Association of acid phosphatase locus 1*C allele with the risk of cardiovascular events in rheumatoid arthritis patients

Introduction

Acid phosphatase locus 1 (ACP1) encodes a low molecular weight phosphotyrosine phosphatase implicated in a number of different biological functions in the cell. The aim of this study was to determine the contribution of ACP1 polymorphisms to susceptibility to rheumatoid arthritis (RA), as well as the potential contribution of these polymorphisms to the increased risk of cardiovascular disease (CV) observed in RA patients.

Methods

A set of 1,603 Spanish RA patients and 1,877 healthy controls were included in the study. Information related to the presence/absence of CV events was obtained from 1,284 of these participants. All individuals were genotyped for four ACP1 single-nucleotide polymorphisms (SNPs), rs10167992, rs11553742, rs7576247, and rs3828329, using a predesigned TaqMan SNP genotyping assay. Classical ACP1 alleles (*A, *B and *C) were imputed with SNP data.

Results

No association between ACP1 gene polymorphisms and susceptibility to RA was observed. However, when RA patients were stratified according to the presence or absence of CV events, an association between rs11553742*T and CV events was found (P = 0.012, odds ratio (OR) = 2.62 (1.24 to 5.53)). Likewise, the ACP1*C allele showed evidence of association with CV events in patients with RA (P = 0.024, OR = 2.43).

Conclusions

Our data show that the ACP1*C allele influences the risk of CV events in patients with RA.     

An Osteoprotegerin Gene Polymorphism Is Associated with an Increased Risk of Hip Fracture in Japanese Patients with Rheumatoid Arthritis: Results from the IORRA Observational Cohort Study

Introduction

Patients with rheumatoid arthritis (RA) have a higher prevalence of osteoporosis and hip fracture than healthy individuals. Multiple genetic loci for osteoporotic fracture were identified in recent genome-wide association studies. The purpose of this study was to identify genetic variants associated with the occurrence of hip fracture in Japanese patients with RA.

Methods

DNA samples from 2,282 Japanese patients with RA were obtained from the DNA collection of the Institute of Rheumatology Rheumatoid Arthritis cohort (IORRA) study. Six single nucleotide polymorphisms (SNPs) that have been reported to be associated with fractures in recent studies were selected and genotyped. Forty hip fractures were identified with a maximum follow-up of 10 years. The genetic risk for hip fracture was examined using a multivariate Cox proportional hazards regression model.

Results

The risk analyses revealed that patients who are homozygous for the major allele of SNP rs6993813, in the OPG locus, have a higher risk for hip fracture (hazard ratio [95% CI]  = 2.53 [1.29–4.95], P  = 0.0067). No association was found for the other SNPs.

Conclusions

Our results indicate that an OPG allele is associated with increased risk for hip fracture in Japanese patients with RA.     

A genome-wide association study of seed protein and oil content in soybean

Background

Association analysis is an alternative to conventional family-based methods to detect the location of gene(s) or quantitative trait loci (QTL) and provides relatively high resolution in terms of defining the genome position of a gene or QTL. Seed protein and oil concentration are quantitative traits which are determined by the interaction among many genes with small to moderate genetic effects and their interaction with the environment. In this study, a genome-wide association study (GWAS) was performed to identify quantitative trait loci (QTL) controlling seed protein and oil concentration in 298 soybean germplasm accessions exhibiting a wide range of seed protein and oil content.

Results

A total of 55,159 single nucleotide polymorphisms (SNPs) were genotyped using various methods including Illumina Infinium and GoldenGate assays and 31,954 markers with minor allele frequency >0.10 were used to estimate linkage disequilibrium (LD) in heterochromatic and euchromatic regions. In euchromatic regions, the mean LD (r ² ) rapidly declined to 0.2 within 360 Kbp, whereas the mean LD declined to 0.2 at 9,600 Kbp in heterochromatic regions. The GWAS results identified 40 SNPs in 17 different genomic regions significantly associated with seed protein. Of these, the five SNPs with the highest associations and seven adjacent SNPs were located in the 27.6-30.0 Mbp region of Gm20. A major seed protein QTL has been previously mapped to the same location and potential candidate genes have recently been identified in this region. The GWAS results also detected 25 SNPs in 13 different genomic regions associated with seed oil. Of these markers, seven SNPs had a significant association with both protein and oil.

