Single nucleotide polymorphism screening and association analysis – exclusion of integrin β7 and vitamin D receptor (chromosome 12q) as candidate genes for asthma

BACKGROUND: The human genes coding for integrin beta 7 (ITGB7) and vitamin D receptor (VDR) are two of the several candidate genes for asthma and related phenotypes found in a promising candidate region on chromosome 12q that has been identified in multiple genomewide screens and candidate gene approaches. METHODS: All exons, including parts of the neighbouring introns, and the predicted promoter region of the ITGB7 gene were screened for common polymorphisms in 32 independent asthmatic and healthy probands, resulting in the detection of two single nucleotide polymorphisms (SNPs) unknown so far. In addition to these SNPs, five already described SNPs of the ITGB7 and one in the human VDR gene were analysed in a Caucasian sib pair study of 176 families with at least two affected children, using matrix assisted laser desorption/ionization time of flight mass spectrometry. All confirmed SNPs were tested for linkage/association with asthma and related traits (total serum IgE level, eosinophil cell count and slope of the dose-response curve after bronchial challenge). RESULTS: Two new variations in the ITGB7 gene were identified. The coding SNP in exon 4 causes a substitution of the amino acid GLU by VAL, whereas the other variation is non-coding (intron 3). None of the eight analysed SNPs, of either the ITGB7 or the VDR genes, showed significant linkage/association with asthma or related phenotypes in the family study. CONCLUSIONS: These findings indicate that neither the human ITGB7 nor the VDR gene seem to be associated with the pathogenesis of asthma or the expression of related allergic phenotypes such as eosinophilia and changes in total IgE level.     

Pathway-based identification of SNPs predictive of survival

In recent years, several association analysis methods for case-control studies have been developed. However, as we turn towards the identification of single nucleotide polymorphisms (SNPs) for prognosis, there is a need to develop methods for the identification of SNPs in high dimensional data with survival outcomes. Traditional methods for the identification of SNPs have some drawbacks. First, the majority of the approaches for case-control studies are based on single SNPs. Second, SNPs that are identified without incorporating biological knowledge are more difficult to interpret. Random forests has been found to perform well in gene expression analysis with survival outcomes. In this paper we present the first pathway-based method to correlate SNP with survival outcomes using a machine learning algorithm. We illustrate the application of pathway-based analysis of SNPs predictive of survival with a data set of 192 multiple myeloma patients genotyped for 500,000 SNPs. We also present simulation studies that show that the random forests technique with log-rank score split criterion outperforms several other machine learning algorithms. Thus, pathway-based survival analysis using machine learning tools represents a promising approach for the identification of biologically meaningful SNPs associated with disease.     

Pathway-based analysis of genome-wide association study of circadian phenotypes

Sleepiness affects normal social life, which attracts more and more attention. Circadian phenotypes contribute to obvious individual differences in susceptibility to sleepiness. We aimed to identify candidate single nucleotide polymorphisms (SNPs) which may cause circadian phenotypes, elucidate the potential mechanisms, and generate corresponding SNP-gene-pathways. A genome-wide association studies (GWAS) dataset of circadian phenotypes was utilized in the study. Then, the Identify Candidate Causal SNPs and Pathways analysis was employed to the GWAS dataset after quality control filters. Furthermore, genotype-phenotype association analysis was performed with HapMap database. Four SNPs in three different genes were determined to correlate with usual weekday bedtime, totally providing seven hypothetical mechanisms. Eleven SNPs in six genes were identified to correlate with usual weekday sleep duration, which provided six hypothetical pathways. Our results demonstrated that fifteen candidate SNPs in eight genes played vital roles in six hypothetical pathways implicated in usual weekday bedtime and six potential pathways involved in usual weekday sleep duration.     

A pure likelihood approach to the analysis of genetic association data: an alternative to Bayesian and frequentist analysis

