Showing papers by "Luis Rodriguez-Rodriguez published in 2017"

A combined large-scale meta-analysis identifies COG6 as a novel shared risk locus for rheumatoid arthritis and systemic lupus erythematosus

Abstract: Objectives During the last years, genome-wide association studies (GWASs) have identified a number of common genetic risk factors for rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). However, the genetic overlap between these two immune-mediated diseases has not been thoroughly examined so far. The aim of the present study was to identify additional risk loci shared between RA and SLE. Methods We performed a large-scale meta-analysis of GWAS data from RA (3911 cases and 4083 controls) and SLE (2237 cases and 6315 controls). The top-associated polymorphisms in the discovery phase were selected for replication in additional datasets comprising 13 641 RA cases and 31 921 controls and 1957 patients with SLE and 4588 controls. Results The rs9603612 genetic variant, located nearby the COG6 gene, an established susceptibility locus for RA, reached genome-wide significance in the combined analysis including both discovery and replication sets (p value=2.95E−13). In silico expression quantitative trait locus analysis revealed that the associated polymorphism acts as a regulatory variant influencing COG6 expression. Moreover, protein–protein interaction and gene ontology enrichment analyses suggested the existence of overlap with specific biological processes, specially the type I interferon signalling pathway. Finally, genetic correlation and polygenic risk score analyses showed cross-phenotype associations between RA and SLE. Conclusions In conclusion, we have identified a new risk locus shared between RA and SLE through a meta-analysis including GWAS datasets of both diseases. This study represents the first comprehensive large-scale analysis on the genetic overlap between these two complex disorders.

Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach

Abstract: We developed and independently validated a rheumatoid arthritis (RA) mortality prediction model using the machine learning method Random Survival Forests (RSF). Two independent cohorts from Madrid (Spain) were used: the Hospital Clinico San Carlos RA Cohort (HCSC-RAC; training; 1,461 patients), and the Hospital Universitario de La Princesa Early Arthritis Register Longitudinal study (PEARL; validation; 280 patients). Demographic and clinical-related variables collected during the first two years after disease diagnosis were used. 148 and 21 patients from HCSC-RAC and PEARL died during a median follow-up time of 4.3 and 5.0 years, respectively. Age at diagnosis, median erythrocyte sedimentation rate, and number of hospital admissions showed the higher predictive capacity. Prediction errors in the training and validation cohorts were 0.187 and 0.233, respectively. A survival tree identified five mortality risk groups using the predicted ensemble mortality. After 1 and 7 years of follow-up, time-dependent specificity and sensitivity in the validation cohort were 0.79–0.80 and 0.43–0.48, respectively, using the cut-off value dividing the two lower risk categories. Calibration curves showed overestimation of the mortality risk in the validation cohort. In conclusion, we were able to develop a clinical prediction model for RA mortality using RSF, providing evidence for further work on external validation.

Mutational profile of rare variants in inflammasome-related genes in Behçet disease: A Next Generation Sequencing approach

Abstract: Behcet’s disease (BD) is an immune-mediated systemic disorder with a well-established association with HLA class I and other genes. BD has clinical overlap with many autoinflammatory diseases (AIDs). The aim of this study was to investigate the role of rare variants in seven genes involved in AIDs: CECR1, MEFV, MVK, NLRP3, NOD2, PSTPIP1 and TNFRSF1A using a next generation sequencing (NGS) approach in 355 BD patients. To check global association of each gene, 4 tests: SKAT, CollapseBt, C(α) and weighted KBAC were used. Databases: 1000 Genomes Project Phase 3, Infevers, HGMD and ClinVar and algorithms: PolyPhen2 and SIFT were consulted to collect information of the 62 variants found. All the genes resulted associated using SKAT but only 3 (MVK, NOD2 and PSTPIP1) with C(α) and weighted KBAC. When all the genes are considered, 40 variants were associated to AIDs in clinical databases and 25 were predicted as pathogenic at least by one of the algorithms. Including only MVK, NOD2 and PSTPIP1, the associated to AIDs variants found in BD were 20 and the predicted as pathogenic, 12. The maxima contribution corresponds to NOD2. This study supports influence of rare variants in genes involved in AIDs in the pathogenesis of BD.