Conclusions

This research indicated that GWAS not only identified most of the previously reported QTL controlling seed protein and oil, but also resulted in narrower genomic regions than the regions reported as containing these QTL. The narrower GWAS-defined genome regions will allow more precise marker-assisted allele selection and will expedite positional cloning of the causal gene(s).     

Highly interconnected genes in disease-specific networks are enriched for disease-associated polymorphisms

Background

Complex diseases are associated with altered interactions between thousands of genes. We developed a novel method to identify and prioritize disease genes, which was generally applicable to complex diseases.

Results

We identified modules of highly interconnected genes in disease-specific networks derived from integrating gene-expression and protein interaction data. We examined if those modules were enriched for disease-associated SNPs, and could be used to find novel genes for functional studies. First, we analyzed publicly available gene expression microarray and genome-wide association study (GWAS) data from 13, highly diverse, complex diseases. In each disease, highly interconnected genes formed modules, which were significantly enriched for genes harboring disease-associated SNPs. To test if such modules could be used to find novel genes for functional studies, we repeated the analyses using our own gene expression microarray and GWAS data from seasonal allergic rhinitis. We identified a novel gene, FGF2, whose relevance was supported by functional studies using combined small interfering RNA-mediated knock-down and gene expression microarrays. The modules in the 13 complex diseases analyzed here tended to overlap and were enriched for pathways related to oncological, metabolic and inflammatory diseases. This suggested that this union of the modules would be associated with a general increase in susceptibility for complex diseases. Indeed, we found that this union was enriched with GWAS genes for 145 other complex diseases.

Conclusions

Modules of highly interconnected complex disease genes were enriched for disease-associated SNPs, and could be used to find novel genes for functional studies.     

Elevated levels of fibrinogen-derived endogenous citrullinated peptides in synovial fluid of rheumatoid arthritis patients

Introduction

Rheumatoid arthritis (RA) is an autoimmune disease characterized by inflammation of the joints and the presence of autoantibodies directed against proteins containing the non-standard arginine-derived amino acid citrulline. The protein fibrinogen, which has an essential role in blood clotting, is one of the most prominent citrullinated autoantigens in RA, particularly because it can be found in the inflamed tissue of affected joints. Here, we set out to analyze the presence of citrullinated endogenous peptides in the synovial fluid of RA and arthritic control patients.

Methods

Endogenous peptides were isolated from the synovial fluid of RA patients and controls by filtration and solid phase extraction. The peptides were identified and quantified using high-resolution liquid chromatography-mass spectrometry.

Results

Our data reveal that the synovial fluid of RA patients contains soluble endogenous peptides, derived from fibrinogen, containing significant amounts of citrulline residues and, in some cases, also phosphorylated serine. Several citrullinated peptides are found to be more abundantly present in the synovial fluid of RA patients compared to patients suffering from other inflammatory diseases affecting the joints.

Conclusions

The increased presence of citrullinated peptides in RA patients points toward a possible specific role of these peptides in the immune response at the basis of the recognition of citrullinated peptides and proteins by RA patient autoantibodies.     

Quantifying Missing Heritability at Known GWAS Loci

Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn''s Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.     

Analysis of TNFAIP3, a feedback inhibitor of nuclear factor-κB and the neighbor intergenic 6q23 region in rheumatoid arthritis susceptibility

Introduction

Genome-wide association studies of rheumatoid arthritis (RA) have identified an association of the disease with a 6q23 region devoid of genes. TNFAIP3, an RA candidate gene, flanks this region, and polymorphisms in both the TNFAIP3 gene and the intergenic region are associated with systemic lupus erythematosus. We hypothesized that there is a similar association with RA, including polymorphisms in TNFAIP3 and the intergenic region.

Methods

To test this hypothesis, we selected tag-single nucleotide polymorphisms (SNPs) in both loci. They were analyzed in 1,651 patients with RA and 1,619 control individuals of Spanish ancestry.

