A large-scale survey of genetic copy number variations among Han Chinese residing in Taiwan

  • Chien-Hsing Lin1,

    Affiliated with

    • Ling-Hui Li2,

      Affiliated with

      • Sheng-Feng Ho2,

        Affiliated with

        • Tzu-Po Chuang2,

          Affiliated with

          • Jer-Yuarn Wu2,

            Affiliated with

            • Yuan-Tsong Chen2 and

              Affiliated with

              • Cathy SJ Fann1, 2Email author

                Affiliated with

                BMC Genetics20089:92

                DOI: 10.1186/1471-2156-9-92

                Received: 04 June 2008

                Accepted: 24 December 2008

                Published: 24 December 2008

                Abstract

                Background

                Copy number variations (CNVs) have recently been recognized as important structural variations in the human genome. CNVs can affect gene expression and thus may contribute to phenotypic differences. The copy number inferring tool (CNIT) is an effective hidden Markov model-based algorithm for estimating allele-specific copy number and predicting chromosomal alterations from single nucleotide polymorphism microarrays. The CNIT algorithm, which was constructed using data from 270 HapMap multi-ethnic individuals, was applied to identify CNVs from 300 unrelated Han Chinese individuals in Taiwan.

                Results

                Using stringent selection criteria, 230 regions with variable copy numbers were identified in the Han Chinese population; 133 (57.83%) had been reported previously, 64 displayed greater than 1% CNV allele frequency. The average size of the CNV regions was 322 kb (ranging from 1.48 kb to 5.68 Mb) and covered a total of 2.47% of the human genome. A total of 196 of the CNV regions were simple deletions and 27 were simple amplifications. There were 449 genes and 5 microRNAs within these CNV regions; some of these genes are known to be associated with diseases.

                Conclusion

                The identified CNVs are characteristic of the Han Chinese population and should be considered when genetic studies are conducted. The CNV distribution in the human genome is still poorly characterized, and there is much diversity among different ethnic populations.

                Background

                The human genome contains many DNA sequence variations, including single nucleotide polymorphisms (SNPs), short nucleotide insertions or deletions, tandem repeat sequences, and transposable elements [1]. Recent human genome studies have revealed that copy number variations (CNVs) are more common than previously thought. Some CNVs are associated with gene expression levels and thus may contribute to phenotypic differences [27]. Each CNV is a DNA segment > 1 kb that shows variation within the population in terms of a deletion and/or an amplification. Although SNPs are regarded as the main source of phenotypic differences among humans, CNVs also have a large impact on differential gene expression [2].

                Microarray-based approaches, so called "molecular karyotyping", have been used to detect subtle chromosomal structure variations on the genome-wide scale [8]. SNP microarrays are high-resolution tools for genotyping that can be used to simultaneously detect copy number (CN) alterations and loss-of-heterozygosity [9, 10]. The copy number inferring tool (CNIT), an algorithm recently developed for use with Affymetrix GeneChips, efficiently predicts regions with subtle CN changes and is based on a hidden Markov model (HMM) [11]. CNIT had higher accuracy and lower variation in CN estimation than other programs, including the copy number analysis tool [9] and the copy number analysis with regression and tree approach [12]. In this study, 100 K GeneChip intensity data from 270 HapMap multi-ethnic individuals were used to determine the parameters of the CNIT algorithm. Intensity data from 300 normal unrelated individuals were then used to predict CNV regions using CNIT.

                Stringent selection criteria were used to classify true and false CN-altered predictions: CN-altered regions found in at least two individuals were classed as CNV regions. A total of 230 copy number-variable regions (CNVRs) were identified in the sample population, 64 of which (27.83%) had a CNV allele frequency ≥ 1%. Of these 230 CNVRs, 133 (57.83%) had previously been reported in the genomic variant database http://​projects.​tcag.​ca/​variation/​. The CNVRs ranged from 1.48 kb to 5.68 Mb (mean = 322 kb), and contained 449 genes and 5 microRNAs (miRNAs). Sixty-three CNVRs (27.39%) were associated with segmental duplications (SDs), which are known to induce non-allelic homologous recombination and produce structural variations.

