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Association study in the 5q31-32 linkage region for schizophrenia using pooled DNA genotyping

  • Irina Zaharieva1,
  • Lyudmila Georgieva2Email author,
  • Ivan Nikolov2,
  • George Kirov2,
  • Michael J Owen2,
  • Michael C O'Donovan2 and
  • Draga Toncheva1
BMC Psychiatry20088:11

DOI: 10.1186/1471-244X-8-11

Received: 31 October 2007

Accepted: 25 February 2008

Published: 25 February 2008

Abstract

Background

Several linkage studies suggest that chromosome 5q31-32 might contain risk loci for schizophrenia (SZ). We wanted to identify susceptibility genes for schizophrenia within this region.

Methods

We saturated the interval between markers D5S666 and D5S436 with 90 polymorphic microsatellite markers and genotyped two sets of DNA pools consisting of 300 SZ patients of Bulgarian origin and their 600 parents. Positive associations were followed-up with SNP genotyping.

Results

Nominally significant evidence for association (p < 0.05) was found for seven markers (D5S0023i, IL9, RH60252, 5Q3133_33, D5S2017, D5S1481, D5S0711i) which were then individually genotyped in the trios. The predicted associations were confirmed for two of the markers: D5S2017, localised in the SPRY4-FGF1 locus (p = 0.004) and IL9, localized within the IL9 gene (p = 0.014). Fine mapping was performed using single nucleotide polymorphisms (SNPs) around D5S2017 and IL9. In each region four SNPs were chosen and individually genotyped in our full sample of 615 SZ trios. Two SNPs showed significant evidence for association: rs7715300 (p = 0.001) and rs6897690 (p = 0.032). Rs7715300 is localised between the TGFBI and SMAD5 genes and rs6897690 is within the SPRY4 gene.

Conclusion

Our screening of 5q31-32 implicates three potential candidate genes for SZ: SMAD5, TGFBI and SPRY4.

Background

Schizophrenia (SZ) is a common, severe and disabling disorder that in most cases requires a long-term medical and social care. The lifetime risk for SZ in the population worldwide is around 1%. Family, adoption and twin studies have shown conclusively that a genetic component plays the most important role in its aetiology [1]. At present, the number of susceptibility loci, the disease risk conferred by each locus and the degree of interaction between them remain unknown [2]. The mode of transmission is complex and non-Mendelian and is probably contributed by a small number of genes of moderate effect, or by many genes of small effect, or a mixture of the two [3].

In the present study we investigated one strong region of linkage to schizophrenia: 5q31-32. This region emerged as one of the five most consistent regions in a meta-analysis of the genome-wide linkage studies [4]. The main linkage findings for 5q23-33 are summarised in Figure 1.
https://static-content.springer.com/image/art%3A10.1186%2F1471-244X-8-11/MediaObjects/12888_2007_Article_455_Fig1_HTML.jpg
Figure 1

Linkage findings in schizophrenia in the 5q23-33 region. Straub et al (1997) [5] reported a maximum LOD score of 3.04 for schizophrenia at D5S393. Schwab et al (1997) [6] reported maximum LOD score of 1.8 for the marker IL9 by two point lod score analysis. Paunio et al (2001) [7] obtained a maximum lod score of 3.56 at D5S414. Gurling et al (2001) [50] reported a LOD score 3.6 at D5S422. Devlin et al (2002) [8] reported a LOD score of 3.4 at D5S1480 and Sklar et al (2004) [9] obtained a NPL score of 3.09 (p = 0.0012) at D5S820.

We concentrated upon a minimal region of interest between markers D5S666 and D5S436 as it includes five of the regions showing linkage to schizophrenia [59]. We decided to concentrate on this region, rather than on the full region, as it is the most likely region to contain susceptibility genes, due to the concentration of five linkage findings. We would have been unable to provide similar dense coverage of the whole interval with the funding we received for this project. This interval is ~14 Mb long and contains ~330 genes (UCSC built 35, May 2004), of which 52 constitute the protocadherin α, β, γ clusters. Protocadherins are expressed throughout the nervous system and are involved in synapse formation, specification and maintaining, which make them potential candidate genes for schizophrenia. This group of genes and their relation to schizophrenia has being investigated by several research groups [1013]. Other promising candidate genes in this region are NRG2 (Neuregulin 2) and IL9 (P40 cytokine). The neuregulins are a family of growth and differentiation factors with a wide range of functions in the nervous system [14]. Neuregulin signalling plays an important role in many neurological disorders including multiple sclerosis, traumatic brain and spinal cord injury, peripheral neuropathy, and possibly schizophrenia [1416]. According to the glial growth factors deficiency and synaptic destabilization hypothesis of SZ, functional deficiency of glial growth factors and of growth factors such as neuregulin, insulin-like growth factor I, insulin, epidermal growth factor, neurotrophic growth factors, erbB receptors and others, are among the distal causes in the genotype-to-phenotype chain leading to the development of SZ [17]. Cytokines are key molecules regulating immune/inflammatory reactions. They are involved in brain development, regulation of dopaminergic and GABAergic differentiation, and synaptic maturation. Certain cytokines are postulated to have a central role in the neurodevelopmental defects in SZ [18, 19].

