Candidate Copy Number Variations Research Articles

Clinical phenotype and genetic analysis of a rare case with 6p duplication and terminal deletion syndrome

To explore the genetic basis for a child with developmental delay and intellectual deficit (DD/ID). A child who was admitted to the Maternal and Child Health Care Hospital of Longhua District of Shenzhen City on June 3, 2023 due to DD/ID, craniofacial malformations, and recurrent infections of upper respiratory tract was selected as the study subject. G-banded chromosomal karyotyping was carried out for the child and her parents. Low-depth genome-wide copy number variation sequencing (CNV-seq) and chromosomal microarray analysis (CMA) were used to screen for genome-wide copy number variations (CNV), and fluorescence in situ hybridization (FISH) was used to verify the origin of candidate CNV. The child, an 8-year-old girl, had featured unexplained growth and intellectual development delay, multiple craniofacial malformations, and recurrent infections of the upper respiratory tract. She was found to have a karyotype of 46,XX,der(6)add(6)(q23), while both of her parents were normal. Both CNV-seq and CMA showed that the child has harbored a 21.38 Mb interstitial duplication at 6p25.3p22.3 and a 0.78 Mb terminal deletion at 6p25. FISH verified that both the duplication and deletion had occurred de novo. The abnormal phenotype of the child may be attributed to the 6p duplication and terminal deletion.

Contribution of copy number variations to education, socioeconomic status and cognition from a genome-wide study of 305,401 subjects.

Educational attainment (EA), socioeconomic status (SES) and cognition are phenotypically and genetically linked to health outcomes. However, the role of copy number variations (CNVs) in influencing EA/SES/cognition remains unclear. Using a large-scale (n = 305,401) genome-wide CNV-level association analysis, we discovered 33 CNV loci significantly associated with EA/SES/cognition, 20 of which were novel (deletions at 2p22.2, 2p16.2, 2p12, 3p25.3, 4p15.2, 5p15.33, 5q21.1, 8p21.3, 9p21.1, 11p14.3, 13q12.13, 17q21.31, and 20q13.33, as well as duplications at 3q12.2, 3q23, 7p22.3, 8p23.1, 8p23.2, 17q12 (105 kb), and 19q13.32). The genes identified in gene-level tests were enriched in biological pathways such as neurodegeneration, telomere maintenance and axon guidance. Phenome-wide association studies further identified novel associations of EA/SES/cognition-associated CNVs with mental and physical diseases, such as 6q27 duplication with upper respiratory disease and 17q12 (105 kb) duplication with mood disorders. Our findings provide a genome-wide CNV profile for EA/SES/cognition and bridge their connections to health. The expanded candidate CNVs database and the residing genes would be a valuable resource for future studies aimed at uncovering the biological mechanisms underlying cognitive function and related clinical phenotypes.

De novo copy number variations in candidate genomic regions in patients of severe autism spectrum disorder in Vietnam.

Autism spectrum disorder (ASD) is a developmental disorder with a prevalence of around 1% children worldwide and characterized by patient behaviour (communication, social interaction, and personal development). Data on the efficacy of diagnostic tests using copy number variations (CNVs) in candidate genes in ASD is currently around 10% but it is overrepresented by patients of Caucasian background. We report here that the diagnostic success of de novo candidate CNVs in Vietnamese ASD patients is around 6%. We recruited one hundred trios (both parents and a child) where the child was clinically diagnosed with ASD while the parents were not affected. We performed genetic screening to exclude RETT syndrome and Fragile X syndrome and performed genome-wide DNA microarray (aCGH) on all probands and their parents to analyse for de novo CNVs. We detected 1708 non-redundant CNVs in 100 patients and 118 (7%) of them were de novo. Using the filter for known CNVs from the Simons Foundation Autism Research Initiative (SFARI) database, we identified six CNVs (one gain and five loss CNVs) in six patients (3 males and 3 females). Notably, 3 of our patients had a deletion involving the SHANK3 gene-which is the highest compared to previous reports. This is the first report of candidate CNVs in ASD patients from Vietnam and provides the framework for building a CNV based test as the first tier screening for clinical management.

Copy number variation as a tool for implementing pregnancy as an aging model.

