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Human Molecular Genetics Advance Access originally published online on March 15, 2008
Human Molecular Genetics 2008 17(13):1890-1903; doi:10.1093/hmg/ddn087
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Identification of a novel asthma susceptibility gene on chromosome 1qter and its functional evaluation

Julia H. White1, Mathias Chiano2, Mark Wigglesworth1, Robert Geske1, John Riley2, Nicola White2, Simon Hall3, Guohua Zhu2, Frank Maurio1, Tony Savage1, Wayne Anderson2, Joanna Cordy1, Melissa Ducceschi1, GAIN investigators4,{dagger}, Jorgen Vestbo5 and Sreekumar G. Pillai2,*

1 Molecular Discovery Research, GlaxoSmithKline Research and Development, RTP, NC, USA/Stevenage, UK 2 Genetics, GlaxoSmithKline, Research Triangle Park, NC, USA/Stevenage, UK 3 Respiratory Center for Excellence in Drug Discovery, GlaxoSmithKline, Stevenage, UK 4 Genetics of Asthma International Network Investigators 5 Institute of Preventive Medicine, Kommunehospitalet, Copenhagen, and Department of Respiratory Medicine, Hvidovre University Hospital, Hvidovre, Denmark

* To whom correspondence should be addressed at: 5 Moore Drive, GlaxoSmithKline, Research Triangle Park, NC 27709, USA. Tel: +1 9194831815; Fax: +1 9193150311; Email: sreekumar.g.pillai{at}gsk.com

Received November 30, 2007; Accepted March 13, 2008


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
Asthma is a multifactorial disease, in which the intricate interplay between genetic and environmental factors underlies the overall phenotype of the disease. Using a genome-wide scan for linkage in a population comprising of Danish families, we identified a novel linked locus on chromosome 1qter (LOD 3.6, asthma) and supporting evidence for this locus was identified for both asthma and atopic-asthma phenotypes in the GAIN (Genetics of Asthma International Network) families. The putative susceptibility gene was progressively localized to a 4.5 Mb region on chromosome 1q adjacent to the telomere, through a series of genotyping screens. Further screening using the pedigree-based association test (PBAT) identified polymorphisms in the OPN3 and CHML genes as being associated with asthma and atopic asthma after correcting for multiple comparisons. We observed that polymorphisms flanking the OPN3 and CHML genes wholly accounted for the original linkage in the Danish population and the genetic association was also confirmed in two separate studies involving the GAIN families. OPN3 and CHML are unique genes with no known function that are related to the pathophysiology of asthma. Significantly, analysis of gene expression at both RNA and protein levels, clearly demonstrated OPN3 expression in lung bronchial epithelia as well as immune cells, while CHML expression appeared minimal. Moreover, OPN3 down-regulation by siRNA knock-down in Jurkat cells suggested a possible role for OPN3 in modulation of T-cell responses. Collectively, these data suggest that OPN3 is an asthma susceptibility gene on 1qter, which unexpectedly may play a role in immune modulation.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
Asthma and atopic disorders are considered common complex genetic disorders with both environmental and genetic etiologies and are an important public health concern, with the treatment of asthma alone in the US costing billions of dollars a year (1). The complex interplay between genetic and environmental factors underlies the overall phenotype of asthma and an individual's environmental exposure to allergens, pollutants and respiratory viruses contribute to the development of asthma (2,3). Genome-wide screens for asthma and related phenotypes have been completed in a number of independent populations (4–17) resulting in a number of genes being reported to be associated with asthma, atopy, or related phenotypes. Evidence of association for the positionally cloned genes DPP10 on 2q14 (18), GPRA on 7p (19), PHF11 on 13q14 (20) and ADAM33 on 20p13 (21) are published, though the replication from independent populations is lacking for some of these studies. Recently, genome-wide association studies in asthma has been reported and ORMDL3 is proposed as a susceptibility gene for asthma (22). Although these initial reports present encouraging data, extensive functional work is nevertheless required to fully confirm the role of these identified genes in the pathophysiology of asthma.

To identify novel asthma susceptibility genes, we conducted linkage analysis in two independent populations followed by association analyses in the linkage region. A region with significant linkage to atopic asthma was identified in the Danish population and was subsequently replicated in the GAIN population. Association analyses in the Danish population identified OPN3/CHML as putative asthma susceptibility genes within the linkage region and subsequent replication analyses in the GAIN population confirmed these results. OPN3 is a Family-A GPCR, belonging to the opsin family of receptors (23) and is a recently discovered extra-retinal photoreceptor, which may play a role in non-visual photic processes such as the entrainment of circadian rhythm or the regulation of pineal melatonin production. CHML [choroideremia-like (Rab escort protein 2)] is a single exon gene that is encoded within the first intron of OPN3 and the product of the CHML gene supports geranylgeranylation of most Rab escort proteins for intracellular transport, regulation of GTPase activities as well as visual perception pathways. To determine which of the OPN3 and/or CHML genes are associated with the pathophysiology of asthma, both expression and functional studies were undertaken. The studies strongly suggested that OPN3 is functionally associated with asthma and the gene product may be involved in the modulation of the immune response.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
Linkage analysis
Summary of the clinical characteristics of the populations analyzed are given in Table 1. Multipoint linkage analysis using 294 families (1151 subjects) from the Danish population identified linkage on chromosome 1qter for both asthma (LOD 1.48) and atopic asthma (LOD 2.15) near the marker D1S1609 (263 cM). This finding was subsequently replicated in 364 GAIN families (1551 subjects) from Europe, Australia and the United States (LOD 1.66 between markers D1S2850 and D1S1609) for atopic asthma (17). To increase genetic confidence in the observed linkage at this locus and also to facilitate subsequent gene identification, further linkage refinement studies were undertaken. Single nucleotide polymorphisms (SNPs) were selected at ~1 cM intervals across the linkage region and were genotyped in the Denmark population (list of the SNPs provided Supplementary Material, Table S1). Multipoint linkage analysis confirmed and improved upon the original linkage data in the Danish cohort generating an LOD of 3.6 at rs595773 for asthma and an LOD of 2.7 at rs914940 for atopic asthma. (Fig. 1). The span of the 1-LOD confidence interval for this locus decreased to 11.5 cM (4.5 Mb) between flanking markers rs947306 and rs1890820.


Figure 1
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  		Figure 1. (A) Linkage refinement analysis in the Danish population. Asthma and atopic asthma phenotypes were analyzed for the multipoint linkage analyses. The list of SNP markers used in the linkage refinement analyses are given in Supplementary Material, Table S1. The list of microsatellite markers used in the linkage analyses are in the supplementary information of a previously published article (44). (B) Genomic region identified from the linkage analyses with a physical size of 4.5 mb (11.5 cM). (C) LD pattern at the OPN3 locus. Founders from the 404 GAIN2 families (Greece, Germany, Australia, Groningen and NC) were used for estimating the LD. D' is shown as D'x 100%. Red squares are blank where D' = 1, dark red indicates high LD (D' < 1; LOD ≥ 2), pink indicates weaker LD (D' < 1; LOD ≥ 2), white indicates weaker LD (D' < 1; LOD < 2) and blue indicates uninformative results (D' = 1; LOD < 2). Haplotype block structure was estimated with the Haploview program. The LD block structure was very similar in all the populations examined. (D) Genomic organization of OPN3 and CHML. CHML is located in the first intron of OPN3.