                Results

                Detection of regions with copy number alterations

                In this study, 646 samples and 5 complicated processes were used to detect genetic regions with CN alterations. A flowchart of the study design is presented in Figure 1. First, the Affymetrix 100 K GeneChip intensity data from HapMap 270 multi-ethnic individuals were used to construct the CNIT algorithm. The intensity data from 376 unrelated Han Chinese individuals in Taiwan were used for CNV region identification. Although the SNP call rate was one of the quality control indexes for the GeneChip experiments, 76 of the 376 Han Chinese individuals (20%) having larger numbers of CN-altered regions were excluded from the following analyses. Using the CNIT algorithm, a total of 13,729 CN-altered events were predicted in the 300 individuals. Certain factors intrinsic to GeneChips are thought to affect probe intensity, so additional criteria were used to eliminate false-positive results. For example, sequence variations within restriction-enzyme cutting sites or SNP probe sequences can directly alter SNP probe intensity and reduce the accuracy of CN estimation. Therefore, quantitative PCR (qPCR) experiments were used to validate more than 200 CNIT-predicted events from the first selection round and the results were analyzed. The following criteria were then used to eliminate false-positive CN-altered findings: (1) the number of consecutive SNPs was required to be ≥ 6 and < 15 withP ≤ 0.05; or (2) the number of consecutive SNPs was required to be ≥ 15. Using these additional criteria, the false-positive rate (FPR) of the validated data was reduced from 0.3 to 0.005; however, 34.15% of true CN-altered events were lost. After filter selection, a total of 4,288 CN-altered events in 549 genomic regions were retained. As an additional measure of cross-platform validation, results from Agilent 244 K comparative genomic hybridization (CGH) arrays from two selected individuals were compared to the results from the CNIT predictions and results were consistent between the two arrays. An example of one extra copy of a genomic segment of chromosome 21 is shown in Figure 2.
                http://static-content.springer.com/image/art%3A10.1186%2F1471-2156-9-92/MediaObjects/12863_2008_Article_658_Fig1_HTML.jpg
                Figure 1

                A flowchart of this study. The parameters of the copy number inferring tool (CNIT) algorithm for single nucleotide polymorphism (SNP) copy number (CN) estimations were determined using a reference group that consisted of 210 unrelated, multi-ethnic HapMap individuals. The emission and transition probabilities of the hidden Markov model were determined using data from 40 HapMap individuals, and were verified using data from 20 additional HapMap individuals. After construction of the CNIT algorithm, data from 300 unrelated Han Chinese individuals in Taiwan were used to predict candidate CN-altered regions. A total of 549 reliable CN-altered regions were identified; 230 regions showed copy number variations (CNVs) in at least two individuals.

                http://static-content.springer.com/image/art%3A10.1186%2F1471-2156-9-92/MediaObjects/12863_2008_Article_658_Fig2_HTML.jpg
                Figure 2

                GeneChip and comparative genomic hybridization (CGH) array data from a Han Chinese individual revealing a single copy-number (CN)-altered segment on chromosome 21. (A) Affymetrix 100 K GeneChip analysis using the copy number inferring tool (CNIT). The size of the amplified segment (red) was 373 kb. There were 17 SNPs within this region (mean CN = 2.70). (B) Agilent 244 K CGH array analysis. The size of the amplified segment (shaded box) was 512 kb based on a threshold z-score of 2.5.

                Regions of copy number variants among Han Chinese population

                Among the 300 individuals, 76 and 224 DNA samples were extracted from peripheral blood and cell lines, respectively. It is known that subtle chromosomal abnormalities can be induced during cell culture. Although all these cell lines were freshly cultured, some chromosomal abnormalities might still have been generated during culture. To exclude this possibility, only those CN-altered regions that were identified in more than two individuals were classed as CNVRs. Of the 549 CN-altered segments, 230 were classified as CNVRs in the Han Chinese population (Additional File 1). Among these 230 CNVRs, 133 (57.83%) had been reported previously and 97 (42.17%) were unique to this study. Ten of these novel CNVRs (10%) were validated using qPCR. Of the 230 CNVRs, 196 (85.22%) had simple deletion alleles, 27 (11.74%) had simple amplification alleles, and 7 (3.04%) were comprised of both deletion and amplification alleles. These CNVRs ranged from 1.48 kb to 5.68 Mb (mean = 322 kb) and covered a total of 2.47% of the human genome. As shown in Figure 3, these CNVRs are widely dispersed throughout the genome, with the exception of chromosomes 20 and X. Moreover, 64 of the 230 CNVRs had a CNV allele frequency greater than 1%. Of the CNVs in this study, 27% were associated with SDs, similar to previous results (24%) [5].

