Novel Genetic Variants for Cartilage Thickness and Hip Osteoarthritis

Osteoarthritis is one of the most frequent and disabling diseases of the elderly. Only few genetic variants have been identified for osteoarthritis, which is partly due to large phenotype heterogeneity. To reduce heterogeneity, we here examined cartilage thickness, one of the structural components of joint health. We conducted a genome-wide association study of minimal joint space width (mJSW), a proxy for cartilage thickness, in a discovery set of 13,013 participants from five different cohorts and replication in 8,227 individuals from seven independent cohorts. We identified five genome-wide significant (GWS, P≤5·0×10−8) SNPs annotated to four distinct loci. In addition, we found two additional loci that were significantly replicated, but results of combined meta-analysis fell just below the genome wide significance threshold. The four novel associated genetic loci were located in/near TGFA (rs2862851), PIK3R1 (rs10471753), SLBP/FGFR3 (rs2236995), and TREH/DDX6 (rs496547), while the other two (DOT1L and SUPT3H/RUNX2) were previously identified. A systematic prioritization for underlying causal genes was performed using diverse lines of evidence. Exome sequencing data (n = 2,050 individuals) indicated that there were no rare exonic variants that could explain the identified associations. In addition, TGFA, FGFR3 and PIK3R1 were differentially expressed in OA cartilage lesions versus non-lesioned cartilage in the same individuals. In conclusion, we identified four novel loci (TGFA, PIK3R1, FGFR3 and TREH) and confirmed two loci known to be associated with cartilage thickness.The identified associations were not caused by rare exonic variants. This is the first report linking TGFA to human OA, which may serve as a new target for future therapies

Novel Genetic Variants for Cartilage Thickness and Hip Osteoarthritis

Osteoarthritis is one of the most frequent and disabling diseases of the elderly. Only few genetic variants have been identified for osteoarthritis, which is partly due to large phenotype heterogeneity. To reduce heterogeneity, we here examined cartilage thickness, one of the structural components of joint health. We conducted a genome-wide association study of minimal joint space width (mJSW), a proxy for cartilage thickness, in a discovery set of 13,013 participants from five different cohorts and replication in 8,227 individuals from seven independent cohorts. We identified five genome-wide significant (GWS, P≤5·0×10−8) SNPs annotated to four distinct loci. In addition, we found two additional loci that were significantly replicated, but results of combined meta-analysis fell just below the genome wide significance threshold. The four novel associated genetic loci were located in/near TGFA (rs2862851), PIK3R1 (rs10471753), SLBP/FGFR3 (rs2236995), and TREH/DDX6 (rs49654

Identification of new susceptibility loci for osteoarthritis (arcOGEN):a genome-wide association study

To access publisher's full text version of this article. Please click on the hyperlink in Additional Links field.Osteoarthritis is the most common form of arthritis worldwide and is a major cause of pain and disability in elderly people. The health economic burden of osteoarthritis is increasing commensurate with obesity prevalence and longevity. Osteoarthritis has a strong genetic component but the success of previous genetic studies has been restricted due to insufficient sample sizes and phenotype heterogeneity. We undertook a large genome-wide association study (GWAS) in 7410 unrelated and retrospectively and prospectively selected patients with severe osteoarthritis in the arcOGEN study, 80% of whom had undergone total joint replacement, and 11,009 unrelated controls from the UK. We replicated the most promising signals in an independent set of up to 7473 cases and 42,938 controls, from studies in Iceland, Estonia, the Netherlands, and the UK. All patients and controls were of European descent. We identified five genome-wide significant loci (binomial test p≤5·0×10(-8)) for association with osteoarthritis and three loci just below this threshold. The strongest association was on chromosome 3 with rs6976 (odds ratio 1·12 [95% CI 1·08-1·16]; p=7·24×10(-11)), which is in perfect linkage disequilibrium with rs11177. This SNP encodes a missense polymorphism within the nucleostemin-encoding gene GNL3. Levels of nucleostemin were raised in chondrocytes from patients with osteoarthritis in functional studies. Other significant loci were on chromosome 9 close to ASTN2, chromosome 6 between FILIP1 and SENP6, chromosome 12 close to KLHDC5 and PTHLH, and in another region of chromosome 12 close to CHST11. One of the signals close to genome-wide significance was within the FTO gene, which is involved in regulation of bodyweight-a strong risk factor for osteoarthritis. All risk variants were common in frequency and exerted small effects. Our findings provide insight into the genetics of arthritis and identify new pathways that might be amenable to future therapeutic intervention.Arthritis Research UK 1803

Genome-wide association and functional studies identify a role for matrix Gla protein in osteoarthritis of the hand

