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Am J Hum Genet. Sep 2006; 79(3): 500–513.
Published online Jul 25, 2006. doi:  10.1086/507471
PMCID: PMC1559542

Oligonucleotide Microarray Analysis of Genomic Imbalance in Children with Mental Retardation

Abstract

The cause of mental retardation in one-third to one-half of all affected individuals is unknown. Microscopically detectable chromosomal abnormalities are the most frequently recognized cause, but gain or loss of chromosomal segments that are too small to be seen by conventional cytogenetic analysis has been found to be another important cause. Array-based methods offer a practical means of performing a high-resolution survey of the entire genome for submicroscopic copy-number variants. We studied 100 children with idiopathic mental retardation and normal results of standard chromosomal analysis, by use of whole-genome sampling analysis with Affymetrix GeneChip Human Mapping 100K arrays. We found de novo deletions as small as 178 kb in eight cases, de novo duplications as small as 1.1 Mb in two cases, and unsuspected mosaic trisomy 9 in another case. This technology can detect at least twice as many potentially pathogenic de novo copy-number variants as conventional cytogenetic analysis can in people with mental retardation.

Mental retardation (MR) produces life-long disability, and its burden on affected families and society is enormous. Moderate-to-severe MR, which occurs in ~1% of the population,1,2 is etiologically heterogeneous. Chromosomal abnormalities are the most common recognized cause, accounting for ~10% of MR in most case series,3,4 but no etiology is recognized in at least one-third to one-half of all affected individuals. Accurate genetic counseling and prenatal diagnosis are not available for families of children with MR in whom no etiology is recognized. These children often endure a “diagnostic odyssey” of repeated testing for many different conditions, in an attempt to find the cause.

Chromosomal abnormalities are usually identified by cytogenetic analysis, a microscopic method of detecting gross gain, loss, or rearrangement of genetic material in dividing cells. There have been evolutionary improvements in karyotyping since its introduction as a routine clinical service >40 years ago,57 but cytogenetic analysis has been resistant to quantum improvements and to automation, because of the requirement for tissue culture and for highly skilled technologists to analyze the microscopic images. Standard cytogenetic analysis has the advantage of surveying the entire genome for gain or loss of genetic material in a single test, but it cannot detect imbalances of genetic segments <5–10 Mb.

Over the past several years, constitutional gain or loss of genomic segments containing only 1–5 Mb of DNA has been found to be another important cause of MR.8 These submicroscopic chromosomal alterations are usually diagnosed by locus-specific FISH,9 a test that provides much higher resolution than that of conventional cytogenetic analysis. However, locus-specific FISH is a labor-intensive microscopic technique that uses probes specifically designed for each locus (or for the relatively small number of loci) tested. FISH is, therefore, not suitable for genomewide searches for DNA copy-number changes. Better methods are needed to perform genomewide surveys for submicroscopic genomic copy-number changes in individuals with MR.

Array-based methods can provide high-resolution surveys of the entire genome for submicroscopic copy-number variants (CNVs). A few small studies using these methods have found apparently pathogenic CNVs among children with MR who had normal conventional cytogenetic analyses.1017 These studies were done with arrays made with large-insert clones, usually BACs. The pathogenic submicroscopic deletions and duplications detected in these studies range in size from 0.5 to 15 Mb. However, smaller deletions and duplications can also cause MR.1824 The ideal technique would, therefore, identify CNVs with an even greater genomewide resolution.

High-density whole-genome SNP arrays have been widely used for genotyping25 and can also be used to measure genomic copy number.26,27 Recent studies have shown that whole-genome sampling analysis (WGSA)28 with Affymetrix GeneChip Human Mapping 100K array sets can identify submicroscopic CNVs as well as uniparental disomy (UPD) without copy-number change.2931 We studied 100 children with idiopathic MR and their parents, using WGSA with Mapping 100K arrays to look for potentially pathogenic submicroscopic genomic changes.