                The individuals in this study shared the same ancestry as HapMap individuals from Beijing, so the CNV regions identified among Han Chinese in Taiwan were compared with those identified among Beijing residents [5]. Sixty CNV regions from the 45 unrelated HapMap Beijing individuals were detected using the 100 K GeneChip and CNIT algorithm, and 12 (20%) of these were also observed in the samples of this study.
                http://static-content.springer.com/image/art%3A10.1186%2F1471-2156-9-92/MediaObjects/12863_2008_Article_658_Fig3_HTML.jpg
                Figure 3

                The copy number variation (CNV) map of a Han Chinese population in Taiwan. There were 230 CNV regions (blue) identified from 300 unrelated individuals. Of these regions, 64 had greater than 1% CNV allele frequency and were classed as copy-number polymorphic regions (red).

                Genes within copy number variation regions

                There were 449 genes located within 81 of the 230 CNVRs, and most of these genes (56.35%) had simple amplifications (Additional File 2). The 449 genes identified are involved in many biological functions, including ion transport, metabolism, and cell-surface functions. Some of the identified genes are involved in potassium and/or sodium transport, such as theKCNT1,KCNJ8,SLC2A6 andSLC16A3 genes, and some are known to be involved in metabolism, such as theAMY genes that catalyze the first step in digestion of dietary starch and glycogen [13]. The copy numbers of theAMY1 gene are positively correlated with salivary amylase protein levels, which might result in differences in the digestion of starchy foods among individuals [14].

                MicroRNAs (miRNAs) have recently been shown to be involved in the regulation of development and physiology in animals and plants [15]. miRNAs can specifically recognize target mRNAs with complementary sequences and cause translational repression or site-specific cleavage of the target. Five miRNAs in four CNVRs were identified in this study (Table 1) and all had previously been reported to have variable CN [5, 7]. Six target genes of these miRNAs had been predicted using the MIRANDA software http://​www.​microrna.​org/​, but none had been validated. Some CN-variable genes have been shown to be correlated with diseases (Table 2), including the egl nine homolog 1 (EGLN1) and dopamine beta-hydroxylase (DBH) genes. TheEGLN1 gene is located on chromosome 1q42.1. Deficiency of this gene causes familial erythrocytosis type 3 (ECYT3) (MIM# 609820), which is characterized by increased serum hemoglobin and hematocrit levels, but normal serum erythropoietin levels. Defects in theDBH gene cause DBH deficiency (MIM# 223360), which is characterized by marked deficits in autonomic and cardiovascular function.
                Table 1

                MicroRNAs in the copy number-variable regions (CNVRs)

                CNVR ID

                Chr

                Position (bp)

                Variant Type

                # of observations a

                miRNA b

                Target Gene (P value) c

                102

                7

                3488259–5891682

                Loss and Gain

                4

                hsa-mir-589

                 

                127

                8

                14713230–14862195

                Loss

                2

                hsa-mir-383

                SNCG (9.71 × 10-6)

                143

                9

                72363501–72631779

                Loss

                2

                hsa-mir-204

                NELF (1 × 10-6)

                PLXDC2 (7.1 × 10-6)

                XYLT1 (1.5 × 10-7)

                212

                17

                72497067–78181864

                Gain

                5

                hsa-mir-338

                 
                     

                hsa-mir-657

                BDKRB2 (6.7 × 10-7) HOXB13 (7.1 × 10-6)

                a Total number of gains and losses observed for a CNVR.

                b MicroRNA overlapping a CNVR based on microRNAs of the UCSC March 2006 assembly.

                c Target gene of microRNA based on MIRANDA software analysis using a 1 × 10-5 cut-off [19].

                Table 2

                Genes in the copy number-variable regions (CNVRs) are associated with disease and disease susceptibility

                CNVR ID

                Chr

                Position (bp)

                Variant Type

                # of observations b

                Gene symbol c

                Disease d

                1 a

                1

                17313441–19641958

                Gain

                4

                ALDH4A1

                Hyperprolinemia, type II

                     

                MRT4

                Mental retardation

                     

                PAX7

                Rhabdomyosarcoma, alveolar

                13 a

                1

                229469402–229804452

                Loss

                11

                EGLN1

                Erythrocytosis, familial, 3

                     

