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Sökning: WFRF:(Dominicus Annica)

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1.
  • Hallberg, J, et al. (författare)
  • Interaction between smoking and genetic factors in the development of chronic bronchitis
  • 2008
  • Ingår i: American Journal of Respiratory and Critical Care Medicine. - Am Thoracic Soc. - 1073-449X. ; 177:5, s. 486-490
  • Tidskriftsartikel (refereegranskat)abstract
    • Rationale: Smoking is a primary risk factor for chronic bronchitis, emphysema, and chronic obstructive pulmonary disease, but since not all smokers develop disease, it has been suggested that some individuals may be more susceptible to exogenous factors, such as smoking, and that this susceptibility could be genetically determined. Objectives: The aim of the present study was to assess, in a population-based sample of twins, the following: (1) to what extent genetic factors contribute to the development of chronic bronchitis, including emphysema, taking sex into consideration, and (2) whether the genetic influences on chronic bronchitis, including emphysema, are separate from those for smoking behavior. Methods: Disease cases and smoking habits were identified in 44,919 twins older than 40 years from the Swedish Twin Registry. Disease was defined as self-reported chronic bronchitis or emphysema, or recurrent cough with phlegm. Individuals who had smoked 10 pack-years or more were defined as smokers. Univariate and bivariate structural equation models were used to estimate the heritability specific for chronic bronchitis and that in common with smoking. Measurements and Main Results: The heritability estimate for chronic bronchitis was a moderate 40% and only 14% of the genetic influences were shared with smoking. Conclusions: Genetic factors independent of those related to smoking habits play a role in the development of chronic bronchitis.
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  • Dominicus, Annica, et al. (författare)
  • Bias in variance components due to nonresponse in twin studies
  • 2006
  • Ingår i: Twin Research and Human Genetics. - 1832-4274 .- 1839-2628. ; 9:2, s. 185-193
  • Tidskriftsartikel (refereegranskat)abstract
    • <p>Incomplete data on trait values may bias estimates of genetic and environmental variance components obtained from twin analyses. If the nonresponse mechanism is 'ignorable' then methods such as full information maximum likelihood estimation will produce consistent variance component estimates. If, however, nonresponse is 'nonignorable', then the situation is more complicated. We demonstrate that a within-pair correlation of nonresponse, possibly different for monozygotic (MZ) and dizygotic (DZ) twins, may well be compatible with 'ignorability'. By means of Monte Carlo simulation, we assess the potential bias in variance component estimates for different types of nonresponse mechanisms. The simulation results guide the interpretation of analyses of data on perceptual speed from the Swedish Adoption/Twin Study of Aging. The results suggest that the dramatic decrease in genetic influences on perceptual speed observed after 13 years of follow-up is not attributable solely to dropout from the study, and thus support the hypothesis that genetic influences on some cognitive abilities decrease with age in late life.</p>
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  • Dominicus, Annica, 1976- (författare)
  • Latent variable models for longitudinal twin data
  • 2006
  • Doktorsavhandling (övrigt vetenskapligt)abstract
    • <p>Longitudinal twin data provide important information for exploring sources of variation in human traits. In statistical models for twin data, unobserved genetic and environmental factors influencing the trait are represented by latent variables. In this way, trait variation can be decomposed into genetic and environmental components. With repeated measurements on twins, latent variables can be used to describe individual trajectories, and the genetic and environmental variance components are assessed as functions of age. This thesis contributes to statistical methodology for analysing longitudinal twin data by (i) exploring the use of random change point models for modelling variance as a function of age, (ii) assessing how nonresponse in twin studies may affect estimates of genetic and environmental influences, and (iii) providing a method for hypothesis testing of genetic and environmental variance components. The random change point model, in contrast to linear and quadratic random effects models, is shown to be very flexible in capturing variability as a function of age. Approximate maximum likelihood inference through first-order linearization of the random change point model is contrasted with Bayesian inference based on Markov chain Monte Carlo simulation. In a set of simulations based on a twin model for informative nonresponse, it is demonstrated how the effect of nonresponse on estimates of genetic and environmental variance components depends on the underlying nonresponse mechanism. This thesis also reveals that the standard procedure for testing variance components is inadequate, since the null hypothesis places the variance components on the boundary of the parameter space. The asymptotic distribution of the likelihood ratio statistic for testing variance components in classical twin models is derived, resulting in a mixture of chi-square distributions. Statistical methodology is illustrated with applications to empirical data on cognitive function from a longitudinal twin study of aging. </p>
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  • Dominicus, Annica, et al. (författare)
  • Likelihood ratio tests in behavioral genetics: Problems and solutions
  • 2006
  • Ingår i: Behavior Genetics. - Springer. - 0001-8244 .- 1573-3297. ; 36:2, s. 331-340
  • Tidskriftsartikel (refereegranskat)abstract
    • <p> </p> <p> </p> <p> <p> <p>The likelihood ratio test of nested models for family data plays an important role in the assessment of genetic and environmental influences on the variation in traits. The test is routinely based on the assumption that the test statistic follows a chi-square distribution under the null, with the number of restricted parameters as degrees of freedom. However, tests of variance components constrained to be non-negative correspond to tests of parameters on the boundary of the parameter space. In this situation the standard test procedure provides too large</p> <p>p-values and the use of the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC) for model selection is problematic. Focusing on the classical ACE twin model for univariate traits, we adapt existing theory to show that the asymptotic distribution for the likelihood ratio statistic is a mixture of chi-square distributions, and we derive the mixing probabilities. We conclude that when testing the AE or the CE model against the ACE model, the p-values obtained from using the v2 (1 df) as the reference distribution should be halved. When the E model is tested against the ACE model, a mixture of v2(0 df), v2(1 df) and v2 (2 df) should be used as the reference distribution, and we provide a simple formula to compute the mixing probabilities. Similar results for tests of the AE, DE and E models against the ADE model are also derived. Failing to use the appropriate reference distribution can lead to invalid conclusions.</p> </p> </p>
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  • Ripatti, Samuli, et al. (författare)
  • GENESTAT : an information portal for design and analysis of genetic association studies
  • 2009
  • Ingår i: European Journal of Human Genetics. - 1018-4813 .- 1476-5438. ; 17:4, s. 533-536
  • Tidskriftsartikel (refereegranskat)abstract
    • <p>We present the rationale, the background and the structure for version 2.0 of the GENESTAT information portal (www.genestat.org) for statistical genetics. The fast methodological advances, coupled with a range of standalone software, makes it difficult for expert as well as non-expert users to orientate when designing and analysing their genetic studies. The ultimate ambition of GENESTAT is to guide on statistical methodology related to the broad spectrum of research in genetic epidemiology. GENESTAT 2.0 focuses on genetic association studies. Each entry provides a summary of a topic and gives links to key papers, websites and software. The flexibility of the internet is utilised for cross-referencing and for open editing. This paper gives an overview of GENESTAT and gives short introductions to the current main topics in GENESTAT, with additional entries on the website. Methods and software developers are invited to contribute to the portal, which is powered by a Wikipedia-type engine and allows easy additions and editing.</p>
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