Copy number variations (CNV) are a major contributor to genome variability and have been linked to aging and other degradable phenotypes such as pregnancy physiology. To demonstrate how pregnancy can be used as a model of aging, we used CNVs from pregnant mice. Candidate CNVs were selected by applying case-control analysis in human centenarians compared with control groups. These CNVs were aligned with the mouse genome and their copy variation was assessed using qRT-PCR in liver and blood tissue samples from pregnant mice throughout pregnancy (baseline; first, second, and third trimester; post-partum). Eight of the ten selected CNVs demonstrated a significant decline/increase trend throughout the pregnancy followed by opposite direction soon after delivery in the liver and blood of the mouse tissues. Furthermore, significant differential expression was detected among the candidate CNVs' close vicinity genes (APA2A, LSS, RBDHF1, PLAAT1, and SCL17A2), but not in the WSCD2 gene. Establishing a genetic link between longevity and pregnancy is a significant step toward implementing the pregnancy process as a model for aging. These results in pregnant mice highlight the mechanism and similarities between pregnancy and aging. Investigating the mechanisms that cause such rejuvenation after labor could change our aging treatment paradigm.

Prenatal genetic analysis of a fetus with Miller-Dieker syndrome

To explore the genetic basis for fetus with bilateral lateral ventriculomegaly. Fetus umbilical cord blood and peripheral blood samples of its parents were collected. The fetus was subjected to chromosomal karyotyping, whilst the fetus and its parents were subjected to array comparative genomic hybridization (aCGH). The candidate copy number variation (CNV) were verified by qPCR, Application goldeneye DNA identification system was used to confirm the parental relationship. The fetus was found to have a normal karyotype. aCGH analysis indicated that it has carried a 1.16 Mb deletion at 17p13.3, which partially overlapped with the critical region of Miller-Dieker syndrome (MDS), in addition with a 1.33 Mb deletion at 17p12 region, which is associated with hereditary stress-susceptible peripheral neuropathy (HNPP). Its mother was also found to harbor the 1.33 Mb deletion at 17p12. qPCR analysis confirmed that the expression levels of genes from the 17p13.3 and 17p12 regions were about the half of that in the normal control, as well as the maternal peripheral blood sample. Parental relationship was confirmed between the fetus and its parents. Following genetic counseling, the parents has chosen to continue with the pregnancy. The fetus was diagnosed with Miller-Dieker syndrome due to the de novo deletion at 17p13.3. Ventriculomegaly may be an important indicator for prenatal ultrasonography in fetuses with MDS.

ParseCNV2: efficient sequencing tool for copy number variation genome-wide association studies.

Improved copy number variation (CNV) detection remains an area of heavy emphasis for algorithm development; however, both CNV curation and disease association approaches remain in its infancy. The current practice of focusing on candidate CNVs, where researchers study specific CNVs they believe to be pathological while discarding others, refrains from considering the full spectrum of CNVs in a hypothesis-free GWAS. To address this, we present a next-generation approach to CNV association by natively supporting the popular VCF specification for sequencing-derived variants as well as SNP array calls using a PennCNV format. The code is fast and efficient, allowing for the analysis of large (>100,000 sample) cohorts without dividing up the data on a compute cluster. The scripts are condensed into a single tool to promote simplicity and best practices. CNV curation pre and post-association is rigorously supported and emphasized to yield reliable results of highest quality. We benchmarked two large datasets, including the UK Biobank (n > 450,000) and CAG Biobank (n > 350,000) both of which are genotyped at >0.5 M probes, for our input files. ParseCNV has been actively supported and developed since 2008. ParseCNV2 presents a critical addition to formalizing CNV association for inclusion with SNP associations in GWAS Catalog. Clinical CNV prioritization, interactive quality control (QC), and adjustment for covariates are revolutionary new features of ParseCNV2 vs. ParseCNV. The software is freely available at: https://github.com/CAG-CNV/ParseCNV2 .

Copy-number analysis by base-level normalization: An intuitive visualization tool for evaluating copy number variations.

Next-generation sequencing (NGS) facilitates comprehensive molecular analyses that help with diagnosing unsolved disorders. In addition to detecting single-nucleotide variations and small insertions/deletions, bioinformatics tools can identify copy number variations (CNVs) in NGS data, which improves the diagnostic yield. However, due to the possibility of false positives, subsequent confirmation tests are generally performed. Here, we introduce Copy-number Analysis by BAse-level NormAlization (CABANA), a visualization tool that allows users to intuitively identify candidate CNVs using the normalized single-base-level read depth calculated from NGS data. To demonstrate how CABANA works, NGS data were obtained from 474 patients with neuromuscular disorders. CNVs were screened using a conventional bioinformatics tool, ExomeDepth, and then we normalized and visualized those data at the single-base level using CABANA, followed by manual inspection by geneticists to filter out false positives and determine candidate CNVs. In doing so, we identified 31 candidate CNVs (7%) in 474 patients and subsequently confirmed all of them to be true using multiplex ligation-dependent probe amplification. The performance of CABANA was deemed acceptable by comparing its diagnostic yield with previous data about neuromuscular disorders. Despite some limitations, we expect CABANA to help researchers accurately identify CNVs and reduce the need for subsequent confirmation testing.