                GNPAT

                Chondrodysplasia punctata, rhizomelic, type 2

                31 a

                3

                15281603–15545420

                Gain

                8

                COLQ

                Endplate acetylcholinesterase deficiency

                38

                3

                130171189–130741211

                Loss

                2

                GP9

                Bernard-Soulier syndrome, type C

                     

                RHO

                Retinitis pigmentosa 4

                40 a

                3

                166493869–167384443

                Loss

                4

                BCHE

                Apnea, postanesthetic

                80

                5

                147335938–147460716

                Loss

                2

                SPINK5

                Atopy

                111

                7

                80115926–80209630

                Loss

                10

                CD36

                Platelet glycoprotein IV deficiency

                120 a

                7

                142035481–142155613

                Loss

                2

                PRSS1

                Trypsinogen deficiency

                121 a

                7

                154254541–155708987

                Gain

                6

                SHH

                Coloboma, ocular

                124 a

                8

                13016678–13270959

                Loss and Gain

                4

                DLC1

                Colorectal cancer

                147

                9

                103072622–103206674

                Loss

                3

                BAAT

                Hypercholanemia, familial

                149 a

                9

                134892783–137859477

                Gain

                8

                ADAMTS13

                Thrombotic thrombocytopenic purpura, familial

                     

                CEL

                Maturity-onset diabetes of the young, type VIII

                     

                COL5A1

                Ehlers-Danlos syndrome, type I

                     

                DBH

                Dopamine beta-hydroxylase deficiency

                     

                SURF1

                Leigh syndrome, due to COX deficiency

                152 a

                10

                49832039–50706976

                Gain

                5

                CHAT

                Myasthenic syndrome, congenital, associated with episodic apnea

                     

                ERCC6

                Cerebrooculofacioskeletal syndrome 1

                156 a

                10

                125651321–126729328

                Loss

                2

                OAT

                Gyrate atrophy of choroid and retina with ornithinemia

                163 a

                12

                2449181–3410784

                Gain

                6

                CACNA1C

                Timothy syndrome

                164

                12

                4794562–5039110

                Gain

                3

                KCNA1

                Episodic ataxia/myokymia syndrome

                167 a

                12

                21603812–21931308

                Loss

                8

                ABCC9

                Cardiomyopathy, dilated, 1O

                     

                GYS2

                Glycogen storage disease, type 0

                     

                KCNJ8

                Prinzmetal angina

                     

                LDHB

                Lactate dehydrogenase-B deficiency

                176 a

                13

                22656237–23918863

                Gain

                2

                SACS

                Spastic ataxia, Charlevoix-Saguenay type

                     

                SGCG

                Muscular dystrophy, limb-girdle, type 2C

                206

                16

                11215600–11869359

                Loss

                4

                LITAF

                Charcot-Marie-Tooth disease, type 1C

                212 a

                17

                72497067–78181864

                Gain

                5

                ACTG1

                Deafness, autosomal dominant 20/26

                     

                ASPSCR1

                Alveolar soft-part sarcoma

                     

                FSCN2

                Retinitis pigmentosa-30

                     

                GAA

                Glycogen storage disease II

                     

                SGSH

                Sanfilippo syndrome, type A

                     

                SOCS3

                Dermatitis, atopic, 4

                215 a

                18

                26241506–27419753

                Loss

                5

                DSC2

                Arrhythmogenic right ventricular dysplasia, familial, 11

                     

                DSG1

                Keratosis palmoplantaris striata I

                     

                DSG2

                Arrhythmogenic right ventricular dysplasia, familial, 10

                     

                DSG4

                Hypotrichosis, localized, autosomal recessive

                217 a

                18

                51204895–51803567

                Loss

                4

                TCF4

                Pitt-Hopkins syndrome

                230 a

                22

                47187524–49023156

                Gain

                4

                ALG12

                Congenital disorder of glycosylation, type Ig

                     

                MLC1

                Megalencephalic leukoencephalopathy with subcortical cysts

                a CNVRs reported in the database of genomic variants.

                b Total number of gains and losses observed for a CNVR.

                c Gene overlapping a CNVR based on ReqSeq of the UCSC March 2006 assembly.

                d Disease or disease susceptibility associated with the gene, according to the OMIM Morbid Map.

