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• W2002079712 abstract "Segmental copy-number variations (CNVs) in the human genome are associated with developmental disorders and susceptibility to diseases. More importantly, CNVs may represent a major genetic component of our phenotypic diversity. In this study, using a whole-genome array comparative genomic hybridization assay, we identified 3,654 autosomal segmental CNVs, 800 of which appeared at a frequency of at least 3%. Of these frequent CNVs, 77% are novel. In the 95 individuals analyzed, the two most diverse genomes differed by at least 9 Mb in size or varied by at least 266 loci in content. Approximately 68% of the 800 polymorphic regions overlap with genes, which may reflect human diversity in senses (smell, hearing, taste, and sight), rhesus phenotype, metabolism, and disease susceptibility. Intriguingly, 14 polymorphic regions harbor 21 of the known human microRNAs, raising the possibility of the contribution of microRNAs to phenotypic diversity in humans. This in-depth survey of CNVs across the human genome provides a valuable baseline for studies involving human genetics. Segmental copy-number variations (CNVs) in the human genome are associated with developmental disorders and susceptibility to diseases. More importantly, CNVs may represent a major genetic component of our phenotypic diversity. In this study, using a whole-genome array comparative genomic hybridization assay, we identified 3,654 autosomal segmental CNVs, 800 of which appeared at a frequency of at least 3%. Of these frequent CNVs, 77% are novel. In the 95 individuals analyzed, the two most diverse genomes differed by at least 9 Mb in size or varied by at least 266 loci in content. Approximately 68% of the 800 polymorphic regions overlap with genes, which may reflect human diversity in senses (smell, hearing, taste, and sight), rhesus phenotype, metabolism, and disease susceptibility. Intriguingly, 14 polymorphic regions harbor 21 of the known human microRNAs, raising the possibility of the contribution of microRNAs to phenotypic diversity in humans. This in-depth survey of CNVs across the human genome provides a valuable baseline for studies involving human genetics. Genetic variation in the human genome exists in different forms. Recent studies have shown that variations exist in the human genome at various levels: the single base pair,1Altshuler D Brooks LD Chakravarti A Collins FS Daly MJ Donnelly P A haplotype map of the human genome.Nature. 2005; 437: 1299-1320Crossref PubMed Scopus (4547) Google Scholar the kilobase pair,2Conrad DF Andrews TD Carter NP Hurles ME Pritchard JK A high-resolution survey of deletion polymorphism in the human genome.Nat Genet. 2006; 38: 75-81Crossref PubMed Scopus (519) Google Scholar, 3Hinds DA Kloek AP Jen M Chen X Frazer KA Common deletions and SNPs are in linkage disequilibrium in the human genome.Nat Genet. 2006; 38: 82-85Crossref PubMed Scopus (294) Google Scholar, 4McCarroll SA Hadnott TN Perry GH Sabeti PC Zody MC Barrett JC Dallaire S Gabriel SB Lee C Daly MJ et al.Common deletion polymorphisms in the human genome.Nat Genet. 2006; 38: 86-92Crossref PubMed Scopus (559) Google Scholar and tens to thousands of kilobase pairs.5Iafrate AJ Feuk L Rivera MN Listewnik ML Donahoe PK Qi Y Scherer SW Lee C Detection of large-scale variation in the human genome.Nat Genet. 2004; 36: 949-951Crossref PubMed Scopus (2234) Google Scholar, 6Sebat J Lakshmi B Troge J Alexander J Young J Lundin P Maner S Massa H Walker M Chi M et al.Large-scale copy number polymorphism in the human genome.Science. 2004; 305: 525-528Crossref PubMed Scopus (1900) Google Scholar, 7Sharp AJ Locke DP McGrath SD Cheng Z Bailey JA Vallente RU Pertz LM Clark RA Schwartz S Segraves R et al.Segmental duplications and copy-number variation in the human genome.Am J Hum Genet. 2005; 77: 78-88Abstract Full Text Full Text PDF PubMed Scopus (690) Google Scholar, 8Tuzun E Sharp AJ Bailey JA Kaul R Morrison VA Pertz LM Haugen E Hayden H Albertson D Pinkel D et al.Fine-scale structural variation of the human genome.Nat Genet. 2005; 37: 727-732Crossref PubMed Scopus (781) Google Scholar Extensive studies, including the recently published haplotype map from HapMap,1Altshuler D Brooks LD Chakravarti A Collins FS Daly MJ Donnelly P A haplotype map of the human genome.Nature. 