Large-scale initiatives in sequencing individual genomes have targeted the identification of a broad range of genetic variants, from SNPs to structural genomic variants (including CNVs)1, to identify the genetic factors that contribute to an individual's phenotype. However, current DNA sequencing strategies produce short reads (in a paired-end or non–paired-end manner), which places substantial limitations on accurately identifying structural genomic variants. Here, we have developed an integrated strategy to combine high-resolution array CGH (aCGH) information with whole-genome sequencing data to comprehensively identify and characterize common CNVs within three Asian populations.

We applied this strategy to the genomic DNA of 30 Asian females (ten Koreans (KOR), ten HapMap Chinese (CHB) and ten HapMap Japanese (JPT) individuals) to develop an accurate and comprehensive common CNV map for Asian populations that would complement a recently published CNV map for West African and European populations2. The genomic DNA of all 30 individuals was applied to a custom-designed aCGH platform comprising 24 million oligonucleotide probes (Supplementary Fig. 1). The platform was empirically determined to have an effective resolution to detect CNVs as small as 438 bp. The reference DNA source for our aCGH experiments in this study was the control individual (NA10851) used in previous CNV discovery studies3,4,5 (Fig. 1). We estimated that studying 30 individuals would provide 95.4% power to detect CNVs with a minor allele frequency of 5% among Asian populations.

Figure 1: Overview of the CNV discovery project for Asian populations.
figure 1

The genomic DNA from ten Altaic Korean individuals, ten CHB HapMap individuals of Chinese ancestry, ten JPT HapMap individuals with Japanese ancestry and three platform-control comparison resource individuals (AK1, NA12878 and NA19240) were used for aCGH experiments. Genome sequence data from three subjects (AK1, AK2 and NA10851) were used to filter out false positive CNV calls and to obtain absolute CNV calls.

Among the 30 Asian individuals studied, we discovered 251,573 putative CNV segments. For algorithmic training purposes, we then conducted aCGH experiments on DNA from a Korean male, AK1, for whom whole-genome sequencing data was already available at 27.8× coverage5, and DNA from a Korean female, AK2, whose genome we sequenced at 32.0× coverage (data not shown, see URLs). Because all aCGH experiments used NA10851 as a reference DNA source, we also sequenced this individual at 28.3× coverage (see URLs). Using read-depth information from AK1 and NA10851, we developed criteria to optimize the concordance between aCGH and the DNA sequencing information. We subsequently used this read-depth data to further test and validate our filtering criteria (Supplementary Tables 1 and 2 and Supplementary Fig. 2). The filtered data included a total of 21,905 CNV segments from 30 Asian individuals. We found that smaller CNVs (<5 kb) with low log2 ratio were for the most part removed based on our filtering criteria.

Comparison of the aCGH results from the DNA of AK1 and AK2 with the read-depth information from whole-genome sequencing data on the same individuals suggested that approximately half of the CNV segments that were identified in AK1 and AK2 (as having relative genomic gains and losses by array CGH) actually had a copy number state of 2 in these individuals and a copy number state of <2 or >2 in the reference individual, NA10851 (Fig. 2a and Supplementary Figs. 3 and 4). The read-depth sequence information from NA10851 revealed that 10,980 out of the filtered 21,905 CNV segments might have been erroneously called because the control sample from NA10851 had copy number values not equal to 2 in these genomic regions (Fig. 2b and Supplementary Note). Out of the 10,980 'obscure' CNV segments, in which NA10851 had copy number other than 2, 4,970 were determined to have a diploid copy number of two in the test individual (NA10851) after we converted them to absolute copy number states and removed them (Supplementary Fig. 4c–e). The remaining 6,010 CNV segments were found to have different copy number states from that predicted by aCGH (Fig. 2a). We also identified an additional 3,164 covert CNV segments that were initially missed by the aCGH experiments owing to their having identical copy numbers both in the test sample and NA10851(Supplementary Fig. 4f–i). By this method, we obtained absolute copy number states for a total of 20,099 CNV segments (Supplementary Table 3), in which 9,174 (3,164 from covert calls and 6,010 from obscure calls) were corrected by the read-depth sequence information for NA10851 (Fig. 2b).