Methods

Patients and Families

We studied 100 children with idiopathic MR and both of their unaffected parents, eight unaffected siblings within these families (as negative controls), and eight trios in which the child had MR and a previously recognized chromosomal abnormality or UPD (as positive controls). Each of the children with idiopathic MR was assessed by a clinical geneticist who was unable to determine the cause of the child's MR despite thorough clinical evaluation and clinical testing that included routine karyotyping with at least 450-band resolution. The children were selected because they had moderate-to-severe MR or developmental delay with at least one of the following additional clinical features: one major malformation, microcephaly, abnormal growth, or multiple minor anomalies. Informed consent was obtained from each family, and assent was also obtained from the child, if possible. The study was approved by the University of British Columbia Clinical Research Ethics Board.

DNA Preparation

DNA was extracted from whole blood by use of a Gentra Puregene DNA Purification Kit by following the manufacturer's instructions. The DNA was precipitated in 70% alcohol, was resuspended in hydration solution, and was stored at 4ºC.

Hybridization to Mapping 100K Arrays

Genomic DNA sample quality was assessed by electrophoresis in a 0.7% agarose gel, followed by SYBR Green staining and visualization by use of a Typhoon 9400 variable mode imager. DNA concentration was measured with a Bio-Tek PowerWave X spectrophotometer. A sample of 500 ng of DNA was processed according to the instructions provided in the Affymetrix GeneChip Human Mapping 100K Assay Manual.31 In brief, 250 ng of high-quality genomic DNA was digested with XbaI or HindIII and was ligated to XbaI or HindIII adaptors. Adaptor-ligated restriction fragments were amplified by PCR, and the purified PCR products were quantified with a Bio-Tek PowerWave X spectrophotometer. Random fragmentation and labeling were performed as described elsewhere.31 Samples were hybridized to GeneChip Human Mapping 50K Xba240 or Hind240 arrays in an Affymetrix Hybridization Oven 640. Washes and staining of the arrays were performed with an Affymetrix Fluidics Station 450, and images were obtained using an Affymetrix GeneChip Scanner 3000.

The protocol used to identify CNVs is summarized in figure 1. Initial analysis and quality assessment of the array data were performed using GeneChip DNA Analysis Software (GDAS) version 3.0.

Figure  1.
Flow diagram showing algorithm used for detection of CNVs by use of WGSA with Mapping 100K arrays. HMM = hidden Markov model.

Copy-Number Analysis of Autosomes with CNAG

Detection of CNVs within trios consisting of an affected child and both unaffected parents was performed using Copy Number Analyser for GeneChip (CNAG)32 version 1.0. For each trio, three comparisons of SNP copy number were made: child versus father (as reference), child versus mother (as reference), and father versus mother (as reference). Regions of copy-number gain or loss in these comparisons were determined using the hidden Markov model output of CNAG. De novo and inherited deletions and duplications in the children were called using the rules described in table 1.

Table 1.
CNV Detection with CNAG in Trios[Note]

Copy-Number Analysis of Autosomes with dChip

An analysis complementary to that described above was performed using a reference set that included all 216 unaffected parents in this study. SNP copy number was assessed in each affected child and each unaffected parent in comparison with this large reference set, by use of DNA-Chip Analyzer (dChip) software (version release November 17, 2005).33 Regions of copy-number gain or loss were detected using the hidden Markov model output of dChip. De novo and inherited deletions and duplications in the children were called as described in table 2.

Table 2.
CNV Detection with dChip in Trios by Use of a Reference Set[Note]

Calculation of t Statistics for Identification of the Most Significant Aberrations

The software packages used to detect CNVs each employ a different algorithm to identify genomic regions of arbitrary size that have higher or lower copy number than adjacent regions. To compare CNV calls made by different packages and to identify the calls that were most likely to be biologically meaningful, we calculated t statistics and the corresponding P values for mean sample versus reference log2 copy-number ratios within each candidate deletion or duplication in comparison with the ratios outside the CNV on the same chromosome. The number of SNPs considered to be part of a CNV varies with the software and the parameters used to identify the aberrations, so we tried a range of window sizes (expressed as the number of contiguous SNPs) around the detected aberration and computed a t score for every window of the same size on the chromosome. We tested all reasonable window sizes for each candidate aberration and defined the optimal window size as the one that produced the best t score or equivalent P value for that CNV. Choice of window size for graphical display of t scores is arbitrary. We chose the optimal window size with small CNVs to maximize the t score and to make the aberration more apparent in the plot. Longer aberrations are generally associated with t scores that differ greatly from the rest of the chromosome, and, in these cases, we chose window sizes smaller than the optimal to produce smoother plots.

