Knowledge-based analysis of genetic associations of rheumatoid arthritis to inform studies searching for pleiotropic genes: a literature review and network analysis

Identifying prioritization of therapeutic targets for ankylosing spondylitis: a multi-omics Mendelian randomization study

BACKGROUND

To investigate the associations of methylation, expression, and protein quantitative trait loci (mQTL, eQTL, and pQTL) with ankylosing spondylitis (AS) and find out genetically supported drug targets for AS.

METHODS

The summary-data-based Mendelian randomization (SMR) and Bayesian co-localization analysis were used to assess the potential causality between AS and relevant genes. The GWAS data obtained from the International Genetics of Ankylosing Spondylitis Consortium (IGAS) were set as the discovery stage, and the FinnGen and UK Biobank databases were used to replicate the analysis as an external validation. We further integrated the multi-omics results to screen overlapped genes at different levels. The protein-protein interaction (PPI) network and enrichment analyses were used to explore the biological effect of SMR-identified genes on AS. Drug prediction and molecular docking were used to validate the medicinal value of candidate drug targets.

RESULTS

Based on the results of multi-omics evidence screening, we identified potential associations of TNFRSF1A, B3GNT2, ERAP1, and FCGR2A with AS at different regulatory levels. At the protein level, AIF1, TNXB, APOM, and B3GNT2 were found to be negatively associated with AS risk, whereas higher levels of FCGR2A, FCGR2B, IL12B, TNFRSF1A, and ERAP1 were associated with an increased risk of AS. The bioinformatics analyses showed that the SMR-identified genes were mainly involved in immune response. Molecular docking results displayed stable binding between predicted candidate drugs and these aforementioned proteins.

CONCLUSION

Our study found four AS-associated genes with multi-omics evidence and nine promising drug targets for AS, which may contribute to the understanding of the genetic mechanisms of AS and provide innovative perspectives into targeted therapy for AS.

Long-range linkage disequilibrium events on the genome of dromedary camels as a signal of epistatic and directional positive selection

The genome of dromedary camels has been subjected to various evolutionary forces, such as genetic admixture, natural positive selection, and epistatic selection. These forces are considered as main factors associated with the formation of long-range linkage disequilibrium (LRLD) events. We have analyzed whole-genome data of 56 dromedary camel samples from different geographical regions across the Arabian Peninsula for two main purposes: first, to assess the level of linkage disequilibrium, and second, to identify autosomal LRLD events. The analysis revealed a mean r 2 value of 0.25 (±0.028) over the dromedary autosomes, with a continuous decay until reaching a plateau at inter-variant distances >400 kb. A total of 1847 LRLD events were identified within the dromedary autosomes, which harbor 36 prevalent haplotypes. A level of genetic admixture was observed among the dromedary populations analyzed, which might be a source for the observed LRLD events. Four functional interactions were revealed among the genes found within the LRLD events, with some genes overlapping with prevalent haplotypes, indicative of potential epistatic selection. Genes related to renal function, fertility, thermal regulation, bone structure, and insulin regulation were found among the LRLD genes. These genes, along with the defined prevalent haplotypes, can be considered as hotspots for natural positive selection associated with the LRLD distribution on dromedary genomes. In this study, we have for the first time analyzed the genome of dromedary camels for LRLD events possibly influenced by forces including genetic admixture, epistatic and positive selection. The revealed LRLD elements and prevalent haplotypes should be accounted for when designing breeding programmes to conserve the genetic stock of this well-adapted domestic species.

Impact of missense TSBP1 variants on the susceptibility to coronary heart disease

BACKGROUND

A genome-wide association study has recognized C6orf10-BTNL2 polymorphism in coronary artery disease. The goal of this study was to explore the potential correlation of nine missense TSBP1 variants with coronary heart disease (CHD) risk in the Chinese Han population.

METHODS

Nine TSBP1 missense single nucleotide polymorphisms (SNPs) were selected for genotyping by the Agena MassARRAY platform. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to analyze the contribution of TSBP1 SNPs to CHD predisposition by logistic regression models adjusted by age, sex, drinking, and smoking. The correlation of TSBP1 variants with clinical data in CHD patients was examined by Kruskal-Wallis test.

RESULTS

rs9268368-C (p = 0.039, OR = 1.18, 95 % CI: 1.01-1.38) was related to an increased risk of CHD, while rs3749966-C (p = 0.032, OR = 0.49, 95 % CI: 0.25-0.96) and rs3129941-A (p = 0.011, OR = 0.74, 95 % CI: 0.59-0.93) might be protective factors against CHD occurrence in the Chinese Han population. We also observed the effects of demographic characteristics (age, sex, alcohol consumption, and smoking) and complications (hypertension and diabetes) on the interactive association of TSBP1 polymorphisms with CHD susceptibility. rs139993810 was related to the levels of high-density lipoprotein cholesterol (HDL-C, p = 0.030).

CONCLUSIONS

Our findings determined the association of TSBP1 rs9268368, rs3749966, and rs3129941 with CHD occurrence in the Chinese Han population, and highlighted the influence of demographic characteristics and complications on the interactive association of TSBP1 polymorphisms with CHD risk.

Evaluating statistical significance in a meta-analysis by using numerical integration

Meta-analysis is a method for enhancing statistical power through the integration of information from multiple studies. Various methods for integrating p-values (i.e., statistical significance), including Fisher's method under an independence assumption, the permutation method, and the decorrelation method, have been broadly used in bioinformatics and computational biotechnology studies. However, these methods have limitations related to statistical assumption, computing efficiency, and accuracy of statistical significance estimation. In this study, we proposed a numerical integration method and examined its theoretical properties. Simulation studies were conducted to evaluate its Type I error, statistical power, computational efficiency, and estimation accuracy, and the results were compared with those of other methods. The results demonstrate that our proposed method performs well in terms of Type I error, statistical power, computing efficiency (regardless of sample size), and statistical significance estimation accuracy. P-value data from multiple large-scale genome-wide association studies (GWASs) and transcriptome-wise association studies (TWASs) were analyzed. The results demonstrate that our proposed method can be used to identify critical genomic regions associated with rheumatoid arthritis and asthma, increase statistical significance in individual GWASs and TWASs, and control for false-positives more effectively than can Fisher's method under an independence assumption. We created the software package Pbine, available at GitHub (https://github.com/Yinchun-Lin/Pbine).

Genetic Polymorphisms Associated with Rheumatoid Arthritis Development and Antirheumatic Therapy Response

Rheumatoid arthritis (RA) is the most common inflammatory arthropathy worldwide. Possible manifestations of RA can be represented by a wide variability of symptoms, clinical forms, and course options. This multifactorial disease is triggered by a genetic predisposition and environmental factors. Both clinical and genealogical studies have demonstrated disease case accumulation in families. Revealing the impact of candidate gene missense variants on the disease course elucidates understanding of RA molecular pathogenesis. A multivariate genomewide association study (GWAS) based analysis identified the genes and signalling pathways involved in the pathogenesis of the disease. However, these identified RA candidate gene variants only explain 30% of familial disease cases. The genetic causes for a significant proportion of familial RA have not been determined until now. Therefore, it is important to identify RA risk groups in different populations, as well as the possible prognostic value of some genetic variants for disease development, progression, and treatment. Our review has two purposes. First, to summarise the data on RA candidate genes and the increased disease risk associated with these alleles in various populations. Second, to describe how the genetic variants can be used in the selection of drugs for the treatment of RA.

Whole Blood Targeted Bisulfite Sequencing and Differential Methylation in the C6ORF10 Gene of Patients with Rheumatoid Arthritis

Objective.

Polymorphisms in human major histocompatibility complex (MHC) are the strongest genetic associations with rheumatoid arthritis (RA). Epigenome-wide methylation studies suggest DNA methylation changes within MHC may contribute to disease susceptibility. We profiled MHC-specific methylated CpG (5′–C–phosphate–G–3′) in autoantibody-positive patients with RA and matched unaffected anticitrullinated protein antibodies–negative first-degree relatives (ACPA−/FDR) from an indigenous North American (INA) population that is known to have prevalent RA.

Methods.

DNA was isolated from whole blood and targeted bisulfite sequencing was used to profile methylated CpG in patients with RA and ACPA−/FDR. Differentially methylated CpG loci (DML) were mapped and gene annotated. Ingenuity pathway analysis (IPA) was used for curating biomolecular networks of mapped genes. Transcript abundance was determined by quantitative (q)PCR.

Results.

