Open Access

Meta-analysis of heterogeneous Down Syndrome data reveals consistent genome-wide dosage effects related to neurological processes

  • Mireia Vilardell1, 2Email author,
  • Axel Rasche1,
  • Anja Thormann1,
  • Elisabeth Maschke-Dutz1,
  • Luis A Pérez-Jurado2, 3,
  • Hans Lehrach1 and
  • Ralf Herwig1Email author
BMC Genomics201112:229

DOI: 10.1186/1471-2164-12-229

Received: 2 December 2010

Accepted: 11 May 2011

Published: 11 May 2011

Abstract

Background

Down syndrome (DS; trisomy 21) is the most common genetic cause of mental retardation in the human population and key molecular networks dysregulated in DS are still unknown. Many different experimental techniques have been applied to analyse the effects of dosage imbalance at the molecular and phenotypical level, however, currently no integrative approach exists that attempts to extract the common information.

Results

We have performed a statistical meta-analysis from 45 heterogeneous publicly available DS data sets in order to identify consistent dosage effects from these studies. We identified 324 genes with significant genome-wide dosage effects, including well investigated genes like SOD1, APP, RUNX1 and DYRK1A as well as a large proportion of novel genes (N = 62). Furthermore, we characterized these genes using gene ontology, molecular interactions and promoter sequence analysis. In order to judge relevance of the 324 genes for more general cerebral pathologies we used independent publicly available microarry data from brain studies not related with DS and identified a subset of 79 genes with potential impact for neurocognitive processes. All results have been made available through a web server under http://​ds-geneminer.​molgen.​mpg.​de/​.

Conclusions

Our study represents a comprehensive integrative analysis of heterogeneous data including genome-wide transcript levels in the domain of trisomy 21. The detected dosage effects build a resource for further studies of DS pathology and the development of new therapies.

Background

Down syndrome (DS) is the most frequent genomic aneuploidy with an incidence of approximately 1 in 700 live-newborn [1] resulting from the presence of an extra copy of human chromosome 21 (HSA21). DS is characterized by a complex phenotype with features that are not fully penetrant. The most frequent manifestations, which are virtually always present, include mental retardation, morphological abnormalities of the head and limbs, short stature, hypotonia and hyperlaxity of ligaments. Other features occur with less frequency such as organ malformations, particularly of the heart (50% of DS newborns), several types of gastrointestinal tract obstructions or dysfunctions (4-5% of DS newborns), increased risk of leukaemia (20 × higher compared to the normal population), and early occurrence of an Alzheimer-like neuropathology [2, 3]. DS has been investigated with multiple functional genomics studies aiming to understand the molecular basis underlying the various aspects of the disease [47].

The most commonly accepted pathogenetic hypothesis is that the dosage imbalance of genes on HSA21 is responsible for the molecular dysfunctions in DS, meaning that genes on the triplicated chromosome are overexpressed due to an extra chromosome 21, as demonstrated for selected genes like SOD1 and DYRK1A [8]. Recent global transcriptome studies with microarrays, however, have generated a more complex picture in the sense that not all HSA21 genes have an elevated expression level as expected [9, 10]. An alternative hypothesis is that the phenotype is due to an unstable environment resulting from the dosage imbalance of the hundreds of genes on HSA21 which determines a non-specific disturbance of genomic regulation and expression. The significantly higher inter-individual variability in DS, as compared to euploid, individuals supports this hypothesis [11]. Moreover, the two hypotheses could be coexistent [3]. In both hypotheses it is understood that besides alterations of gene expression of HSA21 genes there are numerous genome-wide effects that lead to the dysregulation of many non-HSA21 genes through molecular pathways and interactions.

Many studies on the transcriptome and proteome levels have been conducted to understand the causal relationship between genes at dosage imbalance and DS phenotypes [12]. Gene expression profiles have been analysed from DS fetal [13] and adult human tissues [6]. Additionally, two classes of mouse models [14] have been developed for investigating the molecular genetics of DS, either mouse models with partial trisomies of the syntenic regions of HSA21 in mouse chromosomes 10, 16 or 17, such as Ts16 [15], Ts65Dn [16] and Ts1Cje mice [17], or transgenic mice for specific genes such as SOD1 [18]. Studies of gene expression profiles in human DS samples and mouse models have shown high genome-wide variability [11, 1922]. Furthermore, differences due to the applied experimental platforms, specific tissues, developmental stages or the triplicated segments under study introduce a high variation to the assessment of genome-wide effects of DS. Here, integrative and comparative studies are pivotal for the analysis of the complex nature of gene expression and regulation in DS at a more general level [2, 23].

Meta-analysis was proven to be a valid strategy to extract consistent information from heterogeneous data, in particular with respect to complex phenotypes for example cancer [24], Alzheimer [25] and type-2 diabetes mellitus [26]. The purpose of meta-analysis is to compensate experiment-specific variations and to reveal consistent information across a wide range of experiments. To date, such a meta-analysis of DS data is missing.

In this paper we describe a comprehensive meta-analysis from 45 different DS studies on human and mouse on the transcriptome and proteome level including quantitative data such as Affymetrix microarrays, RT-PCR and MALDI studies as well as qualitative data such as SAGE and Western blot analyses. We applied an established computational framework [26] and identified 324 genes with consistent dosage effects in many of these studies. As expected, we observed a high fraction of HSA21 genes (N = 77) but also a large amount of non-HSA21 genes (N = 247). Besides well investigated genes in the context of DS we detected a significant proportion of novel ones (N = 62). The 324 genes were further investigated using functional information, molecular interactions and promoter analysis revealing over-represented motifs of four transcription factors: RUNX1, E2F1, STAF/PAX2 and STAT3. In order to test the relevance of the 324 genes for more general brain phenotypes we used independent publicly available data on cerebral pathologies not related to DS and identified a subset of 79 DS genes that were differentially expressed in these studies. The detected dosage effects can be used as a resource for further studies of DS pathology, functional experiments and the development of therapies. All data have been agglomerated and made available through a web server that tracks results of the meta-analysis http://​ds-geneminer.​molgen.​mpg.​de/​ and that enables the community to validate any gene of interest in the light of the experimental data.

Results

Genome-Wide Dosage Effects

Genome-wide dosage effects were computed with the numerical scoring method described in Material and Methods. In total, 45 case-control experiments were interrogated (Additional file 1, Table S1), the alteration for each gene between the trisomic and normal states was scored in each experiment, gene scores were summarised across all experiments and the significance of the summarised scores was judged with a Bootstrap approach. This procedure resulted in a cut-off score value of 3.67 and identified 324 genes as being predominantly affected by DS. The thirty genes with the highest dosage effects, either on HSA21 or on other chromosomes, are listed in Table 1. The entire gene list is given in Additional file 1, Table S2.
Table 1

Top thirty DS dosage effects on A) HSA21 and B) other chromosomes

A) Direct effects

        

Ensembl

HUGO

Score

Entropy

Chromo-some

Start

position

End

position

Band

CNV

ENSG00000154734

ADAMTS1

18.487

4.083

chr21

28208606

28217728

q21.3

 

ENSG00000159228

CBR1

17.518

4.509

chr21

37442239

37445464

q22.12

 

ENSG00000159140

SON

15.920

4.712

chr21

34914924

34949812

q22.11

 

ENSG00000142168

SOD1

15.817

4.372

chr21

33031935

33041244

q22.11

 

ENSG00000182670

TTC3L, TTC3

15.637

4.542

chr21

38445526

38575413

q22.13

 

ENSG00000142192

APP

15.489

4.412

chr21

27252861

27543446

q21.3

 

ENSG00000159128

IFNGR2

15.006

4.640

chr21

34757299

34851655

q22.11

 

ENSG00000182240

BACE2

14.156

4.140

chr21

42539728

42648524

q22.2

 

ENSG00000156256

USP16

13.713

4.378

chr21

30396950

30426809

q21.3

 

ENSG00000159131

GART

13.570

4.564

chr21

34870940

34915797

q22.11

 

ENSG00000157540

DYRK1A

13.163

4.405

chr21

38739236

38887680

q22.13

 

ENSG00000157557

ETS2

13.088

4.440

chr21

40177231

40196879

q22.2

 

ENSG00000159231

CBR3

12.185

3.954

chr21

37507210

37518864

q22.12

YES

ENSG00000159082

SYNJ1

11.880

4.284

chr21

33997269

34100359

q22.11

 

ENSG00000142188

TMEM50B

11.516

3.803

chr21

34804792

34853499

q22.11

YES

ENSG00000159110

IFNAR2

11.280

4.360

chr21

34602206

34656082

q22.11

 

ENSG00000157538

DSCR3

11.254

4.316

chr21

38591910

38640262

q22.13

 

ENSG00000157601

MX1

10.976

3.545

chr21

42792231

42831141

q22.3

 

ENSG00000159267

HLCS

10.764

4.183

chr21

38123493

38362536

q22.13

YES

ENSG00000159200

RCAN1

10.719

3.356

chr21

35885440

35987441

q22.12

 

ENSG00000159147

DONSON

10.435

4.295

chr21

34947783

34961014

q22.11

 

ENSG00000156261

CCT8

10.361

4.560

chr21

30428126

30446118

q21.3

 

ENSG00000183486

MX2

10.179

3.598

chr21

42733870

42781317

q22.3

 

ENSG00000154727

GABPA

9.936

4.032

chr21

27106881

27144771

q21.3

 

ENSG00000160200

CBS

9.284

3.907

chr21

44473301

44497053

q22.3

 

ENSG00000159216

RUNX1

9.129

3.783

chr21

36160098

37357047

q22.12

 

ENSG00000183527

PSMG1

8.903

3.733

chr21

40546695

40555777

q22.2

 

ENSG00000182093

WRB

8.837

4.149

chr21

40752170

40800454

q22.2

 

ENSG00000154736

ADAMTS5

8.746

4.221

chr21

28290231

28338832

q21.3

 

ENSG00000159197

KCNE2

8.654

3.660

chr21

35736323

35743440

q22.11

YES

B) Indirect effects

        

Ensembl

HUGO

Score

Entropy

Chromo-some

Start

position

End

position

Band

CNV

ENSG00000117289

TXNIP

8.790

3.281

chr1

145438469

145442635

q21.1

YES

ENSG00000133110

POSTN

8.301

2.437

chr13

38136722

38172981

q13.3

YES

ENSG00000118785

SPP1

7.232

3.159

chr4

88896802

88904563

q22.1

 

ENSG00000113140

SPARC

7.035

3.338

chr5

151040657

151066726

q33.1

 

ENSG00000125968

ID1

6.987

3.164

chr20

30193086

30194318

q11.21

 

ENSG00000136235

GPNMB

6.943

2.047

chr7

23275586

23314727

p15.3

 

ENSG00000171951

SCG2

6.747

2.950

chr2

224461658

224467221

q36.1

 

ENSG00000135821

GLUL

6.604

3.702

chr1

182350839

182361341

q25.3

 

ENSG00000123610

TNFAIP6

6.575

2.377

chr2

152214106

152236560

q23.3

 

ENSG00000118523

CTGF

6.567

2.996

chr6

132269316

132272513

q23.2

 

ENSG00000168209

DDIT4

6.477

3.318

chr10

74033678

74035794

q22.1

 

ENSG00000162407

PPAP2B

6.350

3.343

chr1

56960419

57110974

p32.2

 

ENSG00000038427

VCAN

6.240

2.958

chr5

82767284

82878122

q14.2

 

ENSG00000151491

EPS8

6.194

3.143

chr12

15773076

15942510

p12.3

 

ENSG00000189067

LITAF

6.185

3.255

chr16

11641582

11680806

p13.13

 

ENSG00000164692

COL1A2

6.148

2.852

chr7

94023873

94060544

q21.3

 

ENSG00000204388

HSPA1B

6.109

2.550

chr6

31795688

31798031

p21.33

 

ENSG00000162692

VCAM1

6.012

2.533

chr1

101185305

101204601

p21.2

 

ENSG00000154096

THY1

5.974

3.244

chr11

119288888

119293854

q23.3

 

ENSG00000135919

SERPINE2

5.904

3.048

chr2

224839765

224904036

q36.1

 

ENSG00000172201

ID4

5.887

3.037

chr6

19837617

19840915

p22.3

 

ENSG00000114315

HES1

5.884

2.874

chr3

193853934

193856521

q29

 

ENSG00000172893

DHCR7

5.883

3.441

chr11

71145457

71159477

q13.4

 

ENSG00000204262

COL5A2

5.857

3.174

chr2

189896622

190044605

q32.2

 

