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Multivariate Trait (multivariate + trait)
Selected AbstractsNew multivariate test for linkage, with application to pleiotropy: Fuzzy Haseman-ElstonGENETIC EPIDEMIOLOGY, Issue 4 2003Belhassen Kaabi Abstract We propose a new method of linkage analysis based on using the grade of membership scores resulting from fuzzy clustering procedures to define new dependent variables for the various Haseman-Elston approaches. For a single continuous trait with low heritability, the aim was to identify subgroups such that the grade of membership scores to these subgroups would provide more information for linkage than the original trait. For a multivariate trait, the goal was to provide a means of data reduction and data mining. Simulation studies using continuous traits with relatively low heritability (H=0.1, 0.2, and 0.3) showed that the new approach does not enhance power for a single trait. However, for a multivariate continuous trait (with three components), it is more powerful than the principal component method and more powerful than the joint linkage test proposed by Mangin et al. ([1998] Biometrics 54:88,99) when there is pleiotropy. Genet Epidemiol 24:253,264, 2003. © 2003 Wiley-Liss, Inc. [source] Correction of a bootstrap approach to testing for evolution along lines of least resistanceJOURNAL OF EVOLUTIONARY BIOLOGY, Issue 12 2009D. BERNER Abstract Testing for an association between the leading vectors of multivariate trait (co)variation within populations (the ,line of least resistance') and among populations is an important tool for exploring variational bias in evolution. In a recent study of stickleback fish populations, a bootstrap-based test was introduced that takes into account estimation error in both vectors and hence improves the previously available bootstrap method. Because this test was implemented incorrectly, however, I here describe the correct test protocol and provide a reanalysis of the original data set. The application of this new test protocol should improve future investigations of evolution along lines of least resistance and other vector comparisons. [source] A Partially Linear Tree-based Regression Model for Multivariate OutcomesBIOMETRICS, Issue 1 2010Kai Yu Summary In the genetic study of complex traits, especially behavior related ones, such as smoking and alcoholism, usually several phenotypic measurements are obtained for the description of the complex trait, but no single measurement can quantify fully the complicated characteristics of the symptom because of our lack of understanding of the underlying etiology. If those phenotypes share a common genetic mechanism, rather than studying each individual phenotype separately, it is more advantageous to analyze them jointly as a multivariate trait to enhance the power to identify associated genes. We propose a multilