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High-resolution SNP array analysis of patients with developmental disorder and normal array CGH results

  • Linda Siggberg1Email author,
  • Ala-Mello Sirpa2,
  • Linnankivi Tarja3,
  • Avela Kristiina4,
  • Scheinin Ilari1, 5, 7,
  • Kristiansson Kati6, 7,
  • Lahermo Päivi7,
  • Hietala Marja8,
  • Metsähonkala Liisa3,
  • Kuusinen Esa9,
  • Laaksonen Maarit10,
  • Saarela Janna7 and
  • Knuutila Sakari1
BMC Medical Genetics201213:84

DOI: 10.1186/1471-2350-13-84

Received: 30 May 2011

Accepted: 5 September 2012

Published: 17 September 2012

Abstract

Background

Diagnostic analysis of patients with developmental disorders has improved over recent years largely due to the use of microarray technology. Array methods that facilitate copy number analysis have enabled the diagnosis of up to 20% more patients with previously normal karyotyping results. A substantial number of patients remain undiagnosed, however.

Methods and Results

Using the Genome-Wide Human SNP array 6.0, we analyzed 35 patients with a developmental disorder of unknown cause and normal array comparative genomic hybridization (array CGH) results, in order to characterize previously undefined genomic aberrations. We detected no seemingly pathogenic copy number aberrations. Most of the vast amount of data produced by the array was polymorphic and non-informative. Filtering of this data, based on copy number variant (CNV) population frequencies as well as phenotypically relevant genes, enabled pinpointing regions of allelic homozygosity that included candidate genes correlating to the phenotypic features in four patients, but results could not be confirmed.

Conclusions

In this study, the use of an ultra high-resolution SNP array did not contribute to further diagnose patients with developmental disorders of unknown cause. The statistical power of these results is limited by the small size of the patient cohort, and interpretation of these negative results can only be applied to the patients studied here. We present the results of our study and the recurrence of clustered allelic homozygosity present in this material, as detected by the SNP 6.0 array.

Keywords

Developmental disorder SNP array Diagnostic yield

Background

The diagnostic yield of microarray comparative genomic hybridizations (array CGH) has already proven to exceed that of cytogenetic methods, except when it comes to balanced rearrangements. A consensus statement suggests that microarrays should be used as the first line of testing for developmental disorders of unknown cause [1]. However, as our previous study shows, approximately 80 % of patients with a developmental disorder of unknown cause (mental retardation and/or malformations and/or neurological disorders) remain undiagnosed even by array CGH analysis (44 K, 180 K, or 244 K) [2]. Thus, other methods are clearly needed to define the pathogenic mechanisms. It is plausible that small copy number variants (CNVs) may go undetected if the probe coverage is limited, as it may be in low-resolution arrays. By increasing the resolution, one would thus expect to detect increasingly smaller pathogenic CNVs.

The frequency of uniparental disomy (UPD) in newborns is reportedly ~0.029% [3]. Around 1,100 cases of whole chromosome UPD and some 120 reports on segmental UPD are described in the literature [4]. Some recessive diseases are expressed in children who have inherited the mutation form a single carrier parent [5]. The explanation for this is a meiotic or very early mitotic recombination event between parental homologous chromosomes causing segmental UPD of the genomic segment containing the mutation, and thereby causing a reduction to homozygosity.

As the market is flooded with new arrays, most having an increased resolution and a promise of ever higher detection rates, the question remains what the added value is of these ultra-high resolution arrays. To test this, we used an array with 1.8 million probes and an average resolution of 0.7Kb to analyze samples of 35 patients with developmental disorder of unknown cause, normal karyotype, and normal array CGH results by use of Agilent 44 K, 180 K, or 244 K platforms (Agilent Technologies, Santa Clara, CA, USA).

Methods

Participants

Patients with previous normal array CGH results were asked to participate in the project. All 35 patients were Finnish of origin and had mild to severe mental retardation, associated with dysmorphic features and/or congenital anomalies (Table 1). In addition, 16 patients also had epilepsy. For diagnostic purposes patients had previously been analyzed by whole-genome array CGH (Human Genome CGH Microarray, Agilent Technologies, Santa Clara, CA); 20 using the 244 K platform, 8 using a 180 K platform, and 7 using a 44 K platform. Informed consent was given by all participating families. Blood samples were collected from all patients and their parents. Ethical permission for this project was given by the Ethics Review Board of Helsinki and Uusimaa Hospital District.
Table 1

Clinical characteristics of patients studied

P. Nr.

Growth

Head and neck

Eyes and vision

Ears and hearing

Face

Cardiovascular

Genitourinary

Skeletal and limb defects

Neurologic

Other

1.