 


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  		Table 1. Clinical characteristics of the study populations

 
Association analyses
Next, an association screen was performed across the 4.5 Mb interval to identify the potential susceptibility genes responsible for the observed linkage to asthma. SNPs were selected at ~25 kb intervals across the entire locus and genotyped in the primary association population. A Transmission Disequilibrium Test (TDT) was performed using the PBAT program (24,25), using 120 SNP markers that spanned the entire locus resulting in a final average marker spacing of 37.5 kb. We used the screening approach implemented in the PBT program to identify the significant association. PBAT screening using an additive model identified three significant markers associated with asthma: rs628720 (P-value 0.006); HCV1292443 (P-value 0.02); HCV1292455 (P-value 0.03). Screening using the atopic-asthma phenotype identified one significant marker HCV1292455 (P-value 0.024, Table 2). These data reveal that the most consistent and significant association signals in the locus came from SNPs in a region that included the genes encoding both OPN3 and CHML.


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  		Table 2. Results of the family based association analysis using PBAT in the Danish population

 
In order to examine the origin of the linkage signal observed at the 1qter locus, we next tested a series of nine SNPs spanning the OPN3/CHML region to see if they could explain the original linkage result in the Danish cohort. Interestingly, a single SNP (rs614251) within the OPN3 gene accounted for the linkage signal observed. A permutation test for association (10 000 permutations) gave an empirical P-value ≤ 0.0001 for asthma phenotype, implicating this polymorphism or other polymorphisms in linkage disequilibrium (LD) with it. Furthermore, partitioning of LOD scores for the 1qter locus based on individual genotypes values at SNP rs614251 revealed that homozygotes for the minor (T) allele accounted for virtually the entire linkage signal observed at this locus (see Supplementary Material, to see the Methods and Results).

We then conducted a replication analysis in the UK and Norway families from the GAIN1 collection (442 families). A total of 20 SNPs in the OPN3 gene region were analyzed using a family-based association test (FBAT) against an expanded set of phenotypes [Asthma, Atopic asthma and Bronchial hyper-reactivity (BHR)]. Eleven significant SNPs (P-value:0.045–0.003) were identified (Table 3). We then analyzed the data in a second replication population from the GAIN study. This population comprised 404 families from Greece, Germany, Australia, Groningen and United States (NC). Eighteen markers in the OPN3 region were genotyped and analyzed using family-based association analysis. The LD pattern indicated the existence of two LD blocks and the LD block structure was highly similar in all three Caucasian populations examined (Fig. 1) with evidence of association distributed throughout the gene, although the significance level varied. Conditional analysis of association under the assumption of linkage demonstrated significant evidence of association in the LD block 2 (Fig. 1, Table 4). Further evaluation of SNPs within the same LD block provided a similar linkage profile to that observed for rs614251. Similar profiles were not observed for markers outside this LD block, suggesting that any genetic effects are limited to a region around the first intron of the OPN3 gene. Significant SNP level replication was observed in the association analysis as well. The SNP which explained the linkage in the Denmark population (rs614251) was associated with asthma in the Denmark population (P-value 0.043, Supplementary Material, Table S2) and in the GAIN2 population (P-value 0.047, Table4). The same marker was associated with BHR in the GAIN1 population (P-value0.0036, Table3). The significant SNP HCV605574 for asthma was replicated in the Danish (P-value = 0.0314) and GAIN2 (P-value = 0.00379) populations. The significant marker HCV1292455 in the Danish population (P-value 0.01) was associated with BHR in the GAIN1 population (P-value 0.025). These three SNPs were in significant LD (average D' = 96) and is located in the associated LD block 2. Haplotype analysis supported the results of single SNPs association studies. If linkage is assumed, the most significant haplotypic associations were from block 2, which covers the OPN3 region.


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  		Table 3. Results of the family-based association analyses using FBAT from the GAIN1a population

 


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  		Table 4. Results of the family-based association analyses using FBAT from the GAIN2a population

 
The genomic structure for the genes OPN3 and CHML is illustrated in Figure1D. Significantly, the single exon encoding the CHML gene lies within the first intron of OPN3 (26), thus making it impossible to unambiguously distinguish between OPN3 and CHML as potential asthma candidate gene based on LD data alone. Indeed, sequence analysis of both genes failed to identify any coding polymorphisms in a panel of asthmatics (data not shown). However, the linked SNP, rs614251, lies within the 5' flanking region of OPN3, which suggests that gene expression levels of OPN3 could functionally drive the observed association. To investigate this possibility, we performed mRNA expression analyses using quantitative PCR (qPCR; TaqMan, ABI) expression profiling for both CHML and OPN3 across a wide variety of tissues and cell lines of immune origin (Figs 2 and 3). Significantly and in contrast to the published brain-specific expression for murine OPN3 (27), we observed a wide tissue distribution for OPN3 throughout the major tissues in Human with particularly higher expression in lung, placenta and spleen and in accordance with the wide distribution for OPN3 reported by Halford et al., (26). In addition, we also observed a wide expression from CHML, yet overall levels of expression for CHML appeared (significantly) lower than those observed for OPN3, suggesting that OPN3 is more likely to be the gene of interest. However this will not completely discount the role of CHML. Importantly, we noted that OPN3 mRNA was highly expressed in macrophages, bronchiolar epithelium, primary T-cells and dendritic cells, whereas CHML was consistently expressed at lower levels in all immune cells examined (Fig. 2B). Finally, we examined OPN3 transcript levels within asthmatic lung samples (data not shown) and compared their values with normal lung expression. OPN3 transcript levels were ~2.5 times higher in asthmatic tissue in comparison to those in normal lung tissue.


Figure 2
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  		Figure 2. Human tissue distribution for OPN3, CHML and the reference gene GAPDH measured using TaqMan quantitative RT–PCR. (A), OPN3 (B), CHML (C) GAPDH Poly A+ RNA from 20 tissues of four different individuals (two males, two females except prostate) was prepared, reverse transcribed and analysed by TaqMan. TaqMan quantitative PCR (Applied Biosystems, Warrington, UK) was used to assess the level of each gene relative to genomic DNA standards. The data are presented as the means of mRNA copies detected/ng poly A+ RNA from four individuals±SEM (n = 4).

 


Figure 3
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  		Figure 3. Human cell distribution for OPN3, CHML and the reference gene GAPDH measured using TaqMan quantitative RT–PCR. (A), OPN3 (B), CHML (C) GAPDH Gene expression profile for OPN3 and CHML from different human tissues TaqMan quantitative PCR (Applied Biosystems, Warrington, UK) was used to assess the level of each gene relative to genomic DNA standards. Data are single values for each of the samples indicated. The cell types used were as follows: monocytes, NK cells, macrophages, neutrophils, fibroblasts, bronchial epithelial cells, CD4+ T cells, CD8+ cells, TH1 cells, dendritic cells, B cells and primary mast cells.

