Comparative analysis of copy number detection by whole-genome BAC and oligonucleotide array CGH

  • Nicholas J Neill1,

    Affiliated with

    • Beth S Torchia1,

      Affiliated with

      • Bassem A Bejjani1,

        Affiliated with

        • Lisa G Shaffer1 and

          Affiliated with

          • Blake C Ballif1Email author

            Affiliated with

            Molecular Cytogenetics20103:11

            DOI: 10.1186/1755-8166-3-11

            Received: 16 March 2010

            Accepted: 29 June 2010

            Published: 29 June 2010

            Abstract

            Background

            Microarray-based comparative genomic hybridization (aCGH) is a powerful diagnostic tool for the detection of DNA copy number gains and losses associated with chromosome abnormalities, many of which are below the resolution of conventional chromosome analysis. It has been presumed that whole-genome oligonucleotide (oligo) arrays identify more clinically significant copy-number abnormalities than whole-genome bacterial artificial chromosome (BAC) arrays, yet this has not been systematically studied in a clinical diagnostic setting.

            Results

            To determine the difference in detection rate between similarly designed BAC and oligo arrays, we developed whole-genome BAC and oligonucleotide microarrays and validated them in a side-by-side comparison of 466 consecutive clinical specimens submitted to our laboratory for aCGH. Of the 466 cases studied, 67 (14.3%) had a copy-number imbalance of potential clinical significance detectable by the whole-genome BAC array, and 73 (15.6%) had a copy-number imbalance of potential clinical significance detectable by the whole-genome oligo array. However, because both platforms identified copy number variants of unclear clinical significance, we designed a systematic method for the interpretation of copy number alterations and tested an additional 3,443 cases by BAC array and 3,096 cases by oligo array. Of those cases tested on the BAC array, 17.6% were found to have a copy-number abnormality of potential clinical significance, whereas the detection rate increased to 22.5% for the cases tested by oligo array. In addition, we validated the oligo array for detection of mosaicism and found that it could routinely detect mosaicism at levels of 30% and greater.

            Conclusions

            Although BAC arrays have faster turnaround times, the increased detection rate of oligo arrays makes them attractive for clinical cytogenetic testing.

            Introduction

            Molecular cytogenetic techniques such as array-based comparative genomic hybridization (aCGH) have revolutionized cytogenetic diagnostics and, in turn, the clinical management of patients with developmental delays and multiple congenital anomalies [1, 2]. These rapid, high-resolution, and highly accurate techniques have identified numerous previously unrecognized chromosomal syndromes [38], refined critical regions for established genetic defects [9], and broadened our view of the "normal" diploid genome [10]. In addition, aCGH has given the clinician a greater appreciation of variability in the clinical presentation of many well-described conditions [11, 12] and allowed for the discovery of new conditions with relatively mild phenotypes [13, 14]. Furthermore, the application of aCGH has created a paradigm shift in genetics that has moved the description and discovery of genetic conditions from the "phenotype-first" approach, in which patients exhibiting similar clinical features are identified prior to the discovery of an underlying etiology, to a "genotype-first" approach, in which a collection of individuals with similar copy-number imbalances can be examined for common clinical features [15].

            Originally, targeted microarrays constructed from bacterial artificial chromosomes (BAC) were developed for the clinical laboratory because of their ability to clearly identify copy number changes in discrete regions of the human genome known to play a role in genetic disease [16]. This "less is more" idea prevailed in the early years of clinical aCGH because the technology was new and proof of principle was required before it could be adopted for more widespread diagnostic use. Furthermore, the identification of copy number alterations of unclear clinical significance was considered undesirable to the diagnostician, the ordering physician, and the patient's family. Recently, the coverage of microarrays has expanded to include more comprehensive coverage of the human genome, leading many to suggest that whole-genome BAC or oligo arrays are the next step in the continued improvement in the detection rate of cytogenetic abnormalities.

            It has been presumed that whole-genome oligonucleotide arrays, because they have higher resolutions, would detect more copy number aberrations than whole-genome BAC arrays. However, to our knowledge, there has not been a systematic comparison of these two whole-genome copy number screening technologies in a clinical diagnostic environment. Therefore, to determine which platform is most effective in identifying clinically significant DNA copy number alterations, we designed a whole-genome BAC array and a whole-genome oligo array and compared the results in a blinded study of 466 clinical diagnostic specimens. In addition, we prospectively evaluated 3,443 patients by the whole-genome BAC array and 3,096 patients by the whole-genome oligo array and compared the detection rates of clinically significant abnormalities and those of unclear clinical significance. Finally, we validated our oligo array with 48 cases to determine the level of mosaicism that can be reliably detected and compared that level to our previously published cases analyzed using the BAC array.

            Materials and methods

            Whole-genome BAC array design and aCGH

            We constructed a whole-genome BAC array designed for clinical diagnostic use using >4,600 BAC clones. All clones were validated by FISH prior to inclusion on the array using previously described validation procedures [16]. Contigs of 3-6 overlapping clones were selected to cover 1,543 genetic loci, including >150 known microdeletion/microduplication syndromes and increased density of coverage in the 5-10 Mb surrounding the subtelomeric and pericentromeric regions of the genome. In addition, we placed contigs to cover >500 functionally significant genes such as transcription factors and other genes known to play important roles in development. This coverage also includes genome-wide representation with at least one contig in nearly every chromosomal band at the resolution of an 850-band karyotype. The mean gap size for the whole-genome BAC array is ~1.6 Mb. Microarray manufacturing and aCGH analysis using the whole-genome BAC array were performed as previously described [13]. BAC arrays were analyzed after a dye-swap, two-experiment analysis [16], using sex-mismatched controls. Results were then displayed using custom BAC aCGH analysis software (Genoglyphix™; Signature Genomic Laboratories, Spokane, WA).