Combined Copy-Number and Genotype Analysis for Deletions

SNP genotype calls were generated from signal intensity data with GDAS version 3.0 by use of a confidence score threshold of 0.05 for genotype accuracy. Genotypes were assessed for each putative deletion identified by CNAG or dChip. If the number of heterozygous SNPs exceeded 10% of the total within a putatively deleted segment, the deletion call was considered to be a false-positive result. Deletions were accepted as hemizygous if at least 90% of the genotype calls within the segment were either “AA” or “BB.” Genotype calls within a hemizygous deletion in a child often exhibited Mendelian errors (e.g., an “AA” genotype result in the child and a “BB” genotype result in one parent), and such errors were used to determine the parental origin of the child's remaining inherited allele.

Copy-Number Analysis of X Chromosomes with dChip

Copy numbers for SNPs on the X chromosome were analyzed with dChip software33 as follows. Each male (child or father) was compared with a reference set comprising all 108 unaffected fathers included in the study. Each female (child or mother) was compared with a reference set comprising all 108 unaffected mothers included in the study. Regions of copy-number gain or loss were detected using the hidden Markov model output of dChip.

Screen for UPD

A search for UPD was performed using SNP genotypes generated with GDAS version 3.0, with a confidence score threshold of 0.05. SNP genotypes for each child were compared with those of both parents, and Mendelian errors (SNPs homozygous for one allele in the child and the opposite allele in one parent) were identified. Mendelian errors are very infrequent in the absence of deletions or UPD. When such Mendelian errors were found, other SNPs within the same chromosomal region or chromosome were evaluated for the presence of isodisomy or heterodisomy. Uniparental isodisomy was identified as a stretch of homozygous SNPs in the child with exclusively maternal or exclusively paternal origin. Diagnosis of uniparental isodisomy also required confirmation of a normal disomic copy number. Uniparental heterodisomy was detected when a continuous region within a window of 200–1,000 SNPs in the child showed genotypes identical to the same chromosomal region in the mother or father.

Validation of CNVs

Putative CNVs identified by WGSA were validated by FISH with cytogenetic pellets prepared according to standard clinical procedures. FISH was performed with BAC or fosmid probes selected using the University of California at Santa Cruz (UCSC) Genome Browser34 and the May 2004 assembly of the human genome sequence.

The genomic content of BAC inserts used for FISH confirmation of putative CNVs was verified by end sequencing, as necessary. BAC DNA was prepared as described elsewhere,35 was precipitated, and was resuspended in 35 μl of Ultrapure water (Gibco). DNA sequencing reactions were assembled in 384-well clear optical reaction plates (Applied Biosystems) by use of a Biomek FX workstation (Beckman-Coulter). Each reaction was performed in a total volume of 8 μl, consisting of 5 μl of purified BAC DNA, 0.7 μl of T7 or SP6Wan sequencing primer (5 pmol/μl [Invitrogen]), 0.3 μl of Ultrapure water (Gibco), and 2 μl of BigDye v.3.1 Ready Reaction Mix (Applied Biosystems). Thermal cycling was performed on a PTC-225 or DNA Engine Tetrad 2 thermal cycler (MJ Research) with parameters of 85 cycles at 96°C for 10 s, melting temperature (which was specific to each primer) for 5 s, and 60°C for 4 min, followed by incubation at 4°C. Reaction products were precipitated by adding 2 μl of 125-mM EDTA (pH 8.0) and 18 μl of 95% ethanol per well, followed by centrifugation at 2,750 g for 30 min in an Eppendorf 5810R centrifuge. The EDTA/ethanol was immediately decanted, and reaction products were washed with 30 μl of 70% ethanol. The 384-well cycle plates were allowed to dry inverted for 15 min. Samples were resuspended in 10 μl of Ultrapure water and were analyzed using a 3730XL DNA analyzer (Applied Biosystems). The reads were processed, and their quality was assessed, using Phred.36,37 The end sequences were aligned with the human genomic sequence (May 2004 assembly) by use of BLAT,38 via the UCSC Genome Browser.