The systematic association analysis of complex disorders requires genotyping of numerous genetic markers over particular genomic regions, or more recently the entire genome, in large samples. The cost of such studies is prohibitive for most laboratories. DNA pooling is a way to decrease the cost, time and labour that are involved in a large-scale genotyping [20]. Briefly, in DNA pooling equimolar amounts of DNA are taken from each individual mixed to form two sets of pools, cases and controls. Predicted allele frequencies are then estimated on the basis of the intensities produced in each pool. DNA pooling is capable of detecting loci with small effect sizes and decreases the cost of the analysis by orders of magnitude. The power of pooling studies is approximately the same as for individual genotyping of affected and non-affected individuals, with a mean error rate of pooled analysis reported for different pooling techniques in the region of <2% [2023].

In the present study, the initial DNA pooling and individual genotyping was performed using microsatellite markers, and fine mapping was performed with single nucleotide polymorphisms (SNPs). We reasoned that microsatellite markers have several advantages over SNPs for initial screening: they are highly polymorphic with corresponding high degree of heretozygosity (on average ~70%) and they can detect linkage disequilibrium (LD) over larger distances than SNPs (~100 kb range compared to ~30 kb range for SNPs) [24, 25] probably due to the fact that they have a high mutation rate, making it possible for certain of their alleles to capture associations of more recent origin. Therefore, we reasoned that we could cover the 14 Mb interval with fewer microsatellite markers, than with SNPs, thus making it possible to conduct the study within the budget of our research project.

We chose to use an initial screening step of applying pooled analysis to polymorphic microsatellite spanning 5q31-32, and a follow up stage of individual genotyping in the same samples from which the pools were constructed for markers showing nominally significant evidence for association (p < 0.05). SNPs in regions containing a confirmed nominally significant microsatellite marker (p < 0.05) were then examined further for association with SNPs in order to refine the position of the association signal.

Methods

Clinical sample

For the initial screen, we used two parent-proband DNA pools: one from 300 SZ patients and the second one from their 600 parents. All trios are of Bulgarian origin. They were either inpatients from five different psychiatric hospitals, or outpatients from four of the largest psychiatric dispensaries in Bulgaria. All had a history of hospitalization for a schizophrenic episode. Each proband was interviewed with an abbreviated version of the SCAN instrument (Schedules for Clinical Assessment in Neuropsychiatry) [26]. Consensus best-estimate diagnoses were made according to DSM-IV criteria (Diagnostic and Statistical Manual of Mental Disorders, 4th edn.1994) [27] by two raters using information from the interview and hospital notes. All patients included in the study met DSM-IV criteria for schizophrenia. Local ethics committee approval was obtained from all the regions where patients were recruited. All patients and their parents were given information sheets and provided written informed consent for participation in genetic association studies. DNA was extracted by standard phenol-chloroform method from peripheral blood. The selected microsatellite markers for individual genotyping were genotyped in the same sample of 300 trios families that were included in the pools, while the SNPs were genotyped in the full sample of 615 SZ trios that have been recruited by the team in Bulgaria. To inform our choice of SNPs for fine-mapping, we used data from another pooling study on 574 SZ trios (explained in more detail below). This larger pool includes the original pool of 300 trios used in the main microsatellite study, and a second pool of 274 trios from our full sample of 615 trios.

Choice of markers

Microsatellites

Microsatellite markers were selected from JBIRC database [28], developed on the basis of analysis of microsatellite markers covering the whole human genome [25], and the UCSC Genome Browser (built 35, May 2004) [29]. We aimed to place markers at an average density of one every ~100 kb. We first selected validated polymorphic markers within our region of interest using all available information from JBIRC. In cases where the distance between validated markers was greater than 100 kb, we selected non-validated di-, tri- tetra- and penta-nucleotide repeats from UCSC and validated them ourselves by examining whether they were polymorphic. We selected a total of 140 microsatellite markers from the two databases. 50 markers were not included in the analysis because they produced heavy stutter bands or were not polymorphic. We successfully analysed 90 markers, of which 24 were chosen from JBIRC and 66 from UCSC database (39 are novel markers that have not been validated before). The average distance between the analysed 90 polymorphic markers was ~150 kb and 72% of the microsatellite markers were di-nucleotide repeats, the remaining were tri-, tetra- or penta-nucleotide repeats.

SNPs

In order to improve our chances of identifying the most significant SNPs around significant microsatellites for the fine mapping, we selected SNPs from the pooling data of a genome-wide association (GWA) study of schizophrenia in the same Bulgarian trios, conducted by the Department of Psychological Medicine, Cardiff University towards the end of the microsatellite project (manuscript in press) [30]. The results of the study on Illumina arrays became available just at the time when we started selecting SNPs for fine mapping. We wanted to saturate an extended area of ~400 kb around positive microsatellites, in order to identify the peak of the maximum significance in each region. The latest versions of the HapMap database contain hundreds of SNPs in these regions, making it impossible for us to provide dense coverage with the funding available. This is why we decided to make use of our Illumina data. We reasoned that SNPs which are not significantly associated in that study of overlapping samples, were most likely to remain negative after individual genotyping, therefore we could only target those SNPs that produced significant results (at a predicted p-value of ≤ 0.05). The GWA pooling study was carried out with Illumina HumanHap550 Genotyping BeadChip technology using pooled DNA from 574 SZ patients and a pool from all the parents of the cases [30]. The pool of 574 trios includes the pool of 300 trios prepared for the microsatellite association study, plus an additional pool of 274 SZ trios that was prepared from the available SZ trios recruited by our team in Bulgaria. Although the complete number of trios available for individual genotyping was 615, only 574 trios are included in the pools, for the following reasons. 1) A small number of samples had lower DNA concentration, making them unsuitable for pooling. 2) A number of families are multiply affected, providing more than one trio for the analysis of individual genotyping. However, only one trio per family was included in pools.