We identified 74 uniquely methylated CpG sites within the MHC region that were differentially methylated in patients with RA (p < 0.05), compared to ACPA−/FDR. Of these, 32 DML were located on 22 genes. IPA showed these genes are involved in regulating the nuclear factor–κB complex and processes involved in antigen presentation, and immune cell crosstalk in autoimmunity. Pearson correlation analysis demonstrated a negative association between differentially methylated CpG in the C6ORF10 gene and risk factors associated with RA. Analysis by qPCR confirmed differential abundance of C6ORF10, TNXB, and HCG18 mRNA in patients with RA compared to ACPA−/FDR.

Conclusion.

Our results confirm the presence of differential methylation at specific gene loci within the MHC region of INA patients with RA. These epigenetic signatures may precede disease onset, or alternatively, may be a result of developing RA.

Metformin one in a Million Efficient Medicines for Rheumatoid Arthritis Complications: Inflammation, Osteoblastogenesis, Cardiovascular Disease, Malignancies

BACKGROUND

Rheumatoid arthritis is a widespread autoimmune disease and inflammation and bone destruction are two main issues in rheumatoid arthritis.

OBJECTIVE

To discussing metformin effects on rheumatoid arthritis complications.

METHODS

We conducted a narrative literature search including clinical trials, experimental studies on laboratory animals and cell lines. Our search covered Medline, PubMed and Google Scholar databases from 1999 until 2018. We used the terms" Metformin; Rheumatoid arthritis; Cardiovascular disease; Cancer; Osteoblastogenesis.

DISCUSSION

Inflammatory pro-cytokines such as Interlukin-6 play important roles in T. helper 17 cell lineage differentiation. Interlukin-6 and Tumor Necrosis Factor-α activate Janus kinase receptors signal through signaling transducer and activator of transcription signaling pathway which plays important role in inflammation, bone destruction and cancer in rheumatoid arthritis patients. Interlukin-6 and Tumor Necrosis Factor-α synergistically activate signaling transducer and activator of transcription and Nuclear Factor-kβ pathways and both cytokines increase the chance of cancer development in rheumatoid arthritis patients. Metformin is AMPK activators that can suppress mTOR, STAT3 and HIF-1 so AMPK activation plays important role in suppressing inflammation and osteoclastogenesis and decreasing cancer.

CONCLUSION

Metformin effect on AMPK and mTOR pathways gives the capability to change Treg/Th17 balance and decrease Th17 differentiation and inflammation, osteoclastogenesis and cancers in RA patients. Metformin can be useful in protecting bones especially in first stages of RA and it can decrease inflammation, CVD and cancer in RA patients so Metformin beside DAMARs can be useful in increasing RA patients' life quality with less harm and cost.

Comparative study for haplotype block partitioning methods - Evidence from chromosome 6 of the North American Rheumatoid Arthritis Consortium (NARAC) dataset

Haplotype-based methods compete with "one-SNP-at-a-time" approaches on being preferred for association studies. Chromosome 6 contains most of the known genetic biomarkers for rheumatoid arthritis (RA) disease. Therefore, chromosome 6 serves as a benchmark for the haplotype methods testing. The aim of this study is to test the North American Rheumatoid Arthritis Consortium (NARAC) dataset to find out if haplotype block methods or single-locus approaches alone can sufficiently provide the significant single nucleotide polymorphisms (SNPs) associated with RA. In addition, could we be satisfied with only one method of the haplotype block methods for partitioning chromosome 6 of the NARAC dataset? In the NARAC dataset, chromosome 6 comprises 35,574 SNPs for 2,062 individuals (868 cases, 1,194 controls). Individual SNP approach and three haplotype block methods were applied to the NARAC dataset to identify the RA biomarkers. We employed three haplotype partitioning methods which are confidence interval test (CIT), four gamete test (FGT), and solid spine of linkage disequilibrium (SSLD). P-values after stringent Bonferroni correction for multiple testing were measured to assess the strength of association between the genetic variants and RA susceptibility. Moreover, the block size (in base pairs (bp) and number of SNPs included), number of blocks, percentage of uncovered SNPs by the block method, percentage of significant blocks from the total number of blocks, number of significant haplotypes and SNPs were used to compare among the three haplotype block methods. Individual SNP, CIT, FGT, and SSLD methods detected 432, 1,086, 1,099, and 1,322 associated SNPs, respectively. Each method identified significant SNPs that were not detected by any other method (Individual SNP: 12, FGT: 37, CIT: 55, and SSLD: 189 SNPs). 916 SNPs were discovered by all the three haplotype block methods. 367 SNPs were discovered by the haplotype block methods and the individual SNP approach. The P-values of these 367 SNPs were lower than those of the SNPs uniquely detected by only one method. The 367 SNPs detected by all the methods represent promising candidates for RA susceptibility. They should be further investigated for the European population. A hybrid technique including the four methods should be applied to detect the significant SNPs associated with RA for chromosome 6 of the NARAC dataset. Moreover, SSLD method may be preferred for its favored benefits in case of selecting only one method.

