 16p11.2. Using comparative genomic hybridization, we observed the identical deletion in 5 of 512 children referred to Children's Hospital Boston for developmental delay, mental retardation, or suspected autism spectrum disorder, as well as in 3 of 299 persons with autism in an Icelandic population; the deletion was also carried by 2 of 18,834 unscreened Icelandic control subjects. The reciprocal duplication of this region occurred in 7 affected persons in AGRE families and 4 of the 512 children from Children's Hospital Boston. The duplication also appeared to be a high-penetrance risk factor. We have identified a novel, recurrent microdeletion and a reciprocal microduplication that carry substantial susceptibility to autism and appear to account for approximately 1% of cases. We did not identify other regions with similar aggregations of large de novo mutations. Copyright 2008 Massachusetts Medical Society.
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              A novel highly-penetrant form of obesity due to microdeletions on chromosome 16p11.2

              Obesity has become a major worldwide challenge to public health, due to the Western ‘obesogenic’ environment interacting with a strong genetic contribution1. Recent extensive genome-wide association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) associated with obesity, but these loci together account for only a small fraction of the known heritable component1. Thus, the “common disease, common variant” paradigm is increasingly under challenge2. We report a highly-penetrant form of obesity, initially observed in 31 subjects who were heterozygous for deletions of at least 593kb at 16p11.2 and whose ascertainment included cognitive deficits. Nineteen similar deletions were identified from GWAS data in 16053 individuals from 8 European cohorts. Such deletions were absent from healthy non-obese controls and accounted for 0.7% of our morbid obesity cases (body mass index, BMI ≥ 40 kg.m−2 or BMI standard deviation score ≥ 4; p = 6.4×10−8, OR = 43.0), demonstrating the potential importance in common disease of rare variants with strong effects. This highlights a promising strategy for identifying missing heritability in obesity and other complex traits: Cohorts with extreme phenotypes are likely to be enriched for rare variants, thereby improving power for their discovery. Subsequent analysis of the loci so identified may well reveal additional rare variants that further contribute to the missing heritability, as recently reported for SIM13 . Thus, the most productive approach may be to combine the “power of the extreme”4 in small, well-phenotyped cohorts, with targeted follow-up in GWAS and population cohorts. The extent to which copy number variants (CNVs) might contribute to the missing heritability of common disorders is currently much under debate2. Since the majority of common simple CNVs are well-tagged by SNPs, it has recently been suggested that common CNVs are unlikely to contribute substantially to the missing heritability5. However, rare variants or recurring CNVs that have arisen on multiple independent occasions are unlikely to be captured by SNP tagging, and their identification will require alternative approaches. We have previously hypothesised that cohorts with extreme phenotypes that include obesity may be enriched for rare but very potent risk variants4,6. Here we have investigated 312 subjects, from three centres in the UK and France, presenting with congenital malformations and/or developmental delay in addition to obesity as previously defined6,7 (see Methods). Known syndromes (e.g. Prader-Willi, fragile X etc.) were excluded. A combination of array comparative genomic hybridisation (aCGH), genotyping arrays, quantitative PCR (qPCR) and multiplex ligation-dependent probe amplification (MLPA) was used to identify and confirm the presence of a heterozygous deletion on 16p11.2 in 9 individuals (2.9%). Such deletions, estimated to be a total of 740kb in size (one copy of a segmental duplication plus 593kb of unique sequences, Figure 1a), have previously been associated to varying extents with autism, schizophrenia and developmental delay8-11; however, the observed frequency of deletions in our cohort is appreciably higher than the reported frequencies in the cohorts from the previous studies ( 7 mmol/L), all adult carriers of such deletions were obese, the majority being morbidly obese; similarly, each of the 7 child/adolescent carriers had a BMI in the top 0.1% of the population range for their age and gender. None of the individuals ascertained on the basis of their obesity had any reported developmental delay or cognitive deficit; four subjects were reported as having hyperphagia. To enable sufficient statistical power to give robust conclusions, we combined data from the population and obesity cohorts in an overall case-control association analysis (the