Investigators performing genetic association studies grapple with how to measure strength of association evidence, choose sample size, and adjust for multiple testing. We apply the evidential paradigm (EP) to genetic association studies, highlighting its strengths. The EP uses likelihood ratios (LRs), as opposed to P-values or Bayes'' factors, to measure strength of association evidence. We derive EP methodology to estimate sample size, adjust for multiple testing, and provide informative graphics for drawing inferences, as illustrated with a Rolandic Epilepsy (RE) fine-mapping study. We focus on controlling the probability of observing weak evidence for or against association (W) rather than type I errors (M). For example, for LR⩾32 representing strong evidence, at one locus with n=200 cases, n=200 controls, W=0.134, whereas M=0.005. For n=300 cases and controls, W=0.039 and M=0.004. These calculations are based on detecting an OR=1.5. Despite the common misconception, one is not tied to this planning value for analysis; rather one calculates the likelihood at all possible values to assess evidence for association. We provide methodology to adjust for multiple tests across m loci, which adjusts M and W for m. We do so for (a) single-stage designs, (b) two-stage designs, and (c) simultaneously controlling family-wise error rate (FWER) and W. Method (c) chooses larger sample sizes than (a) or (b), whereas (b) has smaller bounds on the FWER than (a). The EP, using our innovative graphical display, identifies important SNPs in elongator protein complex 4 (ELP4) associated with RE that may not have been identified using standard approaches.     

The power of genome-wide association studies of complex disease genes: statistical limitations of indirect approaches using SNP markers

Genome-wide association studies using a dense map of single nucleotide polymorphism (SNP) markers seem to enable us to detect a number of complex disease genes. In such indirect association studies, whether susceptibility genes can be detected is dependent not only on the degree of linkage disequilibrium between the disease variant and the SNP marker but also on the difference in their allele frequencies. These factors, as well as penetrance of the disease variant, influence the statistical power of such approaches. However, the power of indirect association studies is not well understood. We calculated the number of individuals necessary for the detection of the disease variant in both direct and indirect association studies with a case-control design. The result shows that a remarkable reduction in the statistical power of indirect studies, compared with that of direct ones, is unavoidable in the genome-wide screening of complex disease genes. If there is a large difference in allele frequency between the disease variant and the marker, the disease variant cannot be detected. Because the frequency of the disease variant is unknown, SNP markers with various allele frequencies, or a large number of SNP markers, must be used in indirect association studies. However, if the number of SNP markers is increased, the obtained P value may not reach the significance level due to the Bonferroni adjustment. Thus, to test a possible association between functional variants and a complex disease directly, we should identify such SNPs in as many genes as possible for use in genome-wide association studies. Reveived: March 27, 2001 / Accepted: April 21, 2001     

Gains in power for exhaustive analyses of haplotypes using variable-sized sliding window strategy: a comparison of association-mapping strategies

Linkage disequilibrium (LD)-based association mapping is often performed by analyzing either individual SNPs or block-based multi-SNP haplotypes. Sliding windows of several fixed sizes (in terms of SNP numbers) were also applied to a few simulated or real data sets. In comparison, exhaustively testing based on variable-sized sliding windows (VSW) of all possible sizes of SNPs over a genomic region has the best chance to capture the optimum markers (single SNPs or haplotypes) that are most significantly associated with the traits under study. However, the cost is the increased number of multiple tests and computation. Here, a strategy of VSW of all possible sizes is proposed and its power is examined, in comparison with those using only haplotype blocks (BLK) or single SNP loci (SGL) tests. Critical values for statistical significance testing that account for multiple testing are simulated. We demonstrated that, over a wide range of parameters simulated, VSW increased power for the detection of disease variants by ∼1–15% over the BLK and SGL approaches. The improved performance was more significant in regions with high recombination rates. In an empirical data set, VSW obtained the most significant signal and identified the LRP5 gene as strongly associated with osteoporosis. With the use of computational techniques such as parallel algorithms and clustering computing, it is feasible to apply VSW to large genomic regions or those regions preliminarily identified by traditional SGL/BLK methods.     

Investigation of the Ability of Haplotype Association and Logistic Regression to Identify Associated Susceptibility Loci

While finely spaced markers are increasingly being used in case‐control association studies in attempts to identify susceptibility loci, not enough is yet known as to the optimal spacing of such markers, their likely power to detect association, the relative merits of single marker versus multimarker analysis, or which methods of analysis may be optimal. Some investigations of these issues have used markers simulated under different theoretical models of population evolution. However the HapMap project and other sources provide real datasets which can be used to obtain a more realistic view of the performance of these approaches. SNPs around APOE and from two HapMap regions were used to obtain information regarding linkage disequilibrium (LD) relationships between polymorphisms, and these real patterns of LD were used to simulate datasets such as would be obtained in case‐control studies were these SNPs to influence susceptibility to disease. The datasets obtained were analysed using tests for heterogeneity of estimated haplotype frequencies and using logistic regression analyses in which only main effects from each marker were considered. All markers surrounding the putative susceptibility locus were analysed, using sets of either 1, 2, 3 or 4 markers at a time. Some markers within 150 kb of the susceptibility locus were able to detect association. At distances less than 100 kb there was no correlation between the distance from the susceptibility locus and the strength of evidence for association. When the average inter‐locus spacing is 25 kb many loci would not be detected, while when the spacing is as low as 2 kb one can be fairly confident that at least one marker will be in strong enough LD with the susceptibility locus to enable association to be detected, if the susceptibility locus has a strong enough effect relative to the sample size. With an inter‐locus spacing of 4 kb some susceptibility loci did not have a marker locus in strong LD, potentially undermining the ability to detect association. There was little difference in the performance of haplotype‐based analysis compared with logistic regression considering effects of each marker as separate. Multimarker analysis on occasion produced results which were much more highly significant than single marker analysis, but only very rarely. Our results support the view that if markers are randomly selected then a spacing as low as 2 kb is desirable. Multimarker analysis can sometimes be more powerful than single marker analysis so both should be performed. However, because it is rare for multimarker analysis to be much more highly significant than single marker analysis one should strongly suspect that when such results occur they may be due to mistakes in genotyping or through some other artefact. Haplotype analysis may be more prone to such problems than logistic regression, suggesting that the latter method might be preferred.     

Generating Genetic Risk Scores from Intermediate Phenotypes for Use in Association Studies of Clinically Significant Endpoints

While previous results of genetic association studies for common, complex diseases (eg., coronary artery disease, CAD) have been disappointing, examination of multiple related genes within a physiologic pathway may provide improved resolution. This paper describes a method of calculating a genetic risk score (GRS) for a clinical endpoint by integrating data from many candidate genes and multiple intermediate phenotypes (IPs). First, the association of all single nucleotide polymorphisms (SNPs) to an IP is determined and regression β‐coefficients are used to calculate an IP‐specific GRS for each individual, repeating this analysis for every IP. Next, the IPs are assessed by a second regression as predictors of the clinical endpoint. Each IP's individual GRS is then weighted by the regression β‐coefficients from the second step, creating a single, composite GRS. As an example, 3,172 patients undergoing coronary angiography were evaluated for 3 SNPs from the cholesterol metabolism pathway. Although these data provide only a preliminary example, the GRS method detected significant differences in CAD by GRS group, whereas separate genotypes did not. These results illustrate the potential of the GRS methodology for multigenic risk evaluation and suggest that such approaches deserve further examination in common, complex diseases such as CAD.     

Association of polymorphisms in 9p21 region with CAD in North Indian population: replication of SNPs identified through GWAS

Kumar J, Yumnam S, Basu T, Ghosh A, Garg G, Karthikeyan G, Sengupta S. Association of polymorphisms in 9p21 region with CAD in North Indian population: replication of SNPs identified through GWAS. Coronary artery disease (CAD) is one of the leading causes of death worldwide that is influenced by both environmental as well as genetic factors. Several recent genome‐wide association studies (GWAS) have reported the association of multiple single nucleotide polymorphisms (SNPs) mainly in the 9p21 region with CAD. However, the association of these SNPs with CAD has not been rigorously tested in Indian population, which accounts for the largest incidences of CAD in the world. Herein, we genotyped six such SNPs (rs10116277, rs10757274, rs1333040, rs2383206, rs2383207 and rs1994016) identified through GWAS, in 754 individuals (311 angiography‐confirmed CAD patients and 443 treadmill test controls) recruited mainly from North India to evaluate if these SNPs were associated with CAD. The minor allele frequency of these six SNPs was comparable to that reported in the respective GWAS. We found that three of these SNPs (rs10116277, rs1333040 and rs2383206) present at the locus 9p21 were significantly associated with CAD even after controlling for the confounding factors such as age, sex, body mass index, homocysteine, hypertension, diabetes, smoking, diet, etc. In conclusion, the locus 9p21 found to be significantly associated with cardiovascular diseases in the Caucasian populations seems to be also important in North Indian population.     