 
Taken together, the above observations suggest that OPN3 is highly expressed in tissues not normally associated with the expression of opsin receptors and more importantly, that OPN3 appears to be expressed in tissues of importance for immune responses and asthma. We therefore examined the levels of OPN3 protein in key tissues associated with asthma as well as opsin biology (retina and brain) using immunohistochemistry (IHC) (Fig. 4). For this we generated an antiserum raised against OPN3 protein using a immunogenic peptide (amino acids 331–341) and characterized the resulting rabbit polyclonal antiserum for IHC using peptide competition experiments and heterologous OPN3 expression in mammalian cells to fully validate the antiserum for IHC (data not shown). In agreement with the mRNA expression data, we observed protein expression for OPN3 in Human lung bronchiolar epithelium (Fig. 4A and B); clear cell surface staining for OPN3 in T-cells and B-cells as well as certain dendritic cell subtypes (data not shown). Moreover, we observed OPN3 protein expression in several Human tissues using a tissue array format, which again confirmed the mRNA expression observations (Table 5). Finally, we examined OPN3 protein expression in tissues derived from Human asthmatic and non-asthmatic lung tissue. The observed staining pattern suggested that OPN3 is expressed in both asthmatic and non-asthmatic lung samples (Fig. 4A and B) with positivity observed in alveolar macrophages, together with the respiratory epithelium in the trachea and epithelium of the submucosal ducts. Initially, it appeared that OPN3 protein may be over-expressed in asthmatic lung, but this apparent over expression is likely due to the increased infiltration of OPN3 expressing inflammatory cells into the asthmatic lung, rather than a true cellular over expression.


Figure 4
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  		Figure 4. OPN3 expression by IHC in human tissue immunohistochemical localization of OPN3 in human samples of (A) non-asthmatic lung, (B) asthmatic lung, (C) retina and (D) brain. Expression was observed in the respiratory epithelium (broken black arrows), immune cells (filled black arrow heads) and alveolar macrophage (filled red arrow heads) of both non-asthmatic and asthmatic lung. (C) Retinal expression was observed in the photoreceptor cells (P), outer plexiform layer (OPL), inner plexiform layer (IPL) and ganglion cell layer (GCL). (D) Brain expression was noted only in large neurons (open black arrow heads) found within the substantia nigra. Original magnifications: (A, B, D) = 40x (C) = 60x.

 


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  		Table 5. Summary of results from tissue microarray analyses to assess the OPN3 expression in human

 
Opsin receptors are functionally associated with light perception and the regulation of circadian rhythms (23,28) and therefore, we also examined OPN3 expression within the Human retina using IHC (Fig. 4C). We clearly observed staining for OPN3 within the neural layers of the retina, including the ganglion cell layer, the outer and inner plexiform layers as well as positive staining within the photoreceptor layers. Protein expression was also examined for OPN3 in Human brain covering several brain subsections and using the same antiserum, we only detected staining in specific neurons within the substantia nigra (Fig. 4D). Control sections treated with non-immune, isotype specific immunoglobulin under identical staining conditions were negative (data not shown). Finally, we attempted to examine the expression of CHML protein using IHC, but unfortunately, despite several attempts, we were unable to raise antisera of sufficient quality to permit analysis of CHML expression.

Given the wide expression of OPN3 at both mRNA and protein levels within cell types of the immune system and bronchiolar epithelial cells, together with the possible genetic association data to asthma, we were next interested in functionally modulating OPN3 to explore the functional consequences of OPN3 agonism or inverse agonism with respect to immune function. Unfortunately, no pharmacological tools exist for OPN3 and moreover, OPN3, in belonging to the super family of opsin receptors, is likely to be expressed with a covalently bound ligand which is conformationally modulated by photons of light. A well-characterized member of the opsin GPCR family is the rhodopsin receptor, in which the agonist, 11-cis-retinal, is covalently bound to the receptor and isomerased to All-Trans-Retinal (ATR) following light activation before recycling back to the cis-retinal form as part of the retinoid visual cycle (29). Formally OPN3 remains an orphan receptor but the related melanopsin (OPN4) receptor has recently been shown to similarly bind retinaldehyde as a covalently bound ligand and is functionally involved in the entrainment of circadian rhythms (30–32). Moreover, tmt (teleost multiple tissue)-opsin, the zebrafish orthologue to OPN3, is light responsive and involved in circadian rhythm entrainment in zebrafish (33) and therefore by analogy, it is possible that OPN3 likewise possesses an endogenous light responsive ligand and shows a similar function in Humans. Unfortunately, we were unsuccessful in pharmacologically modulating heterologously expressed OPN3 through the binding of various retinals/retinaldehydes; detecting any signaling through classical G-protein signaling cascades in mammalian cells or generating constitutively active mutants of OPN3, despite similar results having been reported for the Rhodopsin receptor (data not shown). Therefore, we decided to employ an siRNA-mediated gene knockdown approach against OPN3 as an alternative technique to mimic the antagonistic modulation of OPN3. We used this approach in a Jurkat cell line, an immortalized Human T-cell line, that is known to endogenously express OPN3 (data not shown), and is therefore likely to express the receptor with an intact endogenous ligand. We hoped that this approach could inform us as to a function for OPN3 in T-cell biology, given the well-documented role of T-cells in the immunopathology of asthma (34,35).

Three siRNA sequences were designed to specifically target OPN3 mRNA together with a random sequence to act as a negative control. These reagents were individually delivered to cultured Human Jurkat T-cells using Amaxa nucleofection and following 48 h incubation, OPN3 gene knockdown was monitored by measuring OPN3 mRNA levels in the cells relative to control housekeeping gene expression. A significant reduction in mRNA expression of OPN3 of over 70% was consistently observed relative to the random sequence control (RSC) for all three siRNA reagents (Fig. 5B). The biological consequences of OPN3 gene knockdown with regard to T-cell activation were next examined in the same Jurkat cells through the measurement of IL-2 secretion in response to T-cell stimulation following T-cell receptor (CD3/CD28) antibody cross-linking in the presence and reduction of OPN3 expression (Fig. 5A). Significantly, all three OPN3 siRNA sequences reproducibly and robustly inhibited IL-2 production after 48 h activation (P < 0.001), and the inhibition specifically correlated with the knockdown of OPN3 mRNA.


Figure 5
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  		Figure 5. siRNA mediated silencing of OPN3 in Human Jurkat T cells (A) IL2 production in Jurkat T cells shows a significant inhibition of the functional response to anti CD3/CD28 activation by all three OPN3 sequences compared with the RSC (P < 0.001). (B) mRNA expression of OPN3 in Jurkat T cells. OPN3 mRNA expression was normalized to that of GAPDH and relative abundance is shown comparing three OPN3 siRNA sequences with an RSC. All three sequences significantly reduced expression levels of OPN3 mRNA (P < 0.001) This data is representative of four separate experiments.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
Our data demonstrate that polymorphisms within the OPN3/CHML gene locus on human chromosome1q are associated with asthma and asthma-related phenotypes. The SNP which explained the linkage (rs614251) was associated with asthma in the Danish and GAIN2 population and with BHR in the GAIN1 population. The SNP HCV605574 was associated in the Danish and GAIN2 populations for asthma phenotype; HCV1292455 with BHR in the Danish and GAIN1 populations. Though these three associated SNPs were in significant LD, none of the SNPs were associated in all three populations for the same phenotype analyzed. So we consider this as a locus replication and not a specific SNP level replication.