ENSG00000149257

SERPINH1

5.846

3.146

chr11

75273170

75283844

q13.5

 

ENSG00000176697

BDNF

5.805

2.416

chr11

27676440

27743605

p14.1

 

ENSG00000182551

ADI1

5.782

3.081

chr2

3501693

3523507

p25.3

 

ENSG00000079739

PGM1

5.661

3.251

chr1

64058947

64125916

p31.3

 

ENSG00000108821

COL1A1

5.527

3.004

chr17

48260650

48278993

q21.33

 

ENSG00000187498

COL4A1

5.514

3.396

chr13

110801318

110959496

q34

 
The meta-analysis identified genes that showed consistent changes in many of the different experiments rather than genes that were affected by a single (or few) experiment(s) (Figure 1A). This is an important fact since, for example, different mouse models have different coverage of triplicated HSA21 genes, and, thus, might introduce model-specific bias [14]. The consistency of the dosage effect was measured for each gene with an entropy criterion (see Materials and Methods) and Figure 1A reveals a strong preference for the selection of high-entropy genes. Highest scores were assigned to HSA21 genes (Figure 1B) what indicates that the meta-analysis scores reflect the effect of an extra chromosome 21 on gene expression (Table 1). While proportionally most dosage effects were identified for HSA21 genes (77 out of 324), the majority of genes (247 out of 324) was located on other chromosomes highlighting the genome-wide impact of DS (Figure 1C).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-229/MediaObjects/12864_2010_Article_3346_Fig1_HTML.jpg
Figure 1

Characterization of dosage effects. A) Entropy (Y-axis) vs. score of dosage effect (X-axis) for all genes, B) Histogram of scores for all 255 HSA21 genes accessible with the experiments under study, C) Distribution of genomic locations of the 324 candidate genes, D) Cytogenetic location of 77 HSA21 genes that show significant dosage effects for all experiments (blue line). Additionally, the same meta-analysis approach has been conducted with human (green line) and mouse (red line) data separately. The yellow line plots the relative number of HSA21 genes within each band (gene density). Y-axis shows percentage of significant genes with respect to all genes annotated for the chromosomal band.

Genome-wide dosage effects underlined the severe phenotypic consequences of DS caused by genes with a major role in human development (Additional file 2, Figure S1). Of the 247 non-HSA21 genes, 72 were associated with development, in particular with respect to organ development (62 genes, GO:0048513), tissue development (34 genes GO:0009888) and cell development (30 genes, GO:0048468). Amongst these genes were known interactors of HSA21 genes, for example REST (RE1-silencing transcription factor). REST modulates expression of genes encoding fundamental neuronal functions including ion channels, synaptic proteins and neurotransmitter receptors and has been linked to an inherited form of mental retardation. Recently, Canzonetta et al. [5] demonstrated that the region capable of affecting REST levels, in both mouse and human cells, could be assigned to the DYRK1A locus on HSA21 which was found among the top-scoring HSA21 genes (Table 1).

TXNIP (thioredoxin interacting protein) had the highest dosage effect (8.79) of all non-HSA21 genes. It has weak association with DS yet (through S100B [27]) but could play a major role for several DS phenotypes. It is a key signalling molecule involved in glucose homeostasis [28], cardiovascular homeostasis [29] and leukaemia [30].

Enrichment of genomic location with respect to the 324 genes was observed in regions of HSA21 and the respective syntenic regions on mouse chromosomes 16, 17 and 10 (Additional file 3, Figure S2). Moreover, in the human genome, additional enrichment on chr3q24 was computed containing the genes GYG1 (glicogenin), PLOD2 (involved in bone morphogenesis), PLSCR4 and CHST2 (involved in inflammatory response in vascular endothelial cells).

Dosage Effects on HSA21

Proportionally HSA21 contributed mostly to the detected dosage effects (Figure 1C). On the other hand, it is remarkable that only a third of all HSA21 genes (77 out of 255 studied here using the Ensembl genome annotation [31]) showed consistent effects across the different experiments (see also Discussion). While 57 genes had a positive score below the significance threshold of 3.67 indicating relevance with respect to specific experiments only, 121 genes had a score near zero indicating that dosage effects were either compensated or not detected with the selected experimental data (Figure 1B).

HSA21 dosage effects included, for example APP (beta-amyloid precursor protein) involved in senile plaque formation in DS and Alzheimer's disease [3], SOD1 (superoxide dismutase 1), a key enzyme in the metabolism of oxygen-derived free radicals [3], DYRK1A (dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A) involved in neuroblast proliferation, crucial for brain function, learning and memory [32], RUNX1 (runt-related transcription factor 1) which plays a critical role in normal hematopoiesis [33], or GABPA (GA binding protein transcription factor, alpha subunit 60 kDa) encoding a DNA binding domain with a huge variety of targets including genes from different cell/tissue specificities and functions [34]. HSA21 genes were mostly up-regulated in gene expression studies (69 out of 77) with the exception of eight genes that were either variable or down-regulated (SLC5A3, MRPS6, B3GALT6, CBS, KCNJ6, KCNJ15, CLDN14, COL18A1). Possible explanations for this observation might be tissue-specificity of gene expression as in the case of MRPS6 which was mostly up-regulated in brain samples and down-regulated in other tissues like heart or kidney, or differences in human and mouse gene expression as in the case of CBS which was up-regulated in human but down-regulated in mouse experiments what might be caused by differential tissue specificity of the orthologous mouse gene [35].

Three genomic regions on HSA21 were enriched with the significant genes using the MSigDB_c1 positional database: chr21q22, chr21q21 and chr21q11, located on the q-terminal arm (Figure 1D). This contradicts the hypothesis that a single region on HSA21 could be responsible for the molecular and phenotypic consequences of DS with only a few responsive genes [36, 37]. Rather our findings support studies that identified more than one HSA21 region causative for DS phenotypes so that the dosage effects were not uniformly distributed along the chromosome but rather enriched in certain regions on HSA21 similar to the results in [38, 39].

