A Pilot Study on Collective Effects of 22q13.31 Deletions on Gray Matter Concentration in Schizophrenia

The association of copy number variation (CNV) with schizophrenia has been reported with evidence of increased frequency of both rare and large CNVs. Yet, little is known about the impact of CNVs in brain structure. In this pilot study, we explored collective effects of all CNVs in each cytogenetic band on the risk of schizophrenia and gray matter variation measured in structural magnetic resonance imaging. With 324 participants’ CNV profiles (151 schizophrenia patients and 173 healthy controls), we first extracted specific CNV features that differ between patients and controls using a two sample t-test, and then tested their associations with gray matter concentration using a linear regression model in a subset of 301 participants. Our data first provided evidence of population structure in CNV features where elevated rare CNV burden in schizophrenia patients was confounded by the levels associated with African American subjects. We considered this ethnic group difference in the following cytoband analyses. Deletions in one cytoband 22q13.31 were observed significantly (p<0.05) more in patients than controls from all samples after controlling ethnicity, and the deletion load was also significantly (p = 1.44×10⁻⁴) associated with reduced gray matter concentration of a brain network mainly comprised of the cingulate gyrus and insula. Since 80% deletion carriers were patients, patients with deletions also showed reduced gray matter concentration compared with patients without deletions (p = 3.36×10⁻⁴). Our findings indicate that regional CNVs at 22q13.31, no matter the size, may influence the risk of schizophrenia with a remarkably increased mutation rate and with reduced gray matter concentration in the peri-limbic cortex. This proof-of-concept study suggests that the CNVs occurring at some ‘hotspots’ may in fact cause biological downstream effects and larger studies are important for confirming our initial results.     

Genomic Landscape of a Three-Generation Pedigree Segregating Affective Disorder

Bipolar disorder (BPD) is a common psychiatric illness with a complex mode of inheritance. Besides traditional linkage and association studies, which require large sample sizes, analysis of common and rare chromosomal copy number variants (CNVs) in extended families may provide novel insights into the genetic susceptibility of complex disorders. Using the Illumina HumanHap550 BeadChip with over 550,000 SNP markers, we genotyped 46 individuals in a three-generation Old Order Amish pedigree with 19 affected (16 BPD and three major depression) and 27 unaffected subjects. Using the PennCNV algorithm, we identified 50 CNV regions that ranged in size from 12 to 885 kb and encompassed at least 10 single nucleotide polymorphisms (SNPs). Of 19 well characterized CNV regions that were available for combined genotype-expression analysis 11 (58%) were associated with expression changes of genes within, partially within or near these CNV regions in fibroblasts or lymphoblastoid cell lines at a nominal P value <0.05. To further investigate the mode of inheritance of CNVs in the large pedigree, we analyzed a set of four CNVs, located at 6q27, 9q21.11, 12p13.31 and 15q11, all of which were enriched in subjects with affective disorders. We additionally show that these variants affect the expression of neuronal genes within or near the rearrangement. Our analysis suggests that family based studies of the combined effect of common and rare CNVs at many loci may represent a useful approach in the genetic analysis of disease susceptibility of mental disorders.     

Both Rare and De Novo Copy Number Variants Are Prevalent in Agenesis of the Corpus Callosum but Not in Cerebellar Hypoplasia or Polymicrogyria