On the basis of the observed expression in immune cells, OPN3 is more likely to encode the gene product functionally associated with asthma, although we are unable to formally discount CHML as being the functionally associated gene product. CHML lies in the first intron of OPN3 (26) and it was therefore impossible to distinguish between these potential asthma candidate genes based on LD data. This is analogous to one of the recent publications on FTO as an obesity susceptibility gene, which is in tight LD with another gene KIAA1005 (36). Another important point is that one of the flanking genes KMO could also be associated with asthma; however, the LD data shows that the association signals are not coming from KMO, but from OPN3/CHML. CHML is required for the prenylation and membrane localization of Rab proteins and is essential for their role in lysosomal trafficking. Loss of Rab function is known to effect membrane trafficking and have a downstream effect upon secretion, uptake and delivery of proteins to the cell surface. It is possible that CHML association with asthma may be due to a defect in the prenylation of specific Rabs leading to an inappropriate immune surveillance or inappropriate responses to inflammatory stimuli.

OPN3 (opsin3, encephalopsin or panopsin) is a member of the guanine nucleotide-binding protein (G-protein)-coupled receptor super-family (GPCR), which characteristically signal via the activation of heterotrimeric G-proteins. As a subgroup within the GPCR super-family, opsins are photosensitive as a result of their fusion with a light sensing chromaphore called retinal and so far, over 1000 different opsins have been identified. As an opsin, OPN3 encodes a 403 amino-acid polypeptide chain that forms the typical seven {alpha}-helical transmembrane structure with the classical opsin lysine residue at position 299, required to form a Schiff base with its retinal as well as an acidic aspartate at position 115, to act as a counter-ion to the basic Schiff base. However, despite all these opsin characteristics, the chromaphore for OPN3 remains undefined and although OPN3 has not been demonstrated as being photosensitive as have other ‘orphan’ opsins such as OPN4 (23,30–32), is a reasonable conjecture that OPN3 is likely to function as a photosensitive opsin, possibly binding a retinaldehyde-based chromaphore. Given the lack of knowledge of the chromaphore associated with OPN3 and our observations that the OPN3 protein is widely expressed throughout Human tissues, including regions where light is unlikely to penetrate, the function(s) of OPN3, whether light sensitive or insensitive, remain unknown. Indeed, the close yet distinct position of OPN3 to that of vertebrate visual and non-visual opsins within the opsin phylogenetic tree suggests that OPN3 may possess a novel function (23). Functional clues to the role of OPN3 have arisen from studies associated with the closely related zebrafish orthologue, tmt-opsin (Telost multiple tissue), whereby tmt-opsin and OPN3 lie on the same branch of the opsin phylogenetic tree and possess common genomic structures and sequence identities (33). Like our observations for OPN3, tmt-opsin is expressed in multiple tissues within zebrafish but importantly has been shown to be functionally light sensing and possibly involved in the photic regulation of the zebrafish peripheral circadian rhythm in several organs (33). OPN3 could share a similar function within vertebrates, but this remains to be investigated.

In these reported studies, we have demonstrated that the OPN3 gene locus is genetically associated to several asthma phenotypes and OPN3 is expressed throughout the body in several peripheral tissues, including the ganglion and plexiform neuronal layers as well as the photoreceptor layers of the retina. Surprisingly we show that OPN3 appears to have a functional role in modulating IL-2 secretion from Jurkat cells, an immortalized T-cell line, following T-cell receptor activation. Taken together, these results are strongly suggestive that even if OPN3 does act as a photosensitive opsin with a functional role relating to circadian entrainment, it may play a further as yet undefined role in T-cell signaling processes and immunomodulation, which could relate back to the observed genetic association with asthma and asthmatic phenotypes. Mechanistically, cytokines released from TH2 and, to a lesser extent, TH1 T-cells promote the recruitment to the lung and mobilization of eosinophils and mast cells, which in turn promote the progressive pathogenesis of asthma. Understanding what governs the polarization of TH2 positive T-cells and their activation may be key for controlling the pathogenesis of asthma. The silencing of OPN3 expression by siRNA, a process which is somewhat analogous to the action of an antagonist at the receptor, has been shown to have an inhibitory effect on cytokine production in a Jurkat T-cell line. This suggests that OPN3 may play a role in TH1 and TH2 cells polarization and thereby could provide an interesting approach to examine effects on polarization of TH cells, their effector functions and how this may relate to asthma pathogenesis.

Given the possible role for OPN3 in asthma as suggested by our genetic and functional studies as well as the likely role of OPN3 in the regulation of peripheral circadian rhythms, it is interesting to note that asthma itself is strongly associated with the circadian cycle (37). Pulmonary function in asthmatic patients frequently deteriorates between midnight and early morning and although relationships between bronchial asthma and the function of circadian clock genes still remain unclear, it is interesting to speculate that OPN3 could play some role to marry circadian regulation and photo-entrainment with the regulation of immune responses in asthma. Indeed, anecdotal evidence for light being involved in the modulation of immune responses has recently arisen from UV light phototherapy for the treatment of allergic rhinitis (38,39). A second link between OPN3 and asthma could arise via its likely retinaldehyde-based ligand and Vitamin A. Asthmatics have been shown to have significantly lower serum concentrations of Vitamin A and dietary supplement of Vitamin A may be beneficial to asthmatics (40,41).

In summary, we identified replicated linkage to asthma at 1qter in two large Caucasian populations. Replicated evidence of association with OPN3/CHML was detected in subsequent association analyses. Gene expression analyses and functional studies of OPN3 suggest that this gene may play a role in the pathophysiology of asthma and immune cell function.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
Subjects
Linkage analyses: primary population
The primary linkage population consisted of family samples collected from the Department of Respiratory Medicine at hospitals in the metropolitan area of Copenhagen, Denmark. For each family, at least two siblings with clinical asthma were required. The Danish population consisted of 294 families with 1151 subjects (42). Clinical characteristic of the subjects used in linkage analyses are provided in Table1.

Linkage study: replication population
Asthma families recruited at eight clinical centers as part of the GAIN collection served as the replication population for linkage. Recruitment procedure, population characteristics and results from the linkage analyses are published elsewhere (17,43,44).

Association analysis: primary population
The subjects used in the primary linkage analysis (Denmark) served as the primary population for family-based association analyses. The clinical characteristics of the children and parents are provided in Table1.

Association analysis: replication population 1 (GAIN1)
Four hundred and forty-two families from the GAIN collection was used as the primary replication cohort. The clinical centers are listed with the number of families and that of subjects in parentheses: Aberdeen, 94 (401); Leicester, 73 (301); Sheffield, 93 (399); Stoke-on-Trent, 82 (345); and Norway 100 (414). In the selection of these families, at least two siblings with clinical asthma were required. The ascertainment procedure for the GAIN collection is described elsewhere (43). The clinical characteristics of the children and parents are provided in Table1.