Functional Annotation Using Gene Enrichment Analysis

Functional annotation of biological pathways was retrieved from the ConsensusPathDB [40], a meta-database that summarizes the content of 22 human interaction databases. A total of 1,695 pre-defined pathways were screened with the 324 genes using gene set enrichment analysis [41]. A total of 277 pathways were found significantly enriched (family-wise error rate (FWER)<0.01) of which several pathways were associated with neurological and neuropathological processes (Table 2). These pathways referred mainly to (i) neurodegeneration (e.g. Huntington's disease, Alzheimer's disease or Parkinson's disease) and (ii) defects in synapsis (e.g. Axon guidance, NGF signaling). Furthermore, the results emphasized the role of tyrosine-kinase receptors in DS pathology (for example P75(NTR)- mediating signalling or NGF signalling via TRKA) which interact directly with BDNF (brain-derived neurotrophic factor). Moreover, our results showed gene dosage effects caused either directly by genes located on HSA21 (e.g. SOD1, APP, DONSON, TIAM1, COL6A2, ITSN1 and BACE2) or indirectly by HSA21 interactors, highlighting the intrinsic complexity of the DS pathology. For example, PIK3R1 de-regulation impacts on many of these pathways and is a direct interactor of IFNAR1, a significant DS gene. A similar effect can be observed for TPJ1A that has interactions with HSA21 genes JAM2 and CDLN8 both showing consistent dosage effects (cf. Figure 2A).
Table 2

Enriched neuropathological pathways.

PATHWAY (Source Database)

Pathway

size

P-value

FWER P-value

Genes on HSA21

HSA21 Interactors

Others

HUNTINGTONS DISEASE (KEGG)

159

0

0

SOD1; DONSON

REST

BDNF; SOD2

ALZHEIMERS DISEASE (KEGG)

147

0

0

APP; BACE2; DONSON

PPP3CA; GSK3B

CAPN2

SIGNALLING BY NGF (REACTOME)

209

0

0

ITSN1; TIAM1

PIK3R1; GSK3B

RPS6KA2; RAP1A; KRAS

AXON GUIDANCE (REACTOME)

256

0

0

COL6A2

GSK3B;COL1A1; COL1A2; COL4A1; COL4A2

COL5A2; DPYSL3; RPS6KA2; LAMB1; COL3A1; COL5A1; ALCAM; KRAS

PARKINSONS DISEASE (KEGG)

105

0

0

DONSON

 

UBE2G2

P75(NTR)-MEDIATED SIGNALING (PID)

68

0

0

APP

PIK3R1

BDNF

NOTCH (NETPATH)

61

0

0

APP

PIK3R1; GSK3B

 

NEUROTROPHIN SIGNALING PATHWAY (KEGG)

121

0

0

 

PIK3R1; GSK3B

BDNF; RPS6KA2; RAP1A; KRAS

NGF SIGNALLING VIA TRKA FROM THE PLASMA MEMBRANE (REACTOME)

127

0

0

 

PIK3R1; GSK3B

RPS6KA2; RAP1A; KRAS

MEMBRANE TRAFFICKING (REACTOME)

87

0

0

 

TJP1

GJA1; COPG

NEUROTROPHIC FACTOR-MEDIATED TRK RECEPTOR SIGNALING (PID)

60

0

0

TIAM1

PIK3R1

BDNF; RAP1A; KRAS

EPO SIGNALING (INOH)

180

0

0

 

PIK3R1; GSK3B

 

CDC42 SIGNALING EVENTS (PID)

68

0

0

TIAM1

PIK3R1; GSK3B

EPS8; YES1

L1CAM INTERACTIONS (REACTOME)

93

0

0

  

LAMB1; ALCAM1; RPS6KA2

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-229/MediaObjects/12864_2010_Article_3346_Fig2_HTML.jpg
Figure 2

Molecular interactions of HSA21 genes. A) Interactions of HSA21 genes (red) with non-HSA21 genes (other colours). Same colours of the gene nodes refer to the same chromosome. B) Example of consistent down-regulation of DNAJB1 as a consequence of HSA21 imbalance visualized in the web browser.

Dosage Effects on Transcriptional Regulation

Dysregulation of transcriptional regulation is widely reported in DS [34]. Among the 324 significant genes were 13 transcription factors (TFs) (PSIP1, RBPJ, TCF4, HES1, ETS2, BACH1, RUNX1, GABPA, SNAI2, REST, LITAF, EGR1, FOS), 6 TFs (PSIP1, HOXC8, DLX5, HIVEP3, ZNF187, ATF6) had significant enrichment of their targets as retrieved by the TRANSFAC [42] database. Additionally, 57 TFs had significant enrichment of their interacting proteins when judged with physical interactions retrieved from the ConsensusPathDB [40]. In total, 70 different TFs were identified as being (directly or indirectly) affected by dosage imbalances. The list of TFs and their associated functional categories is given in Additional file 1, Table S3. GO categories indicate a broad impact of transcriptional regulation for neurological development, the central nervous system development (RUNX1 and TP53), nervous system development (DLX5, FOS, HES1, STAT3 and EP300), axonogenesis (DLX5, NOTCH1 and CREB1), neuron differentiation (HOXC8, NOTCH1 and RUNX1), negative regulation of neuron differentiation (HES1, NOTCH1 and REST) and regulation of long-term neuronal synaptic plasticity and learning or memory (EGR1 and JUN). Other prominent categories refer to organ development (RBPJ, ETS2, GABPA and SNAI2) and stress response (ATF6, FOS and RELA).

We further analyzed the promoter sequences of the 324 genes for enrichment of transcription factor binding sites using the AMADEUS software [43]. Significant enrichment was computed for 4 TF motifs, E2F1, RUNX1, STAF/PAX2 and STAT3 (Table 3). Enrichment was evident for RUNX1, which is among the most studied genes implicated in DS. The implication of E2F1 in DS was also previously reported [34] and could be responsible for impaired cell proliferation documented for hippocampus, cerebellum and astrocytes of DS mouse models.
Table 3

Enriched TFBSs.

TF

Description

Cromo-some

P-Value

Binding motif

Strand

E2F1

E2F transcription factor 1

chr20

9.3*10-18

[C/t][C/a][G/c]C[c/a][C/g][G/c][C/T][G/c]A

-

RUNX1

runt-related transcription factor 1

chr21

4.0*10-18

[C/a/t][T/a/g][G/C]{A}[G/c]{A}T[C/A][G][C/a/t/g]

+

STAF/

PAX2

paired box 2

chr10

8.4*10-18

[A/g][A/g]A[C/T/a][T/g/a][T/c][C/t][C/g][C/a]

+

STAT3

signal transducer and activator of transcription 3 (acute-phase response factor)

chr17

8.4*10-17

GAA[A/T][C/T]G[C/T][C/g/t][A/T][C/T/g]

+

Binding motifs have been represented using the IUPAC nomenclature and incorporating lower case for low frequency bases.