Figure 2: Original approach for calling absolute copy number status.
figure 2

(a) Right: The top, panel shows aCGH data for a genomic region on chromosome 3 in AK1 as compared to the reference sample, NA10851. The second panel down shows read-depth information for the same genomic region, derived from whole-genome sequencing data of NA10851. The third panel down is a 'corrected' absolute copy number result for this genomic region in AK1 using the absolute copy number algorithm analysis method described in this study. The bottom panel displays the same genomic region for AK1 using read-depth information derived from whole-genome sequencing data. (b) Comparison between relative copy number states and absolute copy number values for CNV segments, before and after corrections for NA10851 copy number states. Out of the total 21,905 CNVs identified in the 30 Asian individuals by aCGH (that is, by relative copy number states), the relative copy number values of 10,925 were not affected by CNVs in the NA10851 reference. Among 10,980 'obscure' CNVs, 4,970 were determined to be non-CNVs by absolute calls and were removed from the final list of CNVs. An additional 3,164 CNVs, which were considered 'covert' CNVs and were initially missed by the aCGH experiments, were also identified by the absolute copy number state calling algorithm.

This correction also changed the ratio of total count of copy number losses and gains, with 72.6% of all variants actually having a copy number of <2 per diploid cell (copy number loss), as compared to 44.4% before correction (Fig. 3a,b and Supplementary Fig. 5). This corrected ratio is more compatible with a recent report by Conrad et al.2, in which researchers obtained absolute copy number states by clustering aCGH data from 450 samples. The average lengths of CNV segments with copy number losses and gains were 11.8 kb and 30.3 kb, respectively (Fig. 3b). In genic regions, copy number gains were more frequently recorded than copy number losses, which may be due to the fact that in these regions copy number gains are less likely to be deleterious and therefore less likely to incur a penalty in evolutionary selection6.

Figure 3: Frequency of copy number gains and losses among 33 individuals.
figure 3

(a) Distribution of absolute copy number gains (copy number >2) and losses (copy number <2) in 33 individuals. (b) Distribution of relative and absolute copy number gains and losses by CNV size. The x and y axes represent size and number of CNV segments, respectively.

There were 20,099 CNV segments ultimately identified in this study. On the average, 670 CNV segments were found in each Asian individual studied, which covered 11.31 Mb of the total DNA sequence and involved 389 RefSeq genes per person (Supplementary Table 4). We randomly selected 116 CNV elements and performed 1,881 quantitative PCR (qPCR) experiments on them. A total of 1,717 of the qPCR experiments were correlated with our aCGH data, resulting in a predictive value of 91% (Supplementary Tables 5 and 6).

To compare CNV segments between individuals, CNV segments from this study were merged into groups, termed 'CNV elements' (CNVEs), based on greater than 50% overlap between segments (Supplementary Fig. 6). We obtained absolute copy number states for 5,177 Asian CNVEs, with the group of CNVEs having a median size of 2,667 bp (Supplementary Table 7) and covering 95.40 Mb (3.32%) of the human reference genome. To identify potential Asian-specific CNVEs, we compared these 5,177 CNVEs with 4,978 CNVEs recently identified by Conrad et al.2. Although those researchers found 56 Asian-specific CNVEs, we identified 3,547 putative Asian-specific CNVEs that were not included in their dataset (Fig. 4a and ref. 2).

Figure 4: Putative Asian population–specific copy number variants.
figure 4

(a) Venn diagram showing validated putative Asian-specific CNVEs. The lower part of the figure (blue) indicates the ethnic distribution of 4,959 CNVEs that were discovered by a 42M NimbleGen aCGH platform and validated with a genotyping microarray in the same study2. The upper part of the figure indicates that 3,547 out of 5,177 CNVEs found among the 30 Asian individuals in this study do not reach a 1-bp overlap with CNVEs recently found by the Genome Structural Variation Consortium2. The Genome Structural Variation Consortium reported that they found 4,978 validated CNVEs, but we show only 4,959 of them in this Venn diagram because 19 were nonpolymorphic. (b) Distribution of gene ontology categories for genes in which coding sequences overlap with common copy number–gain regions (outer circle) and copy number–loss regions (inner circle) identified from 30 Asian subjects. (c) CNVE location and number of Asian individuals involved (bar graph, right). Red, copy number gain; green, copy number loss. Selected genes and miRNAs are also shown on the left.