Genetic variation associated with cardiovascular risk in autoimmune diseases.

Abstract: Autoimmune diseases have a higher prevalence of cardiovascular events compared to the general population. The objective of this study was to investigate the genetic basis of cardiovascular disease (CVD) risk in autoimmunity. We analyzed genome-wide genotyping data from 6,485 patients from six autoimmune diseases that are associated with a high socio-economic impact. First, for each disease, we tested the association of established CVD risk loci. Second, we analyzed the association of autoimmune disease susceptibility loci with CVD. Finally, to identify genetic patterns associated with CVD risk, we applied the cross-phenotype meta-analysis approach (CPMA) on the genome-wide data. A total of 17 established CVD risk loci were significantly associated with CVD in the autoimmune patient cohorts. From these, four loci were found to have significantly different genetic effects across autoimmune diseases. Six autoimmune susceptibility loci were also found to be associated with CVD risk. Genome-wide CPMA analysis identified 10 genetic clusters strongly associated with CVD risk across all autoimmune diseases. Two of these clusters are highly enriched in pathways previously associated with autoimmune disease etiology (TNFα and IFNγ cytokine pathways). The results of this study support the presence of specific genetic variation associated with the increase of CVD risk observed in autoimmunity.

An MIF Promoter Polymorphism Is Associated with Susceptibility to Pulmonary Arterial Hypertension in Diffuse Cutaneous Systemic Sclerosis.

Abstract: Objective. Systemic sclerosis (SSc) is a fibrotic immune-mediated disease of unknown etiology. Among its clinical manifestations, pulmonary involvement is the leading cause of mortality in patients with SSc. However, the genetic factors involved in lung complication are not well defined. We aimed to review the association of the MIF gene, which encodes a cytokine implicated in idiopathic pulmonary hypertension among other diseases, with the susceptibility and clinical expression of SSc, in addition to testing the association of this polymorphism with SSc-related pulmonary involvement. Methods. A total of 4392 patients with SSc and 16,591 unaffected controls from 6 cohorts of European origin were genotyped for the MIF promoter variant rs755622. An inverse variance method was used to metaanalyze the data. Results. A statistically significant increase of the MIF rs755622*C allele frequency compared with controls was observed in the subgroups of patients with diffuse cutaneous SSc (dcSSc) and with pulmonary arterial hypertension (PAH) independently (dcSSc: p = 3.20E–2, OR 1.13; PAH: p = 2.19E–02, OR 1.32). However, our data revealed a stronger effect size with the subset of patients with SSc showing both clinical manifestations (dcSSc with PAH: p = 6.91E–3, OR 2.05). Conclusion. We reviewed the association of the MIF rs755622*C allele with SSc and described a phenotype-specific association of this variant with the susceptibility to develop PAH in patients with dcSSc.

Influence of coronary artery disease and subclinical atherosclerosis related polymorphisms on the risk of atherosclerosis in rheumatoid arthritis.

Abstract: A genetic component influences the development of atherosclerosis in the general population and also in rheumatoid arthritis (RA). However, genetic polymorphisms associated with atherosclerosis in the general population are not always involved in the development of cardiovascular disease (CVD) in RA. Accordingly, a study in North-American RA patients did not show the association reported in the general population of coronary artery disease with a series of relevant polymorphisms (TCF21, LPA, HHIPL1, RASD1-PEMT, MRPS6, CYP17A1-CNNM2-NT5C2, SMG6-SRR, PHACTR1, WDR12 and COL4A1-COL4A2). In the present study, we assessed the potential association of these polymorphisms with CVD in Southern European RA patients. We also assessed if polymorphisms implicated in the increased risk of subclinical atherosclerosis in non-rheumatic Caucasians (ZHX2, PINX1, SLC17A4, LRIG1 and LDLR) may influence the risk for CVD in RA. 2,609 Spanish patients were genotyped by TaqMan assays. Subclinical atherosclerosis was determined in 1,258 of them by carotid ultrasonography (assessment of carotid intima media thickness and presence/absence of carotid plaques). No statistically significant differences were found when each polymorphism was assessed according to the presence/absence of cardiovascular events and subclinical atherosclerosis, after adjustment for potential confounder factors. Our results do not show an association between these 15 polymorphisms and atherosclerosis in RA.