A Genome-Wide Copy Number Variant Study of Suicidal Behavior
ABSTRACT: Suicide and suicide attempts are complex behaviors that result from the interaction of different factors, including genetic variants that increase the predisposition to suicidal behaviors. Copy number variations (CNVs) are deletions or duplications of a segment of DNA usually larger than one kilobase. These structural genetic changes, although quite rare, have been associated with genetic liability to mental disorders, such as autism, schizophrenia, and bipolar disorder. No genome-wide level studies have been published investigating the potential role of CNVs in suicidal behaviors. Based on single-nucleotide polymorphism array data, we followed the Penn-CNV standards to detect CNVs in 1,608 subjects, comprising 475 suicide and suicide attempt cases and 1,133 controls. Although the initial algorithms determined the presence of CNVs on chromosomes 6 and 12 in seven and eight cases, respectively, compared with none of the controls, visual inspection of the raw data did not support this finding. Furthermore we were unable to validate these findings by CNV-specific real-time polymerase chain reaction. Additionally, rare CNV burden analysis did not find an association between the frequency or length of rare CNVs and suicidal behavior in our sample population. Although our findings suggest CNVs do not play an important role in the etiology of suicidal behaviors, they are not inconsistent with the strong evidence from the literature suggesting that other genetic variants account for a portion of the total phenotypic variability in suicidal behavior.
Project description:Purpose: Chromosomal microarray analysis (CMA) to assess copy number variation (CNV) content is now used as a first tier genetic diagnostic test for individuals with unexplained neurodevelopmental disorders (NDD) or multiple congenital anomalies (MCA). Over 100 cytogenetic labs worldwide are using the Affymetrix CytoScan HD 2.7M array to genotype >15,000 clinical samples per month. The aim of this study is to develop a CNV resource from a population control cohort that can be used as a community resource for interpretation of clinical and research samples. Methods: We have genotyped a large population control set (1,000 individuals from our Ontario Population Genomics Platform (OPGP)) using the Affymetrix CytoScan HD microarray comprising 2.7 million probes. Four independent algorithms were applied to detect and assess high confidence CNVs. Reproducibility and validations were quantified using sample replicates and Quantitative-PCR (QPCR), respectively. Results: DNA from 873 individuals from the OPGP cohort passed quality control and we have identified 71,178 CNVs (81 CNVs/individual) distributed across 796 different cytogenetic regions in the genome; 9.8% of the CNVs were previously unreported. After applying three layers of filtering criteria, from our high confidence CNVs dataset, we obtained a >95% reproducibility and >90% validation rate. Due to the array's high probe density within genic regions, our high confidence CNV data set show 73% of the detected CNVs overlapped at least one gene. Conclusion: The genotype data and annotated CNVs presented in this study will represent a valuable public resource enabling clinical genetics research and diagnostics. For array quality control, CEL files were processed using modules from the Affymetrix power tools and genotypes were extracted from the CHP file. Samples passing the median of the absolute pairwise differences (MAPD) < 0.20 and waviness-sd < 0.11 were retained for further analysis. After multiple checks, we excluded 52 samples that do not meet quality control (QC) cutoffs. To confirm the sample's self-reported gender, we have matched the sex chromosome information from the array and identified six samples with gender mismatch, which were excluded from the analysis. We also excluded 47 samples due to excessive CNV calls. A final set of 895 samples were used for further analysis. This number included 22 sample replicates (indicated by _1 following the Sample title), which were used to determine reproducibility of the array calls. The CNV data for this study is available from dbVar (NCBI), DGVa (EBI) accession number estd212, and DGV.
Project description:Suicidal behavior (SB) has a complex etiology of genes, environment or both. One of the genetic components in SB could be copy number variations (CNVs), since CNVs are implicated in a range of neurodevelopmental disorders. However, a recently published genome-wide and case-control study failed to observe a significant role of CNVs in SB (see E-MTAB-3519). Here we complement those initial observations by conducting a brief CNV-association study, for the first time in a family-based trio-sample with severe suicide attempt (SA) outcome in offspring (n=660 trios; the GISS sample, see http://www.ncbi.nlm.nih.gov/pubmed/23422793 and refs therein). For association testing, we here used the FBAT-CNV methodology (http://www.ncbi.nlm.nih.gov/pubmed/18228561), which allows for CNV association testing directly on the raw intensity values (Illumina log R ratio), evaluating any type of CNVs (e.g. de novo or inherited) without reliance on CNV calling algorithms and robust to control selection biases. Here we have deposited these raw logR-values for 88,450 on-chip CNV-loci of the HumanOmni1-Quad_v1 chip, arranged in a standard pedigree format used as input for FBAT-CNV analysis in SVS software (goldenhelix.com): column 1 is the family ID, column 2 is the Subject type (1 = offspring, 2 = mother, 3 = father), column 3 is the father ID (? for missing), column 4 is the mother ID (? for missing), column 5 is the sex (1 = female, 0 = male), column 6 is the affection status (1 = suicide attempt, 0 = not affected) and columns 7 and onward are the logR-values for each CNV-loci as is listed in the header row. We observed experiment-wide significant association (P ≤ 5.6 x 10-7) of two proximal CNVs at chromosome 14 (Illumina markers cnvi0108946 and cnvi0118308, with NCBI 36.3 start positions 22794779 and 22582820). However, these CNV-associations mapped to T-cell receptor regions, and therefore most likely reflect inter-individual variation in somatic rearrangements occurring in white blood cells (as our source of DNA was blood), rather than association with SA. In conclusion, our results did not suggest any major role of CNVs in SA etiology, in line with the results of the recent case-control study (see E-MTAB-3519).