            Whole-genome Oligonucleotide Array Design and aCGH

            Oligonucleotide-based microarray analysis was performed using a custom-designed, 105K-feature whole-genome microarray manufactured by Agilent Technologies (Santa Clara, CA) with one probe every 10 kb in regions of interest--microdeletion/microduplication syndromes, the pericentromeric regions, subtelomeres and genes involved in important developmental pathways--for an average of 50 oligos per clinical locus. In addition, to achieve backbone coverage, we placed a probe, on average, every 35 kb throughout the rest of the genome between the regions of interest. Genomic DNA labeling was performed as described for BAC arrays, whereas array hybridization and washing were performed as specified by the manufacturer (Agilent Technologies). A dye swap was not performed for the oligo arrays, and sex-matched controls were used. Arrays were scanned and analyzed as previously described [17]. Results of aberration calls consisting of five or more consecutive oligos were then displayed using custom oligonucleotide aCGH analysis software (Genoglyphix™; Signature Genomic Laboratories). The use of five consecutive oligos achieved a resolution of 40 kb in the regions of interest and a resolution of 140 kb in the backbone.

            Decision Algorithm for Clinically Significant Copy Number Reporting

            We developed a decision algorithm for classifying clinically significant copy number alterations, alterations of unclear clinical significance, and alterations of no currently known clinical significance. Alterations that were associated with established chromosomal syndromes, were large and affected a significant amount of gene content, or were part of a complex rearrangement such as an unbalanced translocation, insertion, or marker chromosome were characterized as clinically significant. Alterations with unclear clinical significance were most commonly those which were not currently associated with a syndrome but which affected gene content which may have contributed to the patient's phenotype and those which could not be precisely refined by the BAC array. Alterations were considered to have no known clinical significance if they were small, affected minimal gene content, and/or were present in regions where common copy-number variation was known to occur in the general population. Signature's own Genoglyphix Chromosome Aberration Database (GCAD) was used as a reference to assist in the interpretation of each alteration. GCAD is a database of >11,000 chromosomal abnormalities identified in >9,500 patients out of >40,000 patients evaluated by our laboratory and contains detailed statistics of each observed alteration (breakpoint coordinates, size, gene content, etc.) as well as clinical information pertaining to patient referral.

            Fluorescence in situ Hybridization (FISH)

            When possible, all copy number alterations detected by microarray analysis were visualized by interphase and/or metaphase FISH using a BAC probe located within the region of gain or loss. FISH was performed as previously described [18].

            Patient Clinical Testing

            To validate the custom-designed 105k oligo array compared to the whole-genome BAC array, 466 cases were run side-by-side in a platform comparison study. In each case, the clinically validated BAC array results were used for interpretation and reporting. Specimens with known chromosome abnormalities, parental specimens, and prenatal cases were excluded from the analysis.

            In addition, we conducted a prospective study of 3,443 consecutive BAC microarray analyses and 3,096 consecutive oligo microarray analyses in our clinical laboratory. The array platform used for testing in each case was chosen by the referring physician at the time of sample submission to our clinical diagnostic laboratory. Cases with previously known chromosomal abnormalities, parental samples, and prenatal specimens were again excluded from the data collection.

            Mosaicism Assessment

            The ability of the oligo platform to detect mosaicism was assessed on 48 patients previously known to carry mosaic abnormalities at levels as low as 5%. The alterations studied included a variety of interstitial, terminal, and whole-chromosome copy-number abnormalities, as well as marker chromosomes. The mosaic alteration in each patient was initially assessed by BAC array and the level of mosaicism determined by interphase FISH analysis when possible. In a separate experiment, mosaicism was assessed using a dilution of cells from a male with trisomy 21 with normal male control cells, as previously described [19]. After FISH verification of the dilutions, DNA was extracted from the diluted cells, labeled and hybridized to the custom-designed oligo array as described above.

            Results

            Platform comparison study

            From the 466 cases analyzed by the BAC array, using the previously described algorithm, we excluded 347 cases that only had aberrations located within regions that contained no genes and/or aberrations that had been established to be normal population variants by Signature Genomic Laboratories or identified in the Toronto Database of Genomic Variants (DGV, http://​projects.​tcag.​ca/​variation/​). After these cases were excluded, 138 copy number alterations in 119 cases (25.5% of the original 466 cases) remained that required FISH analysis. These aberrations included subtelomeric and pericentromeric gains for which FISH was required to exclude an unbalanced translocation or a marker chromosome. After FISH was performed, 60 aberrations in 52 cases were classified as normal variants because marker chromosomes and derivative chromosomes were not identified and because these alterations were located within regions where common copy number variation is known to occur. Thus, alterations of potential clinical significance according to our algorithm were identified in 67 cases, a detection rate of 14.4%. Of these cases, 56 (12.0%) were considered to contain clinically significant copy number alterations (Table 1), and 11 (2.4%) were considered to contain copy number variants of unclear clinical significance for which parental analyses were recommended to further clarify the abnormality (Table 2). aCGH and FISH analysis performed on parental samples revealed that six alterations of unclear significance were inherited from a carrier parent and one was a de novo event in the proband. The origin of the other four unclear alterations could not be determined.
            Table 1

            Cases with Alterations of Clinical Significance Identified by BAC and Oligo aCGH.

