The Netherlands Genomics Initiative Sponsored Netherlands Consortium for Healthy Aging

Rotterdam, Netherlands

The Netherlands Genomics Initiative Sponsored Netherlands Consortium for Healthy Aging

Rotterdam, Netherlands
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Nettleton J.A.,University of Texas Health Science Center at Houston | McKeown N.M.,Tufts University | Kanoni S.,Harokopio University | Lemaitre R.N.,University of Washington | And 73 more authors.
Diabetes Care | Year: 2010

OBJECTIVE - Whole-grain foods are touted for multiple health benefits, including enhancing insulin sensitivity and reducing type 2 diabetes risk. Recent genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) associated with fasting glucose and insulin concentrations in individuals free of diabetes. We tested the hypothesis that whole-grain food intake and genetic variation interact to influence concentrations of fasting glucose and insulin. RESEARCH DESIGN AND METHODS - Via meta-analysis of data from 14 cohorts comprising ∼48,000 participants of European descent, we studied interactions of whole-grain intake with loci previously associated in GWAS with fasting glucose (16 loci) and/or insulin (2 loci) concentrations. For tests of interaction, we considered a P value <0.0028 (0.05 of 18 tests) as statistically significant. RESULTS - Greater whole-grain food intake was associated with lower fasting glucose and insulin concentrations independent of demographics, other dietary and lifestyle factors, and BMI (β [95% CI] per 1-serving-greater whole-grain intake: -0.009 mmol/l glucose [-0.013 to -0.005], P < 0.0001 and -0.011 pmol/l [ln] insulin [-0.015 to -0.007], P = 0.0003). No interactions met our multiple testing-adjusted statistical significance threshold. The strongest SNP interaction with whole-grain intake was rs780094 (GCKR) for fasting insulin (P = 0.006), where greater whole-grain intake was associated with a smaller reduction in fasting insulin concentrations in those with the insulin-raising allele. © 2010 by the American Diabetes Association.


Panoutsopoulou K.,Wellcome Trust Sanger Institute | Southam L.,University of Oxford | Elliott K.S.,University of Oxford | Wrayner N.,University of Oxford | And 92 more authors.
Annals of the Rheumatic Diseases | Year: 2011

Objectives: The genetic aetiology of osteoarthritis has not yet been elucidated. To enable a well-powered genome-wide association study (GWAS) for osteoarthritis, the authors have formed the arcOGEN Consortium, a UK-wide collaborative effort aiming to scan genome-wide over 7500 osteoarthritis cases in a two-stage genome-wide association scan. Here the authors report the findings of the stage 1 interim analysis. Methods: The authors have performed a genome-wide association scan for knee and hip osteoarthritis in 3177 cases and 4894 population-based controls from the UK. Replication of promising signals was carried out in silico in five further scans (44 449 individuals), and de novo in 14 534 independent samples, all of European descent. Results: None of the association signals the authors identified reach genome-wide levels of statistical significance, therefore stressing the need for corroboration in sample sets of a larger size. Application of analytical approaches to examine the allelic architecture of disease to the stage 1 genome-wide association scan data suggests that osteoarthritis is a highly polygenic disease with multiple risk variants conferring small effects. Conclusions: Identifying loci conferring susceptibility to osteoarthritis will require large-scale sample sizes and well-defined phenotypes to minimise heterogeneity.


Evangelou E.,University of Ioannina | Valdes A.M.,King's College London | Kerkhof H.J.M.,Erasmus Medical Center | Kerkhof H.J.M.,The Netherlands Genomics Initiative Sponsored Netherlands Consortium for Healthy Aging | And 86 more authors.
Annals of the Rheumatic Diseases | Year: 2011

Objectives: Osteoarthritis (OA) is the most prevalent form of arthritis and accounts for substantial morbidity and disability, particularly in older people. It is characterised by changes in joint structure, including degeneration of the articular cartilage, and its aetiology is multifactorial with a strong postulated genetic component. Methods: A meta-analysis was performed of four genome-wide association (GWA) studies of 2371 cases of knee OA and 35 909 controls in Caucasian populations. Replication of the top hits was attempted with data from 10 additional replication datasets. Results: With a cumulative sample size of 6709 cases and 44 439 controls, one genome-wide significant locus was identified on chromosome 7q22 for knee OA (rs4730250, p=9.2×10-9), thereby confirming its role as a susceptibility locus for OA. Conclusion: The associated signal is located within a large (500 kb) linkage disequilibrium block that contains six genes: PRKAR2B (protein kinase, cAMP-dependent, regulatory, type II, β), HPB1 (HMG-box transcription factor 1), COG5 (component of oligomeric golgi complex 5), GPR22 (G protein-coupled receptor 22), DUS4L (dihydrouridine synthase 4-like) and BCAP29 (B cell receptor-associated protein 29). Gene expression analyses of the (six) genes in primary cells derived from different joint tissues confirmed expression of all the genes in the joint environment.