The CNVEs identified in our study overlapped with 2,913 RefSeq genes and 1,483 genes present in the OMIM database (Supplementary Tables 7 and 8). These CNVEs were also responsible for copy number changes of 29 microRNA (miRNA) genes (Supplementary Table 9) as well as 35 potential gene fusions (Supplementary Table 10).

We categorized genes overlapping the common CNVEs found in our study using the PANTHER gene ontology (see URLs). Copy number gains had an increased bias toward being contained within genes having functions associated with nucleic acid metabolism and developmental processes. Genic copy number losses were enriched for genes involved in cell adhesion. Subsets of genes involved in signal transduction, immunity and sensory perception were found to have both copy number gains and losses, which is consistent with previous findings involving nonsynonymous SNPs and deletion polymorphisms6,7 (Fig. 4b and Supplementary Table 11).

Notably, 2,183 of the 2,913 RefSeq genes that we identified as being copy number variable among the Asian individuals studied did not appear to be copy number variable in the populations studied by Conrad et al.2. For example, CNVs in CLPS, LPA and CEBPB, which have been reported to be involved in type 2 diabetes, myocardial infarction and cancer, respectively, are found at a frequency of ≥10% among Asian populations, which are not found in the Conrad et al. study2,7,8. Some genes, such as LY9, CNTN5 and PIK3CA, which have been reported to be involved in systemic lupus, cardiovascular disease and oncogenesis, respectively9,10,11,12, were first found to be copy number variable either in this study or in our previous report of the whole genome sequence of AK1 (ref. 5). Examples of genes with CNVs are shown in Figure 4c and Table 1.

Table 1 Summary statistics of selected copy number variants

Because we used a new approach to call absolute copy number states, some of our data showed different genetic variation frequencies than previous reports. For example, we identified copy number losses (specifically, a one-copy loss) in 3 out of 30 Asian subjects in RHD, whereas Conrad et al.2 found copy number gains in all of the 88 Asian individuals genotyped. A deletion in the gene encoding RhD, which is one of the Rhesus blood antigens, results in an RhD-negative blood type when both copies of the gene are deleted13. The incidence of the homozygous deletion resulting in an RhD-negative blood type is between 0.3% and 0.5% in Asians, which is consistent with our observed 10.0% (3 individuals out of 30 total) one-copy loss rate14. Examples of other discrepancies with data obtained from the study conducted by Conrad et al.2 are listed in Supplementary Table 12. Further studies comparing and validating different absolute calling methods, including the one used here, will be required to design the most efficient and accurate method for determining absolute copy number genotypes.

Because the method proposed in this study uses read-depth sequence information from NA10851, which is one of the most commonly used control samples in aCGH, it should be amenable to other aCGH studies that use NA10851 as the control sample. One example in which absolute copy number states could be determined from data generated from other aCGH platforms is shown in Supplementary Figure 7. Alternatively, if an individual other than NA10851 was used as a control sample in aCGH experiments, read-depth sequence information for that individual could subsequently be used to generate an absolute copy number calling algorithm for those studies.

We also developed a custom 180k CNV genotyping array and used this array to simultaneously examine 17,760 CNVs in a large Asian family comprising 13 individuals across three generations. Using relative copy number information, Mendelian inconsistencies for copy number deletions were estimated at a rate of 6.42%. Using absolute copy number information, the Mendelian inconsistencies for copy number deletions dropped to 2.59% (Supplementary Fig. 8), which is probably closer to the true rate of de novo formation of CNVs at this resolution.

