No evidence that genetic predictors of susceptibility predict changes in core outcomes in JIA

Objectives. The clinical progression of JIA is unpredictable. Knowing who will develop severe disease could facilitate rapid intensification of therapies. We use genetic variants conferring susceptibility to JIA to predict disease outcome measures. Methods. A total of 713 JIA patients with genotype data and core outcome variables (COVs) at diagnosis (baseline) and 1 year follow-up were identified from the Childhood Arthritis Prospective Study (CAPS). A weighted genetic risk score (GRS) was generated, including all single nucleotide polymorphisms (SNPs) previously associated with JIA susceptibility (P-value<5 10 08). We used multivariable linear regression to test the GRS for association with COVS (limited joint count, active joint count, physician global assessment, parent/patient general evaluation, childhood HAQ and ESR) at baseline and change in COVS from baseline to 1 year, adjusting for baseline COV and International League of Associations of Rheumatology (ILAR) category. The GRS was split into quintiles to identify high (quintile 5) and low (quintile 1) risk groups. Results. Patients in the high-risk group for the GRS had a younger age at presentation (median low risk 7.79, median high risk 3.51). No association was observed between the GRS and any outcome measures at 1 year follow-up or baseline. Conclusion. For the first time we have used all known JIA genetic susceptibility loci (P¼<5 10 08) in a GRS to predict changes in disease outcome measured over time. Genetic susceptibility variants are poor predictors of changes in core outcome measures, it is likely that genetic factors predicting disease outcome are independent to those predicting susceptibility. The next step will be to conduct a genome-wide association analysis of JIA outcome.Versus Arthritis 20542 22084 20621Centre for Epidemiology Versus Arthritis (UK) 21755National Institute for Health Research (NIHR)Manchester Academic Health Sciences Centre (MAHSC)CLUSTER consortiumUK Research & Innovation (UKRI)Medical Research Council UK (MRC) MR/R013926/1Great Ormond Street Hospital Children's Charity VS0518Olivia's VisionNIHR GOSH BRC'UK's Experimental Arthritis Treatment Centre for Children by Versus Arthritis 20621NIHR GOSH Biomedical Research CentreBritish Society for Rheumatology (BSR

The Effect of Body Fat Distribution on Systemic Sclerosis

Mendelian randomization; Obesity; Systemic sclerosisAleatorización mendeliana; Obesidad; Esclerosis sistémicaAleatorització mendeliana; Obesitat; Esclerosi sistèmicaObesity contributes to a chronic proinflammatory state, which is a known risk factor to develop immune-mediated diseases. However, its role in systemic sclerosis (SSc) remains to be elucidated. Therefore, we conducted a two-sample mendelian randomization (2SMR) study to analyze the effect of three body fat distribution parameters in SSc. As instrumental variables, we used the allele effects described for single nucleotide polymorphisms (SNPs) in different genome-wide association studies (GWAS) for SSc, body mass index (BMI), waist-to-hip ratio (WHR) and WHR adjusted for BMI (WHRadjBMI). We performed local (pHESS) and genome-wide (LDSC) genetic correlation analyses between each of the traits and SSc and we applied several Mendelian randomization (MR) methods (i.e., random effects inverse-variance weight, MR-Egger regression, MR pleiotropy residual sum and outlier method and a multivariable model). Our results show no genetic correlation or causal relationship between any of these traits and SSc. Nevertheless, we observed a negative causal association between WHRadjBMI and SSc, which might be due to the effect of gastrointestinal complications suffered by the majority of SSc patients. In conclusion, reverse causality might be an especially difficult confounding factor to define the effect of obesity in the onset of SSc.This work was supported by grant RTI2018101332-B-100 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” funded by the European Union. Red de Investigación en Inflamación y Enfermedades Reumáticas (RIER) from Instituto de Salud Carlos III (RD16/0012/0013). 115565. LB-C was funded by Grant IJC2018-038026-I funded by MCIN/AEI/10.13039/501100011033. MA-H is a recipient of a Miguel Servet fellowship (CP21/00132) from Instituto de Salud Carlos III (Spanish Ministry of Science and Innovation). EL-I was funded by Grant IJC2019-040080-I funded by MCIN/AEI/10.13039/501100011033. GV-M was funded by Grant PRE2019-087586 funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future”