Genotyping

For microsatellites we designed flanking primers labelled at their 5' end with a fluorescent dye. PCR primers were designed with the Primer 3 program [31]. All primers were checked with BLAT in order to make sure their sequences were unique. Fragments were amplified using a standard PCR touch-down protocol on thermal cycler DNA Engine Tetrad machines (MJ Research, USA). Fragments were separated on an ABI3100 capillary sequencer (Applied Biosystems, USA). Microsatellite markers for individual genotyping were amplified in a multiplex PCR reaction using the same protocol as above.

SNPs were genotyped using the Sequenom MassARRAY™ iPlex™ chemistry (Sequenom, San Diego, California, USA) [32] or Amplifluor™ SNP Genotyping Systems (Serologicals Corporation, USA) [33, 34] according to the recommendations of the manufacturers.

Pooled DNA genotyping

The principles of pooled DNA genotyping have been described before [21, 22]. Briefly, an equimolar amount of DNA was taken from every individual from one sample set and put into a single tube (one pool). The DNA from every sample was quantified using the PicoGreen ds DNA Quantification Reagent (Molecular Probes, Eugene, Oregon, USA). Each pooled DNA was amplified in triplicate and the PCR products were separated on an ABI3100 capillary sequencer. If any of the replicates gave more than 3% difference in any one allele, the experiment was repeated, or this replicate excluded. The peak heights of the signal representing each of the alleles were measured using GENOTYPER 2.5 software. The peak heights were used to estimate the relative allele frequencies in the pools, assuming that peak height is directly proportional to the concentration of that allele in the pool. Thus, the allele frequencies were estimated from the peak height for each allele divided by the sum of the peak heights for all alleles. We didn't apply correction for stutter bands and differential amplification, as described before [21].

Statistical analysis

For analysis of pooling results we used the CLUMP program [35] to compare the predicted allele frequencies of probands with the non-transmitted parental alleles (our pseudo-controls). For each marker we ran 1000 simulations and estimated the nominal p-value. Individual genotyping results were analyzed with the Extended Transmission Disequilibrium Test (ETDT) for multiallelic markers and the Transmission Disequilibrium Test for SNPs [3638]. Analyses of linkage disequilibrium (LD) between SNPs (r 2 and D') were performed using Haploview [39, 40].

Results

Microsatellites

We designed and genotyped 140 microsatellite markers covering a region of ~14 Mb (between markers D5S666 and D5S436, Figure 1) in parents and probands pools. After excluding non-polymorphic markers and those producing heavy stutter bands, we successfully analysed 90 polymorphic microsatellite markers. Seven markers showed suggestive evidence for association with SZ (p < 0.05) and were selected for individual genotyping. These were D5S0023i (p = 0.033), IL9 (p = 0.046), RH60252 (p = 0.041), 5Q31-33_33 (p = 0.031), D5S2017 (p = 0.013), D5S1481 (p = 0.030), D5S0711i (p = 0.028). In order to confirm the results from the pooling experiment, we performed individual genotyping of these markers in the sample of 300 parent-proband trios that were included in the pools. Results from individual genotyping are presented in Table 1. Marker D5S0023i could not be analyzed as it produced heavy stutter bands, making allele calling unreliable. Two markers remained nominally significant after the individual genotyping: marker IL9 (p = 0.014), located within the IL9 (interleukin 9 precursor) gene and marker D5S2017 (p = 0.004), located 28.5 kb 3' from the SPRY4 (Sprouty homolog 4) gene. The distance between the two markers is approximately 6.5 Mb.
Table 1

Pooling and individual genotyping results for microsatellite markers showing suggestive evidence for association.

#

Marker name

Position in bp (UCSC May, 04)

Repeat type

Number of repeats

Pooling p-value

Individual genotyping p-value

1

D5S0023i

chr5:133235407–133235452

AAGG

9

0.033

no data

2

IL9

chr5:135256322–135256367

AC

12

0.046

0.014

3

RH60252

chr5:139324127–139324160

AC

11

0.041

0.51

4

5Q31-33_33

chr5:141503743–141503777

TAA

6

0.031

0.10

5

D5S2017

chr5:141713613–141713660

TG

6

0.013

0.004

6

D5S1481

chr5:144559890–144559927

TAT

10

0.030

0.77

7

D5S0711i

chr5:145722795–145722839

AAACA

9

0.028

0.99

SNPs

In order to further investigate the two candidate regions, we performed fine mapping using SNPs. Eight SNPs were selected from regions of 400 kb surrounding the microsatellites D5S2017 and IL9 from the data of our GWA study in 574 SZ trios carried out in the same population (see Methods). These eight SNPs had shown suggestive significance for association (p = 0.05) in the pooling data. Rs17169180, rs1859430, rs30747 and rs7715300 are around the IL9 area, and rs2961720, rs7443175, rs6897690 and rs153423 are from the D5S2017 area (Figures 2 and 3). We could not produce a robust assay for genotyping rs2961720, so we substituted it with rs2906066 which was in complete LD with rs2961720 (r 2 = 1) in the CEU trios sample (Utah residents with ancestry from Northern and Western Europe) from HapMap [41]. Results from the individual genotyping are presented in Table 2.
https://static-content.springer.com/image/art%3A10.1186%2F1471-244X-8-11/MediaObjects/12888_2007_Article_455_Fig2_HTML.jpg
Figure 2