2005; 437: 1299-1320Crossref PubMed Scopus (4547) Google Scholar have identified millions of SNPs in the human genome. Three recent studies that used the SNP data each identified several hundred sites of deletion in the human population; however, gains could not be deduced from this data set.2Conrad DF Andrews TD Carter NP Hurles ME Pritchard JK A high-resolution survey of deletion polymorphism in the human genome.Nat Genet. 2006; 38: 75-81Crossref PubMed Scopus (519) Google Scholar, 3Hinds DA Kloek AP Jen M Chen X Frazer KA Common deletions and SNPs are in linkage disequilibrium in the human genome.Nat Genet. 2006; 38: 82-85Crossref PubMed Scopus (294) Google Scholar, 4McCarroll SA Hadnott TN Perry GH Sabeti PC Zody MC Barrett JC Dallaire S Gabriel SB Lee C Daly MJ et al.Common deletion polymorphisms in the human genome.Nat Genet. 2006; 38: 86-92Crossref PubMed Scopus (559) Google Scholar By use of a fosmid paired-end sequence analysis, a comprehensive comparison between two genomes quantified 241 sites of insertion or deletion.8Tuzun E Sharp AJ Bailey JA Kaul R Morrison VA Pertz LM Haugen E Hayden H Albertson D Pinkel D et al.Fine-scale structural variation of the human genome.Nat Genet. 2005; 37: 727-732Crossref PubMed Scopus (781) Google Scholar By use of array comparative genomic hybridization (array CGH) techniques, large-scale copy-number variations (CNVs) were demonstrated in a fraction of the human genome.5Iafrate AJ Feuk L Rivera MN Listewnik ML Donahoe PK Qi Y Scherer SW Lee C Detection of large-scale variation in the human genome.Nat Genet. 2004; 36: 949-951Crossref PubMed Scopus (2234) Google Scholar, 6Sebat J Lakshmi B Troge J Alexander J Young J Lundin P Maner S Massa H Walker M Chi M et al.Large-scale copy number polymorphism in the human genome.Science. 2004; 305: 525-528Crossref PubMed Scopus (1900) Google Scholar Each of these studies added to our knowledge about CNVs in the human population, but with little overlap in findings.9Eichler EE Widening the spectrum of human genetic variation.Nat Genet. 2006; 38: 9-11Crossref PubMed Scopus (79) Google Scholar Thus, many characteristics of CNVs in the human population remain unknown, such as the total number, genomic positions, gene content, frequency spectrum, and patterns of linkage disequilibrium with one another. Understanding CNVs is critical for the proper study of disease-associated changes because segmental CNVs have been demonstrated in developmental disorders and susceptibility to disease.10Inoue K Lupski JR Molecular mechanisms for genomic disorders.Annu Rev Genomics Hum Genet. 2002; 3: 199-242Crossref PubMed Scopus (243) Google Scholar Therefore, analysis of CNVs at the whole-genome level is required to create a baseline of human genomic variation. In this study, using a whole-genome tiling-path BAC array CGH approach,11Ishkanian AS Malloff CA Watson SK DeLeeuw RJ Chi B Coe BP Snijders A Albertson DG Pinkel D Marra MA et al.A tiling resolution DNA microarray with complete coverage of the human genome.Nat Genet. 2004; 36: 299-303Crossref PubMed Scopus (525) Google Scholar we measured large scale (>40 kb) segmental gains and losses in >100 individuals to expand our knowledge about CNVs and to estimate the extent of this form of variation in the human population. Samples were collected and were rendered anonymous. These samples included 16 from healthy blood donors, 51 from a British Columbia Cancer Agency (BCCA) screening program, and 26 B-lymphoblast DNA samples encompassing 16 distinct ethnic groups from the Human Variation Collection and 14 CEPH pedigree samples from the Coriell Cell Repository (National Institute of General Medical Sciences, Camden, NJ). The DNA samples from cell lines were included to represent diverse ethnic populations. The 51 samples from the BCCA screening program included 19 from a breast cancer screening program and 32 from a colon cancer screening program. These were constitutional DNA samples obtained from blood that did not contain any neoplastic cells, and none showed CNV association with BRCA1 (MIM 113705), BRCA2 (MIM 600185), APC (MIM 175100), MSH2 (MIM 609309), or MSH6 (MIM 600678). Only 2 of the 32 samples from the colon cancer screening program showed CNV association with