Copy number variations of the KAT6A gene are associated with body measurements of Chinese sheep breeds

Copy number variation (CNV) is one kind of genomic structure variations and presents as gains and losses of genomic fragments. More recently, we have made an atlas of CNV maps for livestock. In the future, it is a primary focus to determine the phenotypic effects of candidate CNVs. Lysine Acetyltransferase 6 A (KAT6A) is a protein coding gene and plays a critical role in many cellular processes. However, the effects of KAT6A CNVs on sheep body measurements remains unknown. In this study, we performed quantitative polymerase chain reaction (qPCR) to detect the presences and distributions of three CNV regions within KAT6A gene in 672 sheep from four Chinese breeds. Association analysis indicated that the three CNVs of KAT6A gene were significantly associated with body measurement(s) in Small-tailed Han sheep (STH) and Hu sheep (HU) (p < 0.05), while no effects on Large-tailed Han sheep (LTH) were observed (p > 0.05) were observed. Additionally, only one CNV was significantly associated with body measurement (body length) in Chaka sheep (CK) (p < 0.05). Our study provided evidence that the CNV(s) of KAT6A gene could be used as candidate marker(s) for molecular breedings of STH, HU, and CK breeds.

Genome-wide selective detection of Mile red-bone goat using next-generation sequencing technology.

The ecotype population of goats (Capra hircus) was created by long‐term artificial selection and natural adaptation. Mile red‐bone goat is an indigenous breed with visible red bones, and its special bone structure has received extensive attention. This study aimed to identify genetic variants and candidate genes associated with specific bone phenotypes using next‐generation sequencing technology (NGS). The results revealed that 31,828,206 single nucleotide polymorphisms (SNPs) were obtained from 72 goats (20 Mile red‐bone goats and 52 common goats) by NGS. A total of 100 candidate genes were identified on the basis top 1% window interaction from nucleotide diversity (π), π ratio (π A/π B), and pairwise fixation index (F ST). Exactly 77 known signaling pathways were enriched. Specifically, three coding genes (NMNAT2, LOC102172983, and PNLIP) were annotated in the vitamin metabolism signaling pathways, and NCF2 was annotated to the osteoclast (OC) differentiation pathway. Furthermore, 5862 reliable copy number variations (CNVs) were obtained, and 14 and 24 genes were annotated with the top 1‰ CNV based on F ST (>0.490) and V ST (>0.527), respectively. Several pathways related to bone development and metabolism of exogenous substances in vivo, including calcium signaling pathway, OC differentiation, and glycerophospholipid metabolism, were annotated. Specifically, six genes from 19 candidate CNVs, which were obtained by interaction of the top 1‰ CNVs with F ST and V ST, were annotated to mucin‐type O‐glycan biosynthesis and metabolic pathways. Briefly, the results implied that pseudopurpurin and specific genetic variants work together to contribute to the red‐bone color and specific bone structure of Mile red‐bone goat. This study is helpful to understanding the genetic basis of the unique bone phenotype of Mile red‐bone goats.

Copy Number Variations of CEP63, FOSL2 and PAQR6 Serve as Novel Signatures for the Prognosis of Bladder Cancer.