Markers and genes in the region around IL9 marker. Included are base pair position according to UCSC built 35, May 2004, chromosome band, gene and the genotyped SNPs and microsatellite markers within the region.

https://static-content.springer.com/image/art%3A10.1186%2F1471-244X-8-11/MediaObjects/12888_2007_Article_455_Fig3_HTML.jpg
Figure 3

Markers and genes in the region around D5S2017 marker. Included are base pair position according to UCSC built 35, May 2004, chromosome band, gene and the genotyped SNPs and microsatellite markers within the region.

Table 2

Summarized individual genotyping results for SNPs and TDT results.

SNP ID

Position

Gene name

Type*

Pooling p-value

Total sample (N trios)

Frequency children

Frequency parents

T/NT

χ2

Individual genotyping total sample p-value

Individual genotyping 300 trios pool p-value

rs17169180

135188954

LOC153328

C/A

0.008

613

0.14

0.15

142/176

3.64

0.06

0.20

rs1859430

135258412

IL9

T/C

0.04

599

0.19

0.20

177/203

1.78

0.18

0.14

rs30747

135366739

TGFBI

C/A

0.04

590

0.05

0.05

60/58

0.03

0.85

0.28

rs7715300

135456919

TGFBI

C/A

0.03

612

0.06

0.08

64/108

11.26

0.001

0.18

rs2906066

141560461

-

T/C

0.05

589

0.14

0.13

122/145

1.98

0.16

0.57

rs7443175

141675612

SPRY4

T/C

0.03

612

0.23

0.25

239/200

3.46

0.06

0.36

rs6897690

141681136

SPRY4

A/G

0.008

615

0.22

0.24

255/209

4.56

0.03

0.0006

rs153423

141794129

-

C/T

0.05

611

0.21

0.20

180/202

1.27

0.26

0.46

Gene names are given for the intragenic SNPs and SNPs within 30 kb of a gene (column 3,"Gene name").

* Minor allele is always presented first and its frequencies in children and in parents are given in columns 7 and 8 ("Frequency children", "Frequency parents"). Transmission/non-transmission counts from heterozygous parents for the minor allele in each case are in column 9 ("T/NT"). P-values from Transmission Disequilibrium Test (TDT) for the total sample and the pool sample are given in the column 11 ("Individual genotyping total sample p-value") and column 12 ("Individual genotyping 300 trios pool p-value"). Predicted pooling p-values are presented in column 5, ("Pooling p-value"). Pooling p-value is also provided for rs2906066, although it is not on the array, because it is in complete LD with rs2961720 which produced that p-value. All markers were in Hardy-Weinberg equilibrium.

Individual genotyping in the total sample of 615 parent-proband trios confirmed one SNP in each region as significantly associated with SZ: rs7715300 (p = 0.001) and rs6897690 (p = 0.032). One more SNP in each region approached significance (p = 0.06). Rs7715300 is located between two genes: 30 kb 3'of TGFBI (transforming growth factor, beta-induced, 68 kDa) and 39.5 kb 5' of SMAD5 (SMAD, mothers against DPP homolog 5). Rs6897690 is located in intron1 of SPRY4 (sprouty homolog 4). All markers were in Hardy-Weinberg equilibrium. No significant linkage disequilibrium (LD) between the studied SNPs was observed (r 2 < 0.1). In order to provide the full information, we also present in the Table the p-values provided by the 300 trios in the original pools used for the microsatellite study, which demonstrate some fluctuation from those in the full sample.

Discussion

Pooling is a fast and cost-effective approach used for systematic screening of complex disease associations, where many markers need to be genotyped in large samples. The power of pooling is approximately the same as individual genotyping and has proved to be an accurate method for detecting allele differences using microsatellite markers or SNPs, with a mean error of <2% for the pooled analysis [2123, 42, 43].

In the present study we wanted to investigate the chromosomal region 5q31.1-q32 between markers D5S666 and D5S436 because it includes five reported linkage regions for SZ [59] and a number of good candidate genes. We started with saturating the 14 Mb region with microsatellite markers using DNA pooling, an approach proven successful for other complex diseases [25, 44, 45]. Based on the knowledge that the average length of LD around microsatellite markers is approximately 100 kb, we covered the region with 140 microsatellite markers of which 90 turned out to be polymorphic and gave reliable traces. We found a suggestive evidence for association with SZ for seven markers. Individual genotyping confirmed two of them to be significantly associated: D5S2017 (p = 0.004) and IL9 (p = 0.014). Marker IL9 is located within the IL9 gene and is the same microsatellite previously reported by Schwab et al [6] to produce a max LOD score of 1.8 in 14 SZ pedigrees.