Agenesis of the corpus callosum (ACC), cerebellar hypoplasia (CBLH), and polymicrogyria (PMG) are severe congenital brain malformations with largely undiscovered causes. We conducted a large-scale chromosomal copy number variation (CNV) discovery effort in 255 ACC, 220 CBLH, and 147 PMG patients, and 2,349 controls. Compared to controls, significantly more ACC, but unexpectedly not CBLH or PMG patients, had rare genic CNVs over one megabase (p = 1.48×10⁻³; odds ratio [OR] = 3.19; 95% confidence interval [CI] = 1.89–5.39). Rare genic CNVs were those that impacted at least one gene in less than 1% of the combined population of patients and controls. Compared to controls, significantly more ACC but not CBLH or PMG patients had rare CNVs impacting over 20 genes (p = 0.01; OR = 2.95; 95% CI = 1.69–5.18). Independent qPCR confirmation showed that 9.4% of ACC patients had de novo CNVs. These, in comparison to inherited CNVs, preferentially overlapped de novo CNVs previously observed in patients with autism spectrum disorders (p = 3.06×10⁻⁴; OR = 7.55; 95% CI = 2.40–23.72). Interestingly, numerous reports have shown a reduced corpus callosum area in autistic patients, and diminished social and executive function in many ACC patients. We also confirmed and refined previously known CNVs, including significantly narrowing the 8p23.1-p11.1 duplication present in 2% of our current ACC cohort. We found six novel CNVs, each in a single patient, that are likely deleterious: deletions of 1p31.3-p31.1, 1q31.2-q31.3, 5q23.1, and 15q11.2-q13.1; and duplications of 2q11.2-q13 and 11p14.3-p14.2. One ACC patient with microcephaly had a paternally inherited deletion of 16p13.11 that included NDE1. Exome sequencing identified a recessive maternally inherited nonsense mutation in the non-deleted allele of NDE1, revealing the complexity of ACC genetics. This is the first systematic study of CNVs in congenital brain malformations, and shows a much higher prevalence of large gene-rich CNVs in ACC than in CBLH and PMG.     

Prioritization of Copy Number Variation Loci Associated with Autism from AutDB–An Integrative Multi-Study Genetic Database

Copy number variants (CNVs) are thought to play an important role in the predisposition to autism spectrum disorder (ASD). However, their relatively low frequency and widespread genomic distribution complicates their accurate characterization and utilization for clinical genetics purposes. Here we present a comprehensive analysis of multi-study, genome-wide CNV data from AutDB (http://mindspec.org/autdb.html), a genetic database that accommodates detailed annotations of published scientific reports of CNVs identified in ASD individuals. Overall, we evaluated 4,926 CNVs in 2,373 ASD subjects from 48 scientific reports, encompassing ∼2.12×10⁹ bp of genomic data. Remarkable variation was seen in CNV size, with duplications being significantly larger than deletions, (P  =  3×10⁻¹⁰⁵; Wilcoxon rank sum test). Examination of the CNV burden across the genome revealed 11 loci with a significant excess of CNVs among ASD subjects (P<7×10⁻⁷). Altogether, these loci covered 15,610 kb of the genome and contained 166 genes. Remarkable variation was seen both in locus size (20 - 4950 kb), and gene content, with seven multigenic (≥3 genes) and four monogenic loci. CNV data from control populations was used to further refine the boundaries of these ASD susceptibility loci. Interestingly, our analysis indicates that 15q11.2-13.3, a genomic region prone to chromosomal rearrangements of various sizes, contains three distinct ASD susceptibility CNV loci that vary in their genomic boundaries, CNV types, inheritance patterns, and overlap with CNVs from control populations. In summary, our analysis of AutDB CNV data provides valuable insights into the genomic characteristics of ASD susceptibility CNV loci and could therefore be utilized in various clinical settings and facilitate future genetic research of this disorder.     

The genetic effect of copy number variations on the risk of type 2 diabetes in a Korean population

Background

Unlike Caucasian populations, genetic factors contributing to the risk of type 2 diabetes mellitus (T2DM) are not well studied in Asian populations. In light of this, and the fact that copy number variation (CNV) is emerging as a new way to understand human genomic variation, the objective of this study was to identify type 2 diabetes–associated CNV in a Korean cohort.

Methodology/Principal Findings

Using the Illumina HumanHap300 BeadChip (317,503 markers), genome-wide genotyping was performed to obtain signal and allelic intensities from 275 patients with type 2 diabetes mellitus (T2DM) and 496 nondiabetic subjects (Total n = 771). To increase the sensitivity of CNV identification, we incorporated multiple factors using PennCNV, a program that is based on the hidden Markov model (HMM). To assess the genetic effect of CNV on T2DM, a multivariate logistic regression model controlling for age and gender was used. We identified a total of 7,478 CNVs (average of 9.7 CNVs per individual) and 2,554 CNV regions (CNVRs; 164 common CNVRs for frequency>1%) in this study. Although we failed to demonstrate robust associations between CNVs and the risk of T2DM, our results revealed a putative association between several CNVRs including chr15:45994758–45999227 (P = 8.6E-04, P^corr = 0.01) and the risk of T2DM. The identified CNVs in this study were validated using overlapping analysis with the Database of Genomic Variants (DGV; 71.7% overlap), and quantitative PCR (qPCR). The identified variations, which encompassed functional genes, were significantly enriched in the cellular part, in the membrane-bound organelle, in the development process, in cell communication, in signal transduction, and in biological regulation.

Conclusion/Significance

We expect that the methods and findings in this study will contribute in particular to genome studies of Asian populations.     

Genome-wide copy-number-variation study identified a susceptibility gene, UGT2B17, for osteoporosis

Osteoporosis, a highly heritable disease, is characterized mainly by low bone-mineral density (BMD), poor bone geometry, and/or osteoporotic fractures (OF). Copy-number variation (CNV) has been shown to be associated with complex human diseases. The contribution of CNV to osteoporosis has not been determined yet. We conducted case-control genome-wide CNV analyses, using the Affymetrix 500K Array Set, in 700 elderly Chinese individuals comprising 350 cases with homogeneous hip OF and 350 matched controls. We constructed a genomic map containing 727 CNV regions in Chinese individuals. We found that CNV 4q13.2 was strongly associated with OF (p = 2.0 × 10⁻⁴, Bonferroni-corrected p = 0.02, odds ratio = 1.73). Validation experiments using PCR and electrophoresis, as well as real-time PCR, further identified a deletion variant of UGT2B17 in CNV 4q13.2. Importantly, the association between CNV of UGT2B17 and OF was successfully replicated in an independent Chinese sample containing 399 cases with hip OF and 400 controls. We further examined this CNV's relevance to major risk factors for OF (i.e., hip BMD and femoral-neck bone geometry) in both Chinese (689 subjects) and white (1000 subjects) samples and found consistently significant results (p = 5.0 × 10⁻⁴ −0.021). Because UGT2B17 encodes an enzyme catabolizing steroid hormones, we measured the concentrations of serum testosterone and estradiol for 236 young Chinese males and assessed their UGT2B17 copy number. Subjects without UGT2B17 had significantly higher concentrations of testosterone and estradiol. Our findings suggest the important contribution of CNV of UGT2B17 to the pathogenesis of osteoporosis.     

Optimizing copy number variation analysis using genome-wide short sequence oligonucleotide arrays

The detection of copy number variants (CNV) by array-based platforms provides valuable insight into understanding human diversity. However, suboptimal study design and data processing negatively affect CNV assessment. We quantitatively evaluate their impact when short-sequence oligonucleotide arrays are applied (Affymetrix Genome-Wide Human SNP Array 6.0) by evaluating 42 HapMap samples for CNV detection. Several processing and segmentation strategies are implemented, and results are compared to CNV assessment obtained using an oligonucleotide array CGH platform designed to query CNVs at high resolution (Agilent). We quantitatively demonstrate that different reference models (e.g. single versus pooled sample reference) used to detect CNVs are a major source of inter-platform discrepancy (up to 30%) and that CNVs residing within segmental duplication regions (higher reference copy number) are significantly harder to detect (P < 0.0001). After adjusting Affymetrix data to mimic the Agilent experimental design (reference sample effect), we applied several common segmentation approaches and evaluated differential sensitivity and specificity for CNV detection, ranging 39–77% and 86–100% for non-segmental duplication regions, respectively, and 18–55% and 39–77% for segmental duplications. Our results are relevant to any array-based CNV study and provide guidelines to optimize performance based on study-specific objectives.     

Whole Genome Distribution and Ethnic Differentiation of Copy Number Variation in Caucasian and Asian Populations

Although copy number variation (CNV) has recently received much attention as a form of structure variation within the human genome, knowledge is still inadequate on fundamental CNV characteristics such as occurrence rate, genomic distribution and ethnic differentiation. In the present study, we used the Affymetrix GeneChip® Mapping 500K Array to discover and characterize CNVs in the human genome and to study ethnic differences of CNVs between Caucasians and Asians. Three thousand and nineteen CNVs, including 2381 CNVs in autosomes and 638 CNVs in X chromosome, from 985 Caucasian and 692 Asian individuals were identified, with a mean length of 296 kb. Among these CNVs, 190 had frequencies greater than 1% in at least one ethnic group, and 109 showed significant ethnic differences in frequencies (p<0.01). After merging overlapping CNVs, 1135 copy number variation regions (CNVRs), covering approximately 439 Mb (14.3%) of the human genome, were obtained. Our findings of ethnic differentiation of CNVs, along with the newly constructed CNV genomic map, extend our knowledge on the structural variation in the human genome and may furnish a basis for understanding the genomic differentiation of complex traits across ethnic groups.     

Identification of copy number variation hotspots in human populations

Copy number variants (CNVs) in the human genome contribute to both Mendelian and complex traits as well as to genomic plasticity in evolution. The investigation of mutational rates of CNVs is critical to understanding genomic instability and the etiology of the copy number variation (CNV)-related traits. However, the evaluation of the CNV mutation rate at the genome level poses an insurmountable practical challenge that requires large samples and accurate typing. In this study, we show that an approximate estimation of the CNV mutation rate could be achieved by using the phylogeny information of flanking SNPs. This allows a genome-wide comparison of mutation rates between CNVs with the use of vast, readily available data of SNP genotyping. A total of 4187 CNV regions (CNVRs) previously identified in HapMap populations were investigated in this study. We showed that the mutation rates for the majority of these CNVRs are at the order of 10⁻⁵ per generation, consistent with experimental observations at individual loci. Notably, the mutation rates of 104 (2.5%) CNVRs were estimated at the order of 10⁻³ per generation; therefore, they were identified as potential hotspots. Additional analyses revealed that genome architecture at CNV loci has a potential role in inciting mutational hotspots in the human genome. Interestingly, 49 (47%) CNV hotspots include human genes, some of which are known to be functional CNV loci (e.g., CNVs of C4 and β-defensin causing autoimmune diseases and CNVs of HYDIN with implication in control of cerebral cortex size), implicating the important role of CNV in human health and evolution, especially in common and complex diseases.     

Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics

The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these 2 parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based likelihood–free inference approaches. We tested the suitability of 2 evolutionary models: a standard Wright–Fisher model and a chemostat model. We evaluated 2 likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models, we show that NPE has several advantages over ABC-SMC and that a Wright–Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in the yeast Saccharomyces cerevisiae to be 10^−4.7 to 10⁻⁴ CNVs per cell division and a fitness coefficient of 0.04 to 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our inference-based estimates using 2 distinct experimental methods—barcode lineage tracking and pairwise fitness assays—which provide independent confirmation of the accuracy of our approach. Our results are consistent with a beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining the outsized importance of CNVs in rapid adaptive evolution. More generally, our study demonstrates the utility of novel neural network–based likelihood–free inference methods for inferring the rates and effects of evolutionary processes from empirical data with possible applications ranging from tumor to viral evolution.

This study shows that simulation-based inference of evolutionary dynamics using neural networks can yield parameter values for fitness and mutation rate that are difficult to determine experimentally, including those of copy number variants (CNVs) during experimental adaptive evolution of yeast.     

Gene-based copy number variation study reveals a microdeletion at 12q24 that influences height in the Korean population

Height is a classic polygenic trait with high heritability (h² = 0.8). Recent genome-wide association studies have revealed many independent loci associated with human height. In addition, although many studies have reported an association between copy number variation (CNV) and complex diseases, few have explored the relationship between CNV and height. Recent studies reported that single nucleotide polymorphisms (SNPs) are highly correlated with common CNVs, suggesting that it is warranted to survey CNVs to identify additional genetic factors affecting heritable traits such as height.This study tested the hypothesis that there would be CNV regions (CNVRs) associated with height nearby genes from the GWASs known to affect height. We identified regions containing > 1% copy number deletion frequency from 3667 population-based cohort samples using the Illumina HumanOmni1-Quad BeadChip. Among the identified CNVRs, we selected 15 candidate regions that were located within 1 Mb of 283 previously reported genes. To assess the effect of these CNVRs on height, statistical analyses were conducted with samples from a case group of 370 taller (upper 10%) individuals and a control group of 1828 individuals (lower 50%).We found that a newly identified 17.7 kb deletion at chromosomal position 12q24.33, approximately 171.6 kb downstream of GPR133, significantly correlated with height; this finding was validated using quantitative PCR. These results suggest that CNVs are potentially important in determining height and may contribute to height variation in human populations.     

Maternal Modifiers and Parent-of-Origin Bias of the Autism-Associated 16p11.2 CNV

Recurrent deletions and duplications at chromosomal region 16p11.2 are a major genetic contributor to autism but also associate with a wider range of pediatric diagnoses, including intellectual disability, coordination disorder, and language disorder. In order to investigate the potential genetic basis for phenotype variability, we assessed the parent of origin of the 16p11.2 copy-number variant (CNV) and the presence of additional CNVs in 126 families for which detailed phenotype data were available. Among de novo cases, we found a strong maternal bias for the origin of deletions (59/66, 89.4% of cases, p = 2.38 × 10⁻¹¹), the strongest such effect so far observed for a CNV associated with a microdeletion syndrome. In contrast to de novo events, we observed no transmission bias for inherited 16p11.2 CNVs, consistent with a female meiotic hotspot of unequal crossover driving this maternal bias. We analyzed this 16p11.2 CNV cohort for the presence of secondary CNVs and found a significant maternal transmission bias for secondary deletions (32 maternal versus 14 paternal, p = 1.14 × 10⁻²). Of the secondary deletions that disrupted a gene, 82% were either maternally inherited or de novo (p = 4.3 × 10⁻³). Nine probands carry secondary CNVs that disrupt genes associated with autism and/or intellectual disability risk variants. Our findings demonstrate a strong bias toward maternal origin of 16p11.2 de novo deletions as well as a maternal transmission bias for secondary deletions that contribute to the clinical outcome on a background sensitized by the 16p11.2 CNV.     

Analysis of Molecular Cytogenetic Alteration in Rhabdomyosarcoma by Array Comparative Genomic Hybridization

Rhabdomyosarcoma (RMS) is the most common pediatric soft tissue sarcoma with poor prognosis. The genetic etiology of RMS remains largely unclear underlying its development and progression. To reveal novel genes more precisely and new therapeutic targets associated with RMS, we used high-resolution array comparative genomic hybridization (aCGH) to explore tumor-associated copy number variations (CNVs) and genes in RMS. We confirmed several important genes by quantitative real-time polymerase chain reaction (QRT-PCR). We then performed bioinformatics-based functional enrichment analysis for genes located in the genomic regions with CNVs. In addition, we identified miRNAs located in the corresponding amplification and deletion regions and performed miRNA functional enrichment analysis. aCGH analyses revealed that all RMS showed specific gains and losses. The amplification regions were 12q13.12, 12q13.3, and 12q13.3–q14.1. The deletion regions were 1p21.1, 2q14.1, 5q13.2, 9p12, and 9q12. The recurrent regions with gains were 12q13.3, 12q13.3–q14.1, 12q14.1, and 17q25.1. The recurrent regions with losses were 9p12–p11.2, 10q11.21–q11.22, 14q32.33, 16p11.2, and 22q11.1. The mean mRNA level of GLI1 in RMS was 6.61-fold higher than that in controls (p = 0.0477) by QRT-PCR. Meanwhile, the mean mRNA level of GEFT in RMS samples was 3.92-fold higher than that in controls (p = 0.0354). Bioinformatic analysis showed that genes were enriched in functions such as immunoglobulin domain, induction of apoptosis, and defensin. Proto-oncogene functions were involved in alveolar RMS. miRNAs that located in the amplified regions in RMS tend to be enriched in oncogenic activity (miR-24 and miR-27a). In conclusion, this study identified a number of CNVs in RMS and functional analyses showed enrichment for genes and miRNAs located in these CNVs regions. These findings may potentially help the identification of novel biomarkers and/or drug targets implicated in diagnosis of and targeted therapy for RMS.     