MLH1 (MIM 120436). In addition, no CNVs were associated with a specific sample type or source, which suggests no obvious selection bias. In total, 105 DNA samples (from 44 males and 61 females) were included in this study (table 1), 95 of which were used for CNV discovery. DNA from the four grandparents of the CEPH pedigree were included in the CNV discovery sample set, whereas DNA from 10 other members of the family were included only for clustering and inheritance analysis. A donor sample was used as the male reference, and a single female sample was used only in control experiments. Genomic DNA from donors was extracted from whole blood by use of the QIAamp DNA Blood Maxi Kit (QIAGEN) and was quantified by spectrophotometry (ND-1000 [NanoDrop]).Table 1Samples Used in This StudySampleSample SourceaCoriell sample numbers are shown in parentheses.SexS1Coriell (NA17755), Han of L.A.MS2Coriell (NA10975), MayanMS3Coriell (NA17392), Mexican IndianMS4Coriell (NA17075), Puerto RicanMS5Coriell (NA15724), CzechoslovakianMS6Coriell (NA15760), IcelandMS7Coriell (NA17384), African North of SaharaMS8Coriell (NA10469), BiakaMS9Coriell (NA10492), MbutiMS10Coriell (NA17361), Ashkenazi JewishMS11Coriell (NA11522), DruzeMS12Coriell (NA13613), Taiwan Ami tribeMS13Coriell (NA13611), Taiwan Ami tribeMS14Coriell (NA13603), Taiwan Atayal tribeMS15Coriell (NA13606), Taiwan Atayal tribeMS16Coriell (NA11587), JapaneseMS17Coriell (NA10540), MelanesianMS18Screening program, ethnicity unknownMS19Screening program, ethnicity unknownMS20Screening program, ethnicity unknownMS21Screening program, ethnicity unknownMS22Screening program, ethnicity unknownMS23Screening program, ethnicity unknownMS24Screening program, ethnicity unknownMS25Screening program, ethnicity unknownMS26Screening program, ethnicity unknownMS27Screening program, ethnicity unknownMS28Screening program, ethnicity unknownMS29Screening program, ethnicity unknownMS30Screening program, ethnicity unknownMS31Screening program, ethnicity unknownMS32Screening program, ethnicity unknownMS33Donor, ethnicity unknownMS34Donor, ethnicity unknownMS35Donor, ethnicity unknownMS36Donor, ethnicity unknownMS37Coriell (NA17766), Han of Los AngelesFS38Coriell (NA17076), Puerto RicanFS39Coriell (NA15729), CzechoslovakianFS40Coriell (NA15766), IcelandicFS41Coriell (NA17348), African South of SaharaFS42Coriell (NA10471), BiakaFS43Coriell (NA11521), DruzeFS44Coriell (NA10539), MelanesianFS45Screening program, ethnicity unknownFS46Screening program, ethnicity unknownFS47Screening program, ethnicity unknownFS48Screening program, ethnicity unknownFS49Screening program, ethnicity unknownFS50Screening program, ethnicity unknownFS51Screening program, ethnicity unknownFS52Screening program, ethnicity unknownFS53Screening program, ethnicity unknownFS54Screening program, ethnicity unknownFS55Screening program, ethnicity unknownFS56Screening program, ethnicity unknownFS57Screening program, ethnicity unknownFS58Screening program, ethnicity unknownFS59Screening program, ethnicity unknownFS60Screening program, ethnicity unknownFS61Screening program, ethnicity unknownFS62Screening program, ethnicity unknownFS63Screening program, ethnicity unknownFS64Screening program, ethnicity unknownFS65Screening program, ethnicity unknownFS66Screening program, ethnicity unknownFS67Screening program, ethnicity unknownFS68Donor, ethnicity unknownFS69Donor, ethnicity unknownFS70Donor, ethnicity unknownFS71Donor, ethnicity unknownFS72Donor, ethnicity unknownFS73Donor, ethnicity unknownFS74Donor, ethnicity unknownMS75Screening program, ethnicity unknownFS76Screening program, ethnicity unknownFS77Coriell (NA17393), Mexican IndianFS78Donor, ethnicity unknownFS79Donor, ethnicity unknownFS80Donor, ethnicity unknownFS81Screening program, ethnicity unknownFS82Screening program, ethnicity unknownFS83Screening program, ethnicity unknownFS84Screening program, ethnicity unknownFS85Screening program, ethnicity unknownFS86Screening program, ethnicity unknownFS87Screening program, ethnicity unknownFS88Screening program, ethnicity unknownFS89Screening program, ethnicity unknownFS90Screening program, ethnicity unknownFS91Screening program, ethnicity unknownFF1Coriell (NA11917, paternal grandfather), UtahMF2Coriell (NA11918, paternal grandmother), UtahFF3Coriell (NA11919, maternal