samples from sib pair families were excluded to avoid complications due to their relatedness). Compared to lean/normal weight subjects (see Table 1 and Methods), 16p11.2 deletions were associated with obesity (p = 5.7×10−7, Fisher’s exact test; odds ratio = 29.8, 95% confidence limits = 4.0, 225) and morbid obesity (p = 6.4×10−8; OR = 43.0 [5.6, 329]) at or near genome-wide levels of significance. Expanding the control group to include all non-obese individuals increased the significance to p = 4.1×10−9 (obese) and p = 6.1×10−10 (morbidly obese). Previous reports have indicated that these deletions are frequently not inherited from either parent but arises de novo, possibly by non-allelic homologous recombination between the >99% sequence identical segmental duplications flanking the deleted region11,14. Therefore, where possible we investigated the parents of carriers of deletions, identifying 11 cases of maternal transmission and 4 of paternal transmission. The available data showed that all first-degree relatives carrying a deletion were also obese (Supplementary Table S1). In 10 instances the deletion was apparently de novo (see Figure 1b). Extrapolation to our full dataset indicates that ~0.4% of all morbidly obese cases are due to an inherited 16p11.2 deletion. The frequency of de novo events is consistent with the previous report where ascertainment was for developmental delay and/or congenital anomalies11; by contrast, deletions are reported to be almost exclusively de novo in autistic subjects8-10. Although they may be heterogeneous in nature, these deletions are highly likely to be the causal variants, representing the second most frequent genetic cause of obesity after point mutations in MC4R 22,23. Their repeated de novo occurrence is likely to result in lack of linkage disequilibrium with any other flanking variant – no consistent haplotype has been identified by analysis of the available surrounding genotypes. To assess the effect of a deletion on the expression of nearby genes (e.g. the obesity GWAS-associated SH2B1 locus 800kb away24), we analysed available transcript data for subcutaneous adipose tissue samples from the discordant sibling cohort. Comparisons of the 2 subjects carrying a deletion with their corresponding non-obese siblings, and with other obese and non-obese subjects (Supplementary Figure S4 and Supplementary Tables S4 and S5), showed that many though not all transcripts from within the deletion had markedly reduced abundance (0.4-0.7 fold). In contrast, no clear evidence was found for consistent cis effects of the deletion on the abundance of mRNAs encoded by genes flanking the deletion. In addition, global analysis of this dataset has not identified any trans expression quantitative trait loci either within or nearby the deletion. Thus, while we cannot completely exclude that a 16p11.2 deletion affects the expression of nearby genes (for instance, its impact may be different in other tissues), the above expression analysis strongly indicates that the observed phenotypes are likely to be due to haploinsufficiency of one or more of the ~30 genes within the deleted region. Indeed, rather than being due to a single haploinsufficiency, the phenotype may well result from the deletion of multiple genes that impact on pathways central to the development of obesity (see Supplementary Table S5). Functional network analysis of the deleted genes has led to the suggestion of a similar multi-gene effect for the cognitive phenotype8. The extent to which there is overlap between the genes involved in the obesity and cognitive phenotypes remains to be elucidated. There is a strong correlation between developmental and cognitive disabilities and the prevalence of obesity: Patients with autism or who have learning disabilities have a greatly increased risk of obesity25; and the severely obese exhibit significant cognitive impairment26. Possible explanations include a direct causal relationship between obesity and developmental delay; the involvement of the same or related regulatory pathways; or different outcomes of the same set of behavioural disorders with complex pleiotropic effects and variable ages of onset and expressivities. The higher frequency of 16p11.2 deletions in the cohort ascertained for both phenotypes (2.9%), compared to cohorts ascertained for either phenotype alone (0.4%, 0.6% respectively), confirms their impact on both obesity and developmental delay, adding to the evidence that these two phenotypes may be fundamentally interrelated. Methods Summary Obesity Definitions for overweight, obesity and morbid obesity were based on previous studies6,7: for adults, BMI ≥ 25, 30 and 40 kg.m−2 respectively; for children, BMI respectively above the 90th, 97th percentiles and ≥ 4 standard deviations above the mean, calculated according to their age and gender from a French reference population27,28. Statistics All reported statistical tests used Fisher’s exact test29, carried out on contingency tables constructed for the number of subjects carrying/lacking a 16p11.2 deletion versus the obesity status/ascertainment of the individual. Since no homozygous deletions were observed, it was unnecessary to make a prior distinction between recessive, additive and dominant models of disease risk. Odds ratios and 95% confidence limits were calculated as described30. CNV discovery Subjects ascertained for cognitive deficit/malformations with or without obesity were selected from those clinically referred for genetic testing; 16p11.2 deletions were identified in these individuals by standard clinical diagnostic procedures. Algorithmic analyses of GWAS data were variously carried out using the cnvHap algorithm; a moving window average intensity procedure; a Gaussian Mixture Model; QuantiSNP; PennCNV; BeadStudio GT module; and Birdseed. Where experimental validation was not possible, at least two independent algorithms were used for each dataset. Online Methods Obesity phenotype We have used previously-defined criteria for to define overweight, obesity and morbid (class III) obesity6,7; in adults, the thresholds were BMI ≥ 25, 30 and 40 kg.m−2 respectively. In children and adolescents, we used age- and sex-specific percentiles of BMI, calculated from a French reference population27,28, that approximately correspond to these thresholds: overweight and obesity were defined by thresholds at the 90th and 97th percentiles respectively. Childhood morbid obesity was defined as BMI ≥ 4 SDs above the age- and sex-specific mean, which corresponds to a BMI of 40 kg.m−2 between the ages of 20 and 30 years for both men and women; this threshold was used in the recruitment of the SCOOP severe early-onset obesity cohorts7. The age- and sex-specific thresholds use to define obesity and morbid obesity are shown in Figure 1 and Supplementary Figures 1-2. No carriers of a 16p11.2 deletion were reported to be taking atypical antipsychotics (known to be associated with weight gain). Patient and population cohorts Patients referred for cognitive delay and obesity A group of 33 patients was selected from those referred for genetic testing at the North West Thames Regional Genetics Service, based at Northwick Park Hospital in Harrow, UK, with approval from the Harrow Research Ethics Committee. Inclusion was based on 3 criteria: mental retardation; dysmorphology; and weight >97th percentile for age and gender. Abnormal karyotype, Fragile X and Prader Willi Syndrome were previously excluded. A second group of 279 French children were selected from those referred to 2 centres (Laboratoire de Diagnostic Génétique, Nouvel Hôpital Civil, Strasbourg; Centre de Génétique Chromosomique, Hôpital Saint-Vincent de Paul, GHICL, Lille). Inclusion was based on obesity plus at least one Prader Willi-like syndromic feature (neonatal hypotonia and difficulty to thrive, mental retardation, developmental delay, behavioural problems, skin picking, facial dysmorphism, hypogenitalism or hypogonadism). Chromosomal abnormalities and Prader Willi Syndrome were excluded by karyotyping and DNA methylation analysis. Patients referred for cognitive delay Patients with cognitive deficits are routinely referred to clinical genetics for etiological work-ups including aCGH. We surveyed 7 cytogenetic centres in France and Switzerland, identifying 3870 patients ascertained for developmental delay and/or malformations. Also included in the study were a further 77 patients, ascertained on similar criteria, who were referred to the Department of Genetics, University of Tartu. These analyses were performed for clinical diagnostic purposes, all available phenotypic data (weight, height) being those provided anonymously by the clinician ordering the analysis. Consequently, research-based informed consent was not required by the institutional review board that approved the study. CoLaus This prospective population cohort was described previously16: 6188 white individuals aged 35–75 years were randomly selected from the general population in Lausanne, Switzerland. These individuals underwent a detailed phenotypic assessment, and were genotyped using the Affymetrix Mapping 500K array; 5612 samples passed genotyping quality control. This study was approved by the institutional review boards of the University of Lausanne, and written consent was obtained from all participants. Because recruitment of this cohort required the ability to give informed consent, it is possible that the (statistically non-significant) lack of 16p11.2 deletions/duplications is due to an ascertainment bias. However, any such bias, if it exists, is very small and affects the identification of only 1-2 subjects carrying a deletion. NFBC1966 The Northern Finland Birth Cohort 1966 is a prospective birth cohort of almost all individuals born in 1966 in the two northernmost provinces of Finland. Expectant mothers were enrolled and clinical data collection took place prenatally, at birth, and at ages six months, one year, 14 years and 31 years. Biochemical and DNA samples were collected with informed consent at age 31 years. Genotyping using the Illumina Infinium 370cnvDuo array and phenotypic characteristics of the cohort were as previously described17. Phenotypic and genotyping data was available for 5246 subjects after quality control. EGPUT The Estonian Genome Project is a biobank coordinated by the University of Tartu (EGPUT)18. The project is conducted according to Estonian Gene Research Act and all participants have given written informed consent. The cohort includes more than 39000 individuals older than 18 years of age and reflects closely the age distribution in the Estonian population (33% male, 67% female; 83% Estonians, 14% Russians, 3% other). Subjects are recruited by general practitioners (GP) and hospital physicians and are then randomly selected. Computer Assisted Personal interview (CAPI) was filled during 1-2 hours at the doctor’s office. The data included personal data (place of birth, place(s) of living, nationality etc.), family history (four generations), educational and occupational history, lifestyle and anthropometric data. 1090 randomly-selected subjects were genotyped using the Illumina 370cnvDuo array, 998 passing the required criteria (nationality, genotyping call rate, phenotype availability). Case-Control familial obesity The adult-obesity case-control groups and the child-obesity case control groups were as previously published6, and were genotyped using the Illumina Human CNV370-duo array. 643 children with familial obesity (BMI ≥ 97th percentile corrected for gender and age, at least one obese first degree relative, age 10 kg.m−2), giving a total of 732 subjects. Genotyping data using the Illumina 610K-Quad array was available for 353 siblings from 149 families. Expression data from subcutaneous adipose tissue (sampled after overnight fasting) were available for 360 siblings from 151 families. Subjects received written and oral information before giving written informed consent. The Regional Ethics Committee in Gothenburg approved the studies. Statistical Methods In view of the low frequency of the 16p11.2 deletions, all reported statistical tests were carried out using Fisher’s exact test29. This was applied to comparisons of separately-ascertained cohorts or categories and was carried out on contingency tables constructed for the number of subjects carrying/lacking a 16p11.2 deletion (zero or one copies, as no homozygous deletions were observed) versus the obesity status/ascertainment of the individual. Since no homozygous deletions were observed, it was unnecessary to make a prior distinction between recessive, additive and dominant models of disease risk. For overall analysis of the obesity risk resulting from a deletion, cohorts were pooled according to their obesity status determined according to the criteria described above, and the described tests were then applied to the pooled data. Odds ratios and 95% confidence limits were calculated as described30. CNV discovery and validation Clinical identification of 16p11.2 deletions All diagnostic procedures (aCGH, qPCR, QMPSF, FISH) were carried out according to the relevant guidelines of good clinical laboratory practice for the respective countries. All rearrangements in probands were confirmed by a second technique and karyotyping was performed in all cases to exclude a complex rearrangement. cnvHap CNVs were detected in the child/adult case-control, bariatric surgery, SOS sibpair and NFBC cohorts using the cnvHap algorithm (Coin et al., manuscript submitted); this method is based on an Hidden Markov Model which models transitions between copy number states at the haplotype level, improving sensitivity and accuracy by capturing LD information between CNVs and SNPs. The compiled JAR and associated parameter files can be downloaded from http://www.imperial.ac.uk/medicine/people/l.coin/. Sample data from the algorithm applied to the NFBC cohort is illustrated in Supplementary Figure S5a. After clustering of genotyping data using the internal Illumina BeadStudio cluster files, values for logR ratio (LRR) and B allele frequency (BAF) were exported from each project and normalised: Effects of %GC content on LRR were removed by regressing on GC and GC2, while wave effects31 were removed by fitting a loess function. Normalised data for probes within 2.5Mb of the 16p11.2 deletion were analysed using cnvHap, and CNV calls intersecting the single-copy sequences within the deletion (chr16:29514353-30107356, build hg18) were extracted. 