Objective Osteoarthritis (OA) is the most common form of arthritis and the leading cause of disability in the elderly. Of all the joints, genetic predisposition is strongest for OA of the hand; however, only few genetic risk loci for hand OA have been identified. Our aim was to identify novel genes associated with hand OA and examine the underlying mechanism. Methods We performed a genome-wide association study of a quantitative measure of hand OA in 12 784 individuals (discovery: 8743, replication: 4011). Genome-wide significant signals were followed up by analysing gene and allele-specific expression in a RNA sequencing dataset (n=96) of human articular cartilage. Results We found two significantly associated loci in the discovery set: at chr12 (p=3.5 × 10⁻¹⁰) near the matrix Gla protein (MGP) gene and at chr12 (p=6.1×10⁻⁹) near the CCDC91 gene. The DNA variant near the MGP gene was validated in three additional studies, which resulted in a highly significant association between the MGP variant and hand OA (rs4764133, Betameta=0.83, Pmeta=1.8*10⁻¹⁵). This variant is high linkage disequilibrium with a coding variant in MGP, a vitamin K-dependent inhibitor of cartilage calcification. Using RNA sequencing data from human primary cartilage tissue (n=96), we observed that the MGP RNA expression of the hand OA risk allele was significantly lowercompared with the MGP RNA expression of the reference allele (40.7%, p<5*10⁻¹⁶). Conclusions Our results indicate that the association between the MGP variant and increased risk for hand OA is caused by a lower expression of MGP, which may increase the burden of hand OA by decreased inhibition of cartilage calcification

Steroid receptor coactivator-1 modulates the function of Pomc neurons and energy homeostasis

Hypothalamic neurons expressing the anorectic peptide Pro-opiomelanocortin (Pomc) regulate food intake and body weight. Here, we show that Steroid Receptor Coactivator-1 (SRC-1) interacts with a target of leptin receptor activation, phosphorylated STAT3, to potentiate Pomc transcription. Deletion of SRC-1 in Pomc neurons in mice attenuates their depolarization by leptin, decreases Pomc expression and increases food intake leading to high-fat diet-induced obesity. In humans, fifteen rare heterozygous variants in SRC-1 found in severely obese individuals impair leptin-mediated Pomc reporter activity in cells, whilst four variants found in non-obese controls do not. In a knock-in mouse model of a loss of function human variant (SRC-1L1376P), leptin-induced depolarization of Pomc neurons and Pomc expression are significantly reduced, and food intake and body weight are increased. In summary, we demonstrate that SRC-1 modulates the function of hypothalamic Pomc neurons, and suggest that targeting SRC-1 may represent a useful therapeutic strategy for weight loss.Peer reviewe

Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits

Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common-and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.Peer reviewe

Genome-wide association study of offspring birth weight in 86 577 women identifies five novel loci and highlights maternal genetic effects that are independent of fetal genetics