Dosage Effects and Molecular Interactions

Molecular interactions among the 324 significant genes on HSA21 and on other chromosomes exhibited a complex network supporting the important role of physical interactions as transmitter of dosage effects (Figure 2A). The consequences of HSA21 triplication on the interacting genes was fairly stable as Figure 2B demonstrates. For example, DNAJB1 (DnaJ (Hsp40) homolog, subfamily B, member 1) and PPP3CA (protein phosphatase 3, catalytic subunit, alpha isozyme, data not shown), both interacting with SOD1, were consistently and significantly down-regulated in the human microarray experiments as the fold-changes and P-values indicate. Opposite trends were observed for TJP1 and RHOQ.

Assessing General Relevance of DS Dosage Effects for Neurological Processes

We were further interested in identifying, among the 324 genes, those which were relevant for other brain disorders. To achieve this, we interrogated 19 independent data sets derived from publicly available microarray data (Additional file 1, Table S4). These studies followed heterogeneous research questions on different cerebral pathologies and identified a total of 623 differentially expressed genes. Gene set enrichment analyses [41] with the 324 genes and the corresponding lists of differentially expressed genes were significant for 10 of these 19 studies with 79 overlapping genes (Figure 3A). Furthermore, we used the HSA21 database http://​chr21.​molgen.​mpg.​de/​hsa21 [4], a resource of RNA in situ hybridizations in postnatal mouse brain sections, in order to provide independent supporting evidence of brain expression of these 79 genes as shown for example for BACH1 (basic leucine zipper transcription factor 1) and TTC3 (tetratricopeptide repeat domain 3) (Figure 3B and 3C).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-229/MediaObjects/12864_2010_Article_3346_Fig3_HTML.jpg
Figure 3

Brain-related dosage effects. A) Venn diagram showing the overlap of the 324 significant genes with 623 genes identified by independent mouse studies related to brain phenotypes; B) RNA in situ hybridisations of BACH1 in postnatal mouse embryonic brain slices. C) In situ hybridisation of TTC3 in the same tissue. Images kindly provided by the HSA21 consortium ([4]; http://​chr21.​molgen.​mpg.​de/​hsa21). D) Hierarchical clustering of 79 genes related to non-DS general brain disorders with the DS gene expression data sets. Clustering was performed with the J-Express 2009 software using Pearson correlation as similarity measure and complete linkage as update rule.

Additionally, we investigated the expression patterns of the 79 genes across the DS microarray experiments used for this meta-analysis and could identify brain-related signatures, for example, a clear up-regulation in brain tissues for the cluster containing C14orf147, IVSNS1ABP, B2M, TPJ1, SPARC, CTGF, COL4A1 and FSTL1 (Figure 3D) .

Novel Dosage Effects

To identify DS-relevant "novel" dosage effects we excluded from the 324 genes (i) HSA21 genes, (ii) genes that interacted with HSA21 genes, as well as (iii) genes that were associated with DS in the literature (Table 4). Remaining candidates (N = 62) comprised BDNF-related genes (SST), MAPK-pathway genes (KRAS, IGF1R, GNG11 and RAP1A), genes related with leukemia (SFRP1) and Rho-Proteins (DHCR7 and RAB21). SST was found as co-expressed in previous studies with TAC1 [44] which is also significant in our meta-analysis and both showed a strong correlation across DS studies (Figure 4A).
Table 4

Novel DS dosage effects

Ensembl

HUGO

Score

Entropy

Chromo-some

Start

position

End

position

Band

CNV

ENSG00000133110

POSTN

8.301

2.437

chr13

38136722

38172981

q13.3

YES

ENSG00000135919

SERPINE2

5.904

3.048

chr2

224839765

224904036

q36.1

 

ENSG00000172893

DHCR7

5.883

3.441

chr11

71145457

71159477

q13.4

 

ENSG00000135744

AGT

5.467

2.799

chr1

230838269

230850043

q42.2

 

ENSG00000159176

CSRP1

5.424

3.136

chr1

201452658

201478584

q32.1

 

ENSG00000178695

KCTD12

5.344

2.373

chr13

77454312

77460540

q22.3

 

ENSG00000183087

GAS6

5.129

2.904

chr13

114523524

114567046

q34

 

ENSG00000164106

SCRG1

5.127

2.728

chr4

174309300

174320617

q34.1

 

ENSG00000166923

GREM1

5.073

1.486

chr15

33010175

33026870

q13.3

YES

ENSG00000163754

GYG1

4.933

3.129

chr3

148709128

148745419

q24

 

ENSG00000155380

SLC16A1

4.878

2.927

chr1

113454469

113499635

p13.2

YES

ENSG00000166033

HTRA1

4.811

3.101

chr10

124221041

124274424

q26.13

YES

ENSG00000145632

PLK2

4.785

2.811

chr5

57749809

57756087

q11.2

 

ENSG00000115380

EFEMP1

4.726

2.257

chr2

56093102

56151274

p16.1

 

ENSG00000060237

WNK1

4.637

2.765

chr12

862089

1020618

p13.33

YES

ENSG00000103888

KIAA1199

4.581

0.885

chr15

81071684

81244117

q25.1

 

ENSG00000113810

SMC4

4.462

3.372

chr3

160117062

160152750

q25.33

 

ENSG00000198356

ASNA1

4.431

3.067

chr19

12848306

12859137

p13.2

 

ENSG00000122952

ZWINT

4.415

3.266

chr10

58116989

58121036

q21.1

 

ENSG00000157005

SST

4.401

1.954

chr3

187386694

187388187

q27.3

 

ENSG00000117519

CNN3

4.384

3.253

chr1

95362507

95392834

p21.3

 

ENSG00000107104

KANK1

4.352

2.508

chr9

470291

746105

p24.3

YES

ENSG00000151414

NEK7

4.329

1.848

chr1

198126121

198291550

q31.3

 

ENSG00000044574

HSPA5

4.261

3.449

chr9

127997132

128003609

q33.3

 

ENSG00000128590

DNAJB9

4.251

3.241

chr7

108210012

108215294

q31.1

 

ENSG00000127920

GNG11

4.226

2.747

chr7

93551011

93555831

q21.3

 