                Discussion

                In this report, a newly developed algorithm, CNIT, was used to identify CNVRs in a normal Han Chinese population. A key assumption of the CNIT algorithm construction is that the CN value of each SNP in the reference group should be two; however, this is not always realistic. Some SNPs with CN aberrations were excluded before constructing the CNIT algorithm due to the availability of CNV data from HapMap individuals. When these CNVs in the reference group were excluded, the identification rate of known CNVs in the test group increased from 23.30% to 25.46%. Owing to the stringent criteria in this study (that is, the parameters of the HMM method and the selection criteria), the false-negative rate (FNR) might be high, resulting in an underestimation of the CNV allele frequency. Nevertheless, these stringent criteria largely reduced the FPR and yielded reliable CNV results. Higher resolution microarrays or other technological approaches are needed to further address these structural variations in detail.

                Most of the CNVRs (85.22%) identified in this study were simple deletions; only a few were simple amplifications (11.74%). The divergence might be due to the emission probability (EP) of the HMM method in the CNIT algorithm. The intensity distribution in a cell line with three copies of the X chromosomes was examined using 100 K GeneChip data and the CNIT algorithm. Intensity differences between samples containing gene amplifications (CN = 3) and those containing normal CNs (CN = 2) were less distinct than the difference between samples containing hemizygous deletions (CN = 1) and normal CNs (CN estimations for one, two and three copies of X chromosomes were 1.38 ± 0.42, 2.08 ± 0.47 and 3.03 ± 1.49, respectively). To eliminate false-positive findings, a stringent EP was used for amplifications (the EP for amplifications was the reverse of the EP for deletions), and as a result, some CNVs with amplifications might have been lost in this CNV survey.

                In this large-scale survey of CNVs in a Han Chinese population in Taiwan, 97 CNVRs (42.17%) were unique; other CNVRs have previously been reported in Asian populations [5]. In the current genomic variant database, most CNVs are rare (allele frequency < 3%). Fewer than 200 CNVRs had an allele frequency of > 5% and these CNVRs were detected using different microarray platforms such as Affymetrix 500 K GeneChips and CGH arrays. The probe distribution of the 100 K GeneChip data was quite different from the 500 K GeneChip or CGH array data. Therefore, it was difficult to precisely compare the results across different platforms. The reliability of the CNVRs found in this study was supported by stringent selection criteria, agreement with previous independent studies, qPCR analysis and CGH experiments.

                Some CN-variable genes identified in this study are known to be correlated with diseases, but individuals in the current study were healthy. Disease models and environmental factors are critical to disease etiology, perhaps explaining why the individuals with gene CN changes were healthy. The well-developed CNIT algorithm for 100 K GeneChip data can be used in future studies to identify subtle chromosome abnormalities http://​www.​csjfann.​ibms.​sinica.​edu.​tw/​EAG/​Program/​CNIT/​CNIT.​htm.

                Conclusion

                CNVs have recently been recognized as an important structural variation in the human genome. In this study, 42.17% of the identified CNVRs were novel, indicating that the CNV loci of the human genome are still not fully understood and that there is much diversity among different ethnic populations. Most CNV allele frequencies were low, and only two CNVRs had a frequency greater than 10%. This observation is consistent with results reported previously by Jakobssonet al [4]. The CNVs reported in this study are characteristic of Han Chinese populations and should be considered when genetic studies are conducted.

                Methods

                SNP microarray intensity data

                Data from 376 unrelated individuals were randomly collected from the Taiwanese Cell and Genome Bank [16]; the raw microarray intensity data have been described in previous studies [11, 17]. Individual genotyping was performed by the National Genotyping Center (Academia Sinica, Taipei, Taiwan) using the Affymetrix 100 K GeneChip Human Mapping set (Affymetrix, Santa Clara, CA, USA) according to the manufacturer's instructions. The SNP call rate of these samples examined in this study was 98.04 ± 0.8%.

                In addition, the intensity data from 100 K GeneChip microarrays of 270 individuals were downloaded from the HapMap project website http://​www.​hapmap.​org/​; the CNV data for these individuals were collected previously [5]. These individuals included 30 Caucasian trios, 30 African trios and 90 unrelated Asian individuals. The HapMap reference group, which included 210 multi-ethnic unrelated individuals, was used to determine the parameters of CNIT for SNP CN estimation. Forty offspring were used as a training group for the HMM method, and 20 additional offspring were used as a test group to evaluate the FPR and FNR.

