Diagnosing idiopathic learning disability: a cost-effectiveness analysis of microarray technology in the National Health Service of the United Kingdom
© Springer Science+Business Media B.V. 2007
Received: 16 January 2007
Accepted: 13 April 2007
Published: 5 June 2007
Array based comparative genomic hybridisation (aCGH) is a powerful technique for detecting clinically relevant genome imbalance and can offer 40 to > 1000 times the resolution of karyotyping. Indeed, idiopathic learning disability (ILD) studies suggest that a genome-wide aCGH approach makes 10–15% more diagnoses involving genome imbalance than karyotyping. Despite this, aCGH has yet to be implemented as a routine NHS service. One significant obstacle is the perception that the technology is prohibitively expensive for most standard NHS clinical cytogenetics laboratories. To address this, we investigated the cost-effectiveness of aCGH versus standard cytogenetic analysis for diagnosing idiopathic learning disability (ILD) in the NHS. Cost data from four participating genetics centres were collected and analysed. In a single test comparison, the average cost of aCGH was £442 and the average cost of karyotyping was £117 with array costs contributing most to the cost difference. This difference was not a key barrier when the context of follow up diagnostic tests was considered. Indeed, in a hypothetical cohort of 100 ILD children, aCGH was found to cost less per diagnosis (£3,118) than a karyotyping and multi-telomere FISH approach (£4,957). We conclude that testing for genomic imbalances in ILD using microarray technology is likely to be cost-effective because long-term savings can be made regardless of a positive (diagnosis) or negative result. Earlier diagnoses save costs of additional diagnostic tests. Negative results are cost-effective in minimising follow-up test choice. The use of aCGH in routine clinical practice warrants serious consideration by healthcare providers.
Learning disability (LD) is a common condition affecting 1–3% of individuals worldwide (Roeleveld et al. 1997). Most with moderate to severe LD (intelligence quotient (IQ) under 50) require life long support and half of those with mild LD (IQ 50–70) are significantly impaired throughout life (Department of Health 2001; Mencap 2001). Despite the clinical, social and psychological challenges associated with LD, up to 80% of cases have no specific causal diagnosis.
Standard testing to detect constitutional anomalies (present at or before birth) is chromosome analysis (karyotyping) at the 450–500 G-band level. Karyotyping can detect large genomic imbalances (losses or gains of DNA) in LD conditions such as Down, Turner and Edwards Syndromes. However, the resolution is insufficient to routinely detect rearrangements smaller than 5 million base pairs (5 Mb) and even abnormalities of 15 Mb may be missed where the banding pattern is indistinct.
As smaller genomic imbalances can be clinically important, demand has increased for higher resolution assays to detect them. This is particularly true for idiopathic (without known cause) LD (ILD) cases, that represent ∼15% of referrals to clinical genetics and paediatrics clinics. Despite ILD being incurable, a diagnosis is important for many reasons including, providing accurate prognostic information and genetic counselling, directing appropriate clinical care and educational needs, considering future preventative and therapeutic regimes and finally helping clinicians to answer the parents’ question “why?”. The clarification of genetic risk for both the immediate and wider family is particularly important because it enables meaningful reproductive choice. For example, a negative result can substantially reduce risk whereas a positive result can open an avenue for prenatal diagnosis (in appropriate cases).
A major advance in diagnosing ILD through genetics was the discovery that cytogenetically invisible genome imbalances involving chromosome tips (telomeres) account for many ILD cases (Flint et al. 1995). Subsequently, a test assaying every telomere of an individual by fluorescence ‘in situ’ hybridisation to chromosomes (‘multi-telomere FISH’) was developed and widely adopted in diagnostic laboratories (Knight et al. 1997). Further technological advances led to a new approach, array comparative genome hybridisation (aCGH), that identifies cryptic genome imbalances at the genome-wide level (Knight and Regan 2006).
Despite this, aCGH is not implemented widely in the NHS. One obstacle is the lack of consensus regarding ‘platform choice’, that is, the best combination of array type, experimental methodology and analysis system. Another obstacle is concern over the proportion of confirmed genome imbalances where the significance of the positive result is unknown e.g. very small ‘de novo’ imbalances and some inherited imbalances. However, the most significant obstacle to date is the perception that the technology is prohibitively expensive for most NHS clinical cytogenetics laboratories. Local commissioners are unable to endorse implementation without considering the clinical utility and economic implications of technology adoption. Whilst the clinical and scientific utility of aCGH in ILD is impressive, information on its economic viability in routine clinical practice is lacking. Therefore, our study aimed to estimate the cost-effectiveness of aCGH compared with standard cytogenetic analysis in ILD.