            Pt. #

            Chr

            Band

            Start pos.

            End pos.

            Gain/Loss

            Size

            # of BACs

            # of Oligos

            21637

            chr16

            p11.2

            29,563,985

            30,066,187

            Loss

            502,202

            5

            46

            21992

            chr16

            p11.2

            29,563,985

            30,066,187

            Loss

            502,202

            5

            46

            22013

            chr16

            p11.2

            29,563,985

            30,066,187

            Loss

            502,202

            5

            46

            21993

            chr10

            q25.2q25.3

            114,306,207

            114,925,368

            Loss

            619,161

            3

            36

            21756

            chr2

            q35

            219,418,281

            220,060,969

            Loss

            642,688

            2

            43

            22002

            chr2

            q37.3

            239,664,393

            240,400,008

            Loss

            735,615

            3

            82

            22334

            chr10

            q25.2q25.3

            114,024,053

            115,677,301

            Gain

            1,653,248

            3

            53

            21688

            chr16

            p13.11p12.3

            15,056,257

            16,742,812

            Gain

            1,686,555

            3

            61

            21667

            chr17

            p12

            14,052,297

            15,742,271

            Gain

            1,689,974

            3

            67

            21896

            chr4

            q35.2

            189,407,487

            191,133,809

            Loss

            1,726,322

            8

            147

            22269

            chr2

            q12.3q13

            107,945,041

            109,784,684

            Loss

            1,839,643

            6

            102

            21640

            chr19

            q13.42

            59,272,450

            61,239,237

            Gain

            1,966,787

            8

            135

            22117

            chr9

            q33.1

            118,991,777

            121,063,590

            Gain

            2,071,813

            3

            67

            22066

            chr8

            p12p11.21

            38,303,146

            40,515,492

            Gain

            2,212,346

            6

            104

            21786

            chr1

            q21.1

            144,973,942

            147,421,814

            Gain

            2,447,872

            5

            63

            22237

            chr1

            q21.1

            144,973,942

            147,421,814

            Gain

            2,447,872

            5

            63

            22310

            chr22

            q11.21

            17,299,742

            19,770,655

            Loss

            2,470,913

            13

            205

            22050

            chr22

            q11.21

            17,007,819

            19,770,655

            Loss

            2,762,836

            13

            208

            21936

            chr1

            q21.1

            144,973,942

            147,966,185

            Gain

            2,992,243

            5

            65

            21719

            chr2

            q31.1

            169,823,689

            172,870,083

            Loss

            3,046,394

            8

            119

            22128

            chr4

            q34.3

            179,065,989

            182,435,119

            Loss

            3,369,130

            3

            91

            22006

            chr17

            p11.2

            16,723,071

            20,145,604

            Loss

            3,422,533

            18

            311

            22174

            chr1

            q41q42.12

            221,260,860

            224,709,317

            Loss

            3,448,457

            9

            178

            21971

            chr22

            q11.23q12.2

            24,025,269

            28,008,109

            Loss

            3,982,840

            3

            111

            22073

            chr15

            q11.2q13.1

            21,208,177

            26,194,049

            Loss

            4,985,872

            11

            281

            21687

            chr16

            q12.2q21

            51,912,655

            57,173,018

            Loss

            5,260,363

            15

            261

            21975

            chr5

            p15.2p14.3

            13,514,464

            18,988,928

            Loss

            5,474,464

            3

            122

            21555

            chr7

            p22.3p22.1

            153,644

            6,230,285

            Gain

            6,076,641

            32

            434

            21547

            chr2

            q24.3q31.1

            168,702,606

            174,842,496

            Loss

            6,139,890

            11

            226

            21755

            chr11

            p12p11.2

            37,540,680

            43,940,573

            Loss

            6,399,893

            14

            298

            21761

            chr3

            p14.1p12.3

            70,738,914

            77,275,908

            Loss

            6,536,994

            6

            151

            22151

            chr15

            q11.2q13.1

            18,809,804

            26,194,049

            Gain

            7,384,245

            14

            331

            22322

            chr8

            p21.3p12

            22,954,212

            30,630,828

            Loss

            7,676,616

            9

            224

            21889

            chr2

            q33.1q34

            202,901,021

            211,366,732

            Gain

            8,465,711

            13

            281

            21723

            chr5

            q23.1q23.3

            121,487,477

            130,306,377

            Loss

            8,818,900

            4

            204

            22337

            chr1

            p36.22p36.13

            9,476,880

            19,436,653

            Loss

            9,959,773

            21

            436

            21795

            chr1

            p34.2p32.3

            41,201,837

            55,191,500

            Gain

            13,989,663

            17

            440

            20986

            chr12

            p13.33p12.3

            84,918

            17,505,135

            Gain

            17,420,217

            46

            800

            21957

            chr11

            q23.3q25

            116,478,434

            134,419,382

            Gain

            17,940,948

            40

            784

            21596

            chr1

            q25.1q32.1

            173,519,967

            203,663,817

            Gain

            30,143,850

            25

            814

            22055

            chr3

            p14.1p13

            71,164,161

            71,958,845

            Loss

            794,684

            3

            46

            21566*

            chr17

            p13.2p13.1

            6,081,457

            6,904,679

            Loss

            823,222

            3

            14

            21558

            chr22

            q13.33

            48,567,185

            49,517,230

            Loss

            950,045

            6

            54

            21770

            chr8