Association analysis: replication population 2 (GAIN2)
A second set of 404 families from the GAIN collection was used as the second replication cohort. The clinical centers are listed with the number of families and that of subjects in parentheses: Greece, 99 (401); Germany, 99 (426); Australia, 92 (412); Groningen, 66 (300); NC, 48 (210). The clinical characteristics of the children and parents are provided in Table1.

Phenotypes
All subjects were evaluated using standard protocols. Baseline spirometry was performed according to American Thoracic Society criteria (45). A methacholine challenge test was performed in all subjects unless the baseline FEV1 was ≤70% of the predicted value (46,47). Skin prick tests for common allergens (mites, animal, insects, pollen and mould) were conducted. The patients were considered atopic if the wheal diameter was ≥3 mm above the negative control (saline) for at least one allergen tested. Total Serum IgE measurements were taken in all but the Danish subjects. The following phenotypes were utilized for the purposes of this study:

  1. Asthma: a participating physician examined these patients and made a conclusive diagnosis of asthma.
  2. Atopic asthma: defined by physician's diagnosis of asthma and a positive result on at least one skin allergen test.
  3. BHR is defined as a positive methacholine response (≥20% reduction in FEV1) at or below 8 mg/ml of methacholine.

Genotyping
Details on the microsatellite markers used for linkage analysis of the GAIN and Danish populations are described previously (17,44). For linkage refinement and association studies, SNPs were selected from public databases and from the Applied Biosystems Assay-on-DemandTM catalog. The list of markers used in the linkage refinement analyses is given in Supplementary Material, Table S1. Genotyping was performed using AmplifluorTM, TaqMan® or Luminex technologies. For the FRET-based genotyping technologies, genotypes were read using an ABI 7900HTs while the SBCE genotypes were read on a Luminex LX100.

Statistical analysis
All the markers typed for linkage analyses were placed on the Decode-NCBI integrated map. Multipoint non-parametric linkage analysis, as implemented in MERLIN, was used in all analyses (48). This software implements the Kong and Cox's likelihood modification (49) which calculates exact likelihoods and reports LOD scores based on a one parameter allele sharing statistic, Zlr, rather than using the perfect data approximation. LOD scores were computed at a grid density of 1 and 0.1 cM for the primary and refinement scans, respectively. Entropy, a measure of information content, was also calculated in Merlin. Where SNPs under a linkage peak were subsequently found to be associated with disease, conditional analysis was performed to evaluate if the associated SNP accounts for the linkage observed (50).

A two-stage testing strategy implemented in PBAT version 3.5 was used to screen the markers in the Denmark population using asthma and atopic-asthma phenotypes for association analyses (51). For analyzing one phenotype, the strategy used the same data set for steps, screening and testing, without any model variation. In the first step (screening), the SNPs and genetic models that best predicted offspring phenotypes were selected by using parental genotypes, and the conditional power of all SNPs was estimated (52). In the second step (testing), the selected SNPs were examined for the association with asthma and BHR, respectively, using FBAT (53). The two steps were independent statistically (52), so it was required to only adjust the FBAT statistic results for the number of comparisons performed on the testing step. A SNP that reaches significance after this adjustment was considered significant at a total-scale level. According to the guidelines of Van Steen et al. (54), a SNP was considered significant at a total-scale level if its unadjusted P-value multiplied by its power rank number was ≤0.05. FBAT (55) was used for the replication analyses. Haplotype analyses were conducted using the HBAT function of the FBAT program with the use of Monte Carlo sampling (56). The LD structure in the OPN3 region was examined with the program, Haploview (57,58).

SNP discovery by sequencing
SNP discovery within OPN3 and CHML was undertaken by direct sequencing of PCR products from all exons (plus ~100 bp flanking introns); 5 kb upstream of transcription start and 500 bp downstream of transcription stop sites. The sequencing panel consisted of 16 asthmatics from the Danish cohort plus 6 random population controls.

TaqMan tissue mRNA profiles
For tissue distribution profiles, poly A+ RNA from 20 tissues of four different individuals (two males, two females except prostate) was prepared, while for cell distribution profiles, total RNA was prepared from the samples indicated. The RNA was reverse transcribed and analyzed by TaqMan quantitative PCR as described previously (59). Briefly, 0.5 µg of poly A+ RNA (tissues) or 10 µg (cells) was reverse transcribed using random priming and the cDNA produced was used to prepare multiple replicate plates with each well containing the cDNA from 0.4 ng poly A+ RNA or 20 ng total RNA. TaqMan quantitative PCR (Applied Biosystems, Warrington, UK) was used to assess the level of each gene relative to genomic DNA standards. The data are presented as the means of mRNA copies detected/ng poly A+ RNA from four individuals±SD (n = 4), or as single measurements for cell data.

Gene-specific reagents for OPN3:

forward primer 5'-TAGGCTGCACTGTGGACTGG-3',
reverse primer 5'-AGCAATGGGCTATGACACCC-3',
TaqMan probe 5'-AAATCCAAGGATGCCAACGATTCCTCC-3';

CHML:

forward primer 5'- GTCTGCTCTGGGCCTGACTG-3',
reverse primer 5'- CTGGATTTGGAGGTGGAGGG-3',
TaqMan probe 5'- TGGGAAATGAGCATGCTGTCAAGCAAG-3';

GAPDH:

forward primer 5'-CAAGGTCATCCATGACAACTTTG-3',
reverse primer 5'-GGGCCATCCACAGTCTTCTG-3',
TaqMan probe 5'-ACCACAGTCCATGCCATCACTGCCA-3'.

Tissue expression: protein
Human lung (asthmatic and non-asthmatic) eye and brain tissue samples were commercially obtained from appropriately consented donors. Four micrometer-thick sections were prepared from paraffin blocks for immunohistochemistry on an automated staining system (Autostainer, Dako Instruments). Briefly, sections were incubated with avidin D then biotin for 15 min each (Vector Laboratories) followed by 3% hydrogen peroxide for 5 min to inhibit endogenous biotin and peroxidase, respectively. The tissues were then blocked using serum free protein block (Dako) then incubated with an antibody directed against human OPN3 (Open Biosystems) applied at a concentration of 0.75 ug/ml for 30 min at room temperature. A biotinylated goat anti-rabbit IgG secondary antibody (Vector Laboratories) was applied for 30 min followed by the Vectastain ABC Elite kit (Vector). Immunoreactive areas were visualized using Diaminobenzidine (DAB) for 5 min (Dako). Non-immune isotype negative controls were performed under the same conditions as the OPN3 antibody. The specificity of the immunostaining was tested using primary antibody pre-adsorbed with OPN3 immunizing peptide (ratio 1:10) overnight at 4°C before incubation on the tissue (data not shown). Examination and photography were performed using a Leica DMLB light microscope and a Leica DC500 camera.

siRNA gene modulation
siRNA reagents were purchased from Ambion as the following sense strand sequences (5'–3'):

OPN3 284755: CCUGGUCACUCCAACAAUAtt,
OPN3 284756: CCUUUACCUUCGUGUCCUGtt,
OPN3 5570: GGCAAACUCCCAUAUAUUUtt.