Results

Weak evidence of association was found both in the 6q23 intergenic region and in the TNFAIP3 locus. The rs582757 SNP and a common haplotype in the TNFAIP3 locus exhibited association with RA. In the intergenic region, two SNPs were associated, namely rs609438 and rs13207033. The latter was only associated in patients with anti-citrullinated peptide antibodies. Overall, statistical association was best explained by the interdependent contribution of SNPs from the two loci TNFAIP3 and the 6q23 intergenic region.

Conclusions

Our data are consistent with the hypothesis that several RA genetic factors exist in the 6q23 region, including polymorphisms in the TNFAIP3 gene, like that previously described for systemic lupus erythematosus.     

Investigation of Caucasian rheumatoid arthritis susceptibility loci in African patients with the same disease

Introduction

The largest genetic risk to develop rheumatoid arthritis (RA) arises from a group of alleles of the HLA DRB1 locus ('shared epitope', SE). Over 30 non-HLA single nucleotide polymorphisms (SNPs) predisposing to disease have been identified in Caucasians, but they have never been investigated in West/Central Africa. We previously reported a lower prevalence of the SE in RA patients in Cameroon compared to European patients and aimed in the present study to investigate the contribution of Caucasian non-HLA RA SNPs to disease susceptibility in Black Africans.

Methods

RA cases and controls from Cameroon were genotyped for Caucasian RA susceptibility SNPs using Sequenom MassArray technology. Genotype data were also available for 5024 UK cases and 4281 UK controls and for 119 Yoruba individuals in Ibadan, Nigeria (YRI, HapMap). A Caucasian aggregate genetic-risk score (GRS) was calculated as the sum of the weighted risk-allele counts.

Results

After genotyping quality control procedures were performed, data on 28 Caucasian non-HLA susceptibility SNPs were available in 43 Cameroonian RA cases and 44 controls. The minor allele frequencies (MAF) were tightly correlated between Cameroonian controls and YRI individuals (correlation coefficient 93.8%, p = 1.7E-13), and they were pooled together. There was no correlation between MAF of UK and African controls; 13 markers differed by more than 20%. The MAF for markers at PTPN22, IL2RA, FCGR2A and IL2/IL21 was below 2% in Africans. The GRS showed a strong association with RA in the UK. However, the GRS did not predict RA in Africans (OR = 0.71, 95% CI 0.29 - 1.74, p = 0.456). Random sampling from the UK cohort showed that this difference in association is unlikely to be explained by small sample size or chance, but is statistically significant with p<0.001.

Conclusions

The MAFs of non-HLA Caucasian RA susceptibility SNPs are different between Caucasians and Africans, and several polymorphisms are barely detectable in West/Central Africa. The genetic risk of developing RA conferred by a set of 28 Caucasian susceptibility SNPs is significantly different between the UK and Africa with p<0.001. Taken together, these observations strengthen the hypothesis that the genetic architecture of RA susceptibility is different in different ethnic backgrounds.     

Genome-wide association study combined with biological context can reveal more disease-related SNPs altering microRNA target seed sites

Background

Emerging studies demonstrate that single nucleotide polymorphisms (SNPs) resided in the microRNA recognition element seed sites (MRESSs) in 3′UTR of mRNAs are putative biomarkers for human diseases and cancers. However, exhaustively experimental validation for the causality of MRESS SNPs is impractical. Therefore bioinformatics have been introduced to predict causal MRESS SNPs. Genome-wide association study (GWAS) provides a way to detect susceptibility of millions of SNPs simultaneously by taking linkage disequilibrium (LD) into account, but the multiple-testing corrections implemented to suppress false positive rate always sacrificed the sensitivity. In our study, we proposed a method to identify candidate causal MRESS SNPs from 12 GWAS datasets without performing multiple-testing corrections. Alternatively, we used biological context to ensure credibility of the selected SNPs.

