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      Genome-Wide Association Study Using Extreme Truncate Selection Identifies Novel Genes Affecting Bone Mineral Density and Fracture Risk

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      1 , 1 , 2 , 1 , 3 , 4 , 3 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 15 , 17 , 18 , 17 , 17 , 18 , 1 , 1 , 7 , 8 , 12 , 19 , 1 , 16 , 20 , 21 , 1 , 22 , 16 , 23 , 24 , 7 , 25 , 7 , 8 , 25 , 22 , 1 , 26 , 16 , 27 , 28 , 29 , 28 , 28 , 4 , 2 , 1 , 19 , *
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          Abstract

          Osteoporotic fracture is a major cause of morbidity and mortality worldwide. Low bone mineral density (BMD) is a major predisposing factor to fracture and is known to be highly heritable. Site-, gender-, and age-specific genetic effects on BMD are thought to be significant, but have largely not been considered in the design of genome-wide association studies (GWAS) of BMD to date. We report here a GWAS using a novel study design focusing on women of a specific age (postmenopausal women, age 55–85 years), with either extreme high or low hip BMD (age- and gender-adjusted BMD z-scores of +1.5 to +4.0, n = 1055, or −4.0 to −1.5, n = 900), with replication in cohorts of women drawn from the general population (n = 20,898). The study replicates 21 of 26 known BMD–associated genes. Additionally, we report suggestive association of a further six new genetic associations in or around the genes CLCN7, GALNT3, IBSP, LTBP3, RSPO3, and SOX4, with replication in two independent datasets. A novel mouse model with a loss-of-function mutation in GALNT3 is also reported, which has high bone mass, supporting the involvement of this gene in BMD determination. In addition to identifying further genes associated with BMD, this study confirms the efficiency of extreme-truncate selection designs for quantitative trait association studies.

          Author Summary

          Osteoporotic fracture is a major cause of early mortality and morbidity in the community. To identify genes associated with osteoporosis, we have performed a genome-wide association study. In order to improve study power and to address the demographic group of highest risk from osteoporotic fracture, we have used a unique study design, studying 1,955 postmenopausal women with either extreme high or low hip bone mineral density. We then confirmed our findings in 20,898 women from the general population. Our study replicated 21 of 26 known osteoporosis genes, and it identified a further six novel loci (in or nearby CLCN7, GALNT3, IBSP, LTBP3, RSPO3, and SOX4). For one of these loci, GALTN3, we demonstrate in a mouse model that a loss-of-function genetic mutation in GALNT3 causes high bone mass. These findings report novel mechanisms by which osteoporosis can arise, and they significantly add to our understanding of the aetiology of the disease.

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          Newly identified loci that influence lipid concentrations and risk of coronary artery disease.

          To identify genetic variants influencing plasma lipid concentrations, we first used genotype imputation and meta-analysis to combine three genome-wide scans totaling 8,816 individuals and comprising 6,068 individuals specific to our study (1,874 individuals from the FUSION study of type 2 diabetes and 4,184 individuals from the SardiNIA study of aging-associated variables) and 2,758 individuals from the Diabetes Genetics Initiative, reported in a companion study in this issue. We subsequently examined promising signals in 11,569 additional individuals. Overall, we identify strongly associated variants in eleven loci previously implicated in lipid metabolism (ABCA1, the APOA5-APOA4-APOC3-APOA1 and APOE-APOC clusters, APOB, CETP, GCKR, LDLR, LPL, LIPC, LIPG and PCSK9) and also in several newly identified loci (near MVK-MMAB and GALNT2, with variants primarily associated with high-density lipoprotein (HDL) cholesterol; near SORT1, with variants primarily associated with low-density lipoprotein (LDL) cholesterol; near TRIB1, MLXIPL and ANGPTL3, with variants primarily associated with triglycerides; and a locus encompassing several genes near NCAN, with variants strongly associated with both triglycerides and LDL cholesterol). Notably, the 11 independent variants associated with increased LDL cholesterol concentrations in our study also showed increased frequency in a sample of coronary artery disease cases versus controls.
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            Genotype imputation.

