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    ABSTRACT: We hypothesised that bone resorption acts to increase bone strength through stimulation of periosteal expansion. Hence, we examined whether bone resorption, as reflected by serum β-C-telopeptides of type I collagen (CTX), is positively associated with periosteal circumference (PC), in contrast to inverse associations with parameters related to bone remodelling such as cortical bone mineral density (BMDC ). CTX and mid-tibial pQCT scans were available in 1130 adolescents (mean age 15.5 years) from the Avon Longitudinal Study of Parents and Children (ALSPAC). Analyses were adjusted for age, gender, time of sampling, tanner stage, lean mass, fat mass and height. CTX was positively related to PC [β= 0.19 (0.13, 0.24)] (coefficient = SD change per SD increase in CTX, 95% CI)], but inversely associated with BMDC [β= -0.46 (-0.52,-0.40)] and cortical thickness [β= -0.11 (-0.18, -0.03)]. CTX was positively related to bone strength as reflected by the strength-strain index (SSI) [β= 0.09 (0.03, 0.14)]. To examine the causal nature of this relationship, we then analysed whether SNPs within key osteoclast regulatory genes, known to reduce areal/cortical BMD, conversely increase PC. Fifteen such genetic variants within or proximal to genes encoding RANK, RANKL and OPG were identified by literature search. Six of the 15 alleles that were inversely related to BMD were positively related to CTX (P < 0.05 cut-off) (n = 2379). Subsequently, we performed a meta-analysis of associations between these SNPs and PC in ALSPAC (n = 3382), Gothenburg Osteoporosis and Obesity Determinants (GOOD) (n = 938) and the Young Finns Study (YFS) (n = 1558). Five of the 15 alleles that were inversely related to BMD were positively related to PC (P < 0.05 cut-off). We conclude that despite having lower BMD, individuals with a genetic predisposition to higher bone resorption have greater bone size, suggesting that higher bone resorption is permissive for greater periosteal expansion.
    Full-text · Article · Apr 2014 · Journal of bone and mineral research: the official journal of the American Society for Bone and Mineral Research
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    ABSTRACT: Coagulation phenotypes show strong intercorrelations, affect cardiovascular disease risk and are influenced by genetic variants. The objective of this study was to search for novel genetic variants influencing the following coagulation phenotypes: factor VII levels, fibrinogen levels, plasma viscosity and platelet count. We genotyped the British Women's Heart and Health Study (n=3,445) and the Whitehall II study (n=5,059) using the Illumina HumanCVD BeadArray to investigate genetic associations and pleiotropy. In addition to previously reported associations (SH2B3, F7/F10, PROCR, GCKR, FGA/FGB/FGG, IL5), we identified novel associations at GRK5 (rs10128498, p=1.30x10⁻⁶), GCKR (rs1260326, p=1.63x10⁻⁶), ZNF259-APOA5 (rs651821, p=7.17x10⁻⁶) with plasma viscosity; andat CSF1 (rs333948, p=8.88x10⁻⁶) with platelet count. A pleiotropic effect was identified in GCKR which associated with factor VII (p=2.16x10⁻⁷) and plasma viscosity (p=1.63x10⁻⁶), and, to a lesser extent, ZNF259-APOA5 which also associated with factor VII and fibrinogen (p<1.00x10⁻²) and plasma viscosity (p<1.00x10⁻⁵). Triglyceride associated variants were overrepresented in factor VII and plasma viscosity associations. Adjusting for triglyceride levels resulted in attenuation of associations at the GCKR and ZNF259-APOA5 loci. In addition to confirming previously reported associations, we identified four single nucleotide polymorphisms (SNPs) associated with plasma viscosity and platelet count and found evidence of pleiotropic effects with SNPs in GCKR and ZNF259-APOA5. These triglyceride-associated, pleiotropic SNPs suggest a possible causal role for triglycerides in coagulation.
    No preview · Article · Oct 2013 · Thrombosis and Haemostasis
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    ABSTRACT: Within medical research there is an increasing trend toward deriving multiple types of data from the same individual. The most effective prognostic prediction methods should use all available data, since this maximizes the amount of information used. In this paper we consider a variety of learning strategies to boost prediction performance based on the use of all available data.Implementation: We consider data integration via the use of multiple kernel learning (MKL) supervised learning methods. We propose a scheme in which feature selection by statistical score is performed separately per data type and by pathway membership. We further consider the introduction of a confidence measure for the class assignment, both to remove some ambiguously labelled datapoints from the training data and to implement a cautious classifier which only makes predictions when the associated confidence is high. We use the METABRIC dataset (Curtis et al, 2012) for breast cancer, with prediction of survival at 2000 days from diagnosis. Predictive accuracy is improved by using kernels which exclusively use those genes, as features, which are known members of particular pathways. We show that yet further improvements can be made by using a range of additional kernels based on clinical covariates such as ER-status. Using this range of measures to improve prediction performance, we show that the test accuracy on new instances is nearly 80%, though predictions are only made on 69.2% of the patient cohort. J.Seoane@bristol.ac.uk.
    Full-text · Article · Oct 2013 · Bioinformatics
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