Results

In 11 out of the 12 GWAS datasets, MRESS SNPs were over-represented in SNPs with p-value ≤ 0.05 (odds ratio (OR) ranged from 1.1 to 2.4). Moreover, host genes of susceptible MRESS SNPs in each of the 11 GWAS dataset shared biological context with reported causal genes. There were 286 MRESS SNPs identified by our method, while only 13 SNPs were identified by multiple-testing corrections with a given threshold of 1 × 10⁻⁵, which is a common cutoff used in GWAS. 27 out of the 286 candidate SNPs have been reported to be deleterious while only 2 out of 13 multiple-testing corrected SNPs were documented in PubMed. MicroRNA-mRNA interactions affected by the 286 candidate SNPs were likely to present negatively correlated expression. These SNPs introduced greater alternation of binding free energy than other MRESS SNPs, especially when grouping by haplotypes (4210 vs. 4105 cal/mol by mean, 9781 vs. 8521 cal/mol by mean, respectively).

Conclusions

MRESS SNPs are promising disease biomarkers in multiple GWAS datasets. The method of integrating GWAS p-value and biological context is stable and effective for selecting candidate causal MRESS SNPs, it reduces the loss of sensitivity compared to multiple-testing corrections. The 286 candidate causal MRESS SNPs provide researchers a credible source to initialize their design of experimental validations in the future.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-669) contains supplementary material, which is available to authorized users.     

Identification of a cytokine network sustaining neutrophil and Th17 activation in untreated early rheumatoid arthritis

Introduction

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease characterized by sustained synovitis. Recently, several studies have proposed neutrophils and Th17 cells as key players in the onset and perpetuation of this disease. The main goal of this work was to determine whether cytokines driving neutrophil and Th17 activation are dysregulated in very early rheumatoid arthritis patients with less than 6 weeks of disease duration and before treatment (VERA).

Methods

Cytokines related to neutrophil and Th17 activation were quantified in the serum of VERA and established RA patients and compared with other very early arthritis (VEA) and healthy controls. Synovial fluid (SF) from RA and osteoarthritis (OA) patients was also analyzed.

Results

VERA patients had increased serum levels of cytokines promoting Th17 polarization (IL-1β and IL-6), as well as IL-8 and Th17-derived cytokines (IL-17A and IL-22) known to induce neutrophil-mediated inflammation. In established RA this pattern is more evident within the SF. Early treatment with methotrexate or corticosteroids led to clinical improvement but without an impact on the cytokine pattern.

Conclusions

VERA patients already display increased levels of cytokines related with Th17 polarization and neutrophil recruitment and activation, a dysregulation also found in SF of established RA. 0 Thus, our data suggest that a cytokine-milieu favoring Th17 and neutrophil activity is an early event in RA pathogenesis.     

The lp13.3 genomic region -rs599839- is associated with endothelial dysfunction in patients with rheumatoid arthritis

Introduction

Rheumatoid arthritis (RA) is an inflammatory disease associated with accelerated atherosclerosis and high risk of cardiovascular (CV) disease. Since genome-wide association studies demonstrated association between rs599839 polymorphism and coronary artery disease, in the present study we assessed the potential association of this polymorphism with endothelial dysfunction, an early step in atherogenesis.

Methods

A total of 128 RA patients without history of CV events were genotyped for rs599839 A/G polymorphism. The presence of endothelial dysfunction was assessed by brachial ultrasonography (brachial flow-mediated endothelium-dependent (FMD)).

Results

Patients carrying the allele G exhibited more severe endothelial dysfunction (FMD%: 4.61 ± 3.94%) than those carrying the wild allele A (FMD%: 6.01 ± 5.15%) (P = 0.08). Adjustment for gender, age at the time of study, follow-up time and classic CV risk factors disclosed a significant association between the rs599839 polymorphism and FMD (G vs. A: P = 0.0062).

Conclusions

Our results confirm an association of the rs599839 polymorphism with endothelial dysfunction in RA.     