Kerkhof H.J.M.,Genetic Laboratory | Kerkhof H.J.M.,The Netherlands Genomics Initiative Sponsored Netherlands Consortium for Healthy Aging | Bierma-Zeinstra S.M.A.,Erasmus University Rotterdam | Arden N.K.,University of Oxford | And 18 more authors.
Annals of the Rheumatic Diseases | Year: 2014

Objective: To develop and validate a prognostic model for incident knee osteoarthritis (KOA) in a general population and determine the value of different risk factor groups to prediction. Methods: The prognostic model was developed in 2628 individuals from the Rotterdam Study-I (RS-I). Univariate and multivariate analyses were performed for questionnaire/easily obtainable variables, imaging variables, genetic and biochemical markers. The extended multivariate model was tested on discrimination (receiver operating characteristic curve and area under the curve (AUC)) in two other populationbased cohorts: Rotterdam Study-II and Chingford Study. Results: In RS-I, there was moderate predictive value for incident KOA based on the genetic score alone in subjects aged <65 years (AUC 0.65), while it was only 0.55 for subjects aged ≥65 years. The AUC for gender, age and body mass index (BMI) in prediction for KOA was 0.66. Addition of the questionnaire variables, genetic score or biochemical marker urinary C-terminal cross-linked telopeptide of type II collagen to the model did not change the AUC. However, when adding the knee baseline KL score to the model the AUC increased to 0.79. Applying external validation, similar results were observed in the Rotterdam Study-II and the Chingford Study. Conclusions: Easy obtainable 'Questionnaire' variables, genetic markers, OA at other joint sites and biochemical markers add only modestly to the prediction of KOA incidence using age, gender and BMI in an elderly population. Doubtful minor radiographic degenerative features in the knee, however, are a very strong predictor of future KOA. This is an important finding, as many radiologists do not report minor degenerative changes in the knee. © 2014, BMJ Publishing Group. All rights reserved.


Ramos Y.F.M.,LUMC | Ramos Y.F.M.,The Netherlands Genomics Initiative Sponsored Netherlands Consortium for Healthy Aging | Metrustry S.,King's College London | Arden N.,University of Oxford | And 32 more authors.
Journal of Medical Genetics | Year: 2014

Background: Research for the use of biomarkers in osteoarthritis (OA) is promising, however, adequate discrimination between patients and controls may be hampered due to innate differences. We set out to identify loci influencing levels of serum cartilage oligomeric protein (sCOMP) and urinary C-telopeptide oftype II collagen (uCTX-II). Methods: Meta-analysis of genome-wide association studies was applied to standardised residuals of sCOMP (N=3316) and uCTX-II (N=4654) levels available in 6 and 7 studies, respectively, from TreatOA. Effects were estimated using a fixed-effects model. Six promising signals were followed up by de novo genotyping in the Cohort Hip and Cohort Knee study (N=964). Subsequently, their role in OA susceptibility was investigated in large-scale genome-wide association studies meta-analyses for OA. Differential expression of annotated genes was assessed in cartilage. Results: Genome-wide significant association with sCOMP levels was found for a SNP within MRC1 (rs691461, p=1.7×10-12) and a SNP within CSMD1 associated with variation in uCTX-II levels with borderline genome-wide significance (rs1983474, p=8.5×10-8). Indication for association with sCOMP levels was also found for a locus close to the COMP gene itself (rs10038, p=7.1×10-6). The latter SNP was subsequently found to be associated with hip OA whereas COMP expression appeared responsive to the OA pathophysiology in cartilage. Conclusions: We have identified genetic loci affecting either uCTX-II or sCOMP levels. The genome wide significant association of MRC1 with sCOMP levels was found likely to act independent of OA subtypes. Increased sensitivity of biomarkers with OA may be accomplished by taking genetic variation into account.

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