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We performed a whole-genome association study of rheumatoid arthritis susceptibility using Illumina 550k single-nucleotide polymorphism (SNP) genotypes of 868 cases and 1194 controls from the North American Rheumatoid Arthritis Consortium (NARAC). Structured association analysis with adjustment for potential population stratification yielded 200 SNPs with p < 1 × 10-8 for association with RA, all of which were on chromosome 6 in a 2.7-Mb region of the major histocompatibility complex (MHC). Given the extensive linkage equilibrium in the region and known risk of HLA-DRB1 alleles, we then applied conditional analyses to ascertain independent signals for RA susceptibility among these 200 candidate SNPs. Conditional analyses incorporating risk categories of the HLA-DRB1 "shared epitope" revealed three SNPs having independent associations with RA (conditional p < 0.001). This supports the presence of significant effects on RA susceptibility in the MHC in addition to the shared epitope.

The Genetic Analysis Workshop 15 Problem 3 simulated rheumatoid arthritis data set provided 100 replicates of simulated single-nucleotide polymorphism (SNP) and covariate data sets for 1500 families with an affected sib pair and 2000 controls, modeled after real rheumatoid arthritis data. The data generation model included nine unobserved trait loci, most of which have one or more of the generated SNPs associated with them. These data sets provide an ideal experimental test bed for evaluating new and old algorithms for selecting SNPs and covariates that can separate cases from controls, because the cases and controls are known as well as the identities of the trait loci. LASSO-Patternsearch is a new multi-step algorithm with a LASSO-type penalized likelihood method at its core specifically designed to detect and model interactions between important predictor variables. In this article the original LASSO-Patternsearch algorithm is modified to handle the large number of SNPs plus covariates. We start with a screen step within the framework of parametric logistic regression. The patterns that survived the screen step were further selected by a penalized logistic regression with the LASSO penalty. And finally, a parametric logistic regression model were built on the patterns that survived the LASSO step. In our analysis of Genetic Analysis Workshop 15 Problem 3 data we have identified most of the associated SNPs and relevant covariates. Upon using the model as a classifier, very competitive error rates were obtained.

Genome-wide association studies (GWAS) have yielded novel genetic loci underlying common diseases. We propose a systems genetics approach to utilize these discoveries for better understanding of the genetic architecture of rheumatoid arthritis (RA). Current evidence of genetic associations with RA was sought through PubMed and the NHGRI GWAS catalog. The associations of 15 single nucleotide polymorphisms and HLA-DRB1 alleles were confirmed in 1,287 cases and 1,500 controls of Japanese subjects. Among these, HLA-DRB1 alleles and eight SNPs showed significant associations and all but one of the variants had the same direction of effect as identified in the previous studies, indicating that the genetic risk factors underlying RA are shared across populations. By receiver operating characteristic curve analysis, the area under the curve (AUC) for the genetic risk score based on the selected variants was 68.4%. For seropositive RA patients only, the AUC improved to 70.9%, indicating good but suboptimal predictive ability. A simulation study shows that more than 200 additional loci with similar effect size as recent GWAS findings or 20 rare variants with intermediate effects are needed to achieve AUC = 80.0%. We performed the random walk with restart (RWR) algorithm to prioritize genes for future mapping studies. The performance of the algorithm was confirmed by leave-one-out cross-validation. The RWR algorithm pointed to ZAP70 in the first rank, in which mutation causes RA-like autoimmune arthritis in mice. By applying the hierarchical clustering method to a subnetwork comprising RA-associated genes and top-ranked genes by the RWR, we found three functional modules relevant to RA etiology: “leukocyte activation and differentiation”, “pattern-recognition receptor signaling pathway”, and “chemokines and their receptors”.

These results suggest that the systems genetics approach is useful to find directions of future mapping strategies to illuminate biological pathways.