BAC or fosmid DNA was isolated by small-scale (miniprep) preparation and was labeled with Spectrum Red or Green (Vysis) by use of a Vysis nick translation reagent kit. The labeled product was mixed with 3 μg of human Cot-1 DNA (Invitrogen) and was isolated by means of a standard DNA precipitation method. Chromosomes and nuclei were visualized by counterstaining with 4′,6-diamidino-2-phenylindole. For deletions, at least 10 metaphase cells were analyzed, and interphase nuclei were examined but not counted. For duplications, at least 10 metaphase cells and at least 50 interphase nuclei were analyzed. All FISH probes were tested on metaphase spreads from unaffected individuals to assure proper hybridization. Of 118 FISH probes successfully tested, 2 hybridized to the wrong location, and 14 produced cross-hybridization signals.

Results

We performed WGSA with Mapping 100K arrays on 100 children with idiopathic MR and on both parents of each affected child. We also tested eight unaffected siblings in these families (as negative controls) and eight trios in which the fetus or child had MR as the result of a previously recognized chromosomal abnormality (as positive controls). In this last group, the Mapping 100K arrays demonstrated the expected findings in all cases, which included the following known segmental aneusomies or UPDs: del(7)(q11.2q11.2), del(11)(p12p14.1), del(10)(q24.32q25.1), dup(10)(p12.2pter), dup(15)(pter), UPD 7, UPD 15, UPD 16, and UPD 22.

Data were analyzed to determine copy number along the length of all chromosomes, and the statistical significance of variations from the expected copy number was estimated in windows of various sizes by t scores calculated in comparison with the estimated copy number of the remainder of the chromosome (fig. 2). The findings for each child were then compared with those for his or her parents. Apparent CNVs seen in the child and in at least one parent were considered likely to be benign polymorphisms. Apparent CNVs found in the child but not in either parent were evaluated by FISH, to confirm the presence of the CNV and its de novo occurrence. Paternity was confirmed in all trios studied by use of the SNP genotyping calls produced in the array experiments.

Figure  2.
A CNV on chromosome 2 in the affected child of family 7551. A, dChip-smoothed copy-number (CN) estimate with a window of 38 SNPs, showing a 1-copy deletion. B, Diagram showing t scores calculated for a sliding window of 38 SNPs that compare the copy number ...

In the course of this study, we identified a total of 3,125 putative CNVs by WGSA in the 100 studied children with idiopathic MR. Apparent CNVs were found in all individuals, with a range of 19–43 per child with idiopathic MR. The median number of apparent CNVs per child was 30 (5th percentile = 22; 95th percentile = 38). Of these variants, 2,669 occurred only once in this data set, and the majority of false-positive CNV calls is likely to be in this subgroup. The putative CNVs with the highest statistical significance—that is, those that were least likely to have occurred by chance alone—were tested by FISH. Of the 3,125 apparent CNVs found in children with MR, 12 were confirmed both in the child and in one of his or her parents. An example is shown in figure 3. Some other apparent CNVs correspond to known polymorphisms (Database of Genomic Variants), but most are unique and have not been independently confirmed. All CNVs confidently detected in the eight unaffected siblings of children with idiopathic MR were inherited from one of the parents.

Figure  3.
Inherited CNV demonstrated by WGSA with Mapping 100K arrays. A, Plot of t scores for chromosome 2 in father, mother, and child in family 0674, by use of a 21-SNP window. The SNPs are ordered along the X-axis from pter to qter, with the location shown ...