Obesity

 

Astigmatism

    

Postaxial polydactyly (one foot) Short meta-carpals V finger clino-dactyly

ID Hypotonia

Bardet-Biedl suspected

2

        

Severe DD No walk/crawl No speech Epilepsy Drooling

 

3.

 

Dolicocephaly Narrow, prominent forehead Low, uneven hairline

Epichantal folds

     

DD Abnormal pons

Hemangiomas

4.

Short stature

   

Low nasal bridge

 

Horseshoe kidney Anal atresia

Small hands and feet

ID

Balanced t(X;13)(q28;q12)

5.

   

Simple ears

Thick and straight eyebrows Broad nasal bridge Long philtrum Retrognathia

   

Autism ID Epilepsy

 

6.

Short stature

Microcephaly

Blindness Optic nerve hypoplasia

    

Scoliosis

ID Epilepsy Severe hypotonia

 

7.

 

Hydrocephalus

      

Brain malformation Severe DD

 

8

 

Microcephaly

Hypertelorism

 

Small nose Low nasal bridge Tented upper lip

   

Severe DD Severe epilepsy

ATRX suspected

9.

  

Severe optic atrophy Impaired vision

     

ID Epilepsy Cortical atrophy

 

10.

  

Mild hypertelorism

Low-set ears

Triangular face Small jaw High palate Thin upper lip

  

Hyper-extensible joints

ID Autistic features Intractable epilepsy

Frax-dna, SCN1A, CLN8 4p-FISH normal

11.

  

Upslanting palpebal fissures

Large earlobes

Small jaw

  

Clubfoot

ID Autistic features Intractable epilepsy

 

12.

  

Epichantic folds

Large earlobes

Flat face

  

Tapering fingers

ID Autistic features ADHD

Balanced t(2;9)(q13q22.3) de novo

13.

 

Macrocephaly

      

Severe ID Hypotonia Autism Epilepsy

Inv 2(p13p25) mat., DMPK mutation negative

14.

  

Severe myopia Cataracta

 

Synophrys Curved eyebrows Upturned pinched nose Big mouth Full lips

Atrial septum defect

  

Severe DD Epilepsy

 

15.

 

Microcephaly

Impaired vision

  

Ventricular septum defect

  

Epilepsy DD

 

16.

        

ID Beahvioural disturbances Autism

No malformation or dysmorphism

17.

        

ID DD

 

18.

Growth retardation

 

Hypertelorism

  

Mild ventricular septum defect

  

ID

 

19. & 20.

        

DD

No structural defects

21

 

Microcephaly

Strabismus

Missing lobuli

Small nose Low nasal bridge Smooth philtrum Thin lips

  

Proximal thumbs Pes planus

ID Intractable epilepsy Ataxia

 

22.

Pre- and postnatal growth retardation

   

Broad nasal root Short nose Bifid nasal tip

 

Cryptorchidism Hypoplastic scrotum

Scoliosis Syndactylies

Slow motor development Hypotonia Expressive language disorder

Congenital contractures Dimples

23.

    

Mild dysmorphism

   

ID Epilepsy

 

24.

 

Microcephaly

Hypertelorism Epicanthic folds Disorder of visual cortex

Low-set and posteriorly rotated ears

Micrognathia Cleft palate

   

ID Epilepsy Hypoplastic cerebellar vermis

Monozygotic twin, twin sister healthy

25.

Tall stature Advanced bone age

 

Deep set eyes Hypotelorism Epicanthic folds Strabismus

 

Short nose Anteverted nares Tented upper lip

 

Cryptorchidism

 

ID No speech Autism

Glypican-3 and PHF6 mutation analyses negative

26.

Short stature

       

ID Intractable epilepsy Tremor Myoclonias Distal spasticity

 

27.

Small for Gestational Age

 

Downslanting palpebral fissures Strabismus

 

Frontal bossing

  

Exostosis (familial) Broad hallux Overriding toes Scoliosis

ID Epilepsy

Inguinal hernia

28.