Using single-nucleotide polymorphisms (SNPs), we sought to predict classical class I and class II human leukocyte antigen (HLA) alleles, and test for their associations with rheumatoid arthritis (RA) in the North American Rheumatoid Arthritis Consortium sample of cases and controls, genotyped on the Illumina HumanHap550 BeadChip. We use publicly available databases of SNP data and HLA data to find SNPs or SNP-haplotypes to be used as surrogates for each HLA allele. To reduce the confounding effects of linkage disequilibrium with the HLA-DRB1 locus, we tested for the association conditional on the presence or absence of a shared epitope allele on the same haplotype as the target HLA allele. Using SNP surrogates, we find that components of the DQ8 serotype (DQA1*0301:DQB1*0302) are associated with RA, irrespective of the presence or absence of a shared epitope allele on their respective haplotypes. Knowledge of the haplotype structure in the HLA region is still necessary for better interpretation of the results.

We compared and evaluated several variable and model selection methods using Bayesian and non-Bayesian approaches for three replicates of the Genetic Analysis Workshop 15 (GAW15) simulated data. In doing so, two phenotypes were utilized: rheumatoid arthritis (RA) affection status as a binary trait and IgM as a continuous measure. The analyses were performed adjusting for sex, age, and smoking status. For both outcomes, all the methods were comparable in finding the single-nucleotide polymorphisms (SNPs) generated to have a genetic signal. We successfully identified the susceptibility SNPs for RA in the HLA region (chromosome 6), and chromosome 18, and the susceptibility SNP for IgM on chromosome 11; however, many of the methods produced false-positive results.

The answers to Problem 3 were requested and known to the authors.

The proteins encoded by the classical HLA class I and class II genes in the major histocompatibility complex (MHC) are highly polymorphic and play an essential role in self/non-self immune recognition. HLA variation is a crucial determinant of transplant rejection and susceptibility to a large number of infectious and autoimmune disease1. Yet identification of causal variants is problematic due to linkage disequilibrium (LD) that extends across multiple HLA and non-HLA genes in the MHC2,3. We therefore set out to characterize the LD patterns between the highly polymorphic HLA genes and background variation by typing the classical HLA genes and >7,500 common single nucleotide polymorphisms (SNPs) and deletion/insertion polymorphisms (DIPs) across four population samples. The analysis provides informative tag SNPs that capture some of the variation in the MHC region and that could be used in initial disease association studies, and provides new insight into the evolutionary dynamics and ancestral origins of the HLA loci and their haplotypes.

The HLA-C locus is distinct relative to the other classical HLA class I loci in that it has relatively limited polymorphism1, lower expression on the cell surface2,3, and more extensive ligand-receptor interactions with killer cell immunoglobulin-like receptors (KIR)4. A single nucleotide polymorphism (SNP) 35Kb upstream of HLA-C (rs9264942; termed −35) associates with control of HIV5–7, and with levels of HLA-C mRNA transcripts8 and cell surface expression7, but the mechanism underlying its varied expression is unknown. We proposed that the −35 SNP is not the causal variant for differential HLA-C expression, but rather is marking another polymorphism that directly affects levels of HLA-C7. Here we show that variation within the 3′ untranslated region of HLA-C regulates binding of the microRNA Hsa-miR-148a to its target site, resulting in relatively low surface expression of alleles that bind this microRNA and high expression of HLA-C alleles that escape post-transcriptional regulation. The 3′UTR variant associates strongly with control of HIV, potentially adding to the effects of genetic variation encoding the peptide-binding region of the HLA class I loci. Variation in HLA-C expression adds another layer of diversity to this highly polymorphic locus that must be considered when deciphering the function of these molecules in health and disease.

Rheumatoid arthritis (RA) is a disorder with important public health implications. It is possible that there are clinically distinctive subtypes of the disorder with different genetic etiologies. We used the data provided to the participants in the Genetic Analysis Workshop 15 to evaluate and describe clinically based subgroups and their genetic associations with single-nucleotide polymorphisms (SNPs) on chromosome 6, which harbors the HLA region. Detailed two- and three-SNP haplotype analyses were conducted in the HLA region. We used demographic, clinical self-report, and biomarker data from the entire sample (n = 8477) to identify and characterize the subgroups. We did not use the RA diagnosis itself in the identification of the subgroups. Nuclear families (715 families, 1998 individuals) were used to examine the genetic association with the HLA region. We found five distinct subgroups in the data. The first comprised unaffected family members. Cluster 2 was a mix of affected and unaffected in which patients endorsed symptoms not corroborated by physicians. Clusters 3 through 5 represented a severity continuum in RA. Cluster 5 was characterized by early onset severe disease. Cluster 2 showed no association on chromosome 6. Clusters 3 through 5 showed association with 17 SNPs on chromosome 6. In the HLA region, Cluster 3 showed single-, two-, and three-SNP association with the centromeric side of the region in an area of linkage disequilibrium. Cluster 5 showed both single- and two-SNP association with the telomeric side of the region in a second area of linkage disequilibrium. It will be important to replicate the subgroup structure and the association findings in an independent sample.