CNVs that were confirmed by FISH in a child with MR and that were found by FISH to be not present in either parent were considered to have arisen de novo. We found such de novo alterations in 11 of the 100 children with idiopathic MR (table 3). Table 4 summarizes the phenotypic abnormalities observed in each of these children.

Table 3.
De Novo CNVs Detected by WGSA with Mapping 100K Arrays and Confirmed by FISH
Table 4.
Summary of Clinical Features in Children with de Novo CNVs Detected by WGSA with Mapping 100K Arrays and Confirmed by FISH

Eight of the 11 de novo cases had submicroscopic deletions that were confirmed by locus-specific FISH (figs. (figs.44 and and5).5). These CNVs ranged in size from 178 kb (in family 5566) to >11 Mb (in family 3476) and involved 5–627 contiguous SNPs.

Figure  4.
De novo submicroscopic genomic deletion, del(2)(p16.3p16.3), identified by WGSA with Mapping 100K arrays in family 5003. A, The t scores for the CNV in comparison with the rest of the chromosome, by use of a window size of 20 SNPs. The SNPs are ordered ...
Figure  5. Figure  5. Figure  5. Figure  5. Figure  5. Figure  5. Figure  5. Figure  5. Figure  5. Figure  5. Figure  5. Figure  5. Figure  5. Figure  5.
De novo submicroscopic genomic deletions identified by WGSA with Mapping 100K arrays in the affected child of families 1895, 3476, 4818, 5566, 6545, and 7807. Panels A, C, E, G, I, K, and M show the t scores for each CNV in comparison with the rest of ...

Two of the 11 de novo cases had submicroscopic duplications that were confirmed by locus-specific FISH (figs. (figs.66 and and7).7). The sizes of these CNVs were 1.1 Mb (15 consecutive SNPs) in family 6168 and 2.9 Mb (29 consecutive SNPs) in family 4794.

Figure  6.
De novo submicroscopic genomic duplication, dup(16)(p13.3p13.3), identified by WGSA with Mapping 100K arrays in family 4794. A, The t scores for the CNV in comparison with the rest of the chromosome, by use of a window size of 20 SNPs. The SNPs are ordered ...
Figure  7.
De novo submicroscopic genomic duplication, dup(17)(q21.33q21.33), identified by WGSA with Mapping 100K arrays in family 6168. A, The t scores for each CNV in comparison with the rest of the chromosome, by use of a window size of 8 SNPs. The SNPs are ...

We also identified mosaic trisomy 9 in family 5994. This abnormality was not detected on initial cytogenetic analysis but was subsequently confirmed in 1 of 44 cells examined on an extended cytogenetic survey of peripheral-blood metaphases and in 60 of 300 peripheral-blood interphase cells studied by FISH with probes for chromosome 9.

Although we were able to demonstrate the UPD in all four control cases with known UPD, we did not find any copy-number–neutral alterations of familial genotype patterns indicative of UPD in the 100 studied children with idiopathic MR. We did not identify any de novo CNVs of the X chromosome or any inherited regions of X-chromosomal nullisomy in males with idiopathic MR.

Discussion

We have shown that WGSA with Affymetrix Mapping 100K arrays can identify apparently pathogenic, submicroscopic CNVs in individuals with idiopathic MR and normal results of conventional cytogenetic analysis. Our observed frequency of de novo submicroscopic CNVs is comparable to that seen in a similarly selected group of patients with idiopathic MR who were studied using comparative genomic hybridization with whole-genome BAC tiling-path arrays.12 However, using WGSA, we were able to detect smaller de novo CNVs than those that have been reported using the BAC tiling-path arrays. In addition, SNP arrays can identify copy-number–neutral UPD2931 and can determine the parent of origin of a child's remaining allele in heterozygous deletions, neither of which can be done by BAC or nonpolymorphic oligonucleotide comparative genomic hybridization. Although we were able to detect the UPD in our control samples, we did not find UPD in any of the 100 studied children with idiopathic MR. The SNP genotypes provided by these mapping arrays were also useful for confirming paternity within the trios.