Funding Information: Researchers were funded by investment from the European Regional Development Fund (ERDF) and the European Social Fund (ESF) Convergence Programme for Cornwall and the Isles of Scilly [J.T.]; European Research Council (ERC) [grant: SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC to T.M.F., A.R.W.], [ERC Consolidator Grant, ERC-2014-CoG-648916 to V.W.V.J.], [P.R.N.]; University of Bergen, KG Jebsen and Helse Vest [P.R.N.]; Wellcome Trust Senior Investigator Awards [A.T.H. (WT098395), M.I.M. (WT098381)]; National Institute for Health Research (NIHR) Senior Investigator Award (NF-SI-0611–10219); Sir Henry Dale Fellowship (Wellcome Trust and Royal Society grant: WT104150) [R.M.F., R.N.B.]; 4-year studentship (Grant Code: WT083431MF) [R.C.R]; the European Research Council under the European Union’s Seventh Framework Programme (FP/2007– 2013)/ERC Grant Agreement (grant number 669545; Develop Obese) [D.A.L.]; US National Institute of Health (grant: R01 DK10324) [D.A.L, C.L.R]; Wellcome Trust GWAS grant (WT088806) [D.A.L] and NIHR Senior Investigator Award (NF-SI-0611–10196) [D.A.L]; Wellcome Trust Institutional Strategic Support Award (WT097835MF) [M.A.T.]; The Diabetes Research and Wellness Foundation Non-Clinical Fellowship [J.T.]; Australian National Health and Medical Research Council Early Career Fellowship (APP1104818) [N.M.W.]; Daniel B. Burke Endowed Chair for Diabetes Research [S.F.A.G.]; UK Medical Research Council Unit grants MC_UU_12013_5 [R.C.R, L.P, S.R, C.L.R, D.M.E., D.A.L.] and MC_UU_12013_4 [D.M.E.]; Medical Research Council (grant: MR/M005070/1) [M.N.W., S.E.J.]; Australian Research Council Future Fellowship (FT130101709) [D.M.E] and (FT110100548) [S.E.M.]; NIHR Oxford Biomedical Research Centre (BRC); Oak Foundation Fellowship and Novo Nordisk Foundation (12955) [B.F.]; FRQS research scholar and Clinical Scientist Award by the Canadian Diabetes Association and the Maud Menten Award from the Institute of Genetics– Canadian Institute of Health Research (CIHR) [MFH]; CIHR— Frederick Banting and Charles Best Canada Graduate Scholarships [C.A.]; FRQS [L.B.]; Netherlands Organization for Health Research and Development (ZonMw–VIDI 016.136.361) [V.W.V.J.]; National Institute on Aging (R01AG29451) [J.M.M.]; 2010–2011 PRIN funds of the University of Ferrara—Holder: Prof. Guido Barbujani, Supervisor: Prof. Chiara Scapoli—and in part sponsored by the European Foundation for the Study of Diabetes (EFSD) Albert Renold Travel Fellowships for Young Scientists, ‘5 per mille’ contribution assigned to the University of Ferrara, income tax return year 2009 and the ENGAGE Exchange and Mobility Program for ENGAGE training funds, ENGAGE project, grant agreement HEALTH-F4–2007-201413 [L.M.]; ESRC (RES-060–23-0011) [C.L.R.]; National Institute of Health Research ([S.D., M.I.M.], Senior Investigator Award (NF-SI-0611–10196) [D.A.L]); Australian NHMRC Fellowships Scheme (619667) [G.W.M]. For study-specific funding, please see Supplementary Material. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Funding to pay the Open Access publication charges for this article was provided by the Charity Open Access Fund (COAF). Funding Information: We are extremely grateful to the participants and families who contributed to all of the studies and the teams of investigators involved in each one. These include interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. This research has been conducted using the UK Biobank Resource (Application numbers 7036 and 12703). For additional study-specific acknowledgements, please see Supplementary Material. Conflict of Interest statement. D.A.L. has received support from Roche Diagnostics and Medtronic for biomarker research unrelated to the work presented here. Funding Researchers were funded by investment from the European Regional Development Fund (ERDF) and the European Social Fund (ESF) Convergence Programme for Cornwall and the Isles of Scilly [J.T.]; European Research Council (ERC) [grant: SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC to T.M.F., A.R.W.], [ERC Consolidator Grant, ERC-2014-CoG-648916 to V.W.V.J.], [P.R.N.]; University of Bergen, KG Jebsen and Helse Vest [P.R.N.]; Wellcome Trust Senior Investigator Awards [A.T.H. (WT098395), M.I.M. (WT098381)]; National Institute for Health Research (NIHR) Senior Investigator Award (NF-SI-0611-10219); Sir Henry Dale Fellowship (Wellcome Trust and Royal Society grant: WT104150) [R.M.F., R.N.B.]; 4-year studentship (Grant Code: WT083431MF) [R.C.R]; the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement (grant number 669545; Develop Obese) [D.A.L.]; US National Institute of Health (grant: R01 DK10324) [D.A.L, C.L.R]; Wellcome Trust GWAS grant (WT088806) [D.A.L] and NIHR Senior Investigator Award (NF-SI-0611-10196) [D.A.L]; Wellcome Trust Institutional Strategic Support Award (WT097835MF) [M.A.T.]; The Diabetes Research and Wellness Foundation Non-Clinical Fellowship [J.T.]; Australian National Health and Medical Research Council Early Career Fellowship (APP1104818) [N.M.W.]; Daniel B. Burke Endowed Chair for Diabetes Research [S.F.A.G.]; UK Medical Research Council Unit grants MC_UU_12013_5 [R.C.R, L.P, S.R, C.L.R, D.M.E., D.A.L.] and MC_UU_12013_4 [D.M.E.]; Medical Research Council (grant: MR/M005070/1) [M.N.W., S.E.J.]; Australian Research Council Future Fellowship (FT130101709) [D.M.E] and (FT110100548) [S.E.M.]; NIHR Oxford Biomedical Research Centre (BRC); Oak Foundation Fellowship and Novo Nordisk Foundation (12955) [B.F.]; FRQS research scholar and Clinical Scientist Award by the Canadian Diabetes Association and the Maud Menten Award from the Institute of Genetics-Canadian Institute of Health Research (CIHR) [MFH]; CIHR-Frederick Banting and Charles Best Canada Graduate Scholarships [C.A.]; FRQS [L.B.]; Netherlands Organization for Health Research and Development (ZonMw-VIDI 016.136.361) [V.W.V.J.]; National Institute on Aging (R01AG29451) [J.M.M.]; 2010-2011 PRIN funds of the University of Ferrara-Holder: Prof. Guido Barbujani, Supervisor: Prof. Chiara Scapoli-and in part sponsored by the European Foundation for the Study of Diabetes (EFSD) Albert Renold Travel Fellowships for Young Scientists, '5 per mille' contribution assigned to the University of Ferrara, income tax return year 2009 and the ENGAGE Exchange and Mobility Program for ENGAGE training funds, ENGAGE project, grant agreement HEALTH-F4-2007-201413 [L.M.]; ESRC (RES-060-23-0011) [C.L.R.]; National Institute of Health Research ([S.D., M.I.M.], Senior Investigator Award (NFSI-0611-10196) [D.A.L]); Australian NHMRC Fellowships Scheme (619667) [G.W.M]. For study-specific funding, please see Supplementary Material. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Funding to pay the Open Access publication charges for this article was provided by the Charity Open Access Fund (COAF). Publisher Copyright: © The Author(s) 2018.Genome-wide association studies of birth weight have focused on fetal genetics, whereas relatively little is known about the role of maternal genetic variation. We aimed to identify maternal genetic variants associated with birth weight that could highlight potentially relevant maternal determinants of fetal growth. We meta-analysed data on up to 8.7 million SNPs in up to 86 577 women of European descent from the Early Growth Genetics (EGG) Consortium and the UK Biobank. We used structural equation modelling (SEM) and analyses of mother-child pairs to quantify the separate maternal and fetal genetic effects. Maternal SNPs at 10 loci (MTNR1B, HMGA2, SH2B3, KCNAB1, L3MBTL3, GCK, EBF1, TCF7L2, ACTL9, CYP3A7) were associated with offspring birth weight at P<5 x 10(-8). In SEM analyses, at least 7 of the 10 associations were consistent with effects of the maternal genotype acting via the intrauterine environment, rather than via effects of shared alleles with the fetus. Variants, or correlated proxies, at many of the loci had been previously associated with adult traits, including fasting glucose (MTNR1B, GCK and TCF7L2) and sex hormone levels (CYP3A7), and one (EBF1) with gestational duration. The identified associations indicate that genetic effects on maternal glucose, cytochrome P450 activity and gestational duration, and potentially on maternal blood pressure and immune function, are relevant for fetal growth. Further characterization of these associations in mechanistic and causal analyses will enhance understanding of the potentially modifiable maternal determinants of fetal growth, with the goal of reducing the morbidity and mortality associated with low and high birth weights.Peer reviewe