Notwithstanding the well established association of HLA-DRB1 shared epitope alleles, interest remains in identifying additional Major Histocompatibility Complex (MHC) region variants associated with rheumatoid arthritis (RA). We used a panel of 1,201 haplotype tagging single nucleotide polymorphisms (SNPs) designed for African Americans to find genetic variants associated with RA in a 3.8 Mb region encompassing the MHC. Conditioning on seven covariates including HLA-DRB1 risk alleles and population structure, we identified a SNP in HLA-DOA (rs9276977) significantly associated with RA; minor allele frequency (MAF) 0.27 in cases versus 0.21 in controls: OR(±95% CI) = 2.86 (1.61, 5.31). Genotyping of rs9276977 in an independent sample of African-American RA patients and controls did not replicate the association (MAF 0.28 in cases versus 0.27 in controls). This study points to the potential association of a SNP in the HLA-DOA gene with RA in African-Americans, but also underscores the importance of replication of findings in larger patient cohorts.

The Genetic Analysis Workshop 16 rheumatoid arthritis data include a set of 868 cases and 1194 controls genotyped at 545,080 single-nucleotide polymorphisms (SNPs) from the Illumina 550 k chip. We focus on investigating chromosomes 6 and 18, which have 35,574 and 16,450 SNPs, respectively. Association studies, including single SNP and haplotype-based analyses, were applied to the data on those two chromosomes. Specifically, we conducted a generalized linear model with regularization (rGLM) approach for detecting disease-haplotype association using unphased SNP data. A total of 444 and 43 four-SNP tests were found to be significant at the Bonferroni corrected 5% significance level on chromosome 6 and 18, respectively.

Multivariate techniques are an important area of investigation for studying contributions of multiple genetic variants to disease onset and pathology. We analyzed the Genetic Analysis Workshop 16 North American Rheumatoid Arthritis Consortium (NARAC) data using a principal-components analysis (PCA) with an orthoblique rotation to identify specific subsets of single-nucleotide polymorphisms (SNP) in the major histocompatibility complex (MHC) region associated with rheumatoid arthritis (RA) and rheumatoid factor IgM (RFUW), and compared this method with a traditional PC approach. Using the orthoblique PC-based clustering method, we identified new clusters of SNPs across the MHC region associated with RA and RFUW, and replicated known SNP cluster associations with RA, such as those in the HLA-DRB region.

Background

The human leukocyte antigen system (HLA) contains many highly variable genes. HLA genes play an important role in the human immune system, and HLA gene matching is crucial for the success of human organ transplantations. Numerous studies have demonstrated that variation in HLA genes is associated with many autoimmune, inflammatory and infectious diseases. However, typing HLA genes by serology or PCR is time consuming and expensive, which limits large-scale studies involving HLA genes. Since it is much easier and cheaper to obtain single nucleotide polymorphism (SNP) genotype data, accurate computational algorithms to infer HLA gene types from SNP genotype data are in need. To infer HLA types from SNP genotypes, the first step is to infer SNP haplotypes from genotypes. However, for the same SNP genotype data set, the haplotype configurations inferred by different methods are usually inconsistent, and it is often difficult to decide which one is true.

Results

In this paper, we design an accurate HLA gene type inference algorithm by utilizing SNP genotype data from pedigrees, known HLA gene types of some individuals and the relationship between inferred SNP haplotypes and HLA gene types. Given a set of haplotypes inferred from the genotypes of a population consisting of many pedigrees, the algorithm first constructs a weighted similarity graph based on a new haplotype similarity measure and derives constraint edges from known HLA gene types. Based on the principle that different HLA gene alleles should have different background haplotypes, the algorithm searches for an optimal labeling of all the haplotypes with unknown HLA gene types such that the total weight among the same HLA gene types is maximized. To deal with ambiguous haplotype solutions, we use a genetic algorithm to select haplotype configurations that tend to maximize the same optimization criterion. Our experiments on a previously typed subset of the HapMap data show that the algorithm is highly accurate, achieving an accuracy of 96% for gene HLA-A, 95% for HLA-B, 97% for HLA-C, 84% for HLA-DRB1, 98% for HLA-DQA1 and 97% for HLA-DQB1 in a leave-one-out test.