grandfather), UtahMF4Coriell (NA11920, maternal grandmother), UtahFF5bThese 10 CEPH family samples were not included in the CNV discovery set of 95.Coriell (NA10842, dad), UtahMF6bThese 10 CEPH family samples were not included in the CNV discovery set of 95.Coriell (NA10843, mom), UtahFF7bThese 10 CEPH family samples were not included in the CNV discovery set of 95.Coriell (NA11909, son), UtahMF8bThese 10 CEPH family samples were not included in the CNV discovery set of 95.Coriell (NA11910, daughter), UtahFF9bThese 10 CEPH family samples were not included in the CNV discovery set of 95.Coriell (NA11911, daughter), UtahFF10bThese 10 CEPH family samples were not included in the CNV discovery set of 95.Coriell (NA11912, son), UtahMF11bThese 10 CEPH family samples were not included in the CNV discovery set of 95.Coriell (NA11913, son), UtahMF12bThese 10 CEPH family samples were not included in the CNV discovery set of 95.Coriell (NA11915, daughter), UtahFF13bThese 10 CEPH family samples were not included in the CNV discovery set of 95.Coriell (NA11916, son), UtahMF14bThese 10 CEPH family samples were not included in the CNV discovery set of 95.Coriell (NA11921, daughter), UtahFa Coriell sample numbers are shown in parentheses.b These 10 CEPH family samples were not included in the CNV discovery set of 95. Open table in a new tab DNA labeling and hybridization was performed as described elsewhere,11Ishkanian AS Malloff CA Watson SK DeLeeuw RJ Chi B Coe BP Snijders A Albertson DG Pinkel D Marra MA et al.A tiling resolution DNA microarray with complete coverage of the human genome.Nat Genet. 2004; 36: 299-303Crossref PubMed Scopus (525) Google Scholar with slight modifications. In brief, 200 ng of sample and reference DNA were differentially labeled with Cyanine 3–dCTP and Cyanine 5–dCTP (Perkin Elmer Life Sciences). The random priming reaction was incubated in the dark at 37°C for 16–18 h. DNA samples were then combined, and unincorporated nucleotides were removed using microcon YM-30 columns (Millipore). Purified samples were mixed with 100 μg of human Cot-1 DNA (Invitrogen) and were precipitated. DNA pellets were resuspended in 45 μl of DIG Easy hybridization solution (Roche) containing 20 mg/ml sheared herring sperm DNA and 10 mg/ml yeast tRNA. Sample mixture was denatured at 85°C for 10 min, and repetitive sequences were blocked at 45°C for 1 h before hybridization. The mixture was then applied onto BAC arrays containing 26,363 clones spotted in duplicate (53,856 elements with controls) on single slides. (These clones were selected from the SMRT clone set, to optimize tiling coverage of the genome; the clone list is available at the SMRT Array Web site.11Ishkanian AS Malloff CA Watson SK DeLeeuw RJ Chi B Coe BP Snijders A Albertson DG Pinkel D Marra MA et al.A tiling resolution DNA microarray with complete coverage of the human genome.Nat Genet. 2004; 36: 299-303Crossref PubMed Scopus (525) Google Scholar) Hybridization was performed in the dark at 45°C for ∼36 h inside a hybridization chamber, followed by washing three times for 3 min each with agitation in 0.1× saline sodium citrate (SSC) and 0.1% SDS at 45°C. Arrays were then rinsed three times for 3 min each in 0.1× SSC at room temperature and were dried by an air stream before imaging. Slides were scanned using a charge-coupled device–based imaging system (arrayWoRx eAuto [Applied Precision]) and were analyzed with the SoftWoRx Tracker Spot Analysis software (Applied Precision). The log2 ratios of the Cyanine 3 to Cyanine 5 intensities for each spot were assessed. To remove systematic effects from nonbiological sources that introduce bias, the ratios were then normalized using a stepwise normalization technique.12Khojasteh M Lam WL Ward RK MacAulay C A stepwise framework for the normalization of array CGH data.BMC Bioinformatics. 2005; 6: 274Crossref PubMed Scopus (76) Google Scholar Custom SeeGH software was used to visualize normalized data as log2 ratio plots (fig. 1).13Chi B deLeeuw RJ Coe BP MacAulay C Lam WL SeeGH—a software tool for visualization of whole genome array comparative genomic hybridization data.BMC Bioinformatics. 