Dataset Information

Children's Hospital of Philadelphia (CHOP) Control Copy Number Variation (CNV) Study

ABSTRACT:

We present a database of copy number variations (CNVs) detected in 2,026 disease-free individuals, using high-density, SNP-based oligonucleotide microarrays. This large cohort analyzed for CNVs in a single study using a uniform array platform and computational tools, comprises mainly of Caucasians (65.2%) and African-Americans (34.2%), We have catalogued and characterized 54,462 individual CNVs, 77.8% of which were identified in multiple unrelated individuals. These non-unique CNVs mapped to 3,272 distinct regions of genomic variation spanning 5.9% of the genome; 51.5% of these were previously unreported, and >85% are rare. Our annotation and analysis confirmed and extended previously reported correlations between CNVs and several genomic features such as repetitive DNA elements, segmental duplications and genes. We demonstrate the utility of this data set in distinguishing CNVs with pathologic significance from normal variants. Together, this analysis and annotation provides a useful resource to assist with the assessment of CNVs in the contexts of human variation, disease susceptibility, and clinical molecular diagnostics. The CNV resource is available at: http://cnv.chop.edu. Reprinted from Shaikh T., et al., High-Resolution Mapping and Analysis of Copy Number Variations in the Human Genome: A Data Resource for Clinical and Research Applications Genome Research. 2009, with permission from Genome Research.

CHOP CNVs from 2,026 disease-free individuals are available through dbVar at http://www.ncbi.nlm.nih.gov/dbvar/studies/nstd21.

PROVIDER: phs000199.v1.p1 | EGA |

REPOSITORIES: EGA

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Publications

High-resolution mapping and analysis of copy number variations in the human genome: a data resource for clinical and research applications.

Shaikh Tamim H TH Gai Xiaowu X Perin Juan C JC Glessner Joseph T JT Xie Hongbo H Murphy Kevin K O'Hara Ryan R Casalunovo Tracy T Conlin Laura K LK D'Arcy Monica M Frackelton Edward C EC Geiger Elizabeth A EA Haldeman-Englert Chad C Imielinski Marcin M Kim Cecilia E CE Medne Livija L Annaiah Kiran K Bradfield Jonathan P JP Dabaghyan Elvira E Eckert Andrew A Onyiah Chioma C CC Ostapenko Svetlana S Otieno F George FG Santa Erin E Shaner Julie L JL Skraban Robert R Smith Ryan M RM Elia Josephine J Goldmuntz Elizabeth E Spinner Nancy B NB Zackai Elaine H EH Chiavacci Rosetta M RM Grundmeier Robert R Rappaport Eric F EF Grant Struan F A SF White Peter S PS Hakonarson Hakon H

Genome research 20090710 9

We present a database of copy number variations (CNVs) detected in 2026 disease-free individuals, using high-density, SNP-based oligonucleotide microarrays. This large cohort, comprised mainly of Caucasians (65.2%) and African-Americans (34.2%), was analyzed for CNVs in a single study using a uniform array platform and computational process. We have catalogued and characterized 54,462 individual CNVs, 77.8% of which were identified in multiple unrelated individuals. These nonunique CNVs mapped t ...[more]

PMID: 19592680

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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).

Dataset Information

Children's Hospital of Philadelphia (CHOP) Control Copy Number Variation (CNV) Study

Publications

High-resolution mapping and analysis of copy number variations in the human genome: a data resource for clinical and research applications.

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