The costs and effects (number of additional diagnoses) of an aCGH test versus standard cytogenetic analysis using karyotyping, were compared. A cost per diagnosis detected was used rather than a cost per life year gained or quality adjusted life year (QALY), as testing is unlikely to save lives and evaluating QALY’s is problematic in children, especially those with LD.
An NHS perspective was adopted and to make the results generally applicable to UK laboratories, four laboratories currently investigating ILD using aCGH, karyotyping or both contributed to data collection: (i) The Wellcome Trust Centre for Human Genetics, University of Oxford (arrays), (ii) Oxford Regional Cytogenetics Laboratory (karyotyping), (iii) Birmingham Regional Genetics Laboratory (arrays and karyotyping) and (iv) South East Scotland Cytogenetics Laboratory, Edinburgh (arrays). These were selected because they employ slightly different testing procedures (e.g. different staff grades or level of automation).
Testing pathways and resource use
Resource information on staff times, consumables and capital was derived from the questionnaires. Salary costs were attached to these based on NHS Agenda for Change figures (Department of Health 2005) and unit costs were attached to equipment and resource information from laboratory price lists including 17.5% VAT, with maintenance and service costs being included under the equipment warranty. For capital items (e.g. array scanners), the cost was spread over the items predicted lifetime and depreciated using equivalent annual costing, discounted at 3.5% (Drummond et al. 2005; HM Treasury 2006). Overheads, including electricity were calculated as a percentage of total costs (around 20%).
The costs of routine cytogenetics analysis include karyotype analysis (see http://www.oup.co.uk/pdf/pas/12–7–1.pdf for standard protocol). Array costs were based on Agilent Technologies Inc. 4 × 44 K genome-wide oligonucleotide multi-sample format arrays, with four different patient DNAs per slide (see www.chem.agilent.com/temp/radAAF6F/00060479.pdf for protocols).
Imbalance is real and clinically relevant; without a positive family history, this would generally be expected to be ‘de novo’ (absent in clinically normal parents) and may or may not have been reported before in similarly affected individuals. However, the imbalance may also be inherited from a clinically normal parent, the phenotype due to a recessive condition, incomplete penetrance or genomic imprinting, for example.
Imbalance is real, but not clinically relevant; it may be a benign polymorphism inherited from a clinically normal parent or a ‘de novo’ benign variant that may or may not have been reported before.
Imbalance is not real; it is a false positive that a different test fails to confirm.
Several testing and reporting scenarios were identified. For arrays, additional tests such as testing parents using arrays or FISH were included. For karyotyping, testing parents and using feasible follow-up tests of multi-telomere FISH and multi-telomere MLPA, were costed. Expert opinion (Laboratory Directors) and laboratory records developed these scenarios. Average test throughput was determined by annual laboratory figures, equipment and staff availability. Additional targeted tests e.g. those for specific gene mutations and biochemical tests were not costed, as they apply to both karyotyping and aCGH approaches when negative.
This explored the impact that changing individual costs has on total costs. The costs varied included: arrays and scanner, percentage used to calculate overheads, array labelling, different staff grades, karyotyping probe costs and test throughput. Ranges were based on expert opinion. Data analyses were conducted in Microsoft Excel 2003 and costs reported in Pounds sterling (£), using 2006 prices. As costs were derived from different laboratories, the results presented are averages of the four laboratories.
To create a cost per diagnosis, cost data were combined with information on the predicted number of diagnoses for 100 hypothetical ILD cases referred via genetics clinics for genomic imbalance testing. Costs were assigned to the karyotyping route (factoring in one additional genome imbalance test, a telomere assay, for karyotypically normal samples) and for aCGH (where few, if any, additional genome imbalance tests are required). The number of diagnoses expected and the testing scenarios were derived from clinical diagnostic laboratory records (karyotyping), research experience (testing scenarios and 44 K aCGH results to date) and published data (karyotyping, subtelomeric studies and aCGH ILD studies) (de Vries et al. 2005; Knight 2005; Knight and Regan 2006; Menten et al. 2006; Miyake et al. 2006; Rauch et al. 2006; Ravnan et al. 2006; Rosenberg et al. 2005; Schoumans et al. 2005; Shaw-Smith et al. 2004; Tyson et al. 2005; Vissers et al. 2003).