We then performed fine mapping with SNPs within an area of ~400 kb surrounding the IL9 and D5S2017 microsatellites. In order to improve our chances of identifying significant association, we chose to genotype only promising SNPs from another pooling study in SZ on Illumina HumanHap550 arrays [30]. That study used an extended sample of 574 Bulgarian trios and was finished just at the time when we were selecting SNPs to follow-up (all 300 trios used in the microsatellite stage of the study were part of that larger sample). We selected 8 SNPs (4 for each region), that had shown nominal significance (p = 0.05) in pools hybridised on Illumina arrays. This considerably reduced the cost of our project, as the two regions contained nearly 200 SNPs on the Illumina arrays (and even more in the HapMap database). Individual genotyping of the SNPs in the full 615 parent-proband trios confirmed the pooling results for two SNPs: rs7715300 (p = 0.001) and rs6897690 (p = 0.032). SNPs rs17169180 and rs7443175 showed only trends toward association (p = 0.06). To show the validity of the pooling approach, we also report data on the 300 trios in our original pools in the Table 2. However our aim was to identify susceptibility loci for SZ which is clearly best achieved by genotyping as large a sample as possible, therefore the full data on the 615 trios constitutes our primary analysis.

Clearly, the strongest signal identified in our study is at rs7715300, located between two genes: 30 kb 3'of TGFBI and 39.5 kb 5' of SMAD5 (Figure 2). We cannot speculate whether rs7715300 is a causal variant itself, or it is in LD with a marker within any of the two genes (however no SNP within the two genes is in high r 2 with rs7715300). A Bonferroni correction for 90 microsatellite markers and 8 SNPs, would however make this result not significant (p = 0.1) indicating the need for replication in other samples, especially from populations that have shown linkage to this region. No Bulgarian sample has been investigated for linkage to SZ, and we don't know if linkage to 5q is present in that population.

SMAD proteins are intracellular mediators in the bone morphogenetic proteins (BMPs) signalling pathway. SMADs are the only known BMP receptor substrates capable of signal transduction. Activated by phosphorilation SMADs move to the nucleus where they assemble complexes directly involved in the control of gene expression. SMAD5 is expressed in early differentiated granule neurons of the developing cerebellar cortex. BMP2 signalling activity is directly mediated by SMAD5 and its expression is sufficient to trigger granule cell precursor differentiation [46, 47]. It can be speculated that dysfunctional SMAD genes can cause disruption in BMP signalling pathway which is directly linked to the transcriptional control of the oligodendrogenesis [48].

Defects in TGFBI are the cause of several types of corneal dystrophies. TGFBI protein binds to different types of collagen and is important for the cell- collagen interactions in cartilage. The function of the protein and its low expression in the brain make TGFBI a less plausible candidate gene for schizophrenia [49].

The second region includes rs6897690 and rs7443175, which are located in intron 1 and intron 2 in the SPRY4 gene. Indeed the signal we identify is not strong, but it points to a plausible candidate gene. SPRY4 encodes for a SPROUTY protein, which is widely expressed, including in brain. SPRY4 was identified as the evolutionarily conserved target of WNT/β-catenin signalling pathway involved in numerous processes during vertebrate CNS development.

It is possible that we have missed to detect a stronger association within the 5q31-33 region, as our coverage with microsatellite markers was still quite sparse and relied on assumptions about strong uniformly-spread LD. We have 13 intervals with intermarker distance over 300 kb due to the absence of polymorphic markers, or failure of the chosen ones to be analysed confidently. Therefore, we might have missed to detect the presence of a disease susceptibility locus due to the large distance exceeding the assumed presence of LD between any markers.

Conclusion

In summary, our screening of the 5q31-32 linkage region using DNA pooling implicates three possible candidate genes: SMAD5, TGFBI and SPRY4. While none of the findings would survive correction for multiple testing, these results were found by a systematic analysis of one of the strongest schizophrenia linkage regions, making them good candidates to study in other samples, especially samples that have shown linkage to this region.

Abbreviations

SZ: 

schizophrenia

SNPs: 

single nucleotide polymorphisms

Mb: 

megabase

kb: 

kilobase

LD: 

linkage disequilibrium

GWA: 

genome-wide association

TDT: 

transmission disequilibrium test

ETDT: 

extended transmission disequilibrium test

CNS: 

Central Nervous System.

Declarations

Acknowledgements

This work was funded by the International Centre for Genetic Engineering and Biotechnology, Trieste grant to the Department of Medical Genetics, Sofia (ref CRP/BUL04-01), a Schizophrenia programme grant from the MRC and by an NIMH Silvio O Conte Centre For The Neuroscience of Mental Disorders Grant to the Department of Psychological Medicine, School of Medicine, Cardiff University (ref G9309834). The collection of families in Bulgaria was funded by the Janssen Research Foundation, Belgium/USA. We would like to thank the psychiatrists in Bulgaria involved in the recruitment and the patients and their families who took part in the study.