                Copy Number Inferring Tool

                The CNIT algorithm showed good performance in CN estimation of SNPs and DNA segments [11], and well-characterized HapMap samples were used to determine the parameters for CNIT. In both single-point and multipoint CN estimations, results obtained from CNIT showed greater accuracy and reduced FPR and FNR compared with that obtained from other programs. When the smoothing procedure was not used, CNIT could detect small CN-altered regions that were missed with other programs. SNPs located in the reported CNV regions of the corresponding individuals were removed, such that the CN for most of the autosomal SNPs from the reference, training and test groups was two. The 100 K GeneChip consists of two chips, each with > 50,000 SNPs (58,960 for theXbaI chip and 57,244 for theHindIII chip). The reference group was used to process probe selection, estimate the coefficient of preferential amplification/hybridization, and construct a CN distribution for each SNP [11]. After probe selection, 94,587 SNPs (mean intermarker distance = 30.9 kb) were used to represent CN dosage. Finally, EP and transition probabilities of the HMM method were determined using the training and test groups, and were used to identify true CN-altered regions and eliminate false-positive CN inference by considering contiguous SNPs [11].

                CNV validations

                Primer Express Software version 3.0 (Applied Biosystems, Foster City, CA, USA) was used to design PCR primers for the selected target CNVs. Quantitative PCR experiments were performed using the ABI PRISM 7900 Sequence Detector (Applied Biosystems). PCR reactions were performed using the Power SYBR-Green PCR reagent kit (Applied Biosystems) and each reaction mix (25 μl total) contained 2.5 ng genomic DNA for each CNV. qPCR comprised initial denaturation at 94°C for 3 minutes, 40 cycles of denaturation at 94°C for 15 seconds, and a combination of annealing and extension at 60°C for 60 seconds. The fluorescence signal was detected in real-time during the qPCR procedure. The primer pair for the long interspersed nuclear elements 1 (LINE1) sequence was used for normalization [18]. The mean estimated CN was calculated from triplicate PCR reactions for each individual.

                The Agilent 244 K CGH array (Agilent Technologies, Palo Alto, CA, USA) contains 236,000 coding and non-coding sequences at a resolution of 6.4 kb. Amplified DNA (2 μg) was used for Cy5/Cy3 labeling according to the manufacturer's protocol. After array hybridization and scanning, CN-altered regions were identified using CGH analytics 3.4 software (Agilent Technologies) with a threshold z-score of 2.5.

                Statistical analysis

                After CN estimation for each SNP, CN-altered regions were predicted using the HMM package http://​www.​cfar.​umd.​edu/​~kanungo/​software/​software.​htmlbased on single-point results. The means of SNPP values and CN represent the statistical significance and CN of state-changed segments, respectively. The above data analyses were performed using SAS/STAT version 8 software (SAS Institute, Cary, NC, USA).

                Abbreviations

                CGH: 

                comparative genomic hybridization

                CN: 

                copy number

                CNIT: 

                copy number inferring tool

                CNV: 

                copy number variation

                CNVR: 

                copy number-variable region

                DBH: 

                dopamine beta-hydroxylase

                ECYT3: 

                erythrocytosis type 3

                EGLN1: 

                egl nine homolog 1

                EP: 

                emission probability

                FNR: 

                false-negative rate

                FPR: 

                false-positive rate

                HMM: 

                hidden Markov model

                LINE1: 

                long interspersed nuclear elements 1

                miRNA: 

                microRNA

                SNP: 

                single nucleotide polymorphism

                SD: 

                segmental duplication.

                Declarations

                Acknowledgements

                We thank the National Genotyping Center and National Clinical Core, Academia Sinica, Taiwan, for providing DNA samples and genotyping. This project was supported by the National Science Council grant of Taiwan (NSC 95-2314-B-001-013) and the Institute of Biomedical Sciences, Academia Sinica, Taiwan. We also thank the two anonymous reviewers for their constructive suggestions, which have largely improved this manuscript.