Genome-wide association study identifies new susceptibility loci for diabetic nephropathy in Korean patients with type 2 diabetes mellitus

Genetic factors are considered to be important in the pathogenesis of diabetic nephropathy (DN). Despite several genome-wide association studies (GWASs) demonstrating that specific polymorphisms of candidate genes were associated with DN, there were some limitations in previous studies. We conducted a GWAS using customized DNA chips to identify novel susceptibility loci for DN in Korean. We analyzed a total of 414 DN cases and 474 normoalbuminuric diabetic hyper-controls across two stages using customized DNA chips containing 98 667 single nucleotide polymorphisms (SNPs). We explored the associations between SNPs and DN in samples from 87 DN cases, mostly confirmed by renal biopsy, and 104 diabetic hyper-controls, and replicated these associations in independent cohort samples with 327 DN cases and 370 diabetic hyper-controls. The top significant SNPs from the discovery samples were selected for replication in the independent cohort. rs3765156 in PIK3C2B was significantly associated with DN in the replication cohort after multiple test. The SNPs identified in our study provide new insights into the pathogenesis of DN in the Korean population. Additional studies are needed to determine biological effects and clinical utility of our findings.     

Linking disease associations with regulatory information in the human genome

Genome-wide association studies have been successful in identifying single nucleotide polymorphisms (SNPs) associated with a large number of phenotypes. However, an associated SNP is likely part of a larger region of linkage disequilibrium. This makes it difficult to precisely identify the SNPs that have a biological link with the phenotype. We have systematically investigated the association of multiple types of ENCODE data with disease-associated SNPs and show that there is significant enrichment for functional SNPs among the currently identified associations. This enrichment is strongest when integrating multiple sources of functional information and when highest confidence disease-associated SNPs are used. We propose an approach that integrates multiple types of functional data generated by the ENCODE Consortium to help identify "functional SNPs" that may be associated with the disease phenotype. Our approach generates putative functional annotations for up to 80% of all previously reported associations. We show that for most associations, the functional SNP most strongly supported by experimental evidence is a SNP in linkage disequilibrium with the reported association rather than the reported SNP itself. Our results show that the experimental data sets generated by the ENCODE Consortium can be successfully used to suggest functional hypotheses for variants associated with diseases and other phenotypes.     

Post‐mortem multiple sclerosis lesion pathology is influenced by single nucleotide polymorphisms

Over the last few decades, several common single nucleotide polymorphisms (SNPs) have been identified that correlate with clinical outcome in multiple sclerosis (MS), but the pathogenic mechanisms underlying the clinical effects of these SNPs are unknown. This is in part because of the difficulty in the functional translation of genotype into disease‐relevant mechanisms. Building on our recent work showing the association of clinical disease course with post‐mortem MS lesion characteristics, we hypothesized that SNPs that correlate with clinical disease course would also correlate with specific MS lesion characteristics in autopsy tissue. To test this hypothesis, 179 MS brain donors from the Netherlands Brain Bank MS autopsy cohort were genotyped for 102 SNPs, selected based on their reported associations with clinical outcome or their associations with genes that show differential gene expression in MS lesions. Three SNPs linked to MS clinical severity showed a significant association between the genotype and either the proportion of active lesions (rs2234978/FAS and rs11957313/KCNIP1) or the proportion of mixed active/inactive lesions (rs8056098/CLEC16A). Three SNPs linked to MS pathology‐associated genes showed a significant association with either proportion of active lesions (rs3130253/MOG), incidence of cortical gray matter lesions (rs1064395/NCAN) or the proportion of remyelinated lesions (rs5742909/CTLA4). In addition, rs2234978/FAS T‐allele carriers showed increased FAS gene expression levels in perivascular T cells and perilesional oligodendrocytes, cell types that have been implicated in MS lesion formation. Thus, by combining pathological characterization of MS brain autopsy tissue with genetics, we now start to translate genotypes linked to clinical outcomes in MS into mechanisms involved in MS lesion pathogenesis.     

Investigation into the Ability of SNP Chipsets and Microsatellites to Detect Association with a Disease Locus