  

Downslanting palpebral fissures

Hearing impairment

Coarse hair Thick eyebrows Thick lips Malposition of teeth

Hypertrophic cardiomyopathy

 

Hip dis-placement Long thin bones

Normal intelligence

 

29.

        

Epilepsy ID Alternating hemiplegia of childhood

 

30.

Small for gestational age Prematurity Short stature

Microcephaly

Severe myopia Coloboma of papillae Optic atrophy Nystagmus Strabismus

 

High palate Gum hypertrophy

Coarctation of aorta

Inguinal hernia

 

ID Intractable epilepsy Hemiparesis (peri-ventricular leukomalacia)

 

31.

  

Central blindness Nystagmus

     

ID Intractable epilepsy Hypotonia Distal spasticity

 

32.

Short stature

       

Normal development

Vomiting Feeding difficulties

33.

 

Neck fistula

  

Dysmorphic malocclusion of teeth

Uni-ventricular heart

  

Brain atrophy Epilepsy

Simian-crease Sinus pilonidalis

34.

        

ID Intractable epilepsy Hypotonia Distal spasticity

 

35.

 

Dolicocephaly

Epichantic fold

Simple ears

Thin upper-lip Long philtrum Broad nasal bridge

   

ID Arnold Chiari malformation

 

The table presents the clinical characteristics of the 35 patients studied. DD = developmental delay, ID = intellectual disability.

SNP array

DNA was extracted from blood samples according to standard protocols. Analysis by the Genome-wide human SNP array 6.0 was performed according to manufacturer protocols (Affymetrix, Santa Clara, CA, USA). In short; DNA was digested, ligated to adapters, and amplified by PCR. Samples were purified using magnetic beads and further fragmented and labelled with biotin. After hybridization arrays were washed and stained with streptavidin and anti-streptavidin antibodies and finally the arrays were scanned using the Affymetrix GeneChip scanner.

Analysis

Data was extracted from the scanned image using the Genotyping console software V.3.0.2, creating a CEL file. Areas containing CNVs and allelic homozygosity were detected using the Hidden-Markow-Model. The resulting data was analyzed using the Chromosome Analysis Suite software V.1.0.

Reference data

Data was extracted by comparison to a reference data set established from 90 Caucasian individuals, which had previously been analyzed using the SNP 6.0 array in the HapMap project (http://www.hapmap.org). As an additional in-house reference set, we used results of 54 individuals studied using the SNP 6.0 array, whereof 19 healthy normal relatives of the patients, and 35 unrelated patients with an unexplained developmental disorder. Sample identities were kept anonymous and the information was only used for reference purposes. These in-house reference sets were used to filter out polymorphic changes in the patient data studied here.

Selected CNVs of the patients were compared to a Finnish population cohort [6]. This population cohort data consist of CNVs detected, using whole-genome SNP analysis, in 2163 healthy Finnish individuals with PennCNV [7]. In the population data, low quality samples (N = 98) with Log R Ratio standard deviation of probe signal intensities > 0.35 or > 115 CNV calls were excluded. Only CNVs with three or more probes were included in the final population data. CNV calls of the study samples were clustered into CNV regions when individual CNVs overlapped by one or more base pairs.

Filtering relevant CNVs and potential UPDs

The first set of default filtering marked all duplications and deletions ≥0.7Kb and all allelic homozygosities ≥100Kb containing at least 10 markers to be included. This was based on the theoretical resolution of the array being 0.7Kb, in addition to information from the HapMap phase 1 study showing that approximately 70% of common haplotype blocks are ≤100Kb [8] . Allelic homozygosity was called by the analysis software where there was a stretch of homozygous SNPs in a chromosomal segment.

The second filtering was based on the comparison of all aberrations detected in the patients of this study (N = 35), an in-house patient reference set (N = 35) and an in-house normal reference set (N = 19) as well as the database of genomic variants (DGV) [9]. Aberrations of one patient that were not present in any of the other groups (potentially “unique”) were further processed by studying their genetic content and association to diseases and traits, as stated in publications or OMIM, and whether these correlated to the patient’s phenotype. If this did not yield a candidate aberration, all aberrations of a patient were reviewed based on only the associated OMIM disease, despite the frequency of similar changes in the reference sets. The CNVs that were picked out as potential candidates were further compared to CNV data from 2,065 healthy Finnish individuals. Only those aberrations that were present in less than 50 individuals of the Finnish population cohort were initially considered as potentially pathogenic.