            p23.3

            202,505

            1,411,517

            Loss

            1,209,012

            5

            96

            22254

            chr8

            p23.2

            2,604,280

            3,966,809

            Loss

            1,362,529

            9

            140

            21937

            chr7

            q11.23

            72,404,049

            73,771,409

            Loss

            1,367,360

            10

            158

            21710

            chr15

            q13.2q13.3

            28,741,818

            30,186,356

            Loss

            1,444,538

            3

            64

            21722

            chr15

            q13.2q13.3

            28,741,818

            30,226,376

            Loss

            1,484,558

            3

            65

            21739

            chr15

            q13.2q13.3

            28,741,818

            30,226,376

            Loss

            1,484,558

            3

            65

            21787

            chr15

            q13.2q13.3

            28,741,818

            30,226,376

            Loss

            1,484,558

            3

            65

            21897*

            chr5

            p15.2

            8,511,592

            9,888,817

            Gain

            1,377,225

            34

            29

            21884

            chrX

            q28

            152,676,750

            153,059,428

            Gain

            382,678

            3

            44

            21592

            chrX

            p22.33q28

            701

            154,888,083

            Gain

            154,887,382

            325

            6888

            22087

            chrY

            p11.32

            262,578

            57,715,879

            Gain

            57,453,301

            49

            49

            22285

            chrX

            p21.1

            31,759,551

            31,830,811

            Loss

            71,260

            2

            11

            22244

            chr22

            q13.33

            49,342,961

            49,514,486

            Loss

            171,525

            2

            56

            *additional alterations identified by oligonucleotide aCGH.

            Table 2

            Cases with Alterations of Unclear Significance Identified by BAC and Oligo aCGH.

            Pt. #

            Chr

            Band

            Start pos.

            End pos.

            Gain/Loss

            Size

            # of BACs

            # of Oligos

            Inheritance

            22365

            chr9

            p23

            9,881,385

            9,984,838

            Loss

            103,453

            2

            15

            Paternal

            21702

            chr16

            p12.2

            21,486,897

            21,641,890

            Loss

            154,993

            2

            16

            Paternal

            21883

            chr16

            p12.1

            21,974,396

            22,338,234

            Gain

            363,838

            3

            49

            Maternal

            21860

            chr16

            p12.1

            21,907,270

            22,338,234

            Loss

            430,964

            3

            50

            Unknown

            22009

            chr22

            q11.21

            19,069,125

            19,770,655

            Loss

            701,530

            4

            70

            Paternal

            22102

            chr16

            p13.11

            14,981,044

            16,166,985

            Gain

            1,185,941

            3

            60

            Maternal

            22246

            chr10

            p11.22

            31,591,310

            32,792,762

            Loss

            1,201,452

            2

            40

            Unknown

            21893

            chrX

            p11.32

            45,930,652

            46,382,140

            Gain

            451,488

            3

            34

            Maternal

            22273

            chrX

            q28

            153,355,101

            154,317,591

            Gain

            962,490

            5

            46

            Unknown

            10245

            chr3

            p14.1

            67,727,841

            69,101,769

            Loss

            1,373,928

            2

            25

            Unknown

            22348

            chr13

            q22.2

            74,989,699

            75,378,640

            Loss

            388,941

            3

            51

            De novo

            Using the oligo array, we identified 1,337 copy number variations among the same 466 cases. Using the algorithm previously described, we excluded 1,172 aberrations that were located within regions that had no gene content or those that were common copy number variants. After these exclusions were made, 165 aberrations in 138 cases (29.6%) remained that required FISH analysis. After FISH analysis was performed, aberrations of potential clinical significance were identified in 73 cases, a detection rate of 15.7%. Of these, the same 56 (12.0%) cases that were identified by the BAC platform were considered to contain clinically significant alterations (Table 1) and 17 (3.7%) were determined to contain copy number variants of unclear clinical significance.

            Table 3 shows the six cases for which aberrations of unclear clinical significance were identified by the oligo array but not by the BAC array. In all six cases, the aberrations either fell within the gaps in the BAC array coverage or were only partially covered by one or more BACs. The average size of the alterations that were not detected by the BAC array was 1.12 Mb (range: 289 kb - 1.42 Mb).
            Table 3

            Cases with Alterations of Unclear Significance Detected by Oligo Array but not Identified by BAC Array.

            Pt. #

            Chr

            Band

            Start pos.

            End pos.

            Gain/Loss

            Size

            # of Oligos

            9756

            chr2

            p25.1

            11,097,126

            12,515,559

            Loss

            1,418,433

            19

            9886

            chr1

            q42.12

            222,702,622

            223,461,255

            Loss

            758,633

            16

            10141

            chr2

            q32.3q33.1

            196,729,308

            197,880,950

            Loss

            1,151,642

            30

            10114

            chr15

            q26.3

            97,299,441

            97,745,782

            Gain

            446,341

            16

            10292

            chr7

            p14.3

            33,202,932

            33,492,136

            Loss

            289,204

            5

            10019

            chr2

            p16.3

            51,079,474

            51,993,245

            Gain

            913,771

            13

            In two cases, the oligo microarray identified additional complexity that was not recognized by the BAC array. In patient 21566, the BAC array identified one interstitial deletion of 17p13.2p13.1, whereas oligo array analysis identified that deletion and an additional interstitial deletion in the same band (data not shown). In patient 21897, the BAC array identified a 6.8 Mb terminal deletion of 5p, whereas oligo array analysis identified that deletion and a 1.4 Mb duplication proximal to the deleted region (Figure 1).
            http://static-content.springer.com/image/art%3A10.1186%2F1755-8166-3-11/MediaObjects/13039_2010_Article_66_Fig1_HTML.jpg
            Figure 1