Staff costs for aCGH and Karyotyping
Medical technical officer
Consultant grade scientist
Cost per hour range (£.p)a
Median cost per hour (£.p)
Hands-on time (minutes)
Cost per sample range (£.p)
Cost per sample (£.p)b
Cost per hour range (£.p)a
Hands-on time range (minutes)
Hands-on time mid-point (minutes)
Cost per sample range (£.p)
Median Cost per sample (£.p)b
Array CGH cost breakdown
Sample reception and initial processing
Digestion/Reference Sample Processing
Arrays, plus preparation and washing b
Analysis and report writing
General resources (e.g. PC and printer)
Karyotyping cost breakdown
Sample reception and initial processing
Media preparation/setting up culture
Analysis and checking
Reporting results and authorisation
General resources (e.g. PC and printer)
Karyotyping costs associated with different testing and reporting scenarios
Karyotyping––Cost of reporting different scenarios
Cost of Karyotypingb
Cost of Multi-telomere MLPAb
Cost of Multi-telomere FISHb
Cost of Targeted telomere FISHb
1 No genome imbalance found.
2 Genome imbalance found of known clinical relevance.
3 Genome imbalance found of unknownclinical relevance. Parental samples karyotyped. Imbalance confirmed as inherited or ‘de novo’.
£351 (£309– £393) (3 tests)
£351 (£309–£393) (3 tests)
4 No genome imbalance found. Multi-telomere MLPA test performed. No imbalance found.
5 No genome imbalance found. Multi-telomere FISH test performed. No imbalance found.
6 No genome imbalance found. Multi-telomere MLPA test performed. Genome imbalance found. Parental samples tested by Multi-telomere MLPA. Imbalance identified as clinically benign (normal parent carries identical anomaly) or imbalance confirmed ‘de novo’ by FISH and likeky to be clinically relevant.
£220 (£200–£240) (max. 2 tests)
7 No genome imbalance found. Multi-telomere FISH test performed. Genome imbalance found. Parental samples tested by Targeted telomere FISH. Imbalance identified as clinically benign (normal parent carries identical anomaly) or clinically relevant.
£324c (£304–£344) (max. 4 tests)
aCGH costs associated with different testing and reporting scenarios
aCGH–Cost of different reporting scenarios
Cost of patient aCGH test
Cost of Targeted FISH test
Cost of MLPA test
Cost of 2 parental aCGH testsb
1 No genome imbalance found; only known benign variants/polymorphisms
2 Genome imbalance (deletion or duplication) found of known clinical relevance.
3 Genome imbalance (deletion or duplication) found of unknownclinical relevance. Targeted FISH test performed on patient sample. Genome imbalance confirmed. Targeted FISH test on parental samples. Imbalance confirmed as inherited or ‘de novo’.
4 Genome imbalance (duplication only) found of unknownclinical relevance. Targeted FISH test performed on patient sample. Genome imbalance not confirmed. Targeted MLPA test on parental samples. Imbalance confirmed as inherited.
5 Genome imbalance (duplication only) found of unknownclinical relevance. Targeted FISH test performed on patient sample. Genome imbalance not confirmed. Targeted MLPA test on parental samples negative. Targeted MLPA test on patient sample performed. Imbalance confirmed as ‘de novo’.
6 Genome imbalance (deletion or duplication) found of unknownclinical relevance. Parental samples tested by aCGH. Imbalance confirmed as inherited
7 Genome imbalance (deletion or duplication) found of unknownclinical relevance. Parental samples tested by aCGH and negative. Targeted FISH test performed on patient sample. Imbalance confirmed as ‘de novo’
For karyotyping, staff time required to perform the test or grade of staff used was the area most likely to impact upon total costs. For instance, substituting a clinical scientist with an MTO reduced the total cost to £95, a difference of £22. Other costs had limited impact upon karyotyping total costs. For aCGH, varying array (slide) costs had the greatest impact upon total cost. Changing the array to £25 per patient, reduced total test cost to £342. By comparison, equipment and staff costs had limited impact.