Conclusions

Our algorithm can infer HLA gene types from neighboring SNP genotype data accurately. Compared with a recent approach on the same input data, our algorithm achieved a higher accuracy. The code of our algorithm is available to the public for free upon request to the corresponding authors.

For Genetic Analysis Workshop 16 Problem 1, we provided data for genome-wide association analysis of rheumatoid arthritis. Single-nucleotide polymorphism (SNP) genotype data were provided for 868 cases and 1194 controls that had been assayed using an Illumina 550 k platform. In addition, phenotypic data were provided from genotyping DRB1 alleles, which were classified according to the rheumatoid arthritis shared epitope, levels of anti-cyclic citrullinated peptide, and levels of rheumatoid factor IgM. Several questions could be addressed using the data, including analysis of genetic associations using single SNPs or haplotypes, as well as gene-gene and genetic analysis of SNPs for qualitative and quantitative factors.

We performed a genome-wide association scan on the North American Rheumatoid Arthritis Consortium (NARAC) data using Hotelling's T2 tests, i.e., TH based on allele coding and TG based on genotype coding. The objective was to identify associations between single-nucleotide polymorphisms (SNPs) or markers and rheumatoid arthritis. In specific candidate gene regions, we evaluated the performance of Hotelling's T2 tests. Then Hotelling's T2 tests were used as a tool to identify new regions that contain SNPs showing strong associations with disease. As expected, the strongest association evidence was found in the region of the HLA-DRB1 locus on chromosome 6. In the region of the TRAF1-C5 genes, we identified two SNPs, rs2900180 and rs3761847, with the largest and the second largest TH and TG scores among all SNPs on chromosome 9. We also identified one SNP, rs2476601, in the region of the PTPN22 gene that had the largest TH score and the second largest TG score among all SNPs on chromosome 1. In addition, SNPs with the largest TH score on each chromosome were identified. These SNPs may be located in the regions of genes that have modest effects on rheumatoid arthritis. These regions deserve further investigation.

Introduction

Transforming growth factor-beta1 (TGF-beta1) is a pleiotropic cytokine that plays important roles in immunity and inflammation. Some studies have suggested that polymorphism in the TGFB1 gene is associated with heart disease in the general population. The purpose of the present study was to determine whether common single-nucleotide polymorphisms (SNP) in the TGFB1 gene are associated with ischaemic heart disease (IHD) and/or myocardial infarction (MI) in patients with rheumatoid arthritis (RA), and to investigate the influence of smoking on any association.

Methods

PCR-based assays were used to determine the genotypes of TGFB1 SNPs including TGFB1-509 C/T (rs1800469, in the promoter region), +868 T/C (rs1800470, in exon 1) and +913 G/C (rs1800471, in exon 1) in 414 subjects with established RA. Genotyping for the +868 SNP was also carried out on a second study population of RA patients (n = 259) with early disease. Serum levels of TGF-beta1 were measured using a commercial ELISA kit. Smoking history and IHD/MI status were obtained on each patient. Associations with IHD/MI were assessed using contingency tables and logistic regression analyses.

Results

The heterozygous genotype of TGFB+868 was associated with an increased risk of IHD (OR 2.14, 95% CI 1.30 - 3.55) and MI (OR 2.42, 95% CI 1.30-4.50), compared to the homozygous genotypes combined. Smoking was an independent risk for IHD and MI, and evidence of interaction between smoking and TGFB+868 was found. Multivariate analyses indicated that the strongest associations with IHD and MI were due to the combined effect of the TGFB1+868 TC genotype and smoking (OR 2.75, 95% CI 1.59-4.75; and OR 2.58 95% CI 1.33-4.99, respectively), independent of other cardiovascular risk factors. The association of the +868 TC genotype and evidence of +868 TC-smoking interaction with IHD were replicated in a second population of RA patients with early disease. Serum TGF-beta1 levels were not associated with TGFB1 genetic variations, smoking or IHD/MI status.