ENSG00000008083

JARID2

4.161

3.203

chr6

15246527

15522253

p22.3

 

ENSG00000119938

PPP1R3C

4.159

3.036

chr10

93388199

93392858

q23.32

 

ENSG00000049245

VAMP3

4.146

3.036

chr1

7831329

7841492

p36.23

 

ENSG00000120694

HSPH1

4.129

3.310

chr13

31710762

31736502

q12.3

 

ENSG00000168214

RBPJ

4.127

3.291

chr4

26321332

26436753

p15.2

 

ENSG00000162909

CAPN2

4.111

3.020

chr1

223889347

223963720

q41

YES

ENSG00000166147

FBN1

4.106

2.070

chr15

48700505

48937918

q21.1

YES

ENSG00000100941

PNN

4.081

3.380

chr14

39644425

39652422

q21.1

 

ENSG00000132640

BTBD3

4.074

3.478

chr20

11871371

11907257

p12.2

YES

ENSG00000128708

HAT1

4.064

3.158

chr2

172778958

172848599

q31.1

YES

ENSG00000176105

YES1

4.047

2.855

chr18

721588

812327

p11.32

 

ENSG00000152377

SPOCK1

4.025

3.083

chr5

136310987

136835037

q31.2

 

ENSG00000136026

CKAP4

4.018

2.754

chr12

106631659

106641908

q23.3

 

ENSG00000198121

LPAR1

3.979

2.858

chr9

113635543

113800738

q31.3

 

ENSG00000140443

IGF1R

3.951

3.376

chr15

99192200

99507759

q26.3

 

ENSG00000198730

CTR9

3.891

3.310

chr11

10772803

10801287

p15.3

 

ENSG00000162616

DNAJB4

3.869

3.035

chr1

78444859

78483648

p31.1

 

ENSG00000104332

SFRP1

3.825

2.587

chr8

41119483

41166992

p11.21

 

ENSG00000116473

RAP1A

3.824

2.769

chr1

112084840

112259313

p13.2

 

ENSG00000172500

FIBP

3.804

3.309

chr11

65651211

65656010

q13.1

YES

ENSG00000133703

KRAS

3.801

3.338

chr12

25358182

25403854

p12.1

 

ENSG00000163032

VSNL1

3.798

3.099

chr2

17720393

17838285

p24.2

 

ENSG00000134684

YARS

3.765

3.431

chr1

33240840

33283754

p35.1

 

ENSG00000105854

PON2

3.764

2.862

chr7

95034179

95064510

q21.3

 

ENSG00000148943

LIN7C

3.763

3.033

chr11

27516124

27528303

p14.1

 

ENSG00000162734

PEA15

3.747

3.418

chr1

160175127

160185166

q23.2

 

ENSG00000103187

COTL1

3.731

3.304

chr16

84599200

84651683

q24.1

YES

ENSG00000198648

STK39

3.722

3.439

chr2

168810530

169104651

q24.3

 

ENSG00000100577

GSTZ1

3.713

2.759

chr14

77787230

77797939

q24.3

 

ENSG00000080371

RAB21

3.707

3.312

chr12

72148658

72181150

q21.1

YES

ENSG00000136108

CKAP2

3.688

2.960

chr13

53029495

53050485

q14.3

 

ENSG00000066583

ISOC1

3.686

2.655

chr5

128430442

128449721

q23.3

 

ENSG00000143420

ENSA

3.681

3.276

chr1

150573327

150602098

q21.3

 

ENSG00000114353

GNAI2

3.680

3.138

chr3

50263724

50296787

p21.31

YES

ENSG00000140105

WARS

3.671

2.994

chr14

100800125

100842680

q32.2

 

ENSG00000018625

ATP1A2

3.670

2.733

chr1

160085549

160113381

q23.2

 
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-229/MediaObjects/12864_2010_Article_3346_Fig4_HTML.jpg
Figure 4

Novel DS dosage effects visualised with the web browser. A) SST and TAC1 have been previously reported as acting in a complex. The deregulated profile of these genes correlates was shown here with the fold-change view of the web browser. B) HSPA5 is a novel gene for DS implicated in neurodegeneration which is also a target of the ATF6 TF whose target set was enriched with significant genes. The histogram displays the p-values for this gene in individual studies. C) KANK1, a gene previously related with paternally inherited cerebral palsy, shows a consistent trend of up-regulation in the considered studies as shown with the fold-change view of the web browser.

Novel candidates are associated with neurodegenerative disorders including Alzheimer's disease (VSNL1), prion disease (SCRG1, HSPH1, HSPA5 (Figure 4B) and CTR9) and age-related degeneration (GAS6 and GNG11). Moreover, candidates could explain evident DS features (Additional file 1, Table S5): (i) genes related to neurogenesis and neurite outgrowth (LPAR1 [45], LIN7C, JARID2, GREM1, SERPINE2, IGFR1 and SPOCK1) that could be related with mental retardation or cognitive impairment, (ii) genes involved in synapsis (AGT, KRAS, ATP1A2, GNAI2, SST and LIN7C) (iii) cytoskeletal related proteins (KANK1 [46]; Figure 4C), CKAP2, CKAP4, HAT1, NEK7 and VAMP3), (iv) macular degeneration genes [47] or genes (HTRA1 and EFEMP1) associated with age-related visual problems [48], (v) genes (AGT, CNN3, FBN1, RBPJ, PON2, POSTN, RAP1A, WNK1 and STK39) that were related with cardiac impairments and could be candidates to explain this DS characteristic [49], and (vi) genes related with cancer (BTBD3 [50], DNAJB4 [51], FIBP [52] and GSTZ1 [53]) [54].

These examples show that the meta-analysis approach identified multiple additional genes that might be involved in DS pathology. In order to enable the community to check any particular gene of interest for DS relevance in the studies under analysis, we have agglomerated all information of the meta-analysis into a WEB-interface http://​ds-geneminer.​molgen.​mpg.​de/​. Examples of possible views and information are shown in Figure 4.