Copy-Number Variation of the Glucose Transporter Gene SLC2A3 and Congenital Heart Defects in the 22q11.2 Deletion Syndrome

The 22q11.2 deletion syndrome (22q11DS; velocardiofacial/DiGeorge syndrome; VCFS/DGS) is the most common microdeletion syndrome and the phenotypic presentation is highly variable. Approximately 65% of individuals with 22q11DS have a congenital heart defect (CHD), mostly of the conotruncal type, and/or an aortic arch defect. The etiology of this phenotypic variability is not currently known. We hypothesized that copy-number variants (CNVs) outside the 22q11.2 deleted region might increase the risk of being born with a CHD in this sensitized population. Genotyping with Affymetrix SNP Array 6.0 was performed on two groups of subjects with 22q11DS separated by time of ascertainment and processing. CNV analysis was completed on a total of 949 subjects (cohort 1, n = 562; cohort 2, n = 387), 603 with CHDs (cohort 1, n = 363; cohort 2, n = 240) and 346 with normal cardiac anatomy (cohort 1, n = 199; cohort 2, n = 147). Our analysis revealed that a duplication of SLC2A3 was the most frequent CNV identified in the first cohort. It was present in 18 subjects with CHDs and 1 subject without (p = 3.12 × 10⁻³, two-tailed Fisher’s exact test). In the second cohort, the SLC2A3 duplication was also significantly enriched in subjects with CHDs (p = 3.30 × 10⁻², two-tailed Fisher’s exact test). The SLC2A3 duplication was the most frequent CNV detected and the only significant finding in our combined analysis (p = 2.68 × 10⁻⁴, two-tailed Fisher’s exact test), indicating that the SLC2A3 duplication might serve as a genetic modifier of CHDs and/or aortic arch anomalies in individuals with 22q11DS.     

Duplication of the SLIT3 locus on 5q35.1 predisposes to major depressive disorder

Major depressive disorder (MDD) is a common psychiatric and behavioral disorder. To discover novel variants conferring risk to MDD, we conducted a whole-genome scan of copy number variation (CNV), including 1,693 MDD cases and 4,506 controls genotyped on the Perlegen 600K platform. The most significant locus was observed on 5q35.1, harboring the SLIT3 gene (P = 2×10⁻³). Extending the controls with 30,000 subjects typed on the Illumina 550 k array, we found the CNV to remain exclusive to MDD cases (P = 3.2×10⁻⁹). Duplication was observed in 5 unrelated MDD cases encompassing 646 kb with highly similar breakpoints. SLIT3 is integral to repulsive axon guidance based on binding to Roundabout receptors. Duplication of 5q35.1 is a highly penetrant variation accounting for 0.7% of the subset of 647 cases harboring large CNVs, using a threshold of a minimum of 10 SNPs and 100 kb. This study leverages a large dataset of MDD cases and controls for the analysis of CNVs with matched platform and ethnicity. SLIT3 duplication is a novel association which explains a definitive proportion of the largely unknown etiology of MDD.     

Genetic Copy Number Variation and General Cognitive Ability

Differences in genomic structure between individuals are ubiquitous features of human genetic variation. Specific copy number variants (CNVs) have been associated with susceptibility to numerous complex psychiatric disorders, including attention-deficit-hyperactivity disorder, autism-spectrum disorders and schizophrenia. These disorders often display co-morbidity with low intelligence. Rare chromosomal deletions and duplications are associated with these disorders, so it has been suggested that these deletions or duplications may be associated with differences in intelligence. Here we investigate associations between large (≥500kb), rare (<1% population frequency) CNVs and both fluid and crystallized intelligence in community-dwelling older people. We observe no significant associations between intelligence and total CNV load. Examining individual CNV regions previously implicated in neuropsychological disorders, we find suggestive evidence that CNV regions around SHANK3 are associated with fluid intelligence as derived from a battery of cognitive tests. This is the first study to examine the effects of rare CNVs as called by multiple algorithms on cognition in a large non-clinical sample, and finds no effects of such variants on general cognitive ability.     