Interaction Effect between Physical Activity and the BDNF Val66Met Polymorphism on Depression in Women from the PISMA-ep Study

This study was partially funded by the Consejeria de Salud, Junta de Andalucia (PI3222009), Consejeria de Innovacion, Proyecto de Excelencia (CTS-2010-6682), the Institute of Health Carlos III (Co-funded by European Regional Development Fund/European Social Fund "A way to make Europe"/"Investing in your future") (projects PI18/00238 and PI18/00467), the Marie Curie Research Grants Scheme (FP7 626235), and by a NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation (22514). Juan Antonio Zarza-Rebollo was supported by the Spanish Ministry of Economy and Competitiveness (BES-2017-082698). Elena Lopez-Isac received financial support from the Spanish Ministry of Science and Innovation Juan de la Cierva Incorporacion Program (grant code IJC2019-040080-I/AEI/10.13039/501100011033). AnaMPerez-Gutierrez was supported by a grant from the Ministry of Economy and Competitiveness and Institute of Health Carlos III (FI19/00228), and Margarita Rivera was supported by the Ministry of Economy and Competitiveness Ramon y Cajal Program (RYC-2014-15774).The relationship between depression and the Val66Met polymorphism at the brain-derived neurotrophic factor gene (BDNF), has been largely studied. It has also been related to physical activity, although the results remain inconclusive. The aim of this study is to investigate the relationship between this polymorphism, depression and physical activity in a thoroughly characterised sample of community-based individuals from the PISMA-ep study. A total of 3123 participants from the PISMAep study were genotyped for the BDNF Val66Met polymorphism, of which 209 had depression. Our results are in line with previous studies reporting a protective effect of physical activity on depression, specifically in light intensity. Interestingly, we report a gene-environment interaction effect in which Met allele carriers of the BDNF Val66Met polymorphism who reported more hours of physical activity showed a decreased prevalence of depression. This effect was observed in the total sample (OR = 0.95, 95%CI = 0.90–0.99, p = 0.027) and was strengthened in women (OR = 0.93, 95%CI = 0.87–0.98, p = 0.019). These results highlight the potential role of physical activity as a promising therapeutic strategy for preventing and adjuvant treatment of depression and suggest molecular and genetic particularities of depression between sexes.Junta de Andalucia PI3222009Consejeria de Innovacion, Proyecto de Excelencia CTS-2010-6682Institute of Health Carlos III (European Regional Development Fund/European Social Fund "A way to make Europe"/"Investing in your future") PI18/00238 PI18/00467Marie Curie Research Grants Scheme FP7 626235NARSAD 22514Spanish Government BES-2017-082698Spanish Ministry of Science and Innovation Juan de la Cierva Incorporacion Program IJC2019-040080-I/AEI/10.13039/501100011033Ministry of Economy and CompetitivenessInstituto de Salud Carlos III FI19/00228Ministry of Economy and Competitiveness Ramon y Cajal Program RYC-2014-1577

Expression Quantitative Trait Locus Analysis in Systemic Sclerosis Identifies New Candidate Genes Associated With Multiple Aspects of Disease Pathology

Objective: To identify the genetic variants that affect gene expression (expression quantitative trait loci [eQTLs]) in systemic sclerosis (SSc) and to investigate their role in the pathogenesis of the disease. Methods: We performed an eQTL analysis using whole-blood sequencing data from 333 SSc patients and 524 controls and integrated them with SSc genome-wide association study (GWAS) data. We integrated our findings from expression modeling, differential expression analysis, and transcription factor binding site enrichment with key clinical features of SSc. Results: We detected 49,123 validated cis-eQTLs from 4,539 SSc-associated single-nucleotide polymorphisms (SNPs) (PGWAS 0.05). As a result, 233 candidates were identified, 134 (58%) of them associated with hallmarks of SSc and 105 (45%) of them differentially expressed in the blood cells, skin, or lung tissue of SSc patients. Transcription factor binding site analysis revealed enriched motifs of 24 transcription factors (5%) among SSc eQTLs, 5 of which were found to be differentially regulated in the blood cells (ELF1 and MGA), skin (KLF4 and ID4), and lungs (TBX4) of SSc patients. Ten candidate genes (4%) can be targeted by approved medications for immune-mediated diseases, of which only 3 have been tested in clinical trials in patients with SSc. Conclusion: The findings of the present study indicate a new layer to the molecular complexity of SSc, contributing to a better understanding of the pathogenesis of the disease