DNArCdb: A database of cancer biomarkers in DNA repair genes that includes variants related to multiple cancer phenotypes

Functioning DNA repair capabilities are vital for organisms to ensure that the biological information is preserved and correctly propagated. Disruptions in DNA repair pathways can result in the accumulation of DNA mutations, which may lead to onset of complex disease such as cancer. The discovery and characterization of cancer-related biomarkers may allow early diagnosis and targeted treatment, which could significantly contribute to the survival rates of cancer patients. To this end, we have applied a hypothesis driven bioinformatics approach to identify biomarkers related to 25 different DNA repair enzymes, in combination with structural analysis of six selected missense mutations of newly discovered SNPs that are associated with cancer phenotypes. Our search on 8 distinct cancer databases uncovered 43 missense SNPs that statistically significantly associated at least one phenotype. Moreover, nine of these missense SNPs are statistically significantly associated with two or more cancers. In addition, we have performed classical molecular dynamics to characterize the impact of rs10018786 on POLN, which results in the M310 L Pol ν variant, and rs3218784 on POLI, which results in the I236 M Pol ι. Our results suggest that both of these cancer-associated variants result in noticeable structural and dynamical changes compared with their respective wild-type proteins.

Identification of novel genetic loci for osteoporosis and/or rheumatoid arthritis using cFDR approach

There are co-morbidity between osteoporosis (OP) and rheumatoid arthritis (RA). Some genetic risk factors have been identified for these two phenotypes respectively in previous research; however, they accounted for only a small portion of the underlying total genetic variances. Here, we sought to identify additional common genetic loci associated with OP and/or RA. The conditional false discovery rate (cFDR) approach allows detection of additional genetic factors (those respective ones as well as common pleiotropic ones) for the two associated phenotypes. We collected and analyzed summary statistics provided by large, multi-center GWAS studies of FNK (femoral neck) BMD (a major risk factor for osteoporosis) (n = 53,236) and RA (n = 80,799). The conditional quantile-quantile (Q-Q) plots can assess the enrichment of SNPs related to FNK BMD and RA, respectively. Furthermore, we identified shared loci between FNK BMD and RA using conjunction cFDR (ccFDR). We found strong enrichment of p-values in FNK BMD when conditional Q-Q was done on RA and vice versa. We identified 30 novel OP-RA associated pleiotropic loci that have not been reported in previous OP or RA GWAS, 18 of which located in the MHC (major histocompatibility complex) region previously reported to play an important role in immune system and bone health. We identified some specific novel polygenic factors for OP and RA respectively, and identified 30 novel OP-RA associated pleiotropic loci. These discovery findings may offer novel pathobiological insights, and suggest new targets and pathways for drug development in OP and RA patients.

Notch4 and mhc class II polymorphisms are associated with hcv-related benign and malignant lymphoproliferative diseases

Mixed cryoglobulinemia (MC), is a HCV-related, clinically benign, lymphoproliferative disorder (LPD) that may evolve into a non Hodgkin's lymphoma (NHL). Significant associations were found between two single nucleotide polymorphisms near NOTCH4 (rs2071286) and the HLA class II (rs9461776) genes and HCV-related MC syndrome (MCS). We analyzed NOTCH4 rs2071286 and HLA-II rs9461776 in 3 HCV-related LPD groups (asymptomatic MC, MCS, NHL) with HCV infection without lymphoproliferative disorders.

We found a positive relationship between NOTCH4 rs207186 T minor allele frequency (MAF) and patients with HCV-related LPDs at risk of NHL (Chi-square test for trend = 14.84 p = 0.0001), in accordance with an over-dominant model in the NHL group (CT vs CC + TT, OR=1.88, 95% CI 1.24–2.83, p = 0.0026).