Conclusions

Interaction between smoking and polymorphism in the TGFB1 gene may influence the risk of IHD and MI in patients with RA.

Introduction

Ankylosing spondylitis (AS) is a severe, chronic inflammatory disease strongly associated with HLA-B27. The presence of additional HLA risk factors has been suggested by several studies. The aim of the current study is to assess the occurrence of an additional HLA susceptibility locus in the region between HLA-E and HLA-C in the Sardinian population.

Methods

200 random controls, 120 patients with AS and 175 HLA-B27 positive controls were genotyped for six single nucleotide polymorphisms (SNPs) spanning the HLA region between HLA-E and HLA-C loci previously shown to harbour an additional susceptibility locus for AS. Allele, genotype and haplotype frequencies were compared.

Results

The data confirm our previous finding of a significant increase in patients with AS of allele A at SNP rs1264457 encoding for an Arg at the functional HLA-E polymorphism (Arg128/Gly128). This was due to a remarkable increase in the frequency of genotype A/A in patients vs HLA-B27-matched controls (51% vs 29%; P for trend: 5 × 10-5). Genotype distribution of three other SNPs mapping in genes (GNL1, PRR3 and ABCF-1) close to HLA-E and showing high LD with it, was also significantly skewed. Accordingly, haplotype distribution was also remarkably different. The frequency of the haplotype AAGA, is 42% in random controls, increases to 53% in the HLA-B27-positive controls, and reaches 68% in patients with AS (P values: 2 × 10-11 vs random and 3 × 10-4 vs HLA-B27 controls).

Conclusions

There is a strong association between the presence of a haplotype in genes mapping between HLA-E and HLA-C and AS due to an increase of homozygous markers in patients. The strongest association however, is with the HLA-E functional polymorphism rs1264457. Since HLA-E is the ligand for the NKG2A receptor, these data point to the natural killer (NK) activity as possible player in the pathogenesis of AS.

In the present paper, we used the North American Rheumatoid Arthritis Consortium data provided for Genetic Analysis Workshop 15 Problem 2 to: 1) estimate the penetrances of PTPN22 and HLA-DRB1 and, 2) test the selected model of PTPN22 conditional on the rheumatoid factor status. To achieve these aims, we used the marker association segregation chi-square method, fitting simultaneously both genotype frequency and identical by descent distributions in a sample of 3690 White individuals from 604 nuclear families. A co-dominant model fitted the rs2476601 (R620W) single-nucleotide polymorphism (SNP) of the PTPN22 gene well, whereas a lack of fit for all models was observed for the HLA-DRB1 locus. Testing genetic models of rheumatoid arthritis that include the PTPN22 SNP in addition to the HLA-DRB1 locus did not affect the results, nor did subgroup analysis of PTPN22 conditional on the rheumatoid factor status. In conclusion, PTPN22 R620W SNP is a risk factor for rheumatoid arthritis. The genetic architecture of the HLA-DRB1 locus is highly complex, and more elaborate modeling of this locus is required.

Variable selection in genome-wide association studies can be a daunting task and statistically challenging because there are more variables than subjects. We propose an approach that uses principal-component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) to identify gene-gene interaction in genome-wide association studies. A PCA was used to first reduce the dimension of the single-nucleotide polymorphisms (SNPs) within each gene. The interaction of the gene PCA scores were placed into LASSO to determine whether any gene-gene signals exist. We have extended the PCA-LASSO approach using the bootstrap to estimate the standard errors and confidence intervals of the LASSO coefficient estimates. This method was compared to placing the raw SNP values into the LASSO and the logistic model with individual gene-gene interaction. We demonstrated these methods with the Genetic Analysis Workshop 16 rheumatoid arthritis genome-wide association study data and our results identified a few gene-gene signals. Based on our results, the PCA-LASSO method shows promise in identifying gene-gene interactions, and, at this time we suggest using it with other conventional approaches, such as generalized linear models, to narrow down genetic signals.

Background

The individual contribution of genes in the HLA region to the risk of developing type 1 diabetes (T1D) is confounded by the high linkage disequilibrium (LD) in this region. Using a novel approach we have combined genetic association data with information on functional protein-protein interactions to elucidate risk independent of LD and to place the genetic association into a functional context.