The Effect of Body Fat Distribution on Systemic Sclerosis

Obesity contributes to a chronic proinflammatory state, which is a known risk factor to develop immune-mediated diseases. However, its role in systemic sclerosis (SSc) remains to be elucidated. Therefore, we conducted a two-sample mendelian randomization (2SMR) study to analyze the effect of three body fat distribution parameters in SSc. As instrumental variables, we used the allele effects described for single nucleotide polymorphisms (SNPs) in different genomewide association studies (GWAS) for SSc, body mass index (BMI), waist-to-hip ratio (WHR) and WHR adjusted for BMI (WHRadjBMI). We performed local (pHESS) and genome-wide (LDSC) genetic correlation analyses between each of the traits and SSc and we applied several Mendelian randomization (MR) methods (i.e., random effects inverse-variance weight, MR-Egger regression, MR pleiotropy residual sum and outlier method and a multivariable model). Our results show no genetic correlation or causal relationship between any of these traits and SSc. Nevertheless, we observed a negative causal association between WHRadjBMI and SSc, which might be due to the effect of gastrointestinal complications suffered by the majority of SSc patients. In conclusion, reverse causality might be an especially difficult confounding factor to define the effect of obesity in the onset of SSc.MCIN/AEI RTI2018101332-B-100 IJC2018-038026-I IJC2019-040080-I PRE2019-087586"ERDF A way of making Europe" - European UnionRed de Investigacion en Inflamacion y Enfermedades Reumaticas (RIER) from Instituto de Salud Carlos III RD16/0012/0013ESF Investing in your futur

Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals

This study was funded by the Spanish Ministry of Health, the Institute of Health Carlos III (ISCIII), and the European Regional Development Fund (grants PS09/02272, PS09/02147, PS09/01095, PS09/00849, PS09/00461, and PI12-02755); the Andalusian Council of Health (grant PI-0569-2010); the Spanish Network of Primary Care Research, redIAPP (grant RD06/ 0018); the Aragon group (grant RD06/0018/0020); the Bizkaya group (grant RD06/0018/0018); the Castilla-Leon group (grant RD06/0018/0027); the Mental Health Barcelona Group (grant RD06/0018/0017); the Mental Health, Services and Primary Care Malaga group (grant RD06/0018/0039); and the projects "PI18/00238" and "PI18/00467" funded by the Institute of Health Carlos III (Co-funded by European Regional Development Fund/European Social Fund "A way tomake Europe"/"Investing in your future"). This study was performed as part of a PhD thesis conducted within the Official Doctoral Programme in Biomedicine of the University of Granada, Spain. Augusto Anguita-Ruiz was supported by a Ministry of Economy and Competitiveness and Institute of Health Carlos III fellowship (IFI17/00048). Juan Antonio Zarza-Rebollo received financial support from the Spanish Ministry of Economy and Competitiveness (BES-2017-082698). Ana M. Perez-Gutierrez was supported by a grant from the Ministry of Economy and Competitiveness and Institute of Health Carlos III (FI19/00228). Elena Lopez-Isac received financial support from the Spanish Ministry of Science and Innovation Juan de la Cierva Incorporacion Program (IJC2019040080-I), and Margarita Rivera was supported by the Ministry of Economy and Competitiveness Ramon y Cajal Program (RYC-2014-15774). The authors thank the Institute of Health Carlos III (ISCIII), the European Regional Development Fund (FEDER), the Andalusian Council of Health and Andalusian Health Service (SAS), the Primary Care Prevention and Health Promotion Research Network (redIAPP), the Biomedical Research Institute of Malaga (IBIMA), and the Biomedical Research Centre (CIBM) from the University of Granada for their economic and logistic support. The authors thank all the patients and General Practitioners who participated in the trial.Depression is strongly associated with obesity among other chronic physical diseases. The latest mega- and meta-analysis of genome-wide association studies have identified multiple risk loci robustly associated with depression. In this study, we aimed to investigate whether a genetic-risk score (GRS) combining multiple depression risk single nucleotide polymorphisms (SNPs) might have utility in the prediction of this disorder in individuals with obesity. A total of 30 depression-associated SNPs were included in a GRS to predict the risk of depression in a large case-control sample from the Spanish PredictD-CCRT study, a national multicentre, randomized controlled trial, which included 104 cases of depression and 1546 controls. An unweighted GRS was calculated as a summation of the number of risk alleles for depression and incorporated into several logistic regression models with depression status as the main outcome. Constructed models were trained and evaluated in the whole recruited sample. Non-genetic-risk factors were combined with the GRS in several ways across the five predictive models in order to improve predictive ability. An enrichment functional analysis was finally conducted with the aim of providing a general understanding of the biological pathways mapped by analyzed SNPs. We found that an unweighted GRS based on 30 risk loci was significantly associated with a higher risk of depression. Although the GRS itself explained a small amount of variance of depression, we found a significant improvement in the prediction of depression after including some non-genetic-risk factors into the models. The highest predictive ability for depression was achieved when the model included an interaction term between the GRS and the body mass index (BMI), apart from the inclusion of classical demographic information as marginal terms (AUC = 0.71, 95% CI = [0.65, 0.76]). Functional analyses on the 30 SNPs composing the GRS revealed an over-representation of the mapped genes in signaling pathways involved in processes such as extracellular remodeling, proinflammatory regulatory mechanisms, and circadian rhythm alterations. Although the GRS on its own explained a small amount of variance of depression, a significant novel feature of this study is that including non-genetic-risk factors such as BMI together with a GRS came close to the conventional threshold for clinical utility used in ROC analysis and improves the prediction of depression. In this study, the highest predictive ability was achieved by the model combining the GRS and the BMI under an interaction term. Particularly, BMI was identified as a trigger-like risk factor for depression acting in a concerted way with the GRS component. This is an interesting finding since it suggests the existence of a risk overlap between both diseases, and the need for individual depression genetics-risk evaluation in subjects with obesity. This research has therefore potential clinical implications and set the basis for future research directions in exploring the link between depression and obesityassociated disorders. While it is likely that future genome-wide studies with large samples will detect novel genetic variants associated with depression, it seems clear that a combination of genetics and non-genetic information (such is the case of obesity status and other depression comorbidities) will still be needed for the optimization prediction of depression in high-susceptibility individuals.Instituto de Salud Carlos III Spanish Government Institute of Health Carlos III (ISCIII) European Commission PS09/02272 PS09/02147 PS09/01095 PS09/00849 PS09/00461 PI12-02755Andalusian Council of Health PI-0569-2010Spanish Network of Primary Care Research, redIAPP RD06/ 0018Gobierno de Aragon RD06/0018/0020Bizkaya group RD06/0018/0018Castilla-Leon group RD06/0018/0027Mental Health Barcelona Group RD06/0018/0017Mental Health, Services and Primary Care Malaga group RD06/0018/0039Instituto de Salud Carlos III PI18/00238 PI18/00467 FI19/00228European Regional Development Fund/European Social Fund "A way tomake Europe"/"Investing in your future"Ministry of Economy and CompetitivenessInstitute of Health Carlos III fellowship IFI17/00048Spanish Government BES-2017-082698Spanish Ministry of Science and Innovation Juan de la Cierva Incorporacion Program IJC2019040080-IMinistry of Economy and Competitiveness Ramon y Cajal Program RYC-2014-15774Andalusian Council of HealthAndalusian Health Service (SAS)Primary Care Prevention and Health Promotion Research Network (redIAPP)Biomedical Research Institute of Malaga (IBIMA)Biomedical Research Centre (CIBM) from the University of GranadaEuropean Commissio

The effect of body fat distribution on systemic sclerosis.