We wished to investigate the ability of different SNP chipsets to detect association with a disease and to investigate the linkage disequilibrium (LD) relationships between microsatellites and nearby SNPs in order to assess their potential usefulness to detect association.
SNP genotypes were obtained from HapMap and microsatellite genotypes from CEPH. 5000 SNPs were simulated as disease genes which increased penetrance from 0.01 to 0.02 in a sample of 400 cases and 400 controls. The power of flanking SNPs to detect association was tested using sets of 1, 2, 3 or 4 markers analysed with haplotype analysis or logistic regression and using either all HapMap markers or those from the Affymetrix 500K, Illumina 300K or Illumina 550K chipsets. Additionally, LD relationships between 10 microsatellites and SNPs within 2Mb of each other were studied.
The power for one of the markers to detect association at p = 0.001 was around 0.4. Power was slightly better for logistic regression than haplotype analysis and for two-marker as opposed to single marker analysis but analysing with larger numbers markers had little benefit. The Illumina 550K marker set was better able to detect association than the other two and was almost as powerful as using all HapMap markers. Microsatellites had detectable LD with only a small number of nearby SNPs and the pattern of LD was very variable.
Available chipsets have quite good ability to detect association although obviously results will be critically dependent on the nature of the genetic effect on risk, sample size and the actual LD relationships of the susceptibility polymorphisms involved. Microsatellites seem ill-suited for systematic studies to detect association.     

Joint Identification of Multiple Genetic Variants via Elastic‐Net Variable Selection in a Genome‐Wide Association Analysis

Unraveling the genetic background of common complex traits is a major goal in modern genetics. In recent years, genome‐wide association (GWA) studies have been conducted with large‐scale data sets of genetic variants. Most of those studies have relied on single‐marker approaches that identify single genetic factors individually and can be limited in considering fully the joint effects of multiple genetic factors on complex traits. Joint identification of multiple genetic factors would be more powerful and would provide better prediction on complex traits since it utilizes combined information across variants. Here we propose a multi‐stage approach for GWA analysis: (1) prescreening, (2) joint identification of putative SNPs based on elastic‐net variable selection, and (3) empirical replication using bootstrap samples. Our approach enables an efficient joint search for genetic associations in GWA analysis. The suggested empirical replication method can be beneficial in GWA studies because one can avoid a costly, independent replication study while eliminating false‐positive associations and focusing on a smaller number of replicable variants. We applied the proposed approach to a GWA analysis, and jointly identified 129 genetic variants having an association with adult height in a Korean population.     

Association Study of Common Mitochondrial Variants and Cognitive Ability

Mitochondria are central to optimal functioning of the nervous system and disruption of mitochondrial function is known to lead to cognitive impairment. However, there has been little focus on whether common mitochondrial DNA polymorphisms contribute to normal variation in cognitive phenotypes. In this study, we use methodology for carrying out whole mitochondrial association studies in family cohorts to test whether 69 common mitochondrial variants and 10 common European haplogroups are associated with a number of measures of cognition, including information processing, word recognition and general cognitive ability, in a sample of Australian adolescent twins and their singleton/non-twin siblings. With data from 1,385 individuals from 665 families, this is by far the largest mitochondrial association study of cognition undertaken to date. We find that there is no significant evidence that either common European mitochondrial SNPs or haplogroups are associated with variation in cognitive performance. In spite of the associations not reaching significance, several of the most highly associated SNPs are in mitochondrial genes that have previously been identified as potentially playing a role in cognitive performance in mice. These genes warrant further investigation in both functional and association studies with larger cohorts. Edited by Chandra Reynolds.     

Genome-wide gene and pathway analysis

Current GWAS have primarily focused on testing association of single SNPs. To only test for association of single SNPs has limited utility and is insufficient to dissect the complex genetic structure of many common diseases. To meet conceptual and technical challenges raised by GWAS, we suggest gene and pathway-based GWAS as complementary to the current single SNP-based GWAS. This publication develops three statistics for testing association of genes and pathways with disease: linear combination test, quadratic test and decorrelation test, which take correlations among SNPs within a gene or genes within a pathway into account. The null distribution of the suggested statistics is examined and the statistics are applied to GWAS of rheumatoid arthritis in the Wellcome Trust Case–Control Consortium and the North American Rheumatoid Arthritis Consortium studies. The preliminary results show that the suggested gene and pathway-based GWAS offer several remarkable features. First, not only can they identify the genes that have large genetic effects, but also they can detect new genes in which each single SNP conferred a small amount of disease risk, and their joint actions can be implicated in the development of diseases. Second, gene and pathway-based analysis can allow the formation of the core of pathway definition of complex diseases and unravel the functional bases of an association finding. Third, replication of association findings at the gene or pathway level is much easier than replication at the individual SNP level.     