2004; 5: 13Crossref PubMed Scopus (74) Google Scholar For each experiment, 1,398 clones from chromosomes X and Y were removed, and the remaining data were median normalized to remove bias introduced because of any sex-mismatched hybridization. In addition, 573 clones were removed from analysis because of printing anomalies or their shift in log2 ratios, possibly due to homology with the X or Y chromosome, leaving a total of 24,392 reliable clones for analysis (see the tab-delimited ASCII file, which can be imported into a spreadsheet, of data set 1 [online only]). Experimental SDs (SDautosome) were calculated for each experiment on the basis of the log2 ratios of the 24,392 reliable clones minus the clones removed because of low signal-to-noise ratio (SNR) or high SD of replicate clone measures (SDclone). Thresholds for determining CNV clones were set at a multiple of the SDautosome value. For each experiment, clones were annotated as uninformative if they were filtered via SNR or SDclone, as a CNV loss if the log2 ratio was less than the negative threshold, as unchanged if the log2 ratio was between the negative and positive thresholds, and as a CNV gain if the log2 ratio was above the positive threshold. To determine the optimal values for SNR, SDclone, and the SDautosome multiplier, eight hybridization experiments (four repeat experiments of male reference versus the single female DNA and four experiments between those two DNAs and two additional DNA pools) were used. On the basis of the possible combinations of copy-number status in the four DNA samples used, we determined the expected CNV patterns in the eight hybridization experiments (table 2). The three parameters were recursively varied until the highest proportion of CNV clones match the expected patterns (table 2); this resulted in the filter settings of SDclone>0.15, SNR<3, and a stringent SDautosome multiplier of 3.3×. On the basis of six self-versus-self hybridizations to calibrate array performance, experiments with >10% uninformative data points or with an SDautosome>0.12 were repeated. Normalized log2 ratio profiles were generated for the 105 individuals from hybridization of sample DNA versus a single male reference DNA. Data points that did not meet our SDclone or SNR criteria were annotated as uninformative, whereas those whose average ratio exceeded the 3.3× SDautosome were identified as CNV clones (see the tab-delimited ASCII file of data set 2 [online only]). CNV clones that overlapped in genomic coverage were considered to represent the same CNV loci. A custom track file for uploading the identified CNV clones to the University of California–Santa Cruz (UCSC) Human Genome Browser is available on request. After submission of the custom track file, clones displayed in blue, red, green, and black represented CNVs seen once or twice, three times, four or five times, and six or more times, respectively.Table 2Expected CNV Patterns of Eight Hybridizations between Four DNA SamplesCNV CombinationsaPossible combinations of copy-number status in the four DNA samples. Blank cells indicate no copy-number change. Gain = copy-number gain; Loss = copy-number loss.Expected CNV PatternsbExpected CNV patterns of eight hybridizations between the four DNA samples. Observed experimental data were compared against these expected patterns. In each hybridization, the first sample is expected to have a net gain in copy-number (+), a net loss in copy-number (−), or the same copy number (blank cell) as the second sample for a CNV with the particular combination of copy-number status shown on the left.MRFSMPFPMR vs. FSMR vs. FSMR vs. FSMR vs. FSMR vs. MPFS vs. MPMR vs. FPFS vs. FPLossLossLoss−−LossLossLossGain−−LossLossLoss−−LossLoss−−−−LossLossGain−−−−LossLossGainLoss−−LossLossGain−−−−LossLossGainGain−−−−LossLossLoss−−−−++LossLoss−−−−+−LossLossGain−−−−+−−LossLoss−−−−−+Loss−−−−−−LossGain−−−−−−−LossGainLoss−−−−−−+LossGain−−−−−−−LossGainGain−−−−−−−−LossGainLossLoss−−−−++LossGainLoss−−−−+−+LossGainLossGain−−−−+−LossGainLoss−−−−−++LossGain−−−−−+−+LossGainGain−−−−−+−LossGainGainLoss−−−−−+LossGainGain−−−−−−+LossGainGainGain−−−−−−LossLossLoss++++++LossLoss+++++−LossLossGain+++++−−LossLoss++++−+Loss++++−−LossGain++++−−−LossGainLoss+++++−+LossGain+++++−−LossGainGain+++++−−−LossLoss++++Loss++LossGain++−−Loss++Gain−−GainLoss−−++Gain−−GainGain−−−−GainLossLoss−−−−++++GainLoss−−−−+++GainLossGain−−−−++−GainLoss−−−−+++Gain−−−−++GainGain−−−−+−GainGainLoss−−−−−++GainGain−−−−−+GainGainGain−−−−−−GainLossLossLoss++++++GainLossLoss++++++−GainLossLossGain+++++−GainLossLoss+++++−+GainLoss+++++−+−GainLossGain+++++−−GainLossGainLoss++++−+GainLossGain++++−+−GainLossGainGain++++−−GainLossLoss++++++++GainLoss+++++++GainLossGain++++++−GainLoss+++++++Gain++++++GainGain+++++−GainGainLoss++++−++GainGain++++−+GainGainGain++++−−GainGainLossLoss++++GainGainLoss++++GainGainLossGain++GainGainLoss++++GainGain++++GainGainGain++GainGainGainLoss++GainGainGain++Note.