The genetic epidemiology of joint shape and the development of osteoarthritis

Congruent, low-friction relative movement between the articulating elements of a synovial joint is an essential pre-requisite for sustained, efficient, function. Where disorders of joint formation or maintenance exist, mechanical overloading and osteoarthritis (OA) follow. The heritable component of OA accounts for ~ 50% of susceptible risk. Although almost 100 genetic risk loci for OA have now been identified, and the epidemiological relationship between joint development, joint shape and osteoarthritis is well established, we still have only a limited understanding of the contribution that genetic variation makes to joint shape and how this modulates OA risk. In this article, a brief overview of synovial joint development and its genetic regulation is followed by a review of current knowledge on the genetic epidemiology of established joint shape disorders and common shape variation. A summary of current genetic epidemiology of OA is also given, together with current evidence on the genetic overlap between shape variation and OA. Finally, the established genetic risk loci for both joint shape and osteoarthritis are discussed

Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors.

Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.The Fenland Study is funded by the Medical Research Council (MC_U106179471) and Wellcome Trust

The UK10K project identifies rare variants in health and disease

M. Kivimäki työryhmäjäsen.The contribution of rare and low-frequency variants to human traits is largely unexplored. Here we describe insights from sequencing whole genomes (low read depth, 7x) or exomes (high read depth, 80x) of nearly 10,000 individuals from population-based and disease collections. In extensively phenotyped cohorts we characterize over 24 million novel sequence variants, generate a highly accurate imputation reference panel and identify novel alleles associated with levels of triglycerides (APOB), adiponectin (ADIPOQ) and low-density lipoprotein cholesterol (LDLR and RGAG1) from single-marker and rare variant aggregation tests. We describe population structure and functional annotation of rare and low-frequency variants, use the data to estimate the benefits of sequencing for association studies, and summarize lessons from disease-specific collections. Finally, we make available an extensive resource, including individual-level genetic and phenotypic data and web-based tools to facilitate the exploration of association results.Peer reviewe