A fast multilocus test with adaptive SNP selection for large-scale genetic-association studies

As increasing evidence suggests that multiple correlated genetic variants could jointly influence the outcome, a multilocus test that aggregates association evidence across multiple genetic markers in a considered gene or a genomic region may be more powerful than a single-marker test for detecting susceptibility loci. We propose a multilocus test, AdaJoint, which adopts a variable selection procedure to identify a subset of genetic markers that jointly show the strongest association signal, and defines the test statistic based on the selected genetic markers. The P-value from the AdaJoint test is evaluated by a computationally efficient algorithm that effectively adjusts for multiple-comparison, and is hundreds of times faster than the standard permutation method. Simulation studies demonstrate that AdaJoint has the most robust performance among several commonly used multilocus tests. We perform multilocus analysis of over 26 000 genes/regions on two genome-wide association studies of pancreatic cancer. Compared with its competitors, AdaJoint identifies a much stronger association between the gene CLPTM1L and pancreatic cancer risk (6.0 × 10⁻⁸), with the signal optimally captured by two correlated single-nucleotide polymorphisms (SNPs). Finally, we show AdaJoint as a powerful tool for mapping cis-regulating methylation quantitative trait loci on normal breast tissues, and find many CpG sites whose methylation levels are jointly regulated by multiple SNPs nearby.     

Chromosome 7p linkage and GPR154 gene association in Italian families with allergic asthma

BACKGROUND: Several genome scans have reported linkage of markers on chromosome 7p with asthma and related phenotypes in different populations. A fine mapping in Finnish and French-Canadian populations has associated the GPR154 gene (also known as G-protein-coupled receptor for asthma susceptibility, GPRA) with elevated IgE or asthma. OBJECTIVE: To confirm chromosome 7p linkage and candidate gene association in Italian families with atopic asthma. METHODS: In a two-phase approach, we first performed a linkage analysis of chromosome 7, and then a family-based association study on the GPR154 gene for allergic asthma phenotypes in the Italian population. RESULTS: The screening of 117 families with 19 microsatellite markers showed potential linkage for elevated IgE (P<0.002 at 22 cM from p-ter), asthma (P<0.005 at 44 cM), or atopy (P<0.005 at 54 cM). In the second phase of the present study, candidate gene GPR154, which is located in the phase one-linked region, was investigated in 211 families with seven single nucleotide polymorphisms (SNPs) that tag most haplotype variability, by the pedigree disequilibrium test. Elevated IgE levels were associated with two GPR154 gene SNPs (SNP 546333, P=0.0046; rs740 347, P=0.006), and with haplotypes in the global test (P=0.013). Haplotype analysis performed in nuclear families having at least 1 asthmatic parent showed a significant association with asthma (P=0.0173), atopy (P=0.0058), SPT (P=0.0025), and bronchial hyper reactivity (P=0.0163). CONCLUSION: These results support a susceptibility locus for asthma and related phenotypes on chromosome 7, and are in agreement with recent reports suggesting that a common susceptibility factor for atopic manifestations in asthma is likely conferred by the locus containing the GPR154 gene.     

Binomial Mixture Model-based Association Tests under Genetic Heterogeneity

Most of the existing association tests for population-based case-control studies are based on comparing the mean genotype scores between the case and control groups, which may not be efficient under genetic heterogeneity. Given that most common diseases are genetically heterogeneous, caused by mutations in multiple loci, it may be beneficial to fully account for genetic heterogeneity in an association test. Here we first propose a binomial mixture model for such a purpose and develop a corresponding mixture likelihood ratio test (MLRT) for a single locus. We also consider two methods to combine single-locus-based MLRTs across multiple loci in linkage disequilibrium to boost power when causal SNPs are not genotyped. We show with a wide spectrum of numerical examples that under genetic heterogeneity the proposed tests are more powerful than some commonly used association tests.     

Novel IL-6 haplotypes and disease association

Interleukin-6 (IL-6) is a pleiotropic cytokine crucial in both adaptive and innate immunity. Numerous genetic studies have shown association with variants of this gene in a multitude of diseases and phenotypes. Most tests of association have focused on a limited set of promoter polymorphisms, in particular, the -174G>C; however, there are many inconsistencies within and between these studies. We propose that there is a more complex regulatory haplotype extending further upstream of the previously characterised promoter region which will provide a more detailed view of the effect of variation on lL-6 regulation. We have exploited two additional single nucleotide polymorphisms (SNPs) in IL-6 that, when examined as a haplotype with existing markers, show an increased level of association with systemic onset juvenile arthritis in a family-based study. This suggests that the haplotype effect may be more functionally relevant to the disease.