Obesity contributes to a chronic proinflammatory state, which is a known risk factor to develop immune-mediated diseases. However, its role in systemic sclerosis (SSc) remains to be elucidated. Therefore, we conducted a two-sample mendelian randomization (2SMR) study to analyze the effect of three body fat distribution parameters in SSc. As instrumental variables, we used the allele effects described for single nucleotide polymorphisms (SNPs) in different genome-wide association studies (GWAS) for SSc, body mass index (BMI), waist-to-hip ratio (WHR) and WHR adjusted for BMI (WHRadjBMI). We performed local (pHESS) and genome-wide (LDSC) genetic correlation analyses between each of the traits and SSc and we applied several Mendelian randomization (MR) methods (i.e., random effects inverse-variance weight, MR-Egger regression, MR pleiotropy residual sum and outlier method and a multivariable model). Our results show no genetic correlation or causal relationship between any of these traits and SSc. Nevertheless, we observed a negative causal association between WHRadjBMI and SSc, which might be due to the effect of gastrointestinal complications suffered by the majority of SSc patients. In conclusion, reverse causality might be an especially difficult confounding factor to define the effect of obesity in the onset of SSc.This work was supported by grant RTI2018101332-B-100 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” funded by the European Union. Red de Investigación en Inflamación y Enfermedades Reumáticas (RIER) from Instituto de Salud Carlos III (RD16/0012/0013). 115565. LB-C was funded by Grant IJC2018-038026-I funded by MCIN/AEI/10.13039/501100011033. MA-H is a recipient of a Miguel Servet fellowship (CP21/00132) from Instituto de Salud Carlos III (Spanish Ministry of Science and Innovation). EL-I was funded by Grant IJC2019-040080-I funded by MCIN/AEI/10.13039/501100011033. GV-M was funded by Grant PRE2019-087586 funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future”

The effect of body fat distribution on systemic sclerosis.

Congresos y Comunicaciones: Comunicación de Congreso - Conferencia invitada

Cross-disorder analysis of schizophrenia and 19 immune-mediated diseases identifies shared genetic risk.

Many immune diseases occur at different rates among people with schizophrenia compared to the general population. Here, we evaluated whether this phenomenon might be explained by shared genetic risk factors. We used data from large genome-wide association studies to compare the genetic architecture of schizophrenia to 19 immune diseases. First, we evaluated the association with schizophrenia of 581 variants previously reported to be associated with immune diseases at genome-wide significance. We identified five variants with potentially pleiotropic effects. While colocalization analyses were inconclusive, functional characterization of these variants provided the strongest evidence for a model in which genetic variation at rs1734907 modulates risk of schizophrenia and Crohn’s disease via altered methylation and expression of EPHB4—a gene whose protein product guides the migration of neuronal axons in the brain and the migration of lymphocytes towards infected cells in the immune system. Next, we investigated genome-wide sharing of common variants between schizophrenia and immune diseases using cross-trait LD score regression. Of the 11 immune diseases with available genome-wide summary statistics, we observed genetic correlation between six immune diseases and schizophrenia: inflammatory bowel disease (rg = 0.12 ± 0.03, P = 2.49 × 10−4), Crohn’s disease (rg = 0.097 ± 0.06, P = 3.27 × 10−3), ulcerative colitis (rg = 0.11 ± 0.04, P = 4.05 × 10–3), primary biliary cirrhosis (rg = 0.13 ± 0.05, P = 3.98 × 10−3), psoriasis (rg = 0.18 ± 0.07, P = 7.78 × 10–3) and systemic lupus erythematosus (rg = 0.13 ± 0.05, P = 3.76 × 10–3). With the exception of ulcerative colitis, the degree and direction of these genetic correlations were consistent with the expected phenotypic correlation based on epidemiological data. Our findings suggest shared genetic risk factors contribute to the epidemiological association of certain immune diseases and schizophrenia.This research was supported in part by a number of funding sources. This research uses resources provided by the Genetic Association Information Network (GAIN), obtained from the database of Genotypes and Phenotypes (dbGaP) found at http://www.ncbi.nlm.nih.gov/gap through dbGaP accession number phs000021.v3.p2; samples and associated phenotype data for this study were provided by the Molecular Genetics of Schizophrenia Collaboration (PI: Pablo V. Gejman, Evanston Northwestern Healthcare and Northwestern University, Evanston, IL, USA). Fulbright Canada, the Weston Foundation, and Brain Canada through the Canada Brain Research Fund—a public-private partnership established by the Government of Canada (to J.G.P.); the National Research Foundation of Korea (NRF) [grant 2016R1C1B2013126 to B.H.] and the Bio & Medical Technology Development Program of the NRF [grant 2017M3A9B6061852 to B.H.] funded by the Korean government, Ministry of Science and ICT; the Finnish Cultural Foundation and Academy of Finland [grant 309643 to H.M.O.]; the Spanish Ministry of Economy and Competitiveness and P12-BIO-1395 from Consejería de Innovación, Ciencia y Tecnología, Junta de Andalucía (Spain) [grant SAF2015-66761-P to J.M.]; the US National Institutes of Health (NIH) [grants R01AR045584, R01AR056292, X01HG007484 and P30AR057212 to Y.J., S.A.S. and R.S.]; the US NIH [grants N01AR02251 and R01AR05528 to M.D.M.]; the US NIH [grants 1R01AR063759, 1R01AR062886, 1UH2AR067677-01 and U19AI111224-01 to S.R.] and Doris Duke Charitable Foundation [grant 2013097 to S.R.]. Funding for the GAIN schizophrenia sample was provided by the US NIH [grants R01 MH67257, R01 MH59588, R01 MH59571, R01 MH59565, R01 MH59587, R01 MH60870, R01 MH59566, R01 MH59586, R01 MH61675, R01 MH60879, R01 MH81800, U01 MH46276, U01 MH46289, U01 MH46318, U01 MH79469 and U01 MH79470] and the genotyping of samples was provided through GAIN. The funding sources did not influence the study design, data analysis or writing of this manuscript

Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals

Depression is strongly associated with obesity among other chronic physical diseases. The latest mega- and meta-analysis of genome-wide association studies have identified multiple risk loci robustly associated with depression. In this study, we aimed to investigate whether a genetic-risk score (GRS) combining multiple depression risk single nucleotide polymorphisms (SNPs) might have utility in the prediction of this disorder in individuals with obesity. A total of 30 depression-associated SNPs were included in a GRS to predict the risk of depression in a large case-control sample from the Spanish PredictD-CCRT study, a national multicentre, randomized controlled trial, which included 104 cases of depression and 1546 controls. An unweighted GRS was calculated as a summation of the number of risk alleles for depression and incorporated into several logistic regression models with depression status as the main outcome. Constructed models were trained and evaluated in the whole recruited sample. Non-genetic-risk factors were combined with the GRS in several ways across the five predictive models in order to improve predictive ability. An enrichment functional analysis was finally conducted with the aim of providing a general understanding of the biological pathways mapped by analyzed SNPs. We found that an unweighted GRS based on 30 risk loci was significantly associated with a higher risk of depression. Although the GRS itself explained a small amount of variance of depression, we found a significant improvement in the prediction of depression after including some non-genetic-risk factors into the models. The highest predictive ability for depression was achieved when the model included an interaction term between the GRS and the body mass index (BMI), apart from the inclusion of classical demographic information as marginal terms (AUC = 0.71, 95% CI = [0.65, 0.76]). Functional analyses on the 30 SNPs composing the GRS revealed an over-representation of the mapped genes in signaling pathways involved in processes such as extracellular remodeling, proinflammatory regulatory mechanisms, and circadian rhythm alterations. Although the GRS on its own explained a small amount of variance of depression, a significant novel feature of this study is that including non-genetic-risk factors such as BMI together with a GRS came close to the conventional threshold for clinical utility used in ROC analysis and improves the prediction of depression. In this study, the highest predictive ability was achieved by the model combining the GRS and the BMI under an interaction term. Particularly, BMI was identified as a trigger-like risk factor for depression acting in a concerted way with the GRS component. This is an interesting finding since it suggests the existence of a risk overlap between both diseases, and the need for individual depression genetics-risk evaluation in subjects with obesity. This research has therefore potential clinical implications and set the basis for future research directions in exploring the link between depression and obesity-associated disorders. While it is likely that future genome-wide studies with large samples will detect novel genetic variants associated with depression, it seems clear that a combination of genetics and non-genetic information (such is the case of obesity status and other depression comorbidities) will still be needed for the optimization prediction of depression in high-susceptibility individuals