            Identification by oligonucleotide microarray of additional complexity missed by BAC microarray. (A) BAC microarray results showing a single-copy loss of 34 BAC clones from the terminus of 5p, approximately 6.8 Mb in size (chr5: 387,034-7,150,950, based on UCSC 2006 hg 18 assembly). Probes are ordered on the x axis according to physical mapping positions, with the p-arm probes to the left and q-arm probes to the right. (B) shows oligonucleotide microarray results of the terminal deletion shown in (A) in addition to single-copy gain of 29 probes from 5p, approximately 1.38 Mb in size (chr5: 8,511,592-9,888,817, based on UCSC 2006 hg 18 assembly). Probes are ordered as in the BAC array. Regions shaded in blue represent deletions detected by microarray, whereas duplications are shaded in pink.

            Prospective Diagnostic Comparison

            Of the 3,443 diagnostic specimens analyzed using our whole-genome BAC array, 605 (17.6%) had copy number alterations. Using the previously described algorithm, 365 (10.6%) had abnormalities that were classified as clinically significant, whereas 240 (6.9%) had copy number variants of unclear clinical significance.

            Of the 3,096 diagnostic specimens analyzed using our whole-genome oligo array during the same time period, 698 (22.5%) had copy number alterations. Using the previously described algorithm, 477 (15.9%) of these cases were determined to contain alterations considered to be clinically significant and 221 (7.0%) were determined to contain copy number variants of unclear clinical significance (Table 4).
            Table 4

            Summary of the Prospective Diagnostic Comparison.

             

            BAC

            Oligo

            Total

            3,443

            3,096

            Abnormal

            605 (17.6%)

            698 (22.5%)

            Significant

            365 (10.6%)

            477 (15.4%)

            Unclear

            240 (7.0%)

            221 (7.1%)

            Mosaic

            16 (0.5%)

            12 (0.4%)

            The increased number of cases with clinically significant alterations detected by the oligo array was found to be statistically significant using a Fisher's Exact Test (OR = 1.5359, p < .0001). The increased number of cases with alterations of unclear significance detected by the oligo array was not statistically significant (OR = 1.0259, p = 0.8090).

            Mosaicism Assessment

            All but three of the 48 previously known mosaic alterations were detected by the oligo array. FISH analysis estimated that the proportion of uncultured cells carrying the alteration was 24% in the first case, while the proportion in cultured cells was 6%. In the second case, 5% of cells were found to carry the alteration by FISH (data not shown). The proportion of cells carrying the alteration in the third case could not be determined because FISH confirmation was not possible on the sample received by our laboratory. Certain alterations, such as tetrasomy 12p, were successfully detected in proportions of cells as low as 10% by the oligo array, although this low threshold of detection was facilitated by the tetrasomic nature of the rearrangement; the 4:2 ratio of patient to control DNA in this case was more readily detected than the 3:2 ratio typically associated with duplications. Figure 2 shows a 2.77 Mb interstitial deletion at 16q12.1 present in 23% of cultured metaphase cells that was detected by the oligo array.
            http://static-content.springer.com/image/art%3A10.1186%2F1755-8166-3-11/MediaObjects/13039_2010_Article_66_Fig2_HTML.jpg
            Figure 2

            Oligonucleotide microarray analysis of a mosaic 16q12.1 deletion (shaded blue region). The zoomed-in microarray plot shows a single-copy loss of 289 probes from 16q12.1, approximately 2.77 Mb in size (chr16: 46,837,260-49,605,054, based on UCSC 2006 hg 18 assembly). Probes are ordered on the x axis according to physical mapping positions, with the most proximal 16q11.2 probes to the left and the most distal 16q12.2 probes to the right.

            In the dilution series of trisomy 21 cells, shifts in the aCGH data were distinguishable down to levels as low as 10%, but could only be readily detected at a level of 30% or greater (Figure 3). As the proportion of trisomy 21 cells was increased from 10% to 30%, the average log2 ratio of chromosome 21 increased from 0.08 to 0.21. During the prospective diagnostic comparison, 16 cases analyzed using the BAC array contained mosaic alterations, whereas only 12 mosaic cases were identified using the oligo array (Table 5). The increased number of mosaic abnormalities detected by the BAC array was determined to be not statistically significant (OR = 1.1999, p = 0.7066).
            http://static-content.springer.com/image/art%3A10.1186%2F1755-8166-3-11/MediaObjects/13039_2010_Article_66_Fig3_HTML.jpg
            Figure 3

            Oligonucleotide microarray analysis of artificially derived mosaic trisomy 21 samples. (A) 10% trisomy 21 showing a very subtle copy-number gain for all clones on chromosome 21. The profile was generated using DNA extracted from a mixture of blood which contained 10% WBCs from a trisomy 21 subject and 90% WBCs from a normal male individual. (B) 15% trisomy 21, generated as in (A), showing a very subtle copy-number gain for all clones on chromosome 21. (C) 20% trisomy 21, generated as in (A) showing a subtle copy-number gain for all clones on chromosome 21. (D) 30% trisomy 21, generated as in (A), showing a clear copy-number gain for all clones on chromosome 21. The inset images to the right of each array plot show the average log2 ratio of all probes mapping to chromosome 21, with the horizontal dotted line representing a log2 ratio of zero and the vertical dotted line representing the centromere. A pink bar plotted above the horizontal line represents a copy-number gain of all probes on chromosome 21. To the left of each inset image is the average log2 ratio at the specified proportion of trisomic cells.