                Authors’ Affiliations

                (1)
                Department of Life Sciences and Institute of Genome Sciences, National Yang-Ming University
                (2)
                Institute of Biomedical Sciences, Academia Sinica

                References

                1. Wright AF:Nature Encyclopedia of the Human Genome. New York: John Wiley & Sons, Inc. 2003.,2:
                2. Freeman JL, Perry GH, Feuk L, Redon R, McCarroll SA, Altshuler DM, Aburatani H, Jones KW, Tyler-Smith C, Hurles ME,et al.:Copy number variation: new insights in genome diversity. Genome research 2006,16(8):949–961.View ArticlePubMed
                3. Iafrate AJ, Feuk L, Rivera MN, Listewnik ML, Donahoe PK, Qi Y, Scherer SW, Lee C:Detection of large-scale variation in the human genome. Nature genetics 2004,36(9):949–951.View ArticlePubMed
                4. Jakobsson M, Scholz SW, Scheet P, Gibbs JR, VanLiere JM, Fung HC, Szpiech ZA, Degnan JH, Wang K, Guerreiro R,et al.:Genotype, haplotype and copy-number variation in worldwide human populations. Nature 2008,451(7181):998–1003.View ArticlePubMed
                5. Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, Andrews TD, Fiegler H, Shapero MH, Carson AR, Chen W,et al.:Global variation in copy number in the human genome. Nature 2006,444(7118):444–454.View ArticlePubMed
                6. Sebat 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(5683):525–528.View ArticlePubMed
                7. Wong KK, deLeeuw RJ, Dosanjh NS, Kimm LR, Cheng Z, Horsman DE, MacAulay C, Ng RT, Brown CJ, Eichler EE,et al.:A comprehensive analysis of common copy-number variations in the human genome. American journal of human genetics 2007,80(1):91–104.View ArticlePubMed
                8. Hochstenbach R, Ploos van Amstel HK, Poot M:Microarray-based genome investigation: molecular karyotyping or segmental aneuploidy profiling? Eur J Hum Genet 2006,14(3):262–265.View ArticlePubMed
                9. Huang J, Wei W, Zhang J, Liu G, Bignell GR, Stratton MR, Futreal PA, Wooster R, Jones KW, Shapero MH:Whole genome DNA copy number changes identified by high density oligonucleotide arrays. Human genomics 2004,1(4):287–299.PubMed
                10. Slater HR, Bailey DK, Ren H, Cao M, Bell K, Nasioulas S, Henke R, Choo KH, Kennedy GC:High-resolution identification of chromosomal abnormalities using oligonucleotide arrays containing 116,204 SNPs. American journal of human genetics 2005,77(5):709–726.View ArticlePubMed
                11. Lin CH, Huang MC, Li LH, Wu JY, Chen YT, Fann CS:Genome-wide copy number analysis using copy number inferring tool (CNIT) and DNA pooling. Human mutation 2008,29(8):1055–1062.View ArticlePubMed
                12. Huang J, Wei W, Chen J, Zhang J, Liu G, Di X, Mei R, Ishikawa S, Aburatani H, Jones KW,et al.:CARAT: a novel method for allelic detection of DNA copy number changes using high density oligonucleotide arrays. BMC bioinformatics 2006,7:83. View ArticlePubMed
                13. Oudjeriouat N, Moreau Y, Santimone M, Svensson B, Marchis-Mouren G, Desseaux V:On the mechanism of alpha-amylase. European journal of biochemistry/FEBS 2003,270(19):3871–3879.View ArticlePubMed
                14. Perry GH, Dominy NJ, Claw KG, Lee AS, Fiegler H, Redon R, Werner J, Villanea FA, Mountain JL, Misra R,et al.:Diet and the evolution of human amylase gene copy number variation. Nature genetics 2007,39(10):1256–1260.View ArticlePubMed
                15. Bartel DP:MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004,116(2):281–297.View ArticlePubMed
                16. Pan WH, Fann CS, Wu JY, Hung YT, Ho MS, Tai TH, Chen YJ, Liao CJ, Yang ML, Cheng AT,et al.:Han Chinese cell and genome bank in Taiwan: purpose, design and ethical considerations. Human heredity 2006,61(1):27–30.View ArticlePubMed
                17. Yang HC, Liang YJ, Huang MC, Li LH, Lin CH, Wu JY, Chen YT, Fann CS:A genome-wide study of preferential amplification/hybridization in microarray-based pooled DNA experiments. Nucleic acids research 2006,34(15):e106.View ArticlePubMed
                18. Li LH, Ho SF, Chen CH, Wei CY, Wong WC, Li LY, Hung SI, Chung WH, Pan WH, Lee MT,et al.:Long contiguous stretches of homozygosity in the human genome. Human mutation 2006,27(11):1115–1121.View ArticlePubMed
                19. Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS:MicroRNA targets in Drosophila. Genome biology 2003,5(1):R1. View ArticlePubMed

                Copyright

                © Lin et al. 2008

                This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Advertisement