Authors’ Affiliations

(1)
Department of Medical Genetics, Medical University Sofia
(2)
Department of Psychological Medicine, School of Medicine, Cardiff University

References

  1. Gottesman II: Schizophrenia genesis. The origin of madness. 1991, WH Freeman and Co, New YorkGoogle Scholar
  2. Kirov G, O'Donovan MC, Owen MJ: Finding schizophrenia genes. J Clin Invest. 2005, 115: 1440-1448. 10.1172/JCI24759.View ArticlePubMedPubMed CentralGoogle Scholar
  3. McGuffin P, Owen MJ, O'Donovan MC, Thapar A, Gottesman II: Seminars in Psychiatric Genetics. 1994, London: Royal College of PsychiatristsGoogle Scholar
  4. Lewis CM, Levinson DF, Wise LH, DeLisi LE, Straub RE, Hovatta I, Williams NM, Schwab SG, Pulver AE, Faraone SV, Brzustowicz LM, Kaufmann CA, Garver DL, Gurling HM, Lindholm E, Coon H, Moises HW, Byerley W, Shaw SH, Mesen A, Sherrington R, O'Neill FA, Walsh D, Kendler KS, Ekelund J, Paunio T, Lönnqvist J, Peltonen L, O'Donovan MC, Owen MJ, Wildenauer DB, Maier W, Nestadt G, Blouin JL, Antonarakis SE, Mowry BJ, Silverman JM, Crowe RR, Cloninger CR, Tsuang MT, Malaspina D, Harkavy-Friedman JM, Svrakic DM, Bassett AS, Holcomb J, Kalsi G, McQuillin A, Brynjolfson J, Sigmundsson T, Petursson H, Jazin E, Zoëga T, Helgason T: Genome scan meta-analysis of schizophrenia and bipolar disorder, part II: Schizophrenia. Am J Hum Genet. 2003, 73: 34-48. 10.1086/376549.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Straub RE, MacLean CJ, O'Neill FA, Walsh D, Kendler KS: Support for a possible schizophrenia vulnerability locus in region 5q22-31 in Irish families. Mol Psychiatry. 1997, 2: 148-155. 10.1038/sj.mp.4000258.View ArticlePubMedGoogle Scholar
  6. Schwab SG, Eckstein GN, Hallmayer J, Lerer B, Albus M, Borrmann M, Lichtermann D, Ertl MA, Maier W, Wildenauer DB: Evidence suggestive of a locus on chromosome 5q31 contributing to susceptibility for schizophrenia in German and Israeli families by multipoint affected sib-pair linkage analysis. Mol Psychiatry. 1997, 2: 156-160. 10.1038/sj.mp.4000263.View ArticlePubMedGoogle Scholar
  7. Paunio T, Ekelund J, Varilo T, Parker A, Hovatta I, Turunen JA, Rinard K, Foti A, Terwilliger JD, Juvonen H, Suvisaari J, Arajärvi R, Suokas J, Partonen T, Lönnqvist J, Meyer J, Peltonen L: Genome-wide scan in a nationwide study sample of schizophrenia families in Finland reveals susceptibility loci on chromosomes 2q and 5q. Hum Mol Genet. 2001, 10: 3037-48. 10.1093/hmg/10.26.3037.View ArticlePubMedGoogle Scholar
  8. Devlin B, Bacanu S-A, Roeder K, Reimherr F, Wender P, Galke B, Novasad D, Chu A, TCuenco K, Tiobek S, Otto C, Byerley : Genome-wide multipoint linkage analyses of multiplex schizophrenia pedigrees from the oceanic nation of Palau. Mol Psychiatry. 2002, 7: 689-694. 10.1038/sj.mp.4001056.View ArticlePubMedGoogle Scholar
  9. Sklar P, Pato MT, Kirby A, Petryshen TL, Medeiros H, Carvalho C, Macedo A, Dourado A, Coelho I, Valente J, Soares MJ, Ferreira CP, Lei M, Verner A, Hudson TJ, Morley CP, Kennedy JL, Azevedo MH, Lander E, Daly MJ, Pato CN: Genome-wide scan in Portuguese Island families identifies 5q31-5q35 as a susceptibility locus for schizophrenia and psychosis. Mol Psychiatry. 2004, 9: 213-8. 10.1038/sj.mp.4001418.View ArticlePubMedGoogle Scholar
  10. Kirov G, Georgieva L, Williams N, Nikolov I, Norton N, Toncheva D, O'Donovan M, Owen MJ: Variation in the protocadherin gamma A gene cluster. Genomics. 2003, 82: 433-40. 10.1016/S0888-7543(03)00167-8.View ArticlePubMedGoogle Scholar
  11. Bray NJ, Kirov G, Owen RJ, Jacobsen NJ, Georgieva L, Williams HJ, Norton N, Spurlock G, Jones S, Zammit S, O'Donovan MC, Owen MJ: Screening the human protocadherin 8 (PCDH8) gene in schizophrenia. Genes Brain Behav. 2002, 1: 187-191. 10.1034/j.1601-183X.2002.10307.x.View ArticlePubMedGoogle Scholar
  12. Durand CM, Kappeler C, Betancur C, Delorme R, Quach H, Goubran-Botros H, Melke J, Nygren G, Chabane N, Bellivier F, Szoke A, Schurhoff F, Rastam M, Anckarsater H, Gillberg C, Leboyer M, Bourgeron TÌ: Expression and genetic variability of PCDH11Y, a gene specific to Homo sapiens and candidate for susceptibility to psychiatric disorders. Am J Med Genet. 2006, 141 (1): 67-70. 10.1002/ajmg.b.30229.View ArticlePubMed CentralGoogle Scholar
  13. Giouzeli M, Williams NA, Lonie LJ, DeLisi LE, Crow TJ: ProtocadherinX/Y, a candidate gene-pair for schizophrenia and schizoaffective disorder: a DHPLC investigation of genomic sequence. Am J Med Genet. 2004, 129 (1): 1-9. 10.1002/ajmg.b.30036.View ArticleGoogle Scholar