BackgroundFinding effective prognostic signatures is of great urgency due to the high risk of recurrence and progression of bladder cancer (BC). Although a lot of genetic alterations are involved in the carcinogenesis, none of them were referred in the current risk group stratifications. In this study, we aimed to find significant copy number variations (CNVs) to predict prognosis for BC patients.MethodsCNVs with high aberration frequencies in BC were explored by array-based comparative genomic hybridization in 65 tumor samples. Candidates were validated in independent groups of BC tumor samples (n=219) and urine samples (n=123). 3D digital PCR was applied for detecting accurate gene copy numbers in BC urine. In order to explore the prognostic value of candidate CNVs, all enrolled patients were followed up for the disease-free survival (DFS). Cox proportional hazards regression analysis was performed to find the independent prognostic factors for DFS.ResultsCNVs of CEP63, FOSL2 and PAQR6 with high aberration frequencies (67.7%, 56.9% and 60.0%, respectively) were found in BC tumors. Copy numbers of CEP63, FOSL2 and PAQR6 were gained in 219 tumor samples. CNVs of CEP63 and FOSL2 were correlated with advanced tumor stage and high grade. Retrospective analysis (median follow-up time: 69 months) revealed that CNVs of CEP63 and FOSL2 were independent prognostic factors for DFS of non-muscle-invasive bladder cancer (NMIBC) patients, while CNVs of FOSL2 and PAQR6 were independent prognostic factors for DFS of muscle-invasive bladder cancer (MIBC) patients. Models for predicting DFS were constructed based on CNVs of three genes. Patients with high prognostic indexes tended to have poor DFS. Prognostic index can also help to identify those with worse outcomes among high risk NMIBC patients. Copy number gains of CEP63 and FOSL2 in urine were found to be significantly correlated with poor DFS of NMIBC patients.ConclusionsCNVs of CEP63, FOSL2 and PAQR6 were capable of predicting DFS and may serve as promising signatures for prognosis of BC.

Accurate detection of CNV based on single-nucleotide variants recalibration and image classification from whole genome sequencing

Copy number variations (CNVs) play an important role in the genome aberrations and human diseases. Comprehensive discovery of CNVs from whole genome sequencing data remains difficult because of low sensitivity and high false detective rate (FDR). We presented a novel framework which integrated SNV-based recalibration probabilistic model and image classification architecture (ImageCNV) for CNVs discovery. A Naive Bayesian model and a deep neural network InceptionV3 were adopted to infer candidate CNVs, and we utilize the benchmark datasets to evaluate the performance of our framework. ImageCNV yielded comparable sensitivity and lower FDR, complementing other methods based on different signals and providing a new perspective for the detection of CNVs. ImageCNV is freely available at https://github.com/minqing1/ImageCNV.

RKDOSCNV: A Local Kernel Density-Based Approach to the Detection of Copy Number Variations by Using Next-Generation Sequencing Data.

Copy number variations (CNVs) are significant causes of many human cancers and genetic diseases. The detection of CNVs has become a common method by which to analyze human diseases using next-generation sequencing (NGS) data. However, effective detection of insignificant CNVs is still a challenging task. In this study, we propose a new detection method, RKDOSCNV, to meet the need. RKDOSCNV uses kernel density estimation method to evaluate the local kernel density distribution of each read depth segment (RDS) based on an expanded nearest neighbor (k-nearest neighbors, reverse nearest neighbors, and shared nearest neighbors of each RDS) data set, and assigns a relative kernel density outlier score (RKDOS) for each RDS. According to the RKDOS profile, RKDOSCNV predicts the candidate CNVs by choosing a reasonable threshold, which it uses split read approach to correct the boundaries of candidate CNVs. The performance of RKDOSCNV is assessed by comparing it with several current popular methods via experiments with simulated and real data at different tumor purity levels. The experimental results verify that the performance of RKDOSCNV is superior to that of several other methods. In summary, RKDOSCNV is a simple and effective method for the detection of CNVs from whole genome sequencing (WGS) data, especially for samples with low tumor purity.

Detection of copy-number variations from NGS data using read depth information: a diagnostic performance evaluation.

The detection of copy-number variations (CNVs) from NGS data is underexploited as chip-based or targeted techniques are still commonly used. We assessed the performances of a workflow centered on CANOES, a bioinformatics tool based on read depth information. We applied our workflow to gene panel (GP) and whole-exome sequencing (WES) data, and compared CNV calls to quantitative multiplex PCR of short fluorescent fragments (QMSPF) or array comparative genomic hybridization (aCGH) results. From GP data of 3776 samples, we reached an overall positive predictive value (PPV) of 87.8%. This dataset included a complete comprehensive QMPSF comparison of four genes (60 exons) on which we obtained 100% sensitivity and specificity. From WES data, we first compared 137 samples with aCGH and filtered comparable events (exonic CNVs encompassing enough aCGH probes) and obtained an 87.25% sensitivity. The overall PPV was 86.4% following the targeted confirmation of candidate CNVs from 1056 additional WES. In addition, our CANOES-centered workflow on WES data allowed the detection of CNVs with a resolution of single exons, allowing the detection of CNVs that were missed by aCGH. Overall, switching to an NGS-only approach should be cost-effective as it allows a reduction in overall costs together with likely stable diagnostic yields. Our bioinformatics pipeline is available at: https://gitlab.bioinfo-diag.fr/nc4gpm/canoes-centered-workflow .