            Table 5

            Mosaic Alterations Detected in the Prospective Diagnostic Comparison.

            Pt. #

            Proportion (%)

            Classification

            BAC

            25885

            10

            45,X

            25838

            10

            47,XX,i(12)(p10)

            27745

            10

            46,XY,trp(12)(p13.33p10)

            26912

            18

            47,XY,i(8)(p10)

            26358

            20

            46,XX,dup(2)(p14p11.2)

            26880

            24

            47,XX,+9

            23302

            27

            47,XX,+der(9)(p21.2q11)

            23919

            46

            47,XY,+der(12)(p13.33q11)

            26127

            53

            46,XY,del(18)(q22.3q23)

            26750

            57

            47,XY,+der(8)(p11.22q11)

            26894

            60

            45,X

            24887

            70

            46,XX,der(14)dup(14)(q32.13q32.2)del(14)(q32.3q32.33)

            23159

            70

            47,XYY

            23155

            77

            46,XX,idic(18)(q21.33)

            23215

            87

            47,XX,+21

            25862

            93

            48,XX,+der(13)(pterq12.12),+der(20)(p11.21q11)

            Oligo

            27978

            21

            48,XY,+der(13)(pterq12.11),+der(?)(?::Xp22.31->Xp22.31::Xp22.2->Xp22.12::?->cen->?)

            32047

            27

            47,XXY

            32374

            27

            46,XX,r(X)(p11.1q21.1)

            32875

            33

            47,XY,+inv dup(22)(q11.21)

            29361

            53

            47,XX,+der(11)(p11.2q11)

            31439

            63

            47,XY,+der(12)(:p13.33::p13.31->p13.2::p11.23::p11.22->p11.21::?->12cen->?::p11.21->p11.22::p11.23::p13.2->::p13.31::p13.33:)

            31633

            63

            48,XX,+der(4)(p13q12),+der(13)(pterq12.11)

            30028

            77

            46,XY,del(7)(q22.1q22.3),del(12)(q21.31q22)

            31336

            80

            46,XX,idic(X)(q21.1)

            27105

            90

            47,XX,+der(13)(pterq12.12)

            29786

            90

            46,X,+der(X)(p11.21q11.1)

            30218

            93

            47,XY,+der(17)(p11q11.2)

            Discussion

            BAC and oligo array platforms each have unique advantages and disadvantages in a diagnostic setting; these may include turnaround times, genomic coverage, and costs. One of the most important characteristics of each platform is the detection rate of clinically significant alterations. Our results demonstrate that our whole-genome oligo array was able to detect such alterations in 15.4% of patients tested, compared to the BAC array detection rate of 10.6%, a statistically significant difference (Fisher's Exact Test, p < 0.0001). The alterations that constitute the 4.8% difference in detection rate between the BAC and oligo arrays are either too small to be detected by the BAC array but are not below the resolution of the oligo platform (Figure 4) or fall within gaps in the BAC array coverage (Figure 5). Figure 4 shows a 44 kb deletion of 17p13.3 detected in a patient referred to our laboratory for convulsions. This deletion encompasses the first exon of PAFAH1B1 (LIS1). While it is not known whether this deletion results in a null allele or simply a truncated gene product, hemizygous deletions and mutations of this gene are found in patients with isolated lissencephaly type 1 (OMIM 607432) and have been linked to epileptic seizures and convulsions [20, 21]. Although RP11-135N5 provides coverage of this region on the BAC array, FISH analysis using this clone could not confirm the deletion in any cells because of the deletion's small size compared to the FISH probe used. Thus, this clinically significant deletion could only have been reliably detected using the oligo platform. Although oligo-based aCGH has the power to detect alterations smaller than the size of a BAC probe, BAC-based aCGH has an advantage in that the analysis makes evident the appropriate probe to be used for FISH confirmation. In addition, this probe is usually readily available because of its inclusion on the microarray platform and will have a high rate of successful confirmation. When oligonucleotide-based aCGH is performed, BAC probes must be specifically selected for the FISH confirmation of each small abnormality that is detected. Once a probe has been selected, it must also be specially prepared or ordered before FISH can be performed. This process increases both the time it takes to perform FISH confirmation of oligo aCGH results and the cost associated with the analysis.
            http://static-content.springer.com/image/art%3A10.1186%2F1755-8166-3-11/MediaObjects/13039_2010_Article_66_Fig4_HTML.jpg
            Figure 4

            Oligonucleotide microarray characterization of an interstitial deletion at 17p13.3. The zoomed-in microarray plot shows a single-copy loss of six probes from the short arm of chromosome 17 at 17p13.3, approximately 44.0 kb in size (chr17: 2,415,074-2,459,051, based on UCSC 2006 hg 18 assembly). Probes are ordered on the x axis according to physical mapping positions, with the most distal 17p13.3 probes to the left and the most proximal 17p13.3 probes to the right. Below is a schematic of the deletion region. The deletion disrupts the PAFAH1B1/LIS1 gene.

            http://static-content.springer.com/image/art%3A10.1186%2F1755-8166-3-11/MediaObjects/13039_2010_Article_66_Fig5_HTML.jpg
            Figure 5

            Oligonucleotide microarray characterization of an interstitial deletion at 6q14.1. The zoomed-in microarray plot shows a single-copy loss of 43 oligonucleotide probes from the long arm of chromosome 6 at 6q14.1, approximately 2.9 Mb in size (chr6: 79,838,518-82,730,466, based on UCSC 2006 hg 18 assembly). Probes are ordered on the x axis according to physical mapping positions, with the most proximal 6q14.1 probes to the left and the most distal 6q14.1 probes to the right. Below is a schematic of the deletion region. Blue and gray boxes represent genes in the deletion region.