  14. Esper RM, Pankonin MS, Loeb JA: Neuregulins: versatile growth and differentiation factors in nervous system development and human disease. Brain Res Rev. 2006, 51: 161-75. 10.1016/j.brainresrev.2005.11.006.View ArticlePubMedGoogle Scholar
  15. Stefansson H, Sarginson J, Kong A, Yates P, Steinthorsdottir V, Gudfinnsson E, Gunnarsdottir S, Walker N, Petursson H, Crombie C, Ingason A, Gulcher JR, Stefansson K, StClair D: Association of Neuregulin 1 with Schizophrenia Confirmed in a Scottish Population. Am J Hum Genet. 2003, 72: 83-87. 10.1086/345442.View ArticlePubMedGoogle Scholar
  16. Stefansson H, Sigurdsson E, Steinthorsdottir V, Bjornsdottir S, Sigmundsson T, Ghosh S, Brynjolfsson J, Gunnarsdottir S, Ivarsson O, Chou TT, Hjaltason O, Birgisdottir B, Jonsson H, Gudnadottir VG, Gudmundsdottir E, Bjornsson A, Ingvarsson B, Ingason A, Sigfusson S, Hardardottir H, Harvey RP, Lai D, Zhou M, Brunner D, Mutel V, Gonzalo A, Lemke G, Sainz J, Johannesson G, Andresson T, Gudbjartsson D, Manolescu A, Frigge ML, Gurney ME, Kong A, Gulcher JR, Petursson H, Stefansson K: Neuregulin 1 and Susceptibility to Schizophrenia. Am J Hum Genet. 2002, 71: 877-892. 10.1086/342734.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Moises HW, Zoega T, Gottesman I: The glial growth factors deficiency and synaptic destabilization hypothesis of schizophrenia. BMC Psychiatry. 2002, 2: 8-10.1186/1471-244X-2-8.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Nawa H, Takei N: Recent progress in animal modelling of immune inflammatory processes in schizophrenia: implication of specific cytokines. Neurosc Res. 2006, 56: 2-13. 10.1016/j.neures.2006.06.002.View ArticleGoogle Scholar
  19. Shirts BH, Wood J, Yolken RH, Nimgaonkar VL: Association study of IL10, IL1beta, and IL1RN and schizophrenia using tag SNPs from a comprehensive database: Suggestive association with rs16944 at IL1beta. Schizophrenia Research. 2006, 88: 235-244. 10.1016/j.schres.2006.06.037.View ArticlePubMedGoogle Scholar
  20. Sham P, Bader J, Craig I, O'Donovan M, Owen M: DNA pooling: a tool for large scale association studies. Nature Genetics. 2002, 3: 862-871.View ArticleGoogle Scholar
  21. Kirov G, Williams N, Sham P, Craddock N, Owen M: Pooled Genotyping of Microsatellite Markers in Parent-Offspring Trios. Genome Res. 2000, 10: 105-115.PubMedPubMed CentralGoogle Scholar
  22. Norton N, Williams NM, Williams HJ, Spurlock G, Kirov G, Morris DW, Hoogendoorn B, Owen MJ, O'Donovan MC: Universal, robust, highly quantitative SNP allele frequency measurement in DNA pools. Hum Genet. 2002, 110: 471-478. 10.1007/s00439-002-0706-6.View ArticlePubMedGoogle Scholar
  23. Bacellos LF, Klitz W, Field LL, Tobias R, Bowcock AM, Wilson R, Nelson MP, Nagatomi J, Thomson G: Association mapping of disease loci by use of a pooled DNA genome screen. Am J Hum Genet. 1997, 61: 734-747. 10.1086/515512.View ArticleGoogle Scholar
  24. Kendler KS, MacLean CJ, Ma Y, O'Neill FA, Walsh D, Straub RE: Marker-to-marker linkage disequilibrium on chromosomes 5q, 6p, and 8p in Irish high-density schizophrenia pedigrees. Am J Med Genet. 1999, 88: 29-33. 10.1002/(SICI)1096-8628(19990205)88:1<29::AID-AJMG5>3.0.CO;2-7.View ArticlePubMedGoogle Scholar
  25. Tamiya G, Shinya M, Imanishi T, Ikuta T, Makino S, Okamoto K, Furugaki K, Matsumoto T, Mano S, Ando S, Nozaki Y, Yukawa W, Nakashige R, Yamaguchi D, Ishibashi H, Yonekura M, Nakami Y, Takayama S, Endo T, Saruwatari T, Yagura M, Yoshikawa Y, Fujimoto K, Oka A, Chiku S, Linsen SE, Giphart MJ, Kulski JK, Fukazawa T, Hashimoto H, Kimura M, Hoshina Y, Suzuki Y, Hotta T, Mochida J, Minezaki T, Komai K, Shiozawa S, Taniguchi A, Yamanaka H, Kamatani N, Gojobori T, Bahram S, Inoko H: Whole genome association study of rheumatoid arthritis using 27 039 microsatellites. Hum Mol Genet. 2005, 14: 2305-2321. 10.1093/hmg/ddi234.View ArticlePubMedGoogle Scholar
  26. Wing JK, Babor T, Brugha J, Cooper JE, Giel R, Jablenski A, Regier D, Sartorius N: Schedules for clinical assessment in neuropsychiatry. Arch Gen Psychiatry. 1990, 47 (6): 589-593.View ArticlePubMedGoogle Scholar
  27. American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders. 1994, American Psychiatric Press: Washington, DC, 4Google Scholar
  28. JBIRC database (Japan Biological Information Research Centre). [http://www.jbirc.aist.go.jp/gdbs]