Comparing the costs of aCGH versus standard karyotyping
Cost comparison of aCGH and karyotyping per sample
For positive results (diagnoses), Fig. 4 shows that with karyotyping and multi-telomere testing, 8/100 diagnoses are expected, costing £39,652 using multi-telomere FISH and £17,032 using multi-telomere MLPA. With aCGH, the most conservative estimate of at least 18 diagnoses is used (10% more than the karyotyping route). Here, the least expensive testing strategy (aCGH followed by patient targeted FISH and parental targeted FISH or targeted MLPA) gives an overall cost of £56,130. Thus, karyotyping with just one additional test of multi-telomere FISH equates to spending £4,957 to obtain a single diagnosis with 92% cases requiring further tests to reach a diagnosis at a later stage. Using multi-telomere MLPA the figure is reduced to £2,129 per diagnosis, again with 92% cases requiring further tests to reach a diagnosis. By contrast, the aCGH route equates to £3,118 per single diagnosis (assuming 10% more diagnoses than karyotyping plus multi-telomere testing combined), with no further tests for genomic imbalance required. This reduces to £2,440 per diagnosis if the diagnostic yield of aCGH is 15% more than karyotyping plus multi-telomere testing.
This paper has reported a cost-effectiveness analysis comparing aCGH with karyotyping for detecting genomic imbalances that diagnose ILD. The average cost of aCGH was £442 per single (patient) sample and the average cost of karyotyping was £117 per sample. The majority of the cost-difference was accounted for by the array cost. Thus, from a single test perspective, aCGH is more expensive than karyotyping, explaining, in part, the hesitation by commissioners to fund aCGH in NHS diagnostic laboratories.
In reality, the situation is more complex because information regarding subsequent tests for genomic imbalance must be considered before the true cost-effectiveness can emerge. We have shown that the overall cost per diagnosis of the karyotyping route, including a single multi-telomere FISH assay (£4,957) is more expensive than that of the aCGH route (£3,118) that yields 10% more diagnoses. However, if the less conservative yield of 15% more diagnoses is correct, then the aCGH cost reduces to £2,440 per diagnosis, a figure more comparable to karyotyping plus the alternative multi-telomere assay, MLPA (£2,129 per diagnosis). Importantly, 92% of cases tested by karyotyping and a multi-telomere assay will require further tests for an eventual diagnosis. By contrast, the aCGH route, which effectively represents karyotyping, multi-telomere testing and not one, but ∼34,000 interstitial FISH tests as well as assaying the entire human genome at higher resolution is unlikely to require further genome-wide tests for genome imbalance.
Stand-alone karyotyping is the cheapest test when considered per diagnosis (£2,067), but this is at the sacrifice of missing ∼75% (12/18) diagnoses achievable by aCGH (Fig. 4). Thus, the crux of the aCGH versus karyotyping argument in ILD comes down to diagnostic capability versus cost; how much is it acceptable to spend and how many diagnoses is it acceptable to miss? aCGH clearly offers the greatest diagnostic capability, providing 10–15% more diagnoses over all other available tests.
One limitation of our study is that we do not know the full magnitude of the cost for additional follow-up tests after karyotyping. However, we do know that such costs would rapidly escalate and even then the majority of diagnoses achievable by aCGH would be missed; clinically relevant genomic imbalances found by genome-wide aCGH are rarely recurrent and therefore targeted approaches are unlikely to improve diagnostic yield (Veltman and de Vries 2006). Even if multi-telomere FISH or multi-telomere MLPA are not the tests chosen following karyotyping, costing in only five targeted tests for genomic imbalance at £100/test for every sample with a normal karyotype would raise the overall cost of testing 100 patients to ∼£59,402 (compared with £56,130 for aCGH) and offer negligible improvement in resolution overall. Thus, even without precise costing of follow-up tests, our results suggest that aCGH is the most cost-effective testing strategy in the long-term for testing ILD patients.
A further study limitation is the use of a simple outcome measure, diagnosis, rather than the more usual cost per quality adjusted life year promoted by the National Institute for Health and Clinical Excellence. However, describing and valuing health states in children is difficult and well documented (Petrou 2003). Current methodological work in health economics may give useable health states for children, but is unlikely to be suited for those with LD.