Regarding HLA II rs9461776, G MAF increased in patients with HCV-related LPDs at risk of NHL (Chi-square test for trend = 8.40 p = 0.0038), in accordance with a recessive genotypic model in the NHL group (G/G vs A/A + A/G, OR = 11.07, 95% CI 2.37–51.64, p = 0.0022).

Both NOTCH4 rs2071286 and HLA-II rs9461776 were present on chromosome 6 and showed D’ and r values of linkage disequilibrium (LD) of about 0.5 values, thereby suggesting there is no extensive LD in the HCV+ population.

This data shows that the previously demonstrated association between NOTCH4 rs2071286 and HLA-II rs9461776 polymorphisms and HCV-related MCS could be extended to overall patients with HCV-related LPDs. The significant relationship between rs2071286 and rs9461776 MAF and the increased risk for NHL, suggests their use as non-invasive markers to categorize patients at risk of lymphoma.

Type-2 diabetes-associated variants with cross-trait relevance: Post-GWAs strategies for biological function interpretation

Genome-wide association studies (GWAs) for type 2 diabetes (T2D) have been successful in identifying many loci with robust association signals. Nevertheless, there is a clear need for post-GWAs strategies to understand mechanism of action and clinical relevance of these variants. The association of several comorbidities with T2D suggests a common etiology for these phenotypes and complicates the management of the disease. In this study, we focused on the genetics underlying these relationships, using systems genomics to identify genetic variation associated with T2D and 12 other traits. GWAs studies summary statistics for pairwise comparisons were obtained for glycemic traits, obesity, coronary artery disease, and lipids from large consortia GWAs meta-analyses. We used a network medicine approach to leverage experimental information about the identified genes and variants with cross traits effects for biological function interpretation. We identified a set of 38 genetic variants with cross traits effects that point to a main network of genes that should be relevant for T2D and its comorbidities. We prioritized the T2D associated genes based on the number of traits they showed association with and the experimental evidence showing their relation to the disease etiology. In this study, we demonstrated how systems genomics and network medicine approaches can shed light into GWAs discoveries, translating findings into a more therapeutically relevant context.

Personalized medicine in rheumatology

In the era of the 21st century, rheumatoid arthritis (RA) is still poorly characterized. Rheumatoid arthritis is a common but heterogeneous disease, not only in the course and clinical symptoms, but also in the clinical response to treatment. Now it is known that early, correct diagnosis and starting treatment with disease-modifying drugs (DMARDs), of which methotrexate (MTX) remains the gold standard in the treatment of RA, is crucial in order to prevent joint destruction, functional disability and an unfavourable disease outcome. Early diagnosis of rheumatoid arthritis is significant in so much as the primary treatment can be started better. Pharmacogenetic and pharmacogenomic studies, which help determine the genetic profile of individual patients, may bring us closer to personalized medicine. Further studies on RA should allow for the identification of disease-specific genes at the stage when their tolerance by the organism is still preserved (before auto-aggression develops).

Whole-exome sequencing analysis in twin sibling males with an anterior cruciate ligament rupture

Familial predisposition is among the major genetic risk factors for non-contact musculoskeletal tissue injuries. Personal genome sequence shows that different polymorphism profiles may account for the number and the degree of injuries and the recovery time. Genotyping studies allow investigation into genome factors with potential impact on pathogenesis of non-contact ligament injuries. We have studied a family with twin sibling males surgically diagnosed of an anterior cruciate ligament non-contact rupture and non-affected progenitors (father and mother) were subjected to whole exome sequencing (WES) analysis. WES analysis previously carried out on 16 individuals, without ACL injury medical records, were also included in this study for single nucleotide variants (SNVs) and small insertions and deletions detection (indels), variant filtering and to prioritize variants relative to the disease. WES analysis to identify SNVs and indels was performed using open web-based bioinformatics tools. A set of 11 new variants shared by family members can be associated to ACL non-contact injury, including SerpinA11, ARSI, NOCHT4, EPB41, FDFT1, POMC, KIF26A, OLFML2B, ATG7, FAH and WDR6. All of them, except ATG7 and WDR6, have shown a damaging predictive pattern by combinatorial standard predictive scores. In combination to the identified SNVs of EPB41 and SerpinA11 genes, ACTL7A gene showed a predicted deleterious variant reinforcing the idea these variants impact on of fibroblast-like cells deformability and ECM misbalance, Differential gene expression and RNA sequencing analysis will help to understand the combined participation of these protein coding genes in ACL non-contact injuries.