Because the Conrad et al.2 dataset used a different aCGH platform than was used here, we considered whether the unique CNVEs found in our study were platform specific. There are some differences between the Agilent aCGH platform used in this study and the NimbleGen array used in the previous study by Conrad et al.2. The Agilent platform we used excluded areas of repetitive sequence using a homology filter, whereas 43% of the NimbleGen probes are in repetitive regions (Supplementary Fig. 1). The use of the Agilent platform in this study resulted in an effectively smaller portion of the genome being assayed for CNVs and resulted in a lower false positive rate. The Agilent and NimbleGen platforms identified 722 and 1,829 CNVs, respectively, in AK1 (Supplementary Fig. 7). Four hundred and fifty CNVs were common to both platforms. Of the 1,282 CNVs specific to the NimbleGen array, 655 were in moderately to highly repetitive regions, whereas 424 had log2 ratios that did not meet the more stringent filter criteria established for the Agilent array. Consequently, only 203 NimbleGen-specific CNVs were relevant in this comparison. Further comparison of CNVs identified in NA12878 (from CEU) and NA19240 (from YRI) revealed that 60% of the calls on the Agilent 24M array were common to the Conrad et al. study, whereas 40% of the calls were specific to our platform only (Fig. 5 and Supplementary Fig. 9). Taken together, these comparisons indicate that 40% of the Agilent 24M CNV calls may be platform dependent and would not be captured by the NimbleGen 42M array. This suggests that about 60% out of the 3,547 potential Asian-specific CNVEs used here are truly specific to the Asian population and were missed in Conrad et al.2 due to the lack of samples from Asian individuals used in their CNV discovery phase.

Figure 5: Effect of aCGH platforms in the CNV discovery.
figure 5

Absolute CNVs found from two HapMap individuals (NA12878 and NA19240) in this study using the Agilent 24M aCGH array were compared with CNVs found by genotyping microarray in the Genomic Structural Variation Consortium data. We also compared CNVs from AK1 obtained by Agilent 24M and Nimblegen 42M microarrays.

In summary, we have comprehensively identified common CNVs (minor allele frequency > 1.7%) among individuals in Asian populations at a resolution sufficient to detect CNVs as small as 438 bp using an integrated high-resolution aCGH approach combined with next-generation sequencing data. The discovery of many more new CNVs in our study, despite the large number of previous studies in this area, is most likely due to the increased resolution and comprehensive nature of our aCGH platform design, which targets smaller variants, combined with a focus on Asian populations, which to date have been relatively neglected in CNV studies15,16,17,18. We also provide a paradigm for large-scale genome sequencing initiatives, such as the 1000 Genomes Project (see URLs), to combine DNA sequencing data with high-resolution CNV mapping via aCGH for more accurate CNV identification and characterization in individual genome sequences. To more accurately apply CNV research to personalized medicine, copy number genotyping must not rely on relative copy number data, but should be able to identify the absolute copy number state in any given individual. Our results also provide guidance for future studies in genomic medicine in the Asian population, especially in those that identify ethnic differences in predisposition to disease and drug response19,20,21,22,23.


Sequence data for AK1, AK2 and NA10851,; PANTHER gene ontology,; 1000 Genomes Project,



DNA from 10 Korean and 13 Mongolian subjects in the same family was obtained from intravenous blood samples using guidelines approved by the Institutional Review Boards of Macrogen (MG_IRB_090105) and Seoul National University (approval C-0806-023-246-555). Informed consent was obtained from all subjects. All other DNA samples, including those from ten HapMap Chinese subjects (CHB), ten HapMap Japanese subjects (JPT), two CEU subjects and one YRI DNA subject were obtained from Coriell Cell Repositories (Coriell Institute). A single control individual (NA10851, a male from the CEU population) was used for all the aCGH experiments as a reference sample.

24 million probe aCGH array.