Endometriosis Is Associated with Rare Copy Number Variants

Endometriosis is a complex gynecological condition that affects 6–10% of women in their reproductive years and is defined by the presence of endometrial glands and stroma outside the uterus. Twin, family, and genome-wide association (GWA) studies have confirmed a genetic role, yet only a small part of the genetic risk can be explained by SNP variation. Copy number variants (CNVs) account for a greater portion of human genetic variation than SNPs and include more recent mutations of large effect. CNVs, likely to be prominent in conditions with decreased reproductive fitness, have not previously been examined as a genetic contributor to endometriosis. Here we employ a high-density genotyping microarray in a genome-wide survey of CNVs in a case-control population that includes 2,126 surgically confirmed endometriosis cases and 17,974 population controls of European ancestry. We apply stringent quality filters to reduce the false positive rate common to many CNV-detection algorithms from 77.7% to 7.3% without noticeable reduction in the true positive rate. We detected no differences in the CNV landscape between cases and controls on the global level which showed an average of 1.92 CNVs per individual with an average size of 142.3 kb. On the local level we identify 22 CNV-regions at the nominal significance threshold (P<0.05), which is greater than the 8.15 CNV-regions expected based on permutation analysis (P<0.001). Three CNV''s passed a genome-wide P-value threshold of 9.3×10⁻⁴; a deletion at SGCZ on 8p22 (P = 7.3×10⁻⁴, OR = 8.5, Cl = 2.3–31.7), a deletion in MALRD1 on 10p12.31 (P = 5.6×10⁻⁴, OR = 14.1, Cl = 2.7–90.9), and a deletion at 11q14.1 (P = 5.7×10⁻⁴, OR = 33.8, Cl = 3.3–1651). Two SNPs within the 22 CNVRs show significant genotypic association with endometriosis after adjusting for multiple testing; rs758316 in DPP6 on 7q36.2 (P = 0.0045) and rs4837864 in ASTN2 on 9q33.1 (P = 0.0002). Together, the CNV-loci are detected in 6.9% of affected women compared to 2.1% in the general population.     

Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization

Background

Large-scale high throughput studies using microarray technology have established that copy number variation (CNV) throughout the genome is more frequent than previously thought. Such variation is known to play an important role in the presence and development of phenotypes such as HIV-1 infection and Alzheimer's disease. However, methods for analyzing the complex data produced and identifying regions of CNV are still being refined.

Results

We describe the presence of a genome-wide technical artifact, spatial autocorrelation or 'wave', which occurs in a large dataset used to determine the location of CNV across the genome. By removing this artifact we are able to obtain both a more biologically meaningful clustering of the data and an increase in the number of CNVs identified by current calling methods without a major increase in the number of false positives detected. Moreover, removing this artifact is critical for the development of a novel model-based CNV calling algorithm - CNVmix - that uses cross-sample information to identify regions of the genome where CNVs occur. For regions of CNV that are identified by both CNVmix and current methods, we demonstrate that CNVmix is better able to categorize samples into groups that represent copy number gains or losses.

Conclusion

Removing artifactual 'waves' (which appear to be a general feature of array comparative genomic hybridization (aCGH) datasets) and using cross-sample information when identifying CNVs enables more biological information to be extracted from aCGH experiments designed to investigate copy number variation in normal individuals.     

Copy number variations at the Prader-Willi syndrome region on chromosome 15 and associations with obesity in whites

Obesity is a serious health problem with strong genetic determination. Copy number variation (CNV) is a common type of genomic variant associated with some complex human diseases. However, it is not clear how CNVs contribute to the etiology of obesity. In this study, we examined 1,000 unrelated US whites to search for CNVs that may predispose to obesity. We focused our analyses on the Prader‐Willi syndrome (PWS) critical region (chromosome 15q11–q13), because the PWS region is a hotspot for CNV generation and obesity is one of the major clinical manifestations for chromosome abnormalities at this region. We constructed a map containing 39 CNVs at the PWS critical region with CNV occurrence rates higher than 1%. Among them, three CNVs were significantly associated with body fat mass (P < 0.05), with a higher copy number (CN) associated with an increase of 5.08–9.77 kg in body fat mass. These three CNVs are close to two known PWS genes, NDN (necdin homolog) and C15orf2 (chromosome 15 open reading frame 2), and partially overlap with another obesity gene PWRN1 (Prader‐Willi region nonprotein‐coding RNA 1). Interestingly, our recently published whole genome association scan study using the same sample by examining single‐nucleotide polymorphisms (SNPs) did not find any significant associations at these CNV regions, suggesting the importance of examining both CNVs and SNPs for better understanding of genetic basis of obesity. Further studies are warranted to validate these CNVs and their importance to obesity.