Jurkat JE6.1 cells were transfected using the Amaxa nucleofection technology. Cells were resuspended in Amaxa Cell Line V nucleofector solution (2x106per reaction) according to the manufacturers' instructions, 5 µM siRNA added and nucleofected using manufacturer program C16. Cells were then immediately transferred to 1.5 ml RPMI-1640 medium (Invitrogen, UK) containing penicillin and streptomycin and supplemented with 10% FCS and 20 ng/ml rhIL2 (recombinantly expressed and purified by GSK, UK). Cells were activated 48 h post-transfection by transfer of 2x105 viable cells per well to a 96 well flat-bottomed tissue culture flask coated with 2 µg/ml anti-CD3 and 3 µg/ml anti-CD28 antiserum. After 48 h, cell-free supernatants were collected and assayed for IL-2 content using commercial kits (Quantikine, R&D Systems) according to the manufacturers' instructions. The amount of cytokine was calculated by reference to a simultaneously prepared standard curve. Total RNA was purified using the Promega SV96 RNA extraction kit and Beckman Coulter Biomek 2000 automation. Reverse transcription was carried out to synthesise cDNA using the ABI High Capacity cDNA synthesis kit and random primers, according to the manufacturers' instructions. Real-time PCR analysis was performed using ABI Universal TaqMan master mix and an ABI 7900 machine. Primers and probes were designed using Primer Express Software (Applied Biosystems). GAPDH was used as an internal control for normalization of the data. Primer sequences used were:

OPN3 For 5'-CCTCTTTGCTAAATCGAACACTGTATAC-3'
OPN3 Rev 5'-CAGTCGGAGGCACAGAAGCT-3',
OPN3 Probe 5'-FAM-CGAAACTTTCTGATCATGAAGACATAAATCACTGGAT-TAMRA-3',
GAPDH For 5'-CAAGGTCATCCATGACAACTTTG-3',
GAPDH Rev 5'-GGCCATCCACAGTCTTCTGG-3',
GAPDH Probe 5'-ACCACAGTCCATGCCATCACTGCCA-TAMRA-3'

Inhibition of IL2 release and relative abundance of OPN3 mRNA were compared for the OPN3 siRNAs relative to an RSC. Averaged data are expressed as means±SE. Significance (P < 0.001) was determined by the multivariate analogues of Dunnett's ANOVA.


   SUPPLEMENTARY MATERIAL
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
FUNDING
 REFERENCES
 
Supplementary Material is available at HMG Online.


   FUNDING
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 FUNDING
REFERENCES
 
Kathleen C. Barnes was supported in part by the Mary Beryl Patch Turnbull Scholar Program.


   ACKNOWLEDGEMENTS
 
We gratefully acknowledge the assistance of the site coordinators for the family collections. We acknowledge Eric Meldrum for the review and discussion of the data, GSK genetics sample management, data management, bioinformatics and sequencing departments for the support in conducting the experiments and respiratory center of excellence in drug discovery for the guidance and discussions in the course of the gene identification and validation process. The authors would like to thank Dr R. Ravid, Netherlands Brain Bank, The Netherlands, for arrangement/donation of brain tissue.

Conflict of Interest statement. Julia White, Mathias Chiano, Mark Wigglesworth, Robert Geske, John Riley Simon Hall, Guohua Zhu, Frank Maurio, Tony Savage, Wayne Anderson, Joanna Cordy, Melissa Ducceschi and Sreekumar Pillai are full-time employees of Glaxo Smithkline.


   FOOTNOTES
 
{dagger} Genetics of Asthma International Network Investigators: Kathleen C. Barnes—Departments of Medicine & Epidemiology, Johns Hopkins University, Baltimore, MD, USA; Karin Carlsen—Ullevaal University Hospital, Oslo, Norway; Jorrit Gerritsen—University Medical Center Groningen, University of Groningen, Groningen, the Netherlands; Warren Lenney—Directorate of Child Health, Academic Department of Pediatrics, North Staffordshire Hospital, Stoke-on-Trent, UK; Michael Silverman—University of Leicester, Division of Child Health, Leicester, UK; Peter Sly—Center for Child Health Research, University of Western Australia, Perth, Australia; John Sundy—Duke University Medical Center, Durham, NC, USA; John Tsanakas—Pediatric Respiratory Unit, Hippokration General Hospital, Thessaloniki, Greece; Andrea von Berg—Abt. Fuer Kinderheilkunde Foschungsinstitut zur Praevention von Allergien und Atemwegserkrankungen im Kindesalter, Wesel, Germany; Moira Whyte—Academic Unit of Respiratory Medicine, University of Sheffield, Sheffield, UK; Peter J. Helms—Department of Child Health, University of Aberdeen Royal Aberdeen Children's Hospital, Aberdeen, UK. Back


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 

  1. Weiss K.B., Sullivan S.D., Lyttle C.S. Trends in the cost of illness for asthma in the United States, 1985–1994. J. Allergy Clin. Immunol (2000) 106:493–499.[CrossRef][Web of Science][Medline]

  2. Boushey H.A., Holtzman M.J., Sheller J.R., Nadel J.A. Bronchial hyper-reactivity. Am. Rev. Respir. Dis (1980) 12:389–413.

  3. Cookson W.O., Moffatt M.F. Genetics of asthma and allergic disease. Hum. Mol. Genet (2000) 9:2359–2364.[Abstract/Free Full Text]

  4. Anonymous. A genome-wide search for asthma susceptibility loci in ethnically diverse populations. The Collaborative Study on the Genetics of Asthma (CSGA). Nat. Genet (1997) 15:389–392.[CrossRef][Web of Science][Medline]

  5. Daniels S.E., Bhattacharrya S., James A., Leaves N.I., Young A., Hill M.R., Faux J.A., Ryan G.F., le Souef P.N., Lathrop G.M., et al. A genome-wide search for quantitative trait loci underlying asthma. Nature (1996) 383:247–250.[CrossRef][Medline]

  6. Dizier M.H., Besse-Schmittler C., Guilloud-Bataille M., Annesi-Maesano I., Boussaha M., Bousquet J., Charpin D., Degioanni A., Gormand F., Grimfeld A., et al. Genome screen for asthma and related phenotypes in the French EGEA study. Am. J. Respir. Crit. Care Med (2000) 162:1812–1818.[Abstract/Free Full Text]

  7. Haagerup A., Bjerke T., Schiotz P.O., Binderup H.G., Dahl R., Kruse T.A. Asthma and atopy—a total genome scan for susceptibility genes. Allergy (2002) 57:680–686.[CrossRef][Web of Science][Medline]

  8. Hakonarson H., Bjornsdottir U.S., Halapi E., Palsson S., Adalsteinsdottir E., Gislason D., Finnbogason G., Gislason T., Kristjansson K., Arnason T., et al. A major susceptibility gene for asthma maps to chromosome 14q24. Am. J. Hum. Genet (2002) 71:483–491.[CrossRef][Web of Science][Medline]

  9. Hersh C.P., Soto-Quiros M.E., Avila L., Lake S.L., Liang C., Fournier E., Spesny M., Sylvia J.S., Lazarus R., Hudson T., et al. Genome-wide linkage analysis of pulmonary function in families of children with asthma in Costa Rica. Thorax (2006) 62:224–230.[CrossRef][Web of Science][Medline]