            Figure 5 shows a 2.9 Mb deletion of 6q14.1 detected in a patient referred to our laboratory for developmental delay and dysmorphic features. This deletion encompasses eight genes: PHIP, HMGN3, LCA5, SH3BGRL2, ELOVL4, TTK, BCKDHB, two of which are known to be associated with human disease [2224]. Although this 2.9 Mb deletion is likely to be clinically significant, it lies within a gap in the coverage of our BAC array and could only be detected using the oligo platform because of its more uniform backbone coverage.

            The detection rate of alterations of unclear clinical significance is also a concern during the selection of a microarray platform in a clinical diagnostic setting. Our data suggest that both the oligo and BAC platforms detect similar numbers of abnormalities of unclear significance (7.0% by BAC and 7.1% by oligo), although the circumstances leading to an unclear clinical interpretation may vary between the platforms. On the BAC platform, unclear results are often associated with gaps in coverage which prevent the precise determination of the breakpoints and gene content of an abnormal region. This lack of information prohibits definitive interpretation of the clinical significance of the alteration. Figure 6 presents a 262 kb deletion of 9q33.1 detected by BAC array in a patient referred for developmental delay, dysmorphic features, and multiple congenital anomalies. The boundaries of this alteration as defined by BAC array include only one gene, TLR4 [25]. However, gaps in BAC coverage on both sides of the alteration span 4.5 Mb proximally and 4.0 Mb distally. As a result of these coverage gaps, this alteration, though estimated to be just 262 kb, may be as large as 8.7 Mb and include up to 48 additional genes. The design of BAC arrays with dense clone coverage is possible; however, probe density is limited by the availability of BAC clones and the presence of potentially interfering genomic architecture such as segmental duplications. In addition, BAC-based microarrays will not reliably detect abnormalities smaller than the size of an individual probe--80-200 kb, on average, for BAC clones.
            http://static-content.springer.com/image/art%3A10.1186%2F1755-8166-3-11/MediaObjects/13039_2010_Article_66_Fig6_HTML.jpg
            Figure 6

            BAC microarray characterization of a 9q33.1 deletion. The zoomed-in microarray plot shows a single-copy loss of three BAC clones from the long arm of chromosome 9 at 9q33.1, approximately 262 kb in size (chr9: 119,452,279-119,714,054 based on UCSC 2006 hg 18 assembly). The nearest distal clone on chromosome 9 that is not deleted is RP11-977E8 and is approximately 4.0 Mb away from the deleted region. The nearest proximal clone on chromosome 9 that is not deleted is RP11-999I23 and is approximately 4.4 Mb away from the deleted region. Probes are ordered on the x axis according to physical mapping positions, with proximal 9q32 clones to the left and distal 9q33.2 clones to the right. Below is a schematic of the deletion region. Vertical blue lines represent the minimum size of this alteration, which encompasses one gene, TLR4.

            Although gaps in coverage and limited breakpoint-resolving power are primarily a concern for BAC platforms, both oligo and BAC platforms produce results that are unclear because a lack of published evidence prevents a conclusive association between the gene content of an alteration and the clinical features of the patient from being made. Figure 7 presents a 160 kb deletion of 4q25 detected by oligo array in a patient referred for developmental delay. Follow-up analysis performed on this patient's parents revealed that this alteration was de novo in origin. This alteration deletes two genes, PAPSS1 and SGMS2. While mutations or alterations of these genes have not been associated with disease in humans, it has been shown that PAPSS1 plays a key role in post-translational modification and SGMS2 mediates the production of sphingomyelin [26, 27]. Thus, although the gene content and inheritance pattern of this deletion suggest a causative role in the patient's clinical features, a lack of published information linking the genes affected by this alteration with a distinct phenotype prevents a clear interpretation from being made based on only aCGH results. This type of unclear result, although more prominent with oligo platforms (4.2% by BAC vs. 7.1% by oligo), is an element of all aCGH analysis regardless of platform and accentuates the need for databases containing aCGH results in combination with phenotypic information. Although the number of characterized genetic disorders and genomic regions is rapidly increasing, the clinical consequences of alterations involving much of the genome still remain unclear.
            http://static-content.springer.com/image/art%3A10.1186%2F1755-8166-3-11/MediaObjects/13039_2010_Article_66_Fig7_HTML.jpg
            Figure 7

            Oligonucleotide microarray characterization of an interstitial deletion at 4q25. The zoomed-in microarray plots shows a single-copy loss of 15 oligonucleotide probes from the long arm of chromosome 4 at 4q25, approximately 159.6 kb in size (chr4: 108,834,399-108,994,048, based on UCSC 2006 hg 18 assembly). Probes are ordered on the x axis according to physical mapping positions, with proximal 4q25 clones to the left and distal 4q25 clones to the right. Below is a schematic of the deletion region. The deletion disrupts the PAPSS1 and SGMS2 genes, represented by blue boxes.