Design. We designed a 24-chip whole-genome tiling array containing 24 million 60-mer oligonucleotide probes. Probe sequences were based on the human genome reference sequence (hg18, see URLs) and a set of human-specific oligonucleotide probes ranging from 45–60 bp in size were obtained from Agilent Technologies (e-array; see URLs). These probe sequences were similarity filtered such that each probe mapped to a unique location in the genome. Probes were also filtered for repeat content (using RepeatMasker version 20050112, Repbase Update 9.11). The remaining set of 'high-quality' sequences consisted of 23,215,944 probes spread in a partially uniform (due to the filtration of repeat contents, see Supplementary Fig. 1) pattern across the genome. These were then ordered by chromosome and chromosomal start position, and individual custom Agilent 1M arrays (containing approximately 1 million probes per slide) were designed by tiling across each chromosome until the maximum number of features were obtained for each array. An additional 11,488 genome-wide normalization probes and 1,000 replicate probes (replicated five times) were included on each array.

aCGH experiments and training the filtering criteria for aCGH CNV calls. aCGH experiments were conducted according to the manufacturer's instructions. Images were analyzed with Feature Extraction Software (Agilent Technologies) using the CGH-105_Jan09 protocol for background subtraction and normalization. The ADM2 statistical algorithm was used to identify CNVs based on the combined log2ratios.

To select parameters for calling CNVs (that is, the statistical threshold of the ADM2 algorithm, the minimum ± log2 ratio and the minimum number of consecutive probes in a CNV interval), we calculated the sensitivity and positive predictive value based on the comparison of aCGH-based CNV calls (using our high-resolution Agilent 24M platform) with read-depth sequence data for two samples from Korean individuals (AK1 and AK2). In order to train the CNV filtering criteria, we first needed to identify genome-wide true CNVs. True CNVs were determined by comparing aCGH log2 ratios with the ratio of sequence read-depth data in the corresponding regions of the Asian individual (AK1) and the reference individual (NA10851). Filter conditions were set by adjusting the log2 ratio thresholds and P values to minimize false positives while maximizing the number of true positives (see Supplementary Note).

Absolute CNV calling. We attempted to obtain 'absolute' copy number status of the sample from NA10851, which was used as the reference sample for aCGH experiments in this study. For this, we used read-depth data for NA10851 obtained from massively parallel sequencing by the Illumina GA II data. The read-depth data represent the copy number status of NA10851 as compared to the human reference genome (hg18) because the short read sequences were aligned to hg18. First, we selected 1,007 candidate copy number–variant loci from 70 aCGH experiment data (30 from this study using 24M Agilent array sets and 40 from SGVC using 42M NimbleGen array sets; see Supplementary Note for detailed explanation). Sequence read-depth data for these regions were used to determine the copy number status of NA10851.

Based on the 'absolute' copy number status of NA10851, filtered CNV calls from aCGH experiments were categorized into 'overt' calls (in which NA10851 had normal read depth; Supplementary Fig. 4a,b) and 'obscure' calls (in which NA10851 seemed to have different copy numbers from the human reference genome, hg18, because it showed aberration in read depth; Supplementary Fig. 4c–e) (Fig. 2b). Log2 ratios of obscure calls were adjusted using NA10851 sequence read-depth data to obtain absolute CNVs based on the human reference genome (hg18) rather than NA10851 (Fig. 2a). In addition, covert CNVs (Supplementary Fig. 4f–i), which were not identified by aCGH owing to their having identical copy numbers in both the test sample of this study and NA10851, were reinstated as CNVs (see Supplementary Note for more details of the absolute call algorithm).

180k probe aCGH array.

Design. Next, we designed a CNV-targeted aCGH platform using the 4 × 180k format on SurePrint G3 Human CGH Microarrays. The 180k format (custom Agilent arrays) provides more than 170,000 probes on one quarter of a microscope glass slide and allows for the interrogation of thousands of known CNV regions simultaneously in a single sample. Recently, a large amount of high resolution CNV data has become available from a number of distinct research projects by Conrad et al. (2009)2, Kim et al. (2009)5 and the 1000 Genome Project, which have complemented and supported variants previously identified in the Database of Genomic Variants (Supplementary Fig. 10). The union of these regions represents the largest and most accurate set of CNVs compiled to date, and therefore they are well suited as targets for this array (Supplementary Fig. 11).

We first used the set of 8,599 CNVs that were identified by the Genome Structural Variation Consortium. Next, we included regions from the 4,317 deletions released in June 2009 as part of the 1000 Genomes Project. Third, we incorporated CNVs discovered through the use of both a high resolution 24M feature probe set as well as the analysis of very deep sequence coverage on the genome of a single individual (AK1). We additionally included a set of known segmental duplications and new sequences identified in the HuRef genome. Lastly, we included the regions catalogued in Database of Genomic Variants (see URLs) that do not overlap with the abovementioned datasets.

qPCR validation and breakpoint validation studies.