Integrating Genome-Wide CNVs Into QTLs and High Confidence GWAScore Regions Identified Positional Candidates for Sheep Economic Traits.

Copy number variations (CNVs) are important source of genetic variation, which can affect diverse economic traits through a variety of mechanisms. In addition, genome scan can identify many quantitative trait loci (QTLs) for the economic traits, while genome-wide association studies (GWAS) can localize genetic variants associated with the phenotypic variations. Here, we developed a method called GWAScore which collected GWAS summary data to identify potential candidates, and integrated CNVs into QTLs and high confidence GWAScore regions to detect crucial CNV markers for sheep growth traits. We got 197 candidate genes which were overlapping with the candidate CNVs. Some crucial genes (MYLK3, TTC29, HERC6, ABCG2, RUNX1, etc.) showed significantly elevated GWAScore peaks than other candidate genes. In this study, we developed the GWAScore method to excavate the potential value of candidate genes as markers for the sheep molecular breeding.

High Detection Rate of Copy Number Variations Using Capture Sequencing Data: A Retrospective Study.

Capture sequencing (CS) is widely applied to detect small genetic variations such as single nucleotide variants or indels. Algorithms based on depth comparison are becoming available for detecting copy number variation (CNV) from CS data. However, a systematic evaluation with a large sample size has not been conducted to evaluate the efficacy of CS-based CNV detection in clinical diagnosis. We retrospectively studied 3010 samples referred to our diagnostic laboratory for CS testing. We used 68 chromosomal microarray analysis-positive samples (true set [TS]) and 1520 reference samples to build a robust CS-CNV pipeline. The pipeline was used to detect candidate clinically relevant CNVs in 1422 undiagnosed samples (undiagnosed set [UDS]). The candidate CNVs were confirmed by an alternative method. The CS-CNV pipeline detected 78 of 79 clinically relevant CNVs in TS samples, with analytical sensitivity of 98.7% and positive predictive value of 49.4%. Candidate clinically relevant CNVs were identified in 106 UDS samples. CNVs were confirmed in 96 patients (90.6%). The diagnostic yield was 6.8%. The molecular etiology includes aneuploid (n = 7), microdeletion/microduplication syndrome (n = 40), and Mendelian disorders (n = 49). These findings demonstrate the high yield of CS-based CNV. With further improvement of our CS-CNV pipeline, the method may have clinical utility for simultaneous evaluation of CNVs and small variations in samples referred for pre- or postnatal analysis.

Systematic analysis of copy-number variations associated with early pregnancy loss.

Embryonic numerical and structural chromosomal abnormalities are the most common cause of early pregnancy loss. However, the role of submicroscopic copy-number variations (CNVs) in early pregnancy loss is unclear, and little is known about the critical regions and candidate genes for miscarriage, because of the large size of structural chromosomal abnormalities. The aim of this study was to identify potential miscarriage-associated submicroscopic CNVs and critical regions of large CNVs as well as candidate genes for miscarriage. Over a 5-year period, 5180 fresh miscarriage specimens were investigated using quantitative fluorescent polymerase chain reaction/CNV sequencing or chromosomal microarray analysis. Statistically significant submicroscopic CNVs were identified by comparing the frequency of recurrent submicroscopic CNVs between cases and a published control cohort. Furthermore, genes within critical regions of miscarriage-associated CNVs were prioritized by integrating the Residual Variation Intolerance Score and the human gene expression dataset for identification of potential miscarriage candidate genes. Results without significant maternal-cell contamination were obtained in 5003 of the 5180 (96.6%) cases. Clinically significant chromosomal abnormalities were identified in 59.1% (2955/5003) of these cases. Three recurrent submicroscopic CNVs (microdeletions in 22q11.21, 2q37.3 and 9p24.3p24.2) were significantly more frequent in miscarriage cases, and were considered to be associated with miscarriage. Moreover, 44 critical regions of large CNVs were observed, including 14 deletions and 30 duplications. There were 309 genes identified as potential miscarriage candidate genes through gene-prioritization analysis. We identified potential miscarriage candidate CNVs and genes. These data demonstrate the importance of CNVs in the etiology of miscarriage and highlight the importance of ongoing analysis of CNVs in the study of miscarriage. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.