Because of the noise inherent in WGSA with Mapping 100K arrays, statistically significant increases or decreases in apparent copy number often occur by chance. Slater et al.31 estimated the false-positive copy-number call rate for WGSA with Mapping 100K arrays to be in the range 2.0%–3.5%. We employed a different approach for copy-number analysis and found that the false-positive call rate varied with the software, parameter settings, and comparison group. To increase the specificity of CNV calls, we calculated t scores for the variation from expected copy number in the region of interest in comparison with the copy number for the rest of a chromosome (fig. 2), and we accepted only calls that were very unlikely to have occurred by chance alone.

Another advantage of using SNP arrays that provide information on genotypes as well as copy number is that some false-positive deletion calls can be recognized by the presence of heterozygous genotypes within a putatively deleted region. More generally, the false-positive CNV call rate can be reduced by requiring a larger number of SNPs that are adjacent in the genome to demonstrate a similar apparent gain or loss of copy number, but doing so reduces the sensitivity and resolution of the analysis. The use of higher density arrays (e.g., 500K rather than 100K arrays) and critical assessment of the characteristics that permit CNVs to be identified by various software algorithms may help to reduce the false-positive call rate without substantially affecting the sensitivity of the assay.

Affymetrix Mapping 100K arrays have one SNP every 23.6 kb, on average, and an 8.5-kb median distance between adjacent SNPs, excluding centromeric and telomeric regions. We considered a false-positive rate of [less-than-or-eq, slant]4 CNV calls per person to be manageable, given the usual error profiles observed in this study. To obtain this false-positive rate, we required that at least four adjacent SNPs exhibit a similar change in apparent copy number to call a CNV. A larger number of adjacent SNPs was needed to call a change in copy number when the hybridization results were unusually noisy.

The smallest confirmed inherited CNV we observed (a 37.6-kb deletion of chromosome 9 in family 9299) approaches the theoretical resolution of our analytical method. The smallest confirmed de novo CNV we found was 178 kb (in family 5566). The fact that we did not find smaller de novo CNVs is unlikely to reflect any biological limit on the minimal CNV size associated with MR. Smaller pathogenic deletions or duplications have been found in children with MR by use of other methods.1823

SNPs included on the Mapping 100K arrays are not evenly spaced throughout the genome, and some genomic regions have denser representation than others. Thus, although we were able to confirm an inherited CNV as small as 37.6 kb, we may have failed to detect CNVs of several hundred kilobases if they happen to lie in regions that are poorly represented on the Mapping 100K arrays. Of particular concern is the fact that subtelomeric regions and regions rich in segmental duplications, which are frequent sites of benign and pathological copy-number variation,3941 are poorly represented on these arrays. 500K SNP arrays have recently become available and should provide better resolution for WGSA, although subtelomeric regions and regions rich in segmental duplications are still underrepresented.42

As expected, we found many CNVs that probably represent normal polymorphisms.43 Our study was not designed to survey these normal variants in detail, and WGSA may not be the best technology to use for this purpose.42

We have used the occurrence of a CNV as a new mutation in a child with MR as a screen for pathogenicity of the variant, because this approach parallels that used clinically to assess cytogenetically apparent chromosomal abnormalities. We recognize that some CNVs that are inherited from an unaffected parent may nevertheless be pathogenic—for example, if an inherited deletion uncovers a region that is imprinted or includes a pathogenic recessive allele on the normal homologous chromosome.43,44 We do not know the frequency of de novo CNVs among unaffected individuals, although we did not observe any de novo CNVs in the eight unaffected siblings studied with these trios. We believe that all the de novo CNVs observed in this study of children with MR are likely to be pathogenic, because all exhibit features, in addition to their occurrence as new mutations, that have been associated with cytogenetically detectable deletions and duplications that cause MR.