Methodology/Principal Findings

Genetic association data from 2300 single nucleotide polymorphisms (SNPs) in the HLA region was analysed in 2200 T1D family trios divided into six risk groups based on HLA-DRB1 genotypes. The best SNP signal in each gene was mapped to proteins in a human protein interaction network and their significance of clustering in functional network modules was evaluated. The significant network modules identified through this approach differed between the six HLA risk groups, which could be divided into two groups based on carrying the DRB1*0301 or the DRB1*0401 allele. Proteins identified in networks specific for DRB1*0301 carriers were involved in stress response and inflammation whereas in DRB1*0401 carriers the proteins were involved in antigen processing and presentation.

Conclusions/Significance

In this study we were able to hypothesise functional differences between individuals with T1D carrying specific DRB1 alleles. The results point at candidate proteins involved in distinct cellular processes that could not only help the understanding of the pathogenesis of T1D, but also the distinction between individuals at different genetic risk for developing T1D.

A genome-wide association study identified single nucleotide polymorphisms (SNPs) rs3077 and rs9277535 located in the 3′ untranslated regions of human leukocyte antigen (HLA) class II genes HLA-DPA1 and HLA-DPB1, respectively, as the independent variants most strongly associated with chronic hepatitis B. We examined whether these SNPs are associated with mRNA expression of HLA-DPA1 and HLA-DPB1. We identified gene expression-associated SNPs (eSNPs) in normal liver samples obtained from 651 individuals of European ancestry by integrating genotype (∼650 000 SNPs) and gene expression (>39 000 transcripts) data from each sample. We used the Kruskal–Wallis test to determine associations between gene expression and genotype. To confirm findings, we measured allelic expression imbalance (AEI) of complementary DNA compared with DNA in liver specimens from subjects who were heterozygous for rs3077 and rs9277535. On a genome-wide basis, rs3077 was the SNP most strongly associated with HLA-DPA1 expression (p=10−48), and rs9277535 was strongly associated with HLA-DPB1 expression (p=10−15). Consistent with these gene expression associations, we observed AEI for both rs3077 (p=3.0 × 10−7; 17 samples) and rs9277535 (p=0.001; 17 samples). We conclude that the variants previously associated with chronic hepatitis B are also strongly associated with mRNA expression of HLA-DPA1 and HLA-DPB1, suggesting that expression of these genes is important in control of HBV.

The genetic association of the major histocompatibility complex (MHC) to rheumatoid arthritis risk has commonly been attributed to HLA-DRB1 alleles. Yet controversy persists about the causal variants in HLA-DRB1 and the presence of independent effects elsewhere in the MHC. Using existing genome-wide SNP data in 5,018 seropositive cases and 14,974 controls, we imputed and tested classical alleles and amino acid polymorphisms for HLA-A, B, C, DPA1, DPB1, DQA1, DQB1, and DRB1 along with 3,117 SNPs across the MHC. Conditional and haplotype analyses reveal that three amino acid positions (11, 71 and 74) in HLA-DRβ1, and single amino acid polymorphisms in HLA-B (position 9) and HLA-DPβ1 (position 9), all located in the peptide-binding grooves, almost completely explain the MHC association to disease risk. This study illustrates how imputation of functional variation from large reference panels can help fine-map association signals in the MHC.

Objective

To identify additional variants in the major histocompatibility complex (MHC) region that independently contribute to risk in 2 disease subsets of rheumatoid arthritis (RA) defined according to the presence or absence of antibodies to citrullinated protein antigens (ACPAs).

Methods

In a multistep analytical strategy using unmatched as well as matched analyses to adjust for HLA–DRB1 genotype, we analyzed 2,221 single-nucleotide polymorphisms (SNPs) spanning 10.7 Mb, from 6p22.2 to 6p21.31, across the MHC. For ACPA-positive RA, we analyzed samples from the Swedish Epidemiological Investigation of Rheumatoid Arthritis (EIRA) and the North American Rheumatoid Arthritis Consortium (NARAC) studies (totaling 1,255 cases and 1,719 controls). For ACPA-negative RA, we used samples from the EIRA study (640 cases and 670 controls). Plink and SAS statistical packages were used to conduct all statistical analyses.