Combinatorial Detection Algorithm for Copy Number Variations Using High-throughput Sequencing Reads

Copy number variation (CNV) is a prevalent kind of genetic structural variation which leads to an abnormal number of copies of large genomic regions, such as gain or loss of DNA segments larger than 1[Formula: see text]kb. CNV exists not only in human genome but also in plant genome. Current researches have testified that CNV is associated with many complex diseases. In this paper, guanine-cytosine (GC) bias, mappability and their effect on read depth signals in sequencing data are discussed first. Subsequently, a new correction method for GC bias and an improved combinatorial detection algorithm for CNV using high-throughput sequencing reads based on hidden Markov model (CNV-HMM) are proposed. The corrected read depth signals have lower correlation with GC content, mappability of reads and the width of analysis window. Then we create a hidden Markov model which maps the reads onto the reference genome and records the unmapped reads. The unmapped reads are counted and normalized. The CNV-HMM detects the abnormal signal of read count and gains the candidate CNVs using the expectation maximization (EM) algorithm. Finally, we filter the candidate CNVs using split reads to promote the performance of our algorithm. The experiment result indicates that the CNV-HMM algorithm has higher accuracy and sensitivity for CNVs detection than most current detection algorithms.

Improved detection algorithm for copy number variations based on hidden Markov model

Aiming at the problems of parameter optimization and insufficient utilization of split reads in the detection for copy number variation (CNV), a new definition of relative read depth (RRD) and a randomized sampling strategy (RGN) are proposed in this paper. Compared to the raw read depth, the RRD parameter has weak correlation with GC content, mappability and the width of analysis windows tiled along the genome. The RGN strategy is based on the weighted sampling strategy which can speed up the read count data analysis. Subsequently, we propose an improved detection algorithm for CNV based on hidden Markov model (CNV-HMM). The HMM detects the abnormal signal of read count data and outputs the detection results of candidate CNVs. At the end of the algorithm, we filter out the results of candidate CNVs using the split reads to improve the performance of CNV-HMM algorithm. Finally, the experiment results show that our CNV-HMM algorithm has higher sensitivity and accuracy for CNVs detection than most of current detection algorithms and applicative both for diploid animal and plant.

Analysis of copy number variation in the NEDD4L gene potentially implicated in body height in the Japanese population.

Recently it has been recognized that a considerable number of copy number variations (CNVs) are associated with diseases and other complex human traits. In our previous study, we developed a simple quantitative real-time PCR (Q-PCR) method for analysis of CNV copy number, which had the advantage of obviating the need for reference DNA with a known copy number. Using DNA samples obtained from 231 Japanese individuals, we applied this method for analyzing the copy number of a candidate CNV associated with body height, located in the neural precursor cell expressed, developmentally down-regulated 4-like, E3 ubiquitin protein ligase (NEDD4L) gene. In addition, the appropriateness of the results was evaluated and confirmed by quantification of amplicons with an Agilent 2100 Bioanalyzer. The NEDD4L gene encodes a member of the Nedd4 family of HECT domain E3 ubiquitin ligases. The target CNV located in the intron has been found to be significantly associated with height variation in Chinese. However, it remains unknown whether such an association exists in other populations, including Japanese. Analysis of the correlations between copy number and body height using ANOVA revealed no statistically significant correlations in Japanese.

A comprehensive screening of copy number variability in dementia with Lewy bodies.

The role of genetic variability in dementia with Lewy bodies (DLB) is now indisputable; however, data regarding copy number variation (CNV) in this disease has been lacking. Here, we used whole-genome genotyping of 1454 DLB cases and 1525 controls to assess copy number variability. We used 2 algorithms to confidently detect CNVs, performed a case-control association analysis, screened for candidate CNVs previously associated with DLB-related diseases, and performed a candidate gene approach to fully explore the data. We identified 5 CNV regions with a significant genome-wide association to DLB; 2 of these were only present in cases and absent from publicly available databases: one of the regions overlapped LAPTM4B, a known lysosomal protein, whereas the other overlapped the NME1 locus and SPAG9. We also identified DLB cases presenting rare CNVs in genes previously associated with DLB or related neurodegenerative diseases, such as SNCA, APP, and MAPT. To our knowledge, this is the first study reporting genome-wide CNVs in a large DLB cohort. These results provide preliminary evidence for the contribution of CNVs in DLB risk.