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      A genome-wide association study in mice reveals a role for Rhbdf2 in skeletal homeostasis

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          Abstract

          Low bone mass and an increased risk of fracture are predictors of osteoporosis. Individuals who share the same bone-mineral density (BMD) vary in their fracture risk, suggesting that microstructural architecture is an important determinant of skeletal strength. Here, we utilized the rich diversity of the Collaborative Cross mice to identify putative causal genes that contribute to the risk of fractures. Using microcomputed tomography, we examined key structural features that pertain to bone quality in the femoral cortical and trabecular compartments of male and female mice. We estimated the broad-sense heritability to be 50–60% for all examined traits, and we identified five quantitative trait loci (QTL) significantly associated with six traits. We refined each QTL by combining information inferred from the ancestry of the mice, ranging from RNA-Seq data and published literature to shortlist candidate genes. We found strong evidence for new candidate genes, particularly Rhbdf2, whose close association with the trabecular bone volume fraction and number was strongly suggested by our analyses. We confirmed our findings with mRNA expression assays of Rhbdf2 in extreme-phenotype mice, and by phenotyping bones of Rhbdf2 knockout mice. Our results indicate that Rhbdf2 plays a decisive role in bone mass accrual and microarchitecture.

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          The recent prevalence of osteoporosis and low bone mass in the United States based on bone mineral density at the femoral neck or lumbar spine.

          The goal of our study was to estimate the prevalence of osteoporosis and low bone mass based on bone mineral density (BMD) at the femoral neck and the lumbar spine in adults 50 years and older in the United States (US). We applied prevalence estimates of osteoporosis or low bone mass at the femoral neck or lumbar spine (adjusted by age, sex, and race/ethnicity to the 2010 Census) for the noninstitutionalized population aged 50 years and older from the National Health and Nutrition Examination Survey 2005-2010 to 2010 US Census population counts to determine the total number of older US residents with osteoporosis and low bone mass. There were more than 99 million adults aged 50 years and older in the US in 2010. Based on an overall 10.3% prevalence of osteoporosis, we estimated that in 2010, 10.2 million older adults had osteoporosis. The overall low bone mass prevalence was 43.9%, from which we estimated that 43.4 million older adults had low bone mass. We estimated that 7.7 million non-Hispanic white, 0.5 million non-Hispanic black, and 0.6 million Mexican American adults had osteoporosis, and another 33.8, 2.9, and 2.0 million had low bone mass, respectively. When combined, osteoporosis and low bone mass at the femoral neck or lumbar spine affected an estimated 53.6 million older US adults in 2010. Although most of the individuals with osteoporosis or low bone mass were non-Hispanic white women, a substantial number of men and women from other racial/ethnic groups also had osteoporotic BMD or low bone mass. © 2014 American Society for Bone and Mineral Research.
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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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              A method for fine mapping quantitative trait loci in outbred animal stocks.

              High-resolution mapping of quantitative trait loci (QTL) in animals has proved to be difficult because the large effect sizes detected in crosses between inbred strains are often caused by numerous linked QTLs, each of small effect. In a study of fearfulness in mice, we have shown it is possible to fine map small-effect QTLs in a genetically heterogeneous stock (HS). This strategy is a powerful general method of fine mapping QTLs, provided QTLs detected in crosses between inbred strains that formed the HS can be reliably detected in the HS. We show here that single-marker association analysis identifies only two of five QTLs expected to be segregating in the HS and apparently limits the strategy's usefulness for fine mapping. We solve this problem with a multipoint analysis that assigns the probability that an allele descends from each progenitor in the HS. The analysis does not use pedigrees but instead requires information about the HS founder haplotypes. With this method we mapped all three previously undetected loci [chromosome (Chr.) 1 logP 4.9, Chr. 10 logP 6.0, Chr. 15 logP 4.0]. We show that the reason for the failure of single-marker association to detect QTLs is its inability to distinguish opposing phenotypic effects when they occur on the same marker allele. We have developed a robust method of fine mapping QTLs in genetically heterogeneous animals and suggest it is now cost effective to undertake genomewide high-resolution analysis of complex traits in parallel on the same set of mice.
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                Author and article information

                Contributors
                roei231@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                24 February 2020
                24 February 2020
                2020
                : 10
                : 3286
                Affiliations
                [1 ]ISNI 0000 0004 1937 0546, GRID grid.12136.37, Department of Anatomy and Anthropology, , Tel Aviv University, ; Tel Aviv, 69978 Israel
                [2 ]ISNI 0000 0004 1937 0546, GRID grid.12136.37, Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, , Tel Aviv University, ; Tel Aviv, 69978 Israel
                [3 ]Dunn School of Pathology, South Parks Road, Oxford, OX1 3RE UK
                [4 ]ISNI 0000000121901201, GRID grid.83440.3b, UCL Genetics Institute, , University College London, ; Gower St., London, WC1E 6BT UK
                Author information
                http://orcid.org/0000-0003-0410-5451
                http://orcid.org/0000-0002-1022-9330
                Article
                60146
                10.1038/s41598-020-60146-8
                7039944
                32094386
                ac9a9b1a-3a22-4357-b82e-c0fadc3c678d
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 7 March 2019
                : 6 February 2020
                Funding
                Funded by: Wellcome Trust 101035/Z/13/Z Medical Research Council (programme number U105178780)
                Funded by: Wellcome Trust grants 085906/Z/08/Z, 075491/Z/04, and 090532/Z/09/Z
                Funded by: FundRef https://doi.org/10.13039/501100004375, Tel Aviv University (TAU);
                Award ID: core funding
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100003977, Israel Science Foundation (ISF);
                Award ID: 1822/12
                Award Recipient :
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                © The Author(s) 2020

                Uncategorized
                computational models,genome-wide association studies
                Uncategorized
                computational models, genome-wide association studies

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