All 11 de novo CNVs we found in children with MR contain genes (or portions of genes) annotated by Ensembl and sequences that are expressed in human fetal brain (table 5). Almost all these de novo CNVs also contain genes from Online Mendelian Inheritance in Man (OMIM) that have been associated with phenotypic abnormalities (table 3), but none of the children has clinical features compatible with known mutations of these OMIM genes. The duplication in child 4794 includes a region that is deleted in some cases of Rubinstein-Taybi syndrome (MIM 180849), a well-characterized multiple congenital anomaly MR syndrome.45

Table 5.
Ensembl Genes and Fetal Brain Gene Expression in Regions Defined by de Novo CNVs in Children with Idiopathic MR[Note]

The de novo deletions we observed that extend across >1 Mb of genomic sequence (in families 1895, 3476, 4818, 6545, 7807, and 8326) are almost certainly pathogenic, because of their large size and consequent gene content. These deletions are similar in size to the pathogenic CNVs that occur in recognized microdeletion syndromes41 and are larger than almost all the deletion polymorphisms reported in unaffected individuals.12,39,40,4651

Two of the de novo deletions we confirmed by FISH are substantially smaller than the others (321 kb in family 5003 and 178 kb in family 5566), demonstrating the improved resolution of WGSA with Mapping 100K arrays in comparison with other array hybridization platforms used to identify CNVs in children with MR.1017 Despite their small size, the deletions in families 5003 and 5566 are also probably pathogenic because, to the best of our knowledge, they have never been observed in unaffected individuals and because each of these CNVs arose as a new mutation in the affected child. Similar arguments regarding possible pathogenicity apply to the de novo duplications found in this study (figs. (figs.66 and and7).7). Both of these duplications are >1 Mb in size, neither has been reported in unaffected individuals, and both apparently arose as a new mutation in the affected child.

The ability of WGSA to detect trisomy 9 mosaicism despite the failure of routine karyotyping to do so may reflect the fact that trisomy 9 cells are selected against under routine cytogenetic culture conditions or that the trisomy line happens to be much less frequent in the T-lymphocytes that were karyotyped than in other nucleated blood cells. Our observation that trisomy 9 was confirmed by FISH in 20% of interphase cells but by cytogenetic analysis in ~2% of metaphase cells studied is consistent with both interpretations.

CNVs frequently occur as benign polymorphisms in healthy individuals,12,39,40,4651 and it is not always clear whether a CNV found in a child with MR is actually pathogenic. There are several features that can be used to differentiate apparently benign polymorphic variants from CNVs that may cause MR. Our primary approach was based on family studies—the method that is used clinically to determine whether a novel chromosomal alteration identified cytogenetically is pathogenic. In this approach, a CNV that has been inherited from an unaffected parent is considered unlikely to be pathogenic, whereas a CNV that occurs de novo in a child with MR is probably causally related. Although there are exceptions—for example, in the case of recessive mutations, imprinted genes, or X-linked genes transmitted by a woman to her son—family studies are often very helpful in determining the likelihood that a CNV is pathogenic.

Another useful rule of thumb is that a CNV observed frequently in unaffected people is unlikely to be pathogenic in a child with MR. None of the de novo CNVs we found in affected children is listed in the Database of Genomic Variants as having been reported in an unaffected individual, and we did not find these changes in any of the unaffected parents or unaffected sibling controls in our study.

Some CNVs are known to be pathogenic, because they have been observed repeatedly in children who share a particular MR phenotype. The familiar forms of MR associated with microdeletions of 7q11.23 (Williams-Beuren syndrome [MIM 194050])52 and 17p11.2 (Smith-Magenis syndrome [MIM 182290])53 provide cogent examples of CNVs that were demonstrated to be pathogenic in this way. Thus, the phenotypic similarity of child 5994 to other children who have been described with mosaic trisomy 954,55 makes it virtually certain that his MR was caused by the trisomy 9 mosaicism that we discovered in him by WGSA.