Results

A total of 299 SNPs reached locus-wide significance (P < 2.3 × 10−5) for ACPA-positive RA, whereas surprisingly, no SNPs reached this significance for ACPA-negative RA. For ACPA-positive RA, we adjusted for known DRB1 risk alleles and identified additional independent associations with SNPs near HLA–DPB1 (rs3117213; odds ratio 1.42 [95% confidence interval 1.17–1.73], Pcombined = 0.0003 for the strongest association).

Conclusion

There are distinct genetic patterns of MHC associations in the 2 disease subsets of RA defined according to ACPA status. HLA–DPB1 is an independent risk locus for ACPA-positive RA. We did not identify any associations with SNPs within the MHC for ACPA-negative RA.

Background

Single nucleotide polymorphisms (SNPs) are an abundant form of genetic variation in the genome of every species and are useful for gene mapping and association studies. Of particular interest are non-synonymous SNPs, which may alter protein function and phenotype. We therefore examined bovine expressed sequences for non-synonymous SNPs and validated and tested selected SNPs for their association with measured traits.

Results

Over 500,000 public bovine expressed sequence tagged (EST) sequences were used to search for coding SNPs (cSNPs). A total of 15,353 SNPs were detected in the transcribed sequences studied, of which 6,325 were predicted to be coding SNPs with the remaining 9,028 SNPs presumed to be in untranslated regions. Of the cSNPs detected, 2,868 were predicted to result in a change in the amino acid encoded. In order to determine the actual number of non-synonymous polymorphic SNPs we designed assays for 920 of the putative SNPs. These SNPs were then genotyped through a panel of cattle DNA pools using chip-based MALDI-TOF mass spectrometry. Of the SNPs tested, 29% were found to be polymorphic with a minor allele frequency >10%. A subset of the SNPs was genotyped through animal resources in order to look for association with age of puberty, facial eczema resistance or meat yield. Three SNPs were nominally associated with resistance to the disease facial eczema (P < 0.01).

Conclusion

We have identified 15,353 putative SNPs in or close to bovine genes and 2,868 of these SNPs were predicted to be non-synonymous. Approximately 29% of the non-synonymous SNPs were polymorphic and common with a minor allele frequency >10%. Of the SNPs detected in this study, 99% have not been previously reported. These novel SNPs will be useful for association studies or gene mapping.

The interaction among multiple genes and environmental factors can affect an individual's susceptibility to disease. Some genes may not show strong marginal associations when they affect disease risk through interactions with other genes. As a result, these genes may not be identified by single-marker methods that are widely used in genome-wide association studies. To explore this possibility in real data, we carried out a two-stage model selection procedure of joint single-nucleotide polymorphism (SNP) analysis to detect genes associated with rheumatoid arthritis (RA) using Genetic Analysis Workshop 16 genome-wide association study data. In the first stage, the genetic markers were screened through an exhaustive two-dimensional search, through which promising SNP and SNP pairs were identified. Then, LASSO was used to choose putative SNPs from the candidates identified in the first stage. We then use the RA data collected by the Wellcome Trust Case Control Consortium to validate the putative genetic factors. Balancing computational load and statistical power, this method detects joint effects that may fail to emerge from single-marker analysis. Based on our proposed approach, we not only replicated the identification of important RA risk genes, but also found novel genes and their epistatic effects on RA. To our knowledge, this is the first two-dimensional scan based analysis for a real genome-wide association study.

Using the North American Rheumatoid Arthritis Consortium (NARAC) candidate gene and genome-wide single-nucleotide polymorphism (SNP) data sets, we applied regression methods and tree-based random forests to identify genetic associations with rheumatoid arthritis (RA) and to predict RA disease status. Several genes were consistently identified as weakly associated with RA without a significant interaction or combinatorial effect with other candidate genes. Using random forests, the tested candidate gene SNPs were not sufficient to predict RA patients and normal subjects with high accuracy. However, using the top 500 SNPs, ranked by the importance score, from the genome-wide linkage panel of 5742 SNPs, we were able to accurately predict RA patients and normal subjects with sensitivity of approximately 90% and specificity of approximately 80%, which was confirmed by five-fold cross-validation. However, in a complete training-testing framework, replication of genetic predictors was less satisfactory; thus, further evaluation of existing methodology and development of new methods are warranted.