Validation of candidate aberrations

Only aberrations that were considered to potentially associate with patient’s phenotype were attempted validation.

Microsatellite marker analysis

Potential segmental UPDs were analyzed by microsatellite marker analysis (chr15: D15S204, D15S124; chr6: D6S468, D6S2418; chr11: D11S4140; chr17: D17S578, D17S1832, D17S1828).

The markers were selected based on their location, and on information that they are highly polymorphic in the Caucasian population. Fragments were labelled with a fluorescent HEX label, and separated on an Applied Biosystem 3730XL (Life Technologies, Carlsbad, CA, USA) capillary electrophoresis instrument, according to manufacturer recommendations. Genotypes were called using Applied Biosystems GeneMapper 3.7 software.

Results

The immense amount of data created by the Genome-wide human SNP array 6.0 warrants filtering for clear interpretation. The Genotyping Console software identified between 200–1000 changes per patient (Figure 1), depending on the technical quality of the result. More changes were detected in samples with lesser quality. After filtering, based on the uniqueness of the CNVs or regions of homozygosity compared to the references, each patient presented 8–20 unique changes (≥90% CNVs) on average. In samples with lesser quality, ≥100 unique changes were detected. Further research on gene content and phenotypes previously mapped to these regions revealed 23 CNVs and 28 regions of homozygosity that putatively correlated with the clinical phenotype in 26 patients. Nine patients had no CNVs or regions of homozygosity spanning known genes or genes known to associate with a disease that correlated with the patient’s phenotype, and their results were thus considered normal. The associated phenotypes related to the aberration found in 26 patients were further evaluated by the patients’ clinicians, and the frequencies of the observed CNVs were monitored in the Finnish population cohort (n = 2,065). As a result, in four patients, a region of allelic homozygosity was considered a potential candidate for causation of their clinical state (Table 2). No CNVs were considered candidates after clinical evaluation.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2350-13-84/MediaObjects/12881_2011_Article_998_Fig1_HTML.jpg
Figure 1

Frequency of CNVs and allelic homozygosity. The figure visualizes the frequency of copy number changes (loss and gain) and regions of allelic homozygosity (LOH) in 70 patients (patients of this study N = 35 and the in-house reference set N = 35) with developmental disorders of unknown cause as seen by the Integrative Genomics Viewer (IGV) software V.1.5 (The Broad Institute, Cambridge, MA, USA). The vertical bars show the percentage of patients that have a CNV in a particular area of a chromsome. The higher the bar, the higher the percentage, thus indicating as CNP.

Table 2

Results from microsatellite marker analysis

Patient

Chromosome location

Marker

Location start

Location stop

Patient

Mother

Father

Associated OMIM disease (gene)

1

15q23q24.1

D15S204

72300758

72300879

123/123

123/125

123/125

MIM #209900, Bardet-Biedl Syndrome (BBS4)

  

D15S124

73092468

73092572

104/106

106/106

104/106

 

17

6q16.3

D6S468

101630330

101630479

155/159

159/159

155/155

MIM #611092, Mental retardation (GRIK2)

  

D6S2418

101352425

101352639

222/230

222/248

230/238

 

32

11q13.4

DS11S4140

71945684

71945874

195/195

195/197

195/197

MIM #270400, Smith-Lemli-Opitz syndrome (DHCR7)

28

17p13.2p13.1

D17S578

6824007

6824153

173/173

173/173

155/173

MIM #201475, AcylCoA dehydrogenase deficiency (ACADVL)

  

D17S1832

5972677

5972867

173/185

173/185

171/173/185/193

 
  

D17S1828

3810467

3810673

220/220

214/220

214/220

 

The table presents the results of microsatellite marker analysis of 4 patients and their parents, suggesting biparental inheritance of the genomic segment, and thus exclude segmental UPD. The numbers in the patient/mother/father column represent the two markers detected. In all cases, the patient has likely inherited one marker from each parent.

We further attempted verification of four potential segmental UPDs by microsatellite marker analysis. In all the four cases we observed two distinct alleles with at least some of the more informative multiallelic markers, suggesting that the observed LOHs were most probably caused by the same allelic SNP haplotypes being inherited from both the parents (Table 2).