            Genotype imputation is now an essential tool in the analysis of genome-wide association scans. This technique allows geneticists to accurately evaluate the evidence for association at genetic markers that are not directly genotyped. Genotype imputation is particularly useful for combining results across studies that rely on different genotyping platforms but also increases the power of individual scans. Here, we review the history and theoretical underpinnings of the technique. To illustrate performance of the approach, we summarize results from several gene mapping studies. Finally, we preview the role of genotype imputation in an era when whole genome resequencing is becoming increasingly common.
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              Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study

              Summary Background Osteoporosis is diagnosed by the measurement of bone mineral density, which is a highly heritable and multifactorial trait. We aimed to identify genetic loci that are associated with bone mineral density. Methods In this genome-wide association study, we identified the most promising of 314 075 single nucleotide polymorphisms (SNPs) in 2094 women in a UK study. We then tested these SNPs for replication in 6463 people from three other cohorts in western Europe. We also investigated allelic expression in lymphoblast cell lines. We tested the association between the replicated SNPs and osteoporotic fractures with data from two studies. Findings We identified genome-wide evidence for an association between bone mineral density and two SNPs (p<5×10−8). The SNPs were rs4355801, on chromosome 8, near to the TNFRSF11B (osteoprotegerin) gene, and rs3736228, on chromosome 11 in the LRP5 (lipoprotein-receptor-related protein) gene. A non-synonymous SNP in the LRP5 gene was associated with decreased bone mineral density (rs3736228, p=6·3×10−12 for lumbar spine and p=1·9×10−4 for femoral neck) and an increased risk of both osteoporotic fractures (odds ratio [OR] 1·3, 95% CI 1·09–1·52, p=0·002) and osteoporosis (OR 1·3, 1·08–1·63, p=0·008). Three SNPs near the TNFRSF11B gene were associated with decreased bone mineral density (top SNP, rs4355801: p=7·6×10−10 for lumbar spine and p=3·3×10−8 for femoral neck) and increased risk of osteoporosis (OR 1·2, 95% CI 1·01–1·42, p=0·038). For carriers of the risk allele at rs4355801, expression of TNFRSF11B in lymphoblast cell lines was halved (p=3·0×10−6). 1883 (22%) of 8557 people were at least heterozygous for these risk alleles, and these alleles had a cumulative association with bone mineral density (trend p=2·3×10−17). The presence of both risk alleles increased the risk of osteoporotic fractures (OR 1·3, 1·08–1·63, p=0·006) and this effect was independent of bone mineral density. Interpretation Two gene variants of key biological proteins increase the risk of osteoporosis and osteoporotic fracture. The combined effect of these risk alleles on fractures is similar to that of most well-replicated environmental risk factors, and they are present in more than one in five white people, suggesting a potential role in screening. Funding Wellcome Trust, European Commission, NWO Investments, Arthritis Research Campaign, Chronic Disease Research Foundation, Canadian Institutes of Health Research, European Society for Clinical and Economic Aspects of Osteoporosis, Genome Canada, Genome Quebéc, Canada Research Chairs, National Health and Medical Research Council of Australia, and European Union.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                April 2011
                April 2011
                21 April 2011
                : 7
                : 4
                : e1001372
                Affiliations
                [1 ]University of Queensland Diamantina Institute, University of Queensland, Princess Alexandra Hospital, Brisbane, Australia
                [2 ]Medical Research Council Centre for Causal Analyses in Translational Epidemiology, University of Bristol, Bristol, United Kingdom
                [3 ]Academic Unit of Bone Metabolism, Metabolic Bone Centre, University of Sheffield, Sheffield, United Kingdom
                [4 ]The University of Melbourne, Department of Clinical and Biomedical Sciences: Barwon Health, Geelong, Australia
                [5 ]School of Medicine and Pharmacology, University of Western Australia, Perth, Australia
                [6 ]Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Perth, Australia
                [7 ]Garvan Institute of Medical Research, Sydney, Australia
                [8 ]St. Vincent's Clinical School, St. Vincent's Hospital Campus, University of New South Wales, Sydney, Australia
                [9 ]Menzies Research Institute, University of Tasmania, Hobart, Australia
                [10 ]Kolling Institute, Royal North Shore Hospital, University of Sydney, Sydney, Australia
                [11 ]Department of Medicine, University of Auckland, Auckland, New Zealand
                [12 ]Medical Research Council Lifecourse Epidemiology Unit, Southampton, United Kingdom
                [13 ]University of Melbourne Department of Medicine and Bone and Mineral Service, Royal Melbourne Hospital, Melbourne, Australia
                [14 ]Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Canada
                [15 ]Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
                [16 ]Department of Internal Medicine and Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
                [17 ]Medical Research Council Mammalian Genetics Unit, Harwell Science and Innovation Campus, Harwell, Oxfordshire, United Kingdom
                [18 ]Academic Endocrine Unit, Nuffield Department of Clinical Medicine, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital, Headington, Oxford, United Kingdom
                [19 ]National Institute for Health and Research Biomedical Research Unit, University of Oxford, Oxford, United Kingdom
                [20 ]Centre of Muscle and Bone Research, Charité – University Medicine Berlin, Campus Benjamin Franklin, Free and Humboldt University, Berlin, Germany
                [21 ]Medizinische Physik, Klinik für Diagnostische Radiologie, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
                [22 ]School of Medicine, Deakin University, Geelong, Australia
                [23 ]Department of Endocrinology and Diabetes, Barwon Health, Geelong, Australia
                [24 ]Institute of Bone Joint Research, University of Sydney, Royal North Shore Hospital, Sydney, Australia
                [25 ]School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia
                [26 ]Division of Applied Medicine, University of Aberdeen, Aberdeen, United Kingdom
                [27 ]Rheumatology Department, AP-HP Cochin Hospital – Paris-Descartes University, Paris, France
                [28 ]deCODE Genetics, Reykjavik, Iceland
                [29 ]University of Iceland, Reykjavik, Iceland
                Georgia Institute of Technology, United States of America; David B. Allison, University of Alabama at Birmingham, United States of America
                Author notes