            The increase in the number of copy number alterations identified by higher-resolution whole-genome arrays underscores the need for a variety of tools to facilitate the interpretation of array results in a clinical diagnostic setting. We propose the use of an algorithm such as the one outlined here in conjunction with databases of normal population variants, clinically significant alterations, and those of unclear significance. Although such databases can provide invaluable context for the analysis of aCGH data, care must be taken by the diagnostician when comparing their data to pre-existing databases of copy-number variations. For example, data in the DGV are pooled from a variety of sources, platforms, and populations using a variety of different controls and without independent verification, and thus may not be appropriate for comparison in all situations. Furthermore, recent evidence suggests that most data in the DGV overestimate the size of the regions involved because they are dependent primarily on BAC array data, which has a tendency to overestimate the true size of small aberrations [28]. Thus, the most useful CNV databases may be those generated by individual laboratories using identical reference controls and array platforms. Based on our experience, we have constructed a database of abnormal copy number aberrations identified by BAC and oligo aCGH in our laboratory and a database of copy-number variations thought to have no significance. Such databases are essential for understanding the various copy number aberrations identified by microarray analysis.

            Genotype-phenotype correlations in a diagnostic setting must address a variety of factors including gene content, potential position effects, aberration size, and inheritance patterns. These factors often present conflicting evidence about the potential clinical significance of a rare alteration. For instance, the size of an abnormality is commonly used as justification for its proposed clinical consequences; however, this association is not always straightforward. High-resolution microarray analysis routinely detects abnormalities smaller than 500 kb that disrupt clinically significant genes and have clear phenotypic impact (Figure 4); conversely, numerous examples of common copy-number variants have been observed that are relatively large but lie in regions with sparse gene content. In addition, although it is generally assumed that de novo abnormalities are causative and inherited abnormalities are not, this is not always the case. There are a number of regions of the genome where both inherited and de novo copy number alterations have been identified, some of which result in mild phenotypes that may be inherited from parents who have a milder or subclinical, presentation. For example, deletions of distinct regions of 1q21 have been associated with both thrombocytopenia absent radius (TAR) syndrome and a variable phenotype including microcephaly/macrocephaly, developmental delay, cardiac abnormalities, and schizophrenia [2931], but in many instances aberrations of these regions are inherited from phenotypically normal parents [32]. Another example is the 16p11.2 region associated with a range of cognitive, developmental, and speech delays, behavioral issues, and autism, deletions and duplications of which can be inherited or de novo [3336]. In regions such as these, copy number changes may unmask recessive alleles or work in conjunction with various genetic modifiers, perhaps even other CNVs, to produce a clinical phenotype. Potentially, non-paternity may also confound genotype-phenotype correlation for copy number alterations in these complex regions of the genome. These reasons underscore the need for thorough databases of normal population variants and clinically significant alterations complete with genotype-phenotype correlations. Such databases expedite the process of determining the potential significance of copy-number alterations in a diagnostic setting; aid in the elucidation of new microdeletion/duplication syndromes and new regions of benign copy-number variation; and help reduce the burden of expensive, time-consuming, and difficult follow-up necessitated by the increased number of alterations of unclear clinical significance detected by microarray analysis.

            We [19] and others [37] have shown that mosaicism can be detected at low frequencies of chromosomally abnormal cells using BAC-based aCGH; however, the ability of oligo platforms to reliably detect mosaic abnormalities has not yet been well established. Our current assessments demonstrate that aCGH using either BAC or oligo platforms can easily detect mosaicism of 30% or greater for a variety of alterations and that levels as low as 10% can be detected with both platforms under optimal conditions. In addition, our retrospective analysis showed that there is no significant difference between the two types of platforms in the number of mosaic abnormalities detected in a clinical diagnostic setting (p = 0.7066). However, BAC-based arrays may still have a greater ability to detect mosaic abnormalities present at very low levels (less than 20%), perhaps due to the routine use of dye-swap experiments which can be cost-prohibitive with oligo arrays but promote the visual identification of mosaic abnormalities. The sensitivity of the BAC array is demonstrated by the detection in three cases of abnormalities in only 10% of cells during the retrospective study, whereas the lowest level of mosaicism detected by our oligo array was 21% (Table 5). The ability of an aCGH platform to detect mosaic abnormalities also depends largely on the effectiveness of the software used to analyze the data, as low-level mosaic alterations are difficult to identify using only visual inspection (Figure 3). For this reason, it is important to select analysis software which facilitates the identification of mosaic alterations.

            These data suggest high-resolution oligo-based aCGH detects a higher proportion of clinically significant abnormalities than BAC-based aCGH. Our results also demonstrate the ability of microarray-based CGH to reliably produce high-yield results in a clinical setting using differing platforms, array designs, and analysis algorithms, supporting the validity of array CGH as a first-tier diagnostic screening tool [38]. Finally, the prevalence of copy number variants of unclear clinical significance detected on both platforms underscores the need for the development of readily accessible diagnostic tools in the form of databases of documented chromosome abnormalities to aid in the interpretation of microarray data.

            Declarations

            Acknowledgements

            We thank Aaron Theisen (Signature Genomic Laboratories, Spokane, WA) for his careful edits of the paper and all of the laboratory staff at Signature Genomics for conducting the aCGH and FISH experiments.

            Authors’ Affiliations

            (1)
            Signature Genomic Laboratories

            References

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