Further comparison, of all patient SNP array data to the normal data from unaffected individuals, revealed 21 regions of clustering (≥40 % frequency) of allelic homozygosity to specific locations of the genome (Table 3). However, no significant differences were detected between the frequencies of clustered regions in patients and the unaffected relatives in this small set of samples.
Table 3

Regions of clustered allelic homozygosity

Chromosome

Band

Appoximate range (Kb)

Frequency patients (n = 70)

Frequency normals (n = 19)

1

p33-p32.3

48 700–53 300

47.7%

47.7%

1

q21.1-q21.2

145 800–148 500

49%

50%

2

q21.2-q21.3

134 334–136 693

42%

58%

3

p21.31-p21.1

46 500–52 500

71%

68%

4

p15.1

31 838–34 524

60%

57%

8

q22.2

99 200–101 200

47%

63%

8

p11.21-p11.1

41 870–43 270

49%

47%

8

q11.1-q11.21

47 040–49 000

46%

68%

10

p11.21

36 720–38 490

43%

31%

10

q22.2-q22.2

73 200–76 460

44%

31%

12

q21.32-q21.33

85 850–89 100

47%

47%

12

q24.11-q24.13

108 600–111 600

55%

68%

14

q23.3-q24.1

65 500–67 100

62%

73%

15

q12-q13.1

25 400–27 200

71%

68%

15

q15.1-q21.1

40 100–43 730

64%

84%

15

q23-q24.1

69 300–71 700

41%

15%

16

p11.2-p11.1

33 394–34 550

62%

68%

16

q11.2-q12.1

45 092–47 450

64%

63%

16

q21-q22.1

64 850–67 100

48%

57%

17

q22-q23.2

54 610–56 850

67%

68%

20

q11.22-q11.23

31 910–35 500

68%

42%

The table presents the frequency (> 40%) of clustered regions of allelic homozygosity in the patient cohort (n = 70, including in-house reference) compared to the unaffected relatives (n = 19). Kilobase range according to Genome build 19 (NCBI 37).

Discussion

In our previous study of 150 patients with developmental disorders of unknown cause and a normal karyotype, we were able to identify a (potential) causative aberration in 18% of the patients, by using a 44 K or 244 K array CGH platform [2].

To determine whether, by increasing the resolution, any additional copy number changes or regions of UPD could be detected, we studied 35 patients with a normal array CGH result.

Allelic homozygosity is typically caused by linkage and co-segregation of certain blocks of DNA, termed haplotypes [8]. In a small founder population, such as the Finnish population, the founder effect increases the likelihood that the parents will have the same haplotype, and is as such not a segmental UPD [10]. True segmental UPD is typically due to a duplication in one chromosome and a reciprocal deletion in the other; or the fertilization of a disomic and monosomic gamete, somatic crossing over and subsequent trisomic rescue [11]. If the genomic segment harbours a recessive mutation, which subsequent to UPD will be present in two copies (reduction to homozygosity), it causes a recessive disease. Equally relevant, the segment can be preferentially imprinted, causing complete silencing, which is the equivalent of a deletion. Such presentations of recessive syndromes that are inherited from one normal parent are known in some 40 patients [12].

We were interested to see whether the regions of allelic homozygosity detected by the SNP array were in fact segmental UPDs and associated with an autosomal recessive disease. We were, however, unable to confirm these results and thus the SNP array did not yield more molecular diagnoses in this study of developmental disorders of unknown cause. This may be due to the fact that all patients had previously been studied by another high-resolution array, with 8.9Kb (244 K), 13Kb (180 K), and 35Kb (44 K) theoretical resolutions. Although several new CNVs, previously undetected by the array CGH platform, as well as regions of homozygosity were detected, the pathogenic relevance of these changes were considered insignificant in correlation to the patient’s phenotype. It is, however, possible that changes dismissed in this study are pathogenic by means of spanning genomic segments that do not directly involve disease genes, but rather their regulatory elements.

Our results differ from previously published studies using similar research settings. Bernardini et al. (2010), using the SNP 6.0 array platform with a 75Kb cut-off value for detected CNVs, reported potentially pathogenic CNVs in 6% of patients with normal array CGH result (44 K) [13]. Mannik et al. (2010), using another SNP array with a 50Kb resolution, reported a 23% detection rate in patients with a normal karyotype [14]. Bernardini et al. and Mannik et al. had higher detection rates than this study; perhaps as their first-line of array analysis was, at least partly, done by a lower-resolution method (35Kb and 50Kb respectively) compared to the first-line of detection in this study (8,9Kb, 13Kb and 35Kb). Also, it is important to note that the statistical power is limited by the small size of our patient cohort, and thus results are not entirely comparable with Bernardini and Mannik’s.