Five of the other de novo CNVs we found have also been reported in other children with MR: dup(16)(p13.3p13.3) in child 4794, del(7)(p22.2p22.1) in child 6545, del(22)(q12.1q12.1) in child 7807, and del(14)(q11.2q11.2) in child 8326 and child 5566. The appearance of child 4794 is strikingly similar to that of other individuals with cytogenetically apparent duplications of 16p13 and especially to the patient recently described by Sommer et al.56 The de novo duplication we found in child 4794 also overlaps that of DECIPHER patient CHG237.

The 1.6-Mb deletion we demonstrated in child 8326 includes a 1.1-Mb deletion reported in DECIPHER patient CAM126, and both of these lesions include the 178-kb deletion that we found in child 5566. These three children share similar minor anomalies that may constitute a recurrent syndrome. The 3.6-Mb deletion in child 6545 includes a 0.2-Mb deletion reported in DECIPHER patient UPP969, and the 1.8-Mb deletion in DECIPHER patient CHG758 overlaps the 1.4-Mb deletion found in child 7807. A detailed comparison of the phenotypic features of these individuals is being undertaken.

In a few instances, a CNV found in a child with MR may include a gene that is known to be dosage sensitive and to produce the specific phenotype seen in the child. This is the case, for example, in children who have neurofibromatosis 1 as a result of deletion of the entire NF1 locus.57 When this occurs, the child’s MR may be attributed to the involved gene, but, even in these instances, there may be other genes included in the CNV that also contribute to the MR.

A most compelling demonstration that MR can be caused by a particular CNV is obtained if sequence mutations of a gene within the affected region produce the same phenotypic abnormalities as the CNV. This approach is illustrated by the finding of CHD7 sequence mutations in 58% of individuals with CHARGE syndrome (MIM 214800)58 after CHD7 was identified within the CNVs of two other children with this phenotype.59 Such investigations have not been performed on the novel CNVs we found in this study, but the small size of some of these CNVs makes the genes within them attractive candidates for mutational analysis in children with MR who do not have detectable CNVs.

The results of our study demonstrate that WGSA with high-density, whole-genome SNP arrays can identify apparently pathogenic CNVs as small as 178 kb in children with MR. This technology appears to be capable of finding the cause of MR in at least twice as many affected individuals as is conventional cytogenetic analysis.

Note Added in Proof.—De novo CNVs were identified by 50K Xba GeneChip WGSA in 2 of 10 children studied who had multiple congenital anomalies and normal results on conventional cytogenetic analysis.60

Acknowledgments

This study was supported by funding from Genome Canada, Genome British Columbia, and the Canada Foundation for Innovation, with additional support from Affymetrix. Research at the British Columbia Cancer Agency is also supported by the British Columbia Cancer Foundation. M.A.M., S.J.M.J., and R.A.H. are Michael Smith Foundation for Health Research Scholars. We thank Dr. Wendy Robinson and Dr. Michael Taylor for providing us samples from control trios with known chromosomal abnormalities. We appreciate the skilled technical assistance of Nassim Aliakbarli, David Chai, Darko Curman, Christie Garbutt, Noriko Vickerson, and Willie Xue. This study was done in partnership with the affected families, to whom we are extremely grateful.

Web Resources

The URLs for data presented herein are as follows:

Affymetrix GeneChip Human Mapping 100K Assay Manual, http://www.affymetrix.com/support/downloads/manuals/100k_manual.pdf
Database of Genomic Variants, http://projects.tcag.ca/variation/
GeneChip DNA Analysis Software (GDAS) Version 3.0, http://www.affymetrix.com/Auth/support/downloads/manuals/gdas_manual.zip
Online Mendelian Inheritance in Man (OMIM), http://www.ncbi.nlm.nih.gov/Omim/ (for Rubinstein-Taybi, Williams-Beuren, Smith-Magenis, and CHARGE syndromes)
SAGE Library LSAGE_Brain_fetal_normal_B_S1, http://cgap.nci.nih.gov/SAGE/SAGELibInfo?LID=656&ORG=Hs

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