  29. UCSC Genome Browser. [http://www.genome.ucsc.edu]
  30. Kirov G, Zaharieva I, Georgieva L, Moskvina V, Nikolov I, Cichon S, Toncheva D, Owen MJ, O'Donovan MC: A genome-wide association study in 574 schizophrenia trios using DNA pooling. Mol Psychiatry. 2007Google Scholar
  31. Primer 3 program. [http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi]
  32. Sequenom system. [http://www.sequenom.com]
  33. Myakishev MV, Khripin Y, Hu S, Hamer DH: High-throughput SNP genotyping by allele-specific PCR with universal energy-transfer-labelled primers. Genome Res. 2001, 11: 163-169. 10.1101/gr.157901.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Hawskins JR, Khripin Y, Valdes AM, Weaver TA: Miniaturized sealed-tubes allele-specific PCR. Hum Mutat. 2002, 19: 543-553. 10.1002/humu.10060.View ArticleGoogle Scholar
  35. Sham PC, Curtis D: Monte Carlo test for association between disease and alleles at highly polymorphic loci. Ann Hum Genet. 1995, 59: 97-105. 10.1111/j.1469-1809.1995.tb01608.x.View ArticlePubMedGoogle Scholar
  36. Sham P, Curtis D: An extended transmission disequilibrium test (TDT) for multiallele marker loci. Ann Hum Genet. 1995, 59: 323-336. 10.1111/j.1469-1809.1995.tb00751.x.View ArticlePubMedGoogle Scholar
  37. Spielman RS, McGinnis RE, Ewens WJ: Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet. 1993, 52: 506-516.PubMedPubMed CentralGoogle Scholar
  38. Spielman RS, Ewens WJ: The TDT and other family-based tests for linkage disequilibrium and association. Am J Hum Genet. 1996, 59: 983-989.PubMedPubMed CentralGoogle Scholar
  39. Haploview. [http://www.broad.mit.edu/mpg/haploview/index.php]
  40. Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005, 21: 263-265. 10.1093/bioinformatics/bth457.View ArticlePubMedGoogle Scholar
  41. HapMap. [http://www.hapmap.org]
  42. Daniels J, Holmans P, Williams N, Turic D, McGuffin P, Polmin R, Owen MJ: A simple method for analyzing microsatellite allele image patterns generated from DNA pools and its application to allelic association studies. Am J Hum Genet. 1998, 62: 1189-1197. 10.1086/301816.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Kirov G, Nikolov I, Georgieva L, Moskvina V, Owen MJ, O'Donovan MC: Pooled DNA genotyping on Affymetrix SNP genotyping arrays. BMC Genomics. 2006, 7: 27-10.1186/1471-2164-7-27.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Szyld P, Jagiello P, Csernok E, Gross WL, Epplen JT: On the Wegener granulomatosis associated region on chromosome 6p21.3. BMC Med Genet. 2006, 7: 21-10.1186/1471-2350-7-21.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Gödde R, Brune S, Jagiello P, Sindern E, Haupts M, Schimrigk S, Müller N, Epplen JT: An extended association screen in multiple sclerosis using 202 microsatellite markers targeting apoptosis-related genes does not reveal new predisposing factors. J Neg Results BioMedicine. 2005, 4: 7-10.1186/1477-5751-4-7.View ArticleGoogle Scholar
  46. Rios I, Alvarez-Rodríguez R, Martí E, Pons S: Bmp2 antagonizes sonic hedgehog-mediated proliferation of cerebellar granule neurones through Smad5 signalling. Development. 2004, 131: 3159-3168. 10.1242/dev.01188.View ArticlePubMedGoogle Scholar
  47. Hester M, Thompson JC, Mills J, Liu Y, El-Hodiri HM, Weinstein M: Smad1 and Smad8 Function Similarly in Mammalian Central Nervous System Development. Mol Cell Biol. 2005, 25: 4683-4692. 10.1128/MCB.25.11.4683-4692.2005.View ArticlePubMedPubMed CentralGoogle Scholar
  48. Nicolay DJ, Doucette JR, Nazaraki AJ: Transcriptional Control of Oligodendrogenesis. Glia. 2007, 55: 1287-1299. 10.1002/glia.20540.View ArticlePubMedGoogle Scholar
  49. Kannabiran C, Klintworth GK: TGFBI gene mutations in corneal dystrophies. Hum Mutat. 2006, 27: 615-625. 10.1002/humu.20334.View ArticlePubMedGoogle Scholar
  50. Gurling HM, Kalsi G, Brynjolfson J, Sigmundsson T, Sherrington R, Mankoo BS, Read T, Murphy P, Blaveri E, McQuillin A, Petursson H, Curtis D: Genomewide genetic linkage analysis confirms the presence of susceptibility loci for schizophrenia, on chromosomes 1q32.2, 5q33.2, and 8p21-22 and provides support for linkage to schizophrenia, on chromosomes 11q23.3-24 and 20q12.1-11.23. Am J Hum Genet. 2001, 68: 661-673. 10.1086/318788.View ArticlePubMedPubMed CentralGoogle Scholar
  51. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-244X/8/11/prepub

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