—FP = female pool; FS = single female sample; MP = male pool; MR = single male reference.a Possible combinations of copy-number status in the four DNA samples. Blank cells indicate no copy-number change. Gain = copy-number gain; Loss = copy-number loss.b Expected CNV patterns of eight hybridizations between the four DNA samples. Observed experimental data were compared against these expected patterns. In each hybridization, the first sample is expected to have a net gain in copy-number (+), a net loss in copy-number (−), or the same copy number (blank cell) as the second sample for a CNV with the particular combination of copy-number status shown on the left. Open table in a new tab Note.— FP = female pool; FS = single female sample; MP = male pool; MR = single male reference. To estimate our false-positive and false-negative rates in this study, six repeat experiments (of the single female vs. the male reference) were analyzed per our CNV algorithm (see above). In total, 803 CNV calls were made, with 340 seen only once, 50 twice, 46 three times, 15 four times, 15 five times, and 15 six times. Given that our false-positive results cannot exceed the total number of calls (i.e., 803), our maximum false-positive rate is 0.5487% (803/24,392 measures × 6 experiments). By use of this maximum false-positive rate of 0.5487%, the binomial probability, p, of detecting the same clone twice within six experiments by random chance is p=0.000445. Therefore, we concluded that any clone detected twice or more was a true CNV in these six repeat experiments. In theory, we expected to detect 141 true CNVs (i.e., 50 calls seen twice, 46 seen three times, and 15 each seen four, five, and six times) in each of the six experiments (846 calls). In practice, 463 were detected, yielding an estimated false-negative rate of 45.3%. Although statistically a fraction of the single-occurrence calls (those seen only once) represent true CNVs, we conservatively considered all 340 as false-positive results, resulting in a false-positive rate of 0.2323% (340 calls/24,392 measures × 6 experiments). In short, we tolerated this high false-negative rate of 45.3% to achieve our very low false-positive rate for confidence in CNV discovery. On the basis of the false-positive and false-negative rates calculated above, in a repeat of the same hybridization experiment, one would expect to see 134 calls (803 calls/6 experiments), of which 57 would be false-positive results (0.2323% × 24,392 measures) and 77 would be true CNVs. On the basis of our false-negative rate, we would have missed 64 true CNVs (of 141 true CNVs). Therefore, of a total of 141 true CNVs, the probability of obtaining the same true CNVs in a repeat hybridization should be 54.7% (77 of 141), and the probability of seeing those same CNVs in a second repeat hybridization would be 54.7% × 54.7% (42 of the 141 true CNVs). This represents 84 calls (2 × 42 CNVs) of the 268 expected total calls (134 × 2) (a 31.3% overlap). To verify our calculated rates, three repeat hybridization experiments were performed using the same samples. The observed overlaps of CNV calls between the three possible comparisons were 31.3%, 28.6%, and 31.2%, which is in complete agreement with the expected value. The above calculations are summarized in Figure A1 (online only). Additionally, 20 samples (F1, F2, F3, S1, S3, S4, S7, S8, S10, S11, S12, S14, S16, S17, S33, S38, S39, S40, S41, and S44) from the discovery set were each repeated once with a fluorochrome reversal. The overlapping calls between repeats ranged from 21% to 46%, with an average of 30%, again consistent with the expected value from our false-positive and false-negative rates. Furthermore, we employed an additional platform to verify our CNV calls. We recognize that oligonucleotide arrays are