Genome wide association studies in presence of misclassified binary responses

Background

Misclassification has been shown to have a high prevalence in binary responses in both livestock and human populations. Leaving these errors uncorrected before analyses will have a negative impact on the overall goal of genome-wide association studies (GWAS) including reducing predictive power. A liability threshold model that contemplates misclassification was developed to assess the effects of mis-diagnostic errors on GWAS. Four simulated scenarios of case–control datasets were generated. Each dataset consisted of 2000 individuals and was analyzed with varying odds ratios of the influential SNPs and misclassification rates of 5% and 10%.

Results

Analyses of binary responses subject to misclassification resulted in underestimation of influential SNPs and failed to estimate the true magnitude and direction of the effects. Once the misclassification algorithm was applied there was a 12% to 29% increase in accuracy, and a substantial reduction in bias. The proposed method was able to capture the majority of the most significant SNPs that were not identified in the analysis of the misclassified data. In fact, in one of the simulation scenarios, 33% of the influential SNPs were not identified using the misclassified data, compared with the analysis using the data without misclassification. However, using the proposed method, only 13% were not identified. Furthermore, the proposed method was able to identify with high probability a large portion of the truly misclassified observations.

Conclusions

The proposed model provides a statistical tool to correct or at least attenuate the negative effects of misclassified binary responses in GWAS. Across different levels of misclassification probability as well as odds ratios of significant SNPs, the model proved to be robust. In fact, SNP effects, and misclassification probability were accurately estimated and the truly misclassified observations were identified with high probabilities compared to non-misclassified responses. This study was limited to situations where the misclassification probability was assumed to be the same in cases and controls which is not always the case based on real human disease data. Thus, it is of interest to evaluate the performance of the proposed model in that situation which is the current focus of our research.

     

Association of IL4R single-nucleotide polymorphisms with rheumatoid nodules in African Americans with rheumatoid arthritis

Introduction

To determine whether IL4R single-nucleotide polymorphisms (SNPs) rs1805010 (I50V) and rs1801275 (Q551R), which have been associated with disease severity in rheumatoid arthritis (RA) patients of European ancestry, relate to the presence of rheumatoid nodules and radiographic erosions in African Americans.

Methods

Two IL4R SNPs, rs1805010 and rs1801275, were genotyped in 749 patients from the Consortium for Longitudinal Evaluation of African-Americans with Early Rheumatoid Arthritis (CLEAR) registries. End points were rheumatoid nodules defined as present either by physical examination or by chest radiography and radiographic erosions (radiographs of hands/wrists and feet were scored using the modified Sharp/van der Heijde system). Statistical analyses were performed by using logistic regression modeling adjusted for confounding factors.

Results

Of the 749 patients with RA, 156 (20.8%) had rheumatoid nodules, with a mean age of 47.0 years, 84.6% female gender, and median disease duration of 1.9 years. Of the 461 patients with available radiographic data, 185 (40.1%) had erosions (score >0); their mean age was 46.7 years; 83.3% were women; and median disease duration was 1.5 years. Patients positive for HLA-DRB1 shared epitope (SE) and autoantibodies (rheumatoid factor (RF) or anti-cyclic citrullinated peptide (CCP)) had a higher risk of developing rheumatoid nodules in the presence of the AA and AG alleles of rs1801275 (odds ratio (OR)_adj = 8.08 (95% confidence interval (CI): 1.60-40.89), P = 0.01 and OR_adj = 2.97 (95% CI, 1.08 to 8.17), P = 0.04, respectively). Likewise, patients positive for the HLA-DRB1 SE and RF alone had a higher risk of developing rheumatoid nodules in presence of the AA and AG alleles of rs1801275 (OR_adj = 8.45 (95% CI, 1.57 to 45.44), P = 0.01, and OR_adj = 3.57 (95% CI, 1.18 to 10.76), P = 0.02, respectively) and in the presence of AA allele of rs1805010 (OR_adj = 4.52 (95% CI, 1.20 to 17.03), P = 0.03). No significant association was found between IL4R and radiographic erosions or disease susceptibility, although our statistical power was limited by relatively small numbers of cases and controls.

Conclusions

We found that IL4R SNPs, rs1801275 and rs1805010, are associated with rheumatoid nodules in autoantibody-positive African-American RA patients with at least one HLA-DRB1 allele encoding the SE. These findings highlight the need for analysis of genetic factors associated with clinical RA phenotypes in different racial/ethnic populations.     