                Conceived and designed the experiments: EL Duncan, P Danoy, E McCloskey, GC Nicholson, R Eastell, RL Prince, JA Eisman, G Jones, JB Richards, AG Uitterlinden, TD Spector, C Esapa, RD Cox, SDM Brown, RV Thakker, K Estrada, F Rivadeneira, K Stafansson, U Styrkarsdottir, G Thorleifsson, MA Brown. Performed the experiments: EL Duncan, P Danoy, C Esapa, RD Cox, KA Addison, LA Bradbury, C Cremin, K Estrada, CC Glüer, J Hadler, K Pryce. Analyzed the data: EL Duncan, P Danoy, JP Kemp, PJ Leo, JB Richards, AG Uitterlinden, TD Spector, C Esapa, RD Cox, SDM Brown, RV Thakker, K Estrada, CC Glüer, J Hadler, F Rivadeneira, K Stefansson, U Styrkarsdottir, G Thorleifsson, DM Evans, MA Brown. Contributed reagents/materials/analysis tools: EL Duncan, P Danoy, JP Kemp, PJ Leo, E McCloskey, GC Nicholson, R Eastell, RLPrince, JA Eisman, G Jones, PN Sambrook, IR Reid, EM Dennison, J Wark, JB Richards, AG Uitterlinden, TD Spector, C Esapa, SDM Brown, RV Thakker, LA Bradbury, JR Center, C Cooper, K Estrada, D Felsenberg, CC Glüer, MJ Henry, A Hofman, MA Kotowicz, J Makovey, SC Nguyen, TV Nguyen, JA Pasco, DM Reid, F Rivadeneira, C Roux, K Stefansson, U Styrkarsdottir, G Thorleifsson, R Tichawangana, DM Evans, MA Brown. Wrote the paper: EL Duncan, PJ Leo, DM Evans, MA Brown.

                Article
                10-PLGE-RA-NV-4180R4
                10.1371/journal.pgen.1001372
                3080863
                21533022
                2eadc884-d10c-49e0-a68b-f895f28d2cab
                Duncan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 16 September 2010
                : 13 March 2011
                Page count
                Pages: 10
                Categories
                Research Article
                Diabetes and Endocrinology/Bone and Mineral Metabolism
                Genetics and Genomics/Genetics of Disease
                Rheumatology/Bone and Mineral Metabolism

                Genetics
                Genetics

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