  10. Hizawa N., Collins G., Rafnar T., Huang S.K., Duffy D.L., Weber J.L., Freidhoff L.R., Ehrlich E., Marsh D.G., Beaty T.H., et al. Linkage analysis of Dermatophagoides pteronyssinus-specific IgE responsiveness with polymorphic markers on chromosome 6p21 (HLA-D region) in Caucasian families by the transmission/disequilibrium test. Collaborative Study on the Genetics of Asthma (CSGA). J. Allergy Clin. Immunol (1998) 102:443–448.[CrossRef][Web of Science][Medline]

  11. Juan C.C, Soto-Quiros M.E., Lydiana A., Stephen L.L., Catherine L., Eduardo F., Mitzi S., Craig P.H., Sylvia J.S., Thomas J.H., et al. Significant linkage to airway responsiveness on chromosome 12q24 in families of children with asthma in Costa Rica. Hum. Genet (2007) V120:691–699.[Web of Science][Medline]

  12. Koppelman G.H., Stine O.C., Xu J., Howard T.D., Zheng S.L., Kauffman H.F., Bleecker E.R., Meyers D.A., Postma D.S. Genome-wide search for atopy susceptibility genes in Dutch families with asthma. J. Allergy Clin. Immunol (2002) 109:498–506.[CrossRef][Web of Science][Medline]

  13. Laitinen T., Daly M.J., Rioux J.D., Kauppi P., Laprise C., Petays T., Green T., Cargill M., Haahtela T., Lander E.S., et al. A susceptibility locus for asthma-related traits on chromosome 7 revealed by genome-wide scan in a founder population. Nat. Genet (2001) 28:87–91.[CrossRef][Web of Science][Medline]

  14. Ober C., Cox N.J., Abney M., Di Rienzo A., Lander E.S., Changyaleket B., Gidley H., Kurtz B., Lee J., Nance M., et al. Genome-wide search for asthma susceptibility loci in a founder population. The Collaborative Study on the Genetics of Asthma. Hum. Mol. Genet (1998) 7:1393–1398.[Abstract/Free Full Text]

  15. Wjst M., Fischer G., Immervoll T., Jung M., Saar K., Rueschendorf F., Reis A., Ulbrecht M., Gomolka M., Weiss E.H., et al. A genome-wide search for linkage to asthma. German Asthma Genetics Group. Genomics (1999) 58:1–8.[CrossRef][Web of Science][Medline]

  16. Xu J., Meyers D.A., Ober C., Blumenthal M.N., Mellen B., Barnes K.C., King R.A., Lester L.A., Howard T.D., Solway J., et al. Genomewide screen and identification of gene-gene interactions for asthma-susceptibility loci in three U.S. populations: collaborative study on the genetics of asthma. Am. J. Hum. Genet (2001) 68:1437–1446.[CrossRef][Web of Science][Medline]

  17. Pillai S.G., Chiano M.N., White N.J., Speer M., Barnes K.C., Carlsen K., Gerritsen J., Helms P., Lenney W., Silverman M., et al. A genome-wide search for linkage to asthma phenotypes in the genetics of asthma international network families: evidence for a major susceptibility locus on chromosome 2p 8. Eur. J. Hum. Genet (2006) 14:307–316.[CrossRef][Web of Science][Medline]

  18. Allen M., Heinzmann A., Noguchi E., Abecasis G., Broxholme J., Ponting C.P., Bhattacharyya S., Tinsley J., Zhang Y., Holt R., et al. Positional cloning of a novel gene influencing asthma from chromosome 2q14. Nat. Genet (2003) 35:258–263.[CrossRef][Web of Science][Medline]

  19. Laitinen T., Polvi A., Rydman P., Vendelin J., Pulkkinen V., Salmikangas P., Makela S., Rehn M., Pirskanen A., Rautanen A., et al. Characterization of a common susceptibility locus for asthma-related traits. Science (2004) 304:300–304.[Abstract/Free Full Text]

  20. Zhang Y., Leaves N.I., Anderson G.G., Ponting C.P., Broxholme J., Holt R., Edser P., Bhattacharyya S., Dunham A., Adcock I.M., et al. Positional cloning of a quantitative trait locus on chromosome 13q14 that influences immunoglobulin E levels and asthma. Nat. Genet (2003) 34:181–186.[Web of Science][Medline]

  21. Van Eerdewegh P., Little R.D., Dupuis J., Del Mastro R.G., Falls K., Simon J., Torrey D., Pandit S., McKenny J., Braunschweiger K., et al. Association of the ADAM33 gene with asthma and bronchial hyperresponsiveness. Nature (2002) 418:426–430.[CrossRef][Medline]

  22. Moffatt M.F., Kabesch M., Liang L., Dixon A.L., Strachan D., Heath S., Depner M., von Berg A., Bufe A., Rietschel E., et al. Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature (2007) 448:470–473.[CrossRef][Medline]

  23. Terakita A. The opsins. Genome Biology (2005) 6:213.[CrossRef][Medline]

  24. Lange C., DeMeo D., Silverman E.K., Weiss S.T., Laird N.M. PBAT: tools for family-based association studies. Am. J. Hum. Genet (2004) 74:367–369.[CrossRef][Web of Science][Medline]

  25. Lange C., Lyon H., DeMeo D., Raby B., Silverman E.K., Weiss S.T. A new powerful non-parametric two-stage approach for testing multiple phenotypes in family-based association studies. Hum. Hered (2003) 56:10–17.[Web of Science][Medline]

  26. Halford S., Freedman M.S., Bellingham J., Inglis S.L., Poopalasundaram S., Soni B.G., Foster R.G., Hunt D.M. Characterization of a novel human opsin gene with wide tissue expression and identification of embedded and flanking genes on chromosome 1q43. Genomics (2001) 72:203–208.[CrossRef][Web of Science][Medline]

  27. Blackshaw S., Snyder S.H. Encephalopsin: a novel mammalian extraretinal opsin discretely localized in the brain. J. Neurosci (1999) 19:3681–3690.[Abstract/Free Full Text]

  28. Kumbalasiri T., Provencio I. Melanopsin and other novel mammalian opsins. Exp. Eye Res (2005) 81:368–375.[CrossRef][Web of Science][Medline]

  29. Blomhoff R., Blomhoff H.K. Overview of retinoid metabolism and function. J. Neurobiol (2006) 66:606–630.[CrossRef][Web of Science][Medline]

  30. Melyan Z., Tarttelin E.E., Bellingham J., Lucas R.J., Hankins M.W. Addition of human melanopsin renders mammalian cells photoresponsive. Nature (2005) 433:741–745.[CrossRef][Medline]

  31. Panda S., Nayak S.K., Campo B., Walker J.R., Hogenesch J.B., Jegla T. Illumination of the melanopsin signaling pathway. Science (2005) 307:600–604.[Abstract/Free Full Text]