In this study, one scenario that we were unable to cost either for karyotyping or array CGH was that of further researching apparently inherited genome imbalances for which clinical relevance cannot be excluded. Such cases may reflect benign variants or may cause disease through unmasking recessive mutations, through variable penetrance or through imprinting, for example. Currently these account for up to 32% cases (see Fig. 4) and therefore follow-up tests such as sequencing would be prohibitively expensive (not all inherited imbalances are small). However, as more and more studies are performed and more data regarding benign/relevant genome imbalances and genotype/phenotype correlations are added to databases, it is anticipated that the clinical relevance of a significant number of these cases will be defined earlier, thereby minimising the need for parental testing or additional follow up tests. This in turn will lead to reduced aCGH testing costs and costs arising from doctor/counsellor time taken to discuss uncertain results. Furthermore, it may become possible to reduce costs more by employing better defined clinical ‘gatekeeping’ criteria to help clinical geneticists direct testing (thereby minimising total tests done). Databases such as The Database of Genomic Variants (http://projects.tcag.ca/variation/), The Human Structural Variation database, (http://humanparalogy.gs.washington.edu/structuralvariation/), ECARUCA (http://agserver01.azn.nl:8080/ecaruca/ecaruca.jsp) and the DatabasE of Chromosomal Imbalance and Phenotype in Humans using Ensembl Resources (DECIPHER http://www.sanger.ac.uk/PostGenomics/decipher/) have all been designed to expedite these processes. Another possibility may be to use targeted arrays as an initial screening test for paediatrician referrals, though currently these offer no cost advantage over genome-wide arrays and utility will depend on a high diagnostic pick-up rate.
In the meantime, it will continue to be important for families to be counselled in possible outcomes before taking up the test, for both parental samples to be available for testing and for any outgoing laboratory reports to be carefully designed with clearly defined results e.g. array batch, controls used, imbalances found, confirmatory method and database search results that might help inform clinical relevance.
In conclusion, our study demonstrates that in the context of ILD, genome-wide aCGH is viable for NHS diagnostic use. Indeed, where possible, it may be appropriate to replace karyotyping with aCGH as the first-line test for genomic imbalance in ILD. If needed, samples that are normal by aCGH could then be karyotyped in order to identify truly balanced rearrangements or further characterise genome imbalances. Additionally, aCGH is expected to be useful for clarifying previous equivocal karyotyping results (e.g. enabling definition of a cryptic translocation in a family where only one of two unbalanced outcomes is cytogenetically visible).
In the future, improved diagnostic yields of aCGH and reduced follow-up tests will lower the costs of clinical follow-up and additional investigations. Advances in technology will also reduce costs (e.g. automation, increased probe density, multi-sample and cheaper array production and hybridisation methods) and software improvements may reduce analysis time.
Finally, it is important to note that potential applications of microarray technology extend beyond the genetic diagnosis of ILD to include a range of other conditions with suspected genome imbalance and/or aberrant gene expression e.g. haematological malignancies, colorectal cancer and other fields including oncology, immunology, neurology and pathology. The UK Department of Health is keen for the NHS to adopt new technologies (Department of Health 2003), yet commissioners are unable to endorse implementation without considering the clinical utility and economic implications of technology adoption. Our costing, with the results divided into different testing stages provides a framework for costing array implementation in different settings. Not least, it is intended that the study will be useful for healthcare providers faced with the decision of introducing aCGH testing into NHS laboratories before the availability of substantial effectiveness information.
The authors are grateful for financial support from the Oxford Genetics Knowledge Park (Dept of Health and Dept of Trade and Industry: RR, SK and JT). SW is supported by a UK Department of Health Fellowship. We acknowledge support from the UK Microarray Working Party, especially Hilary Burton, John Barber and Phillipa Brice.