Project description:Genome instability is a potential limitation to the research and therapeutic application of induced pluripotent stem cells (iPSCs). Observed genomic variations reflect the combined activities of DNA damage, cellular DNA damage response (DDR), and selection pressure in culture. To understand the contribution of DDR on the distribution of copy number variations (CNVs) in iPSCs, we mapped CNVs of iPSCs with mutations in the central DDR gene ATM onto genome organization landscapes defined by genome-wide replication timing profiles. We show that following reprogramming the early and late replicating genome is differentially affected by CNVs in ATM deficient iPSCs relative to wild type iPSCs. Specifically, the early replicating regions had increased CNV losses during retroviral reprogramming. This differential CNV distribution was not present after later passage or after episomal reprogramming. Comparison of different reprogramming methods in the setting of defective DNA damage response reveals unique vulnerability of early replicating open chromatin to retroviral vectors. We isolated genomic DNA from Ataxia-telangiectasia (A-T) iPSC cells derived from patient fibroblasts virus and episomal vectors, coresponding fibroblasts, normal human fibroblast derived iPSCcells, for hybridization to the Affymetrix Genome-Wide Human SNP 6.0 Array.
Project description:Assays in bile duct cancer patients showed 984 CNVs in 306 CNV regions (CNVR) distributed throughout all 22 chromosomes. Bile duct cancer patients had a mean of 21.8 gains and 19.2 losses of genes, with an average of 35.9 CNVRs per patient. Frequent sites of gains were at chromosomes 22q11.22, 2p11.2-p.11.1, 14q32.33 and 17q12, whereas frequent sites of losses were at 19q12-q13.43. Investigation of CNV in 24 bile duct cancer tissue samples
Project description:Background: The chicken (Gallus gallus) is an important model organism that bridges the evolutionary gap between mammals and other vertebrates. Copy number variations (CNVs) are a form of genomic structural variation widely distributed in the genome. CNV analysis has recently gained greater attention and momentum, as the identification of CNVs can contribute to a better understanding of traits important to both humans and other animals. To detect chicken CNVs, we genotyped 475 animals derived from two broiler chicken lines divergently selected for abdominal fat content using chicken 60K SNP array, which is a high-throughput method widely used in chicken genomics studies. Results: Using PennCNV algorithm, we detected 438 and 291 CNVs in the lean and fat lines, respectively, corresponding to 271 and 188 CNV regions (CNVRs), which were obtained by merging overlapping CNVs. Out of these CNVRs, 99% were confirmed also by the CNVPartition program. These CNVRs covered 40.26 and 30.60 Mb of the chicken genome in the lean and fat lines, respectively. Moreover, CNVRs included 176 loss, 68 gain and 27 both (i.e. loss and gain within the same region) events in the lean line, and 143 loss, 25 gain and 20 both events in the fat line. Ten CNVRs were chosen for the validation experiment using qPCR method, and all of them were confirmed in at least one qPCR assay. We found a total of 886 genes located within these CNVRs, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed they could play various roles in a number of biological processes. Integrating the results of CNVRs, known quantitative trait loci (QTL) and selective sweeps for abdominal fat content suggested that some genes (including SLC9A3, GNAL, SPOCK3, ANXA10, HELIOS, MYLK, CCDC14, SPAG9, SOX5, VSNL1, SMC6, GEN1, MSGN1 and ZPAX) may be important for abdominal fat deposition in the chicken. Conclusions: Our study provided a genome-wide CNVR map of the chicken genome, thereby contributing to our understanding of genomic structural variations and their potential roles in abdominal fat content in the chicken. In total, 475 birds (203 and 272 individuals from the lean and fat lines, respectively) from the 11th generation population of Northeast Agricultural University broiler lines divergently selected for abdominal fat content (NEAUHLF) were used. These 475 birds were genotyped by the chicken 60k SNP chip and PennCNV method were used to perform genome-wide CNV detection.