All 30 samples from Asian women, together with the reference sample NA10851, were used in validation studies by real-time qPCR using SYBR green dye in 116 preselected CNV regions. RNase P was used as an endogenous control locus. The list of primers and sequence information are shown in Supplementary Table 5a.

SYBR green validations were run on an Applied Biosystems 7900HT Fast Real-Time PCR instrument. SYBR Premix Ex Taq #RR041A was ordered from Takara Bio Inc. The conditions for the qPCR experiments were 5 ng of genomic DNA, 2× of SYBR, 50× of ROXII reference dye and 10 μM of primers in a 20 μl total reaction volume. Each experiment was run in triplicate. PCR reactions were incubated for 2 min at 95 °C followed by 40 cycles of 5 s at 95 °C and 30 s at 60 °C. Data was collected and processed by SDS 2.3 software (Life Technologies) provided by the manufacturer and subsequently analyzed by Microsoft Excel. Fold change for each sample relative to the NA10851 was calculated using the standard ΔΔCt method.

The real time PCR and aCGH results were compared for our validation. In each validation region, samples were clustered into three groups (CN loss, no change and CN gain) by ΔΔCt values and the corresponding log2 ratios independently, and a 3 × 3 table was generated. With this table, we calculated the predictive value and false discovery rate of SYBR green real time PCR validation (Supplementary Table 6).

We validated 42 pairs of CNV deletion breakpoints using Sanger sequencing. We validated these regions using genomic DNA from each sample, which were then amplified by PCR with flanking primers targeted against the ends of each region (Supplementary Table 5b). PCR amplification was performed in 50 μl total volume with 50 ng genomic DNA, 10 pmol of forward and reverse primer each, standard volume of Ex Taq (Takara Bio), Ex Taq buffer (Takara Bio) and dNTPs (Takara Bio) at 95 °C for 10 min, 40 cycles of 95 °C for 30 s, 60 °C for 30 s, 72 °C for 30 s and, finally, 72 °C for 10 min. PCR products were purified with the AccuPrep PCR purification kit (Bioneer), and the purified products were sequenced on ABI 3730xl DNA analyzers using ABI BigDye Terminator cycle sequencing (Applied Biosystems). Quantitative PCR experiments were performed by Psoma Therapeutics Inc. and DNA sequencing experiments for breakpoints were conducted by Macrogen Inc. See URLs for raw data from aCGH experiments and a complete list of absolute CNV segments.

Comparison study using Genome Structural Variation (GSV) Consortium data.

To identify potential Asian population–specific CNV loci, we compared our 5,177 CNVEs from 30 Asian women with the 4,978 nonredundant CNV loci used by the GSV to genotype 450 HapMap individuals2. Of the 4,978 loci, 19 were removed from our consideration because they were not polymorphic in any of the 450 individuals. Asian-specific CNVEs were determined by exclusion that do not overlap with GSV CNVs at all.

Gene annotation.

We used RefSeq Genes data (hg18 assembly) downloaded from the UCSC annotation database for gene annotation analysis (accessed 3 July 2009). We reported genes that overlapped by at least 1 bp at the CNV segments or elements and defined a promoter region as 500 bp forward and backward of a gene. We used miRBase data (see URLs; accessed September 2009) for identifying miRNA involved by our CNVs.

Gene Ontology.

We used PANTHER ontology for classifying genes in which coding sequences overlap with common CN gains or CN losses (see Supplementary Note for more details).


Hg18 human reference sequence,; Agilent e-array,; Database of Genomic Variants,; raw data from aCGH experiments and a list of CNV segments,; miRBase data,

Accession codes.

Massively parallel sequencing data of AK1, AK2 and NA10851 have been deposited in the NCBI short read archive under accession number SRA008370, SRA010321 and SRA010320, respectively. Array CGH data have been deposited in the NCBI GEO (gene expression omnibus) under accession number GSE19651.