- Aitman (2001) Science, medicine, and the future: DNA microarrays in medical practice. BMJ 323:611–615PubMedPubMed CentralView ArticleGoogle Scholar
- de Vries B, Pfundt R, Leisink M, Koolen D et al. (2005) Diagnostic genome profiling in mental retardation. Am J Hum Genet 77:606–616PubMedPubMed CentralView ArticleGoogle Scholar
- Department of Health (2001) A new strategy for learning disability for the 21st Century. (http://www.archive.official-documents.co.uk/document/cm50/5086/5086.pdf)Google Scholar
- Department of Health (2003) Genetics white paper: our inheritance, our future–realising the potential of genetics in the NHS. (http://www.dh.gov.uk/PublicationsAndStatistics/Publications/PublicationsPolicyAndGuidance/PublicationsPolicyAndGuidanceArticle/fs/en?CONTENT_ID = 4006538&chk = enskFb)Google Scholar
- Department of Health (2005) NHS reference costs. (www.doh.gov.uk/nhsexec/refcosts.htm)Google Scholar
- Drummond M, Sculpher M, Torrance G, O’Brien B et al. (2005) Methods for the economic evaluation of health care programmes. Oxford University Press, OxfordGoogle Scholar
- Flint J, Wilkie A, Buckle V, Winter R et al. (1995) The detection of subtelomeric chromosomal rearrangements in idiopathic mental retardation. Nat Genet 9:132–140PubMedView ArticleGoogle Scholar
- HM Treasury (2006) Green book, appraisal and evaluation in central government. (http://greenbook.treasury.gov.uk/)Google Scholar
- Knight S (2005) Subtelomeric rearrangements in unexplained mental retardation. In: Fuchs P (eds) Encyclopedia of medical genomics and proteomics. Marcel Dekker Inc, New York, pp 1246–1252Google Scholar
- Knight S, Horsley S, Regan R, Lawrie N et al (1997) Development and clinical application of an innovative fluorescence in situ hybridisation technique which detects submicroscopic rearrangements involving telomeres. Eur J Hum Gen 5:1–8Google Scholar
- Knight S, Regan R (2006) Idiopathic learning disability and genome imbalance. Cytogen Gen Res 115:215–224View ArticleGoogle Scholar
- Mencap (2001) No ordinary life: The support needs for families caring for children and adults with profound and multiple learning difficulties. (http://www.mencap.org.uk/download/no_ordinary_life.pdf)Google Scholar
- Menten B, Maas N, Thienpont B, Buysse K et al (2006) Emerging patterns of cryptic chromosomal imbalances in patients with idiopathic mental retardation and multiple congenital anomalies: a new series of 140 patients and review of the literature. Dig J Med Genet: doi:10.1136/jmg.2005.039453 (http://jmg.bmj.com/cgi/rapidpdf/jmg.032005.039453v039451)Google Scholar
- Miyake N, Shimokawa O, Harada N, Sosonkina N et al (2006) BAC array CGH reveals genomic aberrations in idiopathic mental retardation. Am J Med Genet Part A 140A:205–211View ArticleGoogle Scholar
- Petrou S (2003) Methodological issues raised by preference-based approaches to measuring the health status of children. Health Eco 12:697–702View ArticleGoogle Scholar
- Rauch A, Hoyer J, Guth S, Zweier C et al (2006) Diagnostic yield of various genetic approaches in patients with unexplained developmental delay or mental retardation. Am J Med Genet Part A 140A:2063–2074View ArticleGoogle Scholar
- Ravnan JB, Tepperberg JH, Papenhausen P, Lamb AN et al (2006) Subtelomere FISH analysis of 11,688 cases: an evaluation of the frequency and pattern of subtelomere rearrangements in individuals with developmental disabilities. J Med Genet 43:478–489PubMedPubMed CentralView ArticleGoogle Scholar
- Roeleveld N, Zielhuis G, Gabreels F (1997) The prevalence of mental retardation: a critical review of recent literature. Develop Med Child Neurol 39:125–132PubMedView ArticleGoogle Scholar
- Rosenberg C, Knijnenburg J, Chauffaille M, Brunoni D et al (2005) Array CGH detection of a cryptic deletion in a complex chromosome rearrangement. Human Genet 116:390–394View ArticleGoogle Scholar
- Schoumans J, Ruivenkamp C, Holmberg E, Kyllerman M et al (2005) Detection of chromosomal imbalances in children with idiopathic mental retardation by array based comparative genomic hybridisation (array-CGH). J Med Genet 42:699–705PubMedPubMed CentralView ArticleGoogle Scholar
- Shaw-Smith C, Redon R, Rickman L, Rio M et al (2004) Microarray based comparative genomic hybridisation (array-CGH) detects submicroscopic chromosomal deletions and duplications in patients with learning disability/mental retardation and dysmorphic features. J Med Genet 41:241–248PubMedPubMed CentralView ArticleGoogle Scholar
- Tyson C, Harvard C, Locker R, Friedman J et al (2005) Submicroscopic deletions and duplications in individuals with intellectual disability detected by array-CGH. Am J Med Genet Part A 139A:173–185View ArticleGoogle Scholar
- Veltman J, de Vries B (2006) Diagnostic genome profiling: unbiased whole genome or targeted analysis? J Mole Diag 8:534–537View ArticleGoogle Scholar
- Vissers L, deVries B, Osoegawa K, Janssen I et al (2003) Array-based comparative genomic hybridization for the genome-wide detection of submicroscopic chromosomal abnormalities. Am J Human Genet 73:1261–1270View ArticleGoogle Scholar