16p11.2 deletions were identified by a minimum 90% of probes within the deleted region being called as having reduced copy number. All called 16p11.2 deletions were validated by direct analysis of LRR. Data for each probe were normalised by first subtracting the median value across all samples (so that the distribution of LRR for each probes was centred on zero), and then dividing by the variance across all samples (to correct for variation in the sensitivity of different probes to copy number variation). The normalised data were then smoothed by application of a 9-point moving average and visualised graphically (see Supplementary Figure S6); putative deletions were checked by subsequent manual confirmation of loss-of-heterozygosity across the entire region. Equally, all deletions called by this method were confirmed by cnvHap. Gaussian Mixture Model For the CoLaus cohort, raw genotyping data were normalized using the aroma.affymetrix framework32. Normalization steps included allelic cross-talk calibration33,34, intensity summarization using robust median average and correction for any PCR amplification bias. CN ratios for a given sample, at a given SNP or CN probe, were computed as the log2 ratio of the normalized intensity of this probe divided by the median across all the samples. CN ratios were subsequently smoothed by fitting a Loess function31. CNV calling was done using a new method based on a Gaussian mixture model (Valsesia et al., manuscript in preparation). This Gaussian mixture model fits four components (deletion, copy neutral, 1 and 2 additional copy) to CN ratios. The final copy number at each probe location is determined as the expected (dosage) copy number. The method has been validated by comparing test datasets with results from the CNAT35 and CBS36,37 algorithms and by replicating a subset of CoLaus subject on Illumina arrays. All calls at the 16p11.2 locus made by the highly stringent CBS algorithm were replicated by the Gaussian mixture model. Principal components analysis detected no significant batch effects. Sample data from the algorithm applied to the CoLaus cohort is illustrated in Supplementary Figure S5b. PennCNV, QuantiSNP and Birdsuite CNV discovery in the EGPUT cohort was carried out using QuantiSNP38, PennCNV39 and BeadStudio GT module (Illumina Inc.). All analyses were carried out using the recommended settings, except changing EMiters to 25 and L to 1,000,000 in QuantiSNP. For PennCNV, the Estonian population-specific B-allele frequency file was used. Data from the SCOOP cohort were analysed using Affymetrix Power Tools and Birdsuite software40 Multiplex ligation-dependent probe amplification (MLPA) MLPA was carried out according to standard methods41 using reagents obtained from MRC-Holland (Amsterdam NL). The SALSA MLPA Kit P343-B1 Autism-1 probemix was used, which contained 9 probes within the deleted region on 16p11.2, plus one probe upstream and one downstream of this locus (see Figure 1a). MLPA products were separated using an AB3130 Genetic Analyser (Applied Biosystems) and outputs were analysed using GeneMarker software (Soft Genetics) and Microsoft Excel. Data normalisation was carried out by dividing the peak areas for each of the 11 test probes by the mean of 9 control probe peak areas. Normalised peak area data were then compared across the tested samples to determine which ones carried the 16p11.2 deletion. Supplementary Material 1 2
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                Author and article information

                Journal
                Tremor Other Hyperkinet Mov (N Y)
                Tremor Other Hyperkinet Mov (N Y)
                TOHM
                Tremor and Other Hyperkinetic Movements
                Columbia University Libraries/Information Services
                2160-8288
                2014
                17 November 2014
                : 4
                : 274
                Affiliations
                [1 ]Movement Disorder Division, Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                [2 ]Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                Columbia University, USA
                Author notes
                *To whom correspondence should be addressed. E-mail: Pichet.Termsarasab@ 123456mountsinai.org
                Article
                10.7916/D8N58K0Q
                4303604
                25667815
                ca387a16-2922-4e87-bd53-10b0d876f8bd
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution–Noncommerical–No Derivatives License, which permits the user to copy, distribute, and transmit the work provided that the original author and source are credited; that no commercial use is made of the work; and that the work is not altered or transformed.

                History
                : 24 September 2014
                : 13 October 2014
                Page count
                Pages: 7
                Categories
                Case Reports

                paroxysmal kinesigenic dyskinesia,16p11.2 microdeletion,movement disorders

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