Prognostic value of interleukin-1 receptor antagonist gene polymorphism and cytomegalovirus seroprevalence in patients with coronary artery disease

Background

Chronic inflammatory stimuli such as cytomegalovirus (CMV) infection and various genetic polymorphisms determining the inflammatory response are assumed to be important risk factors in atherosclerosis. We investigated whether patients with stable coronary artery disease (CAD) and homozygous for allele 2 of the interleukin 1 receptor antagonist (IL-1RA) gene and seropositive for CMV represent a group particular susceptible for recurrent cardiovascular events.

Methods

In a series of 300 consecutive patients with angiographically defined CAD a prospective follow-up was conducted (mean age 57.9 years, median follow-up time 38.2 months).

Results

No statistically significant relationship was found between CMV serostatus and IL-1RN*2 (alone or in combination) and risk for future cardiovascular events (CVE). The hazard ratio (HR) for a CVE given positive CMV-serology and IL-1RN*2 was 1.07 (95% confidence interval (CI) 0.32–3.72) in the fully adjusted model compared to seronegative CMV patients not carrying the IL-1RN*2 allele. In this prospective cohort study involving 300 patients with angiographically defined CAD at baseline, homozygousity for allele 2 of the IL-1 RA and seropositivity to CMV alone and in combination were not associated with an increased risk for cardiovascular events during follow-up; in addition, combination of the CMV-seropositivity and IL-1RN*2 allele were not associated with a proinflammatory response

Conclusion

Our study suggests that seropositivity to CMV and IL-1RA*2 genotype alone or in combination might not be a strong risk factor for recurrent cardiovascular events in patients with manifest CAD, and is not associated with levels of established inflammatory markers.     

DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions

Background

Genomic variations are associated with the metabolism and the occurrence of adverse reactions of many therapeutic agents. The polymorphisms on over 2000 locations of cytochrome P450 enzymes (CYP) due to many factors such as ethnicity, mutations, and inheritance attribute to the diversity of response and side effects of various drugs. The associations of the single nucleotide polymorphisms (SNPs), the internal pharmacokinetic patterns and the vulnerability of specific adverse reactions become one of the research interests of pharmacogenomics. The conventional genomewide association studies (GWAS) mainly focuses on the relation of single or multiple SNPs to a specific risk factors which are a one-to-many relation. However, there are no robust methods to establish a many-to-many network which can combine the direct and indirect associations between multiple SNPs and a serial of events (e.g. adverse reactions, metabolic patterns, prognostic factors etc.). In this paper, we present a novel deep learning model based on generative stochastic networks and hidden Markov chain to classify the observed samples with SNPs on five loci of two genes (CYP2D6 and CYP1A2) respectively to the vulnerable population of 14 types of adverse reactions.

Methods

A supervised deep learning model is proposed in this study. The revised generative stochastic networks (GSN) model with transited by the hidden Markov chain is used. The data of the training set are collected from clinical observation. The training set is composed of 83 observations of blood samples with the genotypes respectively on CYP2D6*2, *10, *14 and CYP1A2*1C, *1 F. The samples are genotyped by the polymerase chain reaction (PCR) method. A hidden Markov chain is used as the transition operator to simulate the probabilistic distribution. The model can perform learning at lower cost compared to the conventional maximal likelihood method because the transition distribution is conditional on the previous state of the hidden Markov chain. A least square loss (LASSO) algorithm and a k-Nearest Neighbors (kNN) algorithm are used as the baselines for comparison and to evaluate the performance of our proposed deep learning model.

Results

There are 53 adverse reactions reported during the observation. They are assigned to 14 categories. In the comparison of classification accuracy, the deep learning model shows superiority over the LASSO and kNN model with a rate over 80 %. In the comparison of reliability, the deep learning model shows the best stability among the three models.

Conclusions

Machine learning provides a new method to explore the complex associations among genomic variations and multiple events in pharmacogenomics studies. The new deep learning algorithm is capable of classifying various SNPs to the corresponding adverse reactions. We expect that as more genomic variations are added as features and more observations are made, the deep learning model can improve its performance and can act as a black-box but reliable verifier for other GWAS studies.