generally not designed for measuring CNVs in certain loci, since many segmental duplications and repeat sequences are excluded from array design, and thus we constructed a custom oligonucleotide array (NimbleGen Systems) covering our 3,654 CNV loci with 389,027 elements (∼2 kb spacing between elements). Five samples (S70, S71, S72, S73, and S80) were assayed using this custom platform. Each of these DNA samples were hybridized against the same single male reference DNA used for BAC array analysis onto the oligonucleotide array. As described elsewhere,14Locke DP Sharp AJ McCarroll SA McGrath SD Newman TL Cheng Z Schwartz S Albertson DG Pinkel D Altshuler DM et al.Linkage disequilibrium and heritability of copy-number polymorphisms within duplicated regions of the human genome.Am J Hum Genet. 2006; 79: 275-290Abstract Full Text Full Text PDF PubMed Scopus (250) Google Scholar to identify gains or losses from the oligonucleotide array, thresholds of 2 SDs of the mean log2 ratio for all elements in the hybridization were used. On the basis of the detection sensitivity of BAC array CGH,15Pinkel D Segraves R Sudar D Clark S Poole I Kowbel D Collins C Kuo WL Chen C Zhai Y et al.High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays.Nat Genet. 1998; 20: 207-211Crossref PubMed Scopus (1744) Google Scholar a moving window size of 19 elements (for a total of ∼40 kb, with ∼2 kb spacing between elements) was applied. In each window, the number of elements reporting a loss (beyond the threshold) was subtracted from the number of elements reporting a gain. The difference was then divided by 19—the total number of elements in the window. Gains or losses were scored for results at >0.1 or <−0.1, respectively. Calls from the oligonucleotide array were then directly compared with CNVs detected by BAC array analysis. To confirm a BAC CNV gain (or loss), at least 10 gains (or losses) were required from the oligonucleotide probe calls covering the same BAC. To obtain the genomic loci of our identified copy-number–altered clones, we used UCSC May 2004 mapping annotations from BACPAC Resources. For comparison, locations of previously identified CNVs obtained from the Database of Genomic Variants and from various publications were also anchored to the UCSC May 2004 assembly (from UCSC Genome Bioinformatics).2Conrad DF Andrews TD Carter NP Hurles ME Pritchard JK A high-resolution survey of deletion polymorphism in the human genome.Nat Genet. 2006; 38: 75-81Crossref PubMed Scopus (519) Google Scholar, 3Hinds DA Kloek AP Jen M Chen X Frazer KA Common deletions and SNPs are in linkage disequilibrium in the human genome.Nat Genet. 2006; 38: 82-85Crossref PubMed Scopus (294) Google Scholar, 4McCarroll SA Hadnott TN Perry GH Sabeti PC Zody MC Barrett JC Dallaire S Gabriel SB Lee C Daly MJ et al.Common deletion polymorphisms in the human genome.Nat Genet. 2006; 38: 86-92Crossref PubMed Scopus (559) Google Scholar These were then converted to elements (i.e., clones) within our clone set by comparison of chromosome number, base-pair start position, and base-pair end position. RefSeq gene information was downloaded from the UCSC May 2004 assembly and was viewed in relation to our CNVs. A gene with any overlap across a CNV boundary was considered to be associated with the CNV. Genes overlapping our CNVs were then used to match genes downloaded from the Online Mendelian Inheritance in Man (OMIM) Morbid Map. The locations of human microRNAs were downloaded from the Sanger miRBase database, were converted to the UCSC May 2004 mapping annotations, and were viewed in relation to our CNVs as described above.16Griffiths-Jones S The microRNA Registry.Nucleic Acids Res. 2004; 32: D109-D111Crossref PubMed Google Scholar BAC clones and segmental duplication data were mapped to the UCSC May 2004 assembly. CNV loci were assessed for duplication content on the basis of whole-genome assembly comparison (WGAC) and whole-genome shotgun sequence detection (WSSD) analyses of hum" @default.
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• W2002079712 date "2007-01-01" @default.
• W2002079712 modified "2023-10-11" @default.
• W2002079712 title "A Comprehensive Analysis of Common Copy-Number Variations in the Human Genome" @default.
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