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Sökning: WFRF:(Gertow Karl) > Kungliga Tekniska Högskolan

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1.
  • Bruzelius, Maria, et al. (författare)
  • Influence of coronary artery disease-associated genetic variants on risk of venous thromboembolism
  • 2014
  • Ingår i: Thrombosis Research. - : Elsevier BV. - 0049-3848 .- 1879-2472. ; 134:2, s. 426-432
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction: We investigated whether genetic variations robustly associated with coronary artery disease are also associated with risk of venous thromboembolism in a well-defined, female case-control study (n = 2753) from Sweden. Materials and Methods: 39 single nucleotide polymorphisms in 32 loci associated with coronary artery disease in genome-wide association studies were identified in a literature search and genotyped in the ThromboEmbolism Hormone Study (TEHS). Association with venous thromboembolism was assessed by logistic regression. Results: Only rs579459 in the ABO locus demonstrated a significant association with VTE. A tentative association between ANRIL and VTE in the discovery analysis failed to replicate in a meta-analysis of 4 independent cohorts (total n = 7181). Conclusions: It appears that only the ABO locus is a shared risk factor for coronary artery disease and VTE.
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2.
  • Frånberg, Mattias, 1985-, et al. (författare)
  • Discovering Genetic Interactions in Large-Scale Association Studies by Stage-wise Likelihood Ratio Tests
  • 2015
  • Ingår i: PLOS Genetics. - : Public Library of Science (PLoS). - 1553-7390 .- 1553-7404. ; 11:9
  • Tidskriftsartikel (refereegranskat)abstract
    • Despite the success of genome-wide association studies in medical genetics, the underlying genetics of many complex diseases remains enigmatic. One plausible reason for this could be the failure to account for the presence of genetic interactions in current analyses. Exhaustive investigations of interactions are typically infeasible because the vast number of possible interactions impose hard statistical and computational challenges. There is, therefore, a need for computationally efficient methods that build on models appropriately capturing interaction. We introduce a new methodology where we augment the interaction hypothesis with a set of simpler hypotheses that are tested, in order of their complexity, against a saturated alternative hypothesis representing interaction. This sequential testing provides an efficient way to reduce the number of non-interacting variant pairs before the final interaction test. We devise two different methods, one that relies on a priori estimated numbers of marginally associated variants to correct for multiple tests, and a second that does this adaptively. We show that our methodology in general has an improved statistical power in comparison to seven other methods, and, using the idea of closed testing, that it controls the family-wise error rate. We apply our methodology to genetic data from the PRO-CARDIS coronary artery disease case/control cohort and discover three distinct interactions. While analyses on simulated data suggest that the statistical power may suffice for an exhaustive search of all variant pairs in ideal cases, we explore strategies for a priori selecting subsets of variant pairs to test. Our new methodology facilitates identification of new disease-relevant interactions from existing and future genome-wide association data, which may involve genes with previously unknown association to the disease. Moreover, it enables construction of interaction networks that provide a systems biology view of complex diseases, serving as a basis for more comprehensive understanding of disease pathophysiology and its clinical consequences.
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