Discussion

The statistical meta-analysis approach was described previously by Rasche et al. [26]. The score computed with meta-analysis correlates with entropy (Figure 1A) indicating the ability to identify general dosage effects across many experiments that might be of more phenotypic relevance than very specific ones. Additional file 4, Figures S3A and B provide an overview of the different sources of data, including two organisms (human and mouse), different tissues (brain, heart and others), different stages of development (adult, postnatal, embryonic) and different mouse models (Ts65DN, Ts1Cje, Tc1). It is per se interesting that, in spite of such heterogeneity, common dosage effects could be identified at all and it should be highlighted that whole-genome data was fairly robust across experiments. Additional file 4, Figure S3D shows the overall correlation of the quantitative values of PCR and microarray values averaged from all experiments with only few genes in the non-concordant sectors of the graph (red points).

The score used in this analysis allows detecting genes that could be either up- or down-regulated in different studies. An overview of the fold-changes for the genes across the different experiments is given in Additional file 1, Table S6. Because genes might change their expression level depending on the developmental state, tissue or because of other variables, we expected that this flexibility allows checking the hypothesis of random disturbances as well as the hypothesis of increased expression of HSA21 genes. We detected a clear enrichment of up-regulated genes on the q-terminal part of HSA21 (Figure 1D and Additional file 3, Figure S2). However, not a single region was identified but rather several smaller regions on HSA21 that agglomerate a large amount of significant dosage effects. This finding was also elaborated before (Korbel et al. [38] and Lyle R et al. [39]) using two independent data sets to characterize the molecular HSA21 regions in a set of DS-patients with partial duplications.

We studied 255 HSA21 genes matched with the probe sets from the microarrays. Of these only 77 showed consistent dosage effects (Figure 1). While 165 HSA21 genes had score values different from zero indicating response in some of the microarray studies, 90 HSA21 genes were not responsive at all and provide evidence for a strong mechanism of dosage compensation. On the other hand, these figures could also reflect the limitation of detecting reliable fold-changes of low magnitude with microarray technology. Furthermore, experiments covered only a limited amount of tissues so that it is likely that some genes were missed simply because they were not responsive in the tissues under analysis. However, having brain as the dominant whole-genome sample source this should ensure expression of most of the genes. Microarray data was focused on the Affymetrix platform in order to reduce variance arising from platform inconsistencies. We have also compared our results with additional studies including own previous research [9] and others [55] and found relevance of selected dosage effects with respect to other tissues as well (data not shown). Additional cross-validation was performed with an independent microarray data set [10]. These authors compared human lymphoblastoid cell lines derived from DS patients and normal controls with a custom-made HSA21 array. Yahya-Graison et al. [10] divided the expression ratios in four classes: class I and class II genes were significantly up-regulated, while class III and class IV genes were either compensated or showed variable response. Our meta-analysis revealed a high-degree of concordance taking into account that the cell model, platform and the methodology used were completely different. The meta-analysis scores were significantly higher for class I and II genes than for class III and IV genes (P-value <0.01, Additional file 5, Figure S4). 25 out of 39 class I-II genes revealed a significant score in our meta-analysis (75%).

In this study we monitored molecular interactions of HSA21 genes that might function as drivers of dosage effects (Figure 2A). For example, (i) TJP1 (Tight junction protein ZO-1) interacts with two HSA21 genes, JAM2 and CLDN8, (ii) FOS (FBJ murine osteosarcoma viral oncogene homolog) interacts with HSA21 genes ETS2, SUMO3, RUNX1 and indirectly with ERG, (iii) RHOQ (ras homolog gene family, member Q) interacts directly with ITSN1 and TIAM1 and indirectly with SYNJ, and (iv) PIK3R1 interacts directly with IFNAR1 and indirectly with IFNAR2. It should be emphasized that current information on molecular interactions is far from complete, thus we either might miss important interactions not yet detected and/or we might count false positive interactions due to the high error rates of current annotations of interactions.

Several of the DS genes (N = 79) extrapolated to more general neurological phenotypes (Figure 3A). The dendrogram (Figure 3D) shows further interesting profiles of these genes in the DS samples under analysis: (i) differential gene expression in the cerebellum region versus whole "brain" or cerebrum areas which has been reported in other studies (e.g. Moldrich et al. [56]), (ii) different patterns of gene expression associated to particular developmental stages (P0, P15 and P30); these changes were reported before by Dauphinot el al. [57], and (iii) differences in ES studies.

We further analyzed human and mouse studies separately and found 182 significant dosage effects using only human and 107 dosage effects using only mouse data. The Venn diagram in Additional file 4, Figure S3C clearly shows the benefit in detecting additional dosage effects when mixing the two species. Overlapping dosage effects were detected for 29 genes with both analyses (Additional file 1, Table S7). Results for the human and mouse specific analyses can be found in Additional file 1, Tables S8 and S9. It should be noted here that comparisons between human and mouse using microarrays are inherently difficult and have limitations since the probes for the orthologous mouse and human genes do not correspond well. Furthermore, gene expression variation is generally higher in human individuals compared to mouse inbred strains. Nonetheless, the 107 genes found in the analysis of mouse data (derived from the different mouse models for trisomy 21) represent a core set of genes responsive across different DS mouse models and, thus, could be highly relevant for DS pathogenesis.

In addition to genes commonly related to DS, we have identified novel genes that can be associated with DS phenotypes, in particular with neural development and neurodegeneration. To our best knowledge, this study is the first meta-analysis of genome-wide transcript levels along with other data domains in DS research. The agglomerated data can be accessed through the WEB server at http://​ds-geneminer.​molgen.​mpg.​de and the identified dosage effects are a resource for further functional testing and therapeutic development.

Conclusions

We have identified a set of 324 genes with consistent dosage effects from 45 different experiments related to DS. Since the meta-analysis was enriched with brain experiments, we were able to detect a high fraction of genes related to neuro-development, synapsis and neuro-degeneration. Moreover, our results give more information about known and new pathways related to DS and also about 62 novel candidates. The results of the meta-analysis as well as the source data have been made accessible for the community through a WEB interface.

Material and methods

Selection and integration of DS resources

Data sets were selected from heterogeneous technical platforms, different model systems (human cell lines, human tissues, mouse models) and different developmental stages (Additional file 1, Table S1). For each gene and for each source we computed a numerical value that measures its dosage effect. Data categories were either qualitative or quantitative. Qualitative data incorporated a total of 30 published manuscripts including reviews and semi-quantitative studies as well as two SAGE studies [21, 58] and were summarised to one score point in order to avoid over-scoring. Here, a "1" referred to the case that the gene was found as DS relevant in one (or more) studies. Quantitative data from differential gene expression studies such as Affymetrix microarrays, RT-PCR, MALDI and other quantitatives techniques were evaluated in order to extract comparable information across the different studies. We considered Affymetrix studies that provided the raw data (CEL file level). Raw data were extracted from Gene Omnibus Expression (GEO, [59]), Array Express [60] or were retrieved from the author's web pages (in total 16 data sets including human tissues and four different mouse models (Ts65Dn, Ts1Cje, Tc1 and Ts + HSA21). Furthermore, we incorporated 18 RT-PCR and MALDI data sets for which the authors displayed the information for all genes under study (either significant or not).