     

How to manage rheumatoid arthritis according to classic biomarkers and polymorphisms?

Objectives

Single nucleotide polymorphisms (SNPs), genetic background, and epigenetics play important roles in rheumatoid arthritis (RA). These factors can be useful in RA diagnosis, prognosis, and treatment response evaluation, particularly with the growing trends in personalized medicine. Therefore, categorizing classic genes and SNPs in RA can present an appropriate guideline for RA management.

Discussion

Prognostic and diagnostic biomarkers play important roles in RA diagnosis and treatment. Categorizing SNPs is not an easy process yet, but selecting classic SNPs can be useful worldwide, according to basic similarities that exist in genomes. In this review, we compiled some of these RA-associated SNPs and biomarkers in a table, according to newly identified factors. The role of epigenetics in RA is undeniable; using epigenetic biomarkers like histone deacetylase (HDACs) can be useful in RA diagnosis and treatment. miRs such as miR-146a, miR-155, and miR-222 are useful in diagnosis and can be used in treatment by interfering with other factors’ functions. Interleukins (ILs) seem to be good prognostic and diagnostic markers and can be targeted in RA treatment.

Conclusion

Using multiple types of biomarkers, such as genes, SNPs, and epigenetic biomarkers like HDACs can be useful in RA management and treatment. PTPN22, HLA-DR polymorphisms, miRs, and HDACs are considerable in RA susceptibility; hence, they can be valuable biomarkers in future studies. This article gathered separate information from approximately 100 articles to present useful biomarkers and polymorphisms in one review.

     

Targeted resequencing of GWAS loci reveals novel genetic variants for milk production traits

Background

Genome wide association study (GWAS) has been proven to be a powerful tool for detecting genomic variants associated with complex traits. However, the specific genes and causal variants underlying these traits remain unclear.

Results

Here, we used target-enrichment strategy coupled with next generation sequencing technique to study target regions which were found to be associated with milk production traits in dairy cattle in our previous GWAS. Among the large amount of novel variants detected by targeted resequencing, we selected 200 SNPs for further association study in a population consisting of 2634 cows. Sixty six SNPs distributed in 53 genes were identified to be associated significantly with on milk production traits. Of the 53 genes, 26 were consistent with our previous GWAS results. We further chose 20 significant genes to analyze their mRNA expression in different tissues of lactating cows, of which 15 were specificly highly expressed in mammary gland.

Conclusions

Our study illustrates the potential for identifying causal mutations for milk production traits using target-enrichment resequencing and extends the results of GWAS by discovering new and potentially functional mutations.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1105) contains supplementary material, which is available to authorized users.     

Analysis of genome-wide association study data using the protein knowledge base

Background

Genome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few utilise the multiple-hit model of complex disease to identify causal candidates. A major benefit of multi-locus comparison is that it compensates for some shortcomings of current statistical analyses that test the frequency of each SNP in isolation for the phenotype population versus control.

Results

Here we developed and benchmarked several protocols for GWAS data analysis using different in-silico gene prediction and prioritisation methodologies. We adopted a high sensitivity approach to the data, using less conservative statistical SNP associations. Multiple gene search spaces, either of fixed-widths or proximity-based, were generated around each SNP marker. We used the candidate disease gene prediction system Gentrepid to identify candidates based on shared biomolecular pathways or domain-based protein homology. Predictions were made either with phenotype-specific known disease genes as input; or without a priori knowledge, by exhaustive comparison of genes in distinct loci. Because Gentrepid uses biomolecular data to find interactions and common features between genes in distinct loci of the search spaces, it takes advantage of the multi-locus aspect of the data.

Conclusions

Results suggest testing multiple SNP-to-gene search spaces compensates for differences in phenotypes, populations and SNP platforms. Surprisingly, domain-based homology information was more informative when benchmarked against gene candidates reported by GWA studies compared to previously determined disease genes, possibly suggesting a larger contribution of gene homologs to complex diseases than Mendelian diseases.