  32. Qiu X., Kumbalasiri T., Carlson S.M., Wong K.Y., Krishna V., Provencio I., Berson D.M. Induction of photosensitivity by heterologous expression of melanopsin. Nature (2005) 433:745–749.[CrossRef][Medline]

  33. Moutsaki P., Whitmore D., Bellingham J., Sakamoto K., David-Gray Z.K., Foster R.G. Teleost multiple tissue (tmt) opsin: a candidate photopigment regulating the peripheral clocks of zebrafish? Brain Res. Mol. Brain Res (2003) 112:135–145.[Medline]

  34. Cohn L., Elias J.A., Chupp G.L. Asthma: mechanisms of disease persistence and progression. Ann. Rev. Immunol (2004) 22:789–815.[CrossRef][Web of Science][Medline]

  35. Umetsu D.T., DeKruyff R.H. Th1 and Th2 CD4+ cells in the pathogenesis of allergic diseases. Proc. Soc. Exp. Biol. Med (1997) 215:11–20.[CrossRef][Medline]

  36. Frayling T.M., Timpson N.J., Weedon M.N., Zeggini E., Freathy R.M., Lindgren C.M., Perry J.R.B., Elliott K.S., Lango H., Rayner N.W., et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science (2007) 316:889–894.[Abstract/Free Full Text]

  37. Calhoun W.J. Nocturnal asthma. Chest (2003) 123:399S–405S.[CrossRef][Web of Science][Medline]

  38. Koreck A.I., Csoma Z., Bodai L., Ignacz F., Kenderessy A.S., Kadocsa E., Szabo G., Bor Z., Erdei A., Szony B., et al. Rhinophototherapy: a new therapeutic tool for the management of allergic rhinitis. J. Allergy Clin. Immunol (2005) 115:541–547.[CrossRef][Web of Science][Medline]

  39. Kemeny L., Koreck A. Ultraviolet light phototherapy for allergic rhinitis. J. Photochem. Photobiol. B-Biol (2007) 87:58–65.[CrossRef][Medline]

  40. Riccioni G., Barbara M., Bucciarelli T., Di Ilio C., D'Orazio N. Antioxidant vitamin supplementation in asthma. Ann. Clin. Lab Sci (2007) 37:96–101.[Abstract/Free Full Text]

  41. Kull I., Bergstrom A., Lilja G., Pershagen G., Wickman M. Fish consumption during the first year of life and development of allergic diseases during childhood 6. Allergy (2006) 61:1009–1015.[CrossRef][Web of Science][Medline]

  42. Vestbo J., Thomas W., Asthmagen study group. Asthma and genes in a Danish Population: Outline of an ongoing study. Eur. Respir. Rev (2000) 75:396–399.

  43. Pillai S.G., Tang D., van den Oord E.J., Klotsman M., Barnes K., Carlsen K., Gerritsen J., Lenney W., Silverman M., Sly P., et al. Factor analysis in the Genetics of Asthma International Network (GAIN) family study identifies five major quantitative asthma phenotypes. Clin. Exp. Allergy (2008) 38:421–429.[CrossRef][Web of Science][Medline]

  44. Webb B.T., van den O.E., Akkari A., Wilton S., Ly T., Duff R., Barnes K.C., Carlsen K., Gerritsen J., Lenney W., et al. Quantitative linkage genome scan for atopy in a large collection of Caucasian families. Hum. Genet (2007) 121:83–92.[CrossRef][Web of Science][Medline]

  45. Anonymous. Standardization of Spirometry, 1994 Update. American Thoracic Society. Am. J. Respir. Crit. Care Med (1994) 152:1107–1136.[Web of Science]

  46. Cockcroft D.W., Killian D.N., Mellon J.J., Hargreave F.E. Bronchial reactivity to inhaled histamine: a method and clinical survey. Clin. Allergy (1977) 7:235–243.[CrossRef][Web of Science][Medline]

  47. Juniper E.F., Syty-Golda M., Hargreave F.E. Histamine inhalation tests: inhalation of aerosol via a facemask versus a valve box with mouthpiece. Thorax (1984) 39:556–557.[Free Full Text]

  48. Abecasis G.R., Cherny S.S., Cookson W.O., Cardon L.R. Merlin–rapid analysis of dense genetic maps using sparse gene flow trees. Nat. Genet (2002) 30:97–101.[CrossRef][Web of Science][Medline]

  49. Kong A., Cox N.J. Allele-sharing models: LOD scores and accurate linkage tests. Am. J. Hum. Genet (1997) 61:1179–1188.[CrossRef][Web of Science][Medline]

  50. Horikawa Y., Oda N., Cox N.J., Li X., Orho-Melander M., Hara M., Hinokio Y., Lindner T.H., Mashima H., Schwarz P.E., et al. Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nat. Genet (2000) 26:163–175. [see comment][erratum appears in Nat. Genet. 2000, 26(4): 502].[CrossRef][Web of Science][Medline]

  51. Lange C., DeMeo D., Silverman E.K., Weiss S.T., Laird N.M. Using the noninformative families in family-based association tests: a powerful new testing strategy. Am. J. Hum. Genet (2003) 73:801–811.[CrossRef][Web of Science][Medline]

  52. Lange C., Laird N.M. On a general class of conditional tests for family-based association studies in genetics: the asymptotic distribution, the conditional power, and optimality considerations. Genet. Epidemiol (2002) 23:165–180.[CrossRef][Web of Science][Medline]

  53. Horvath S., Xu X., Laird N.M. The family based association test method: strategies for studying general genotype–phenotype associations. Eur. J. Hum. Genet (2001) 9:301–306.[CrossRef][Web of Science][Medline]

  54. Van Steen K., McQueen M.B., Herbert A., Raby B., Lyon H., DeMeo D.L., Murphy A., Su J., Datta S., Rosenow C., et al. Genomic screening and replication using the same data set in family-based association testing. Nat. Genet (2005) 37:683–691.[CrossRef][Web of Science][Medline]

  55. Horvath S., Xu X., Laird N.M. The family based association test method: strategies for studying general genotype–phenotype associations. Eur. J. Hum. Genet (2001) 9:301–306.[CrossRef][Web of Science][Medline]

  56. Horvath S., Xu X., Lake S.L., Silverman E.K., Weiss S.T., Laird N.M. Family-based tests for associating haplotypes with general phenotype data: application to asthma genetics. Genet. Epidemiol (2004) 26:61–69.[CrossRef][Web of Science][Medline]

  57. Barrett J.C., Fry B., Maller J., Daly M.J. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics (2005) 21:263–265.[Abstract/Free Full Text]

  58. Gabriel S.B., Schaffner S.F., Nguyen H., Moore J.M., Roy J., Blumenstiel B., Higgins J., DeFelice M., Lochner A., Faggart M., et al. The structure of haplotype blocks in the human genome. Science (2002) 296:2225–2229.[Abstract/Free Full Text]

  59. Chapman C.G., Meadows H.J., Godden R.J., Campbell D.A., Duckworth M., Kelsell R.E., Murdock P.R., Randall A.D., Rennie G.I., Gloger I.S. Cloning, localisation and functional expression of a novel human, cerebellum specific, two pore domain potassium channel 1. Brain Res. Mol. Brain Res (2000) 82:74–83.[Medline]


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