Mapping of gene IDs

A central pre-requisiste of any meta-analysis approach is the consolidation of the different ID types, for example coming from different organisms and from different versions of chips. We used the Ensembl database (version 56) as the backbone annotation for all studies. IDs were mapped to human Ensembl gene IDs. Mapping and merging of the information was done within R and the BioConductor package. In total, information on 19,388 ENSEMBL genes was mapped.

Mapping SAGE IDs

Differential expressed tags were extracted from additional files of the studies. Identifiers (based on sequences) were cross-tagged with the information displayed in the updating tables (SAGEmap_Hs and SAGEmap_Mn) from the SAGE site ftp://​ftp.​ncbi.​nlm.​nih.​gov/​pub/​sage/​mappings.

Transcriptome data pre-processing and normalization

We incorporated only case-control studies in the meta-analysis in order to derive expression fold-changes. Affymetrix gene chip annotations were adapted from the latest genome annotation (version 12). Affymetrix data were normalized with GC RMA. For transcriptome case-control studies three pieces of information were stored for each gene; (i) the fold-change (DS vs. controls), (ii) the standard error of the fold-change from the replicated experiments in that study and (iii) the expression p-value (presence-call) that indicates whether or not the gene is expressed in the target samples under study. For RT-PCR and MALDI experiments we computed the fold-change of the mean expression (DS vs. controls) as well as the reported standard error of the ratio. When mean and standard variation for each group (DS and controls) was provided we calculated the ratios as well as their associated standard errors.

Scoring DS dosage effects across studies

In order to score the different categories of information such as binary counts and quantitative gene expression values, we summarized the scores of the individual experiments for each category. For microarray studies, the score of the i-th gene in the j-th study, sij, was computed as described in Rasche et al. [26]:
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-229/MediaObjects/12864_2010_Article_3346_Equa_HTML.gif

Here rij is the fold-change, pij is the average detection p-value and eij is the standard error of the ratio derived from the experimental replicates of the study. Thus, the fold-change is weighted with its reproducibility across the experimental replicates and with the likelihood of the gene being expressed in the study's case or control samples.

For RT-PCR and MALDI studies we applied the following equation:
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-229/MediaObjects/12864_2010_Article_3346_Equb_HTML.gif

Here rij is the fold-change and eij is the standard error of the ratio.

The total score of the gene was computed as the sum across all individual study scores.

Sampling for significance

In order to assess the significance of the overall gene scores we generated random scores by re-sampling the scores 50,000 times with replacement within the same study. Using the random distribution as background we assigned as significant those genes that were above the 99.9 percentile of the background distribution.

Judging consistency of dosage effects

For each gene, entropy of the score distribution was computed in order to quantify the relevance of the gene across many experiments. Let sij be the score of the ith gene in the jth study, then Ei is a measure for the uniformity of the score distribution over the individual experiments:
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-229/MediaObjects/12864_2010_Article_3346_Equc_HTML.gif

High entropy is assigned to a gene if many experiments contribute to the overall score whereas low entropy is assigned if only a few experiments contribute to the overall score.

Enrichment analysis

Gene Set Enrichment Analysis (GSEA, [41]) of the 324 genes was performed with respect to pre-defined human pathways agglomerated from 22 pathway resources from the ConsensusPathDB ([40], http://​cpdb.​molgen.​mpg.​de. Over-representation analysis of TF target sets was performed with Fisher's test based on annotation from TRANSFAC [42]. Motif enrichment analyses were performed using AMADEUS [43] with significant genes as target sets and all the genes considered in the meta-analysis as background set.

Selection of independent brain experiments

In order to proof general brain relevance of the 324 genes, we collected DS-independent gene expression studies to decipher brain features, performed with Affymetrix technology and, with experiments deposited in GEO or ArrayExpress (Additional file 1, Table S4). Mostly, these experiments were performed in mouse tissues. For each study we collected one or more resulting gene lists that were evaluated using Gene Set Enrichment Analysis (GSEA, [41]) against the complete list of 19,388 genes ranked by score.

Abbreviations

DS: 

Down Syndrome

HSA21: 

human chromosome 21

TF: 

Transcription Factor

PCR: 

Polymerase Chain Reaction

RT-PCR: 

real-time Polymerase Chain Reaction

MALDI: 

Matrix-Assisted Laser Desorption/Ionization

SAGE: 

Serial Analysis of Gene Expression

GO: 

Gene Ontology

ES: 

Embryonic Stem Cells

ID: 

Identifier

GEO: 

Gene Omnibus Expression

GSEA: 

Gene Set Enrichment Analysis

CNV: 

Copy Number Variation

TFBS: 

Transcriptor Factor Binding Site

Declarations

Acknowledgements

We want to express our gratitude to all researchers that made DS data available for the community. The free access to high quality experimental data is the necessary pre-requisite for all integrative studies. Furthermore, we apologize for all data sets that could not be integrated into the analysis because of specific constraints such as chip platforms, access to raw data etc. We thank Bernhard Herrmann for giving access to the in situ mouse brain images shown in Figure 3. We thank Marie-Laure Yaspo for discussions, James Adjaye for proof-reading of the manuscript and Reha Yildirimman and Atanas Kamburov for computational support. This work was funded by the European Commission within its 6th Framework Programme with the grant AnEUploidy (LSHG-CT-2006-037627), by the Max Planck Society and the Beatriu de Pinos postdoctoral fellowship (2008 BP-A 00184).

Authors’ Affiliations

(1)
Department of Vertebrate Genomics, Max-Planck-Institute for Molecular Genetics
(2)
Unitat de Genètica, Universitat Pompeu Fabra, y CIBER de Enfermedades Raras (CIBERER), Parc de Recerca Biomèdica de Barcelona
(3)
Programa de Medicina Molecular I Genetica, Hospital Vall d'Hebron

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