UPDs have not been reported in either of the above mentioned studies. However, in a study of 117 patients with a normal karyotype analysed using the 250 K SNP array (Affymetrix, Santa Clara, CA, USA), pathogenic CNVs were detected in 18 patients, and potentially pathogenic segmental UPDs ≥5 Mb in 5, verified by microsatellite marker analysis [15]. The presence of UPDs was also evaluated in another study of 120 patients, using a 500 K SNP array platform [16]. In that study they were unable to verify UPD in any of 121 detected regions of homozygosity in 72 patients with developmental disorder of unknown cause. In addition, in a study of 100 patients with developmental disorder, using a 500 K SNP array, two patients were found to have UPD, the clinical significance of which remained unclear [17]. Thus, UPDs are detectable using SNP arrays, but their clinical significance is difficult to interpret.

Conclusions

Although there is a clear added value of high-resolution arrays in various fields of genetics, it seems that there is a limit to how much the yield can be increased by increasing the theoretical resolution of the analysis platform. Despite the fact that the SNP array has increased probe spacing compared to the 44 K, 180 K, and 244 K array, and is able to detect more CNVs and regions of homozygosity, interpretation of the vast amount of data and pinpointing of pathogenic changes is difficult. One benefit of using a SNP based platform is the possibility to detect UPDs; however these are relatively rare findings.

This study had a limited number of patients, and so it can only be said that for this study group the optimal yield was conceived when using a resolution of approximately 9Kb [2]. Increasing the resolution beyond that did not confer more diagnoses. It must be emphasized, however, that a larger patient cohort needs to be studied in order to draw final conclusions on the added value of an ultra-high resolution array compared to others. Furthermore, as several reports have shown, some pathogenic aberrations span only a few exons and for detecting such small changes the sensitivity of the SNP 6.0 platform is adequate [18]. It is, however a challenge to filter results correctly and so for diagnostic purposes the choice of platform needs to be carefully considered. Patients with developmental disorders of unknown cause and normal array results may also harbour such small genomic changes (i.e. mutations and unbalanced rearrangements) that are difficult to interpret using microarrays and would require higher resolution methods, such as whole-exome sequencing. Interestingly, a recent study suggests that 80% of patients with a developmental disorder of unknown cause and normal array results can be diagnosed using whole-exome sequencing [19]. Only the future can tell.

Availability of supporting data

The data set supporting the results of this article is available in the CanGEM repository, http://www.cangem.org/browse.php.

Declarations

Acknowledgements

We thank all the participating patients and their families. A thanks to Marketta Dalla Valle for her contribution of clinical information. The staff from the Genotyping Facilities at the Wellcome Trust Sanger Institute is acknowledged for genotyping of the Health 2000 study sample. This study was kindly supported by Rinnekoti Research Foundation, Medicinska Understödsföreningen Liv och Hälsa rf., and The Helsinki and Uusimaa Hospital District State Appropriations. K.K. was supported by the Orion-Farmos Research Foundation and the Academy of Finland (grant no. 125973).

Authors’ Affiliations

(1)
Department of Pathology, Haartman Institute, University of Helsinki, and Laboratory of Helsinki and Uusimaa University Hospital
(2)
Rinnekoti Foundation, Rehabilitation Home for Children
(3)
Department of Pediatric Neurology, Helsinki University Central Hospital
(4)
Väestöliitto, The Family Federation of Finland, Department of Medical Genetics
(5)
Department of Pathology, VU University Medical Center
(6)
Public Health Genomics Unit, Department of Chronic Disease Prevention, National Institute for Health and Welfare
(7)
Institute for Molecular Medicine Finland FIMM, University Helsinki
(8)
Department of Clinical Genetics, Turku University Hospital and Department of Medical Biochemistry and Genetics, University of Turku
(9)
Department of Pediatrics, Satakunta Hospital District
(10)
Population Health Unit, Department of Health, Functional Capacity and Welfare, National Institute for Health and Welfare

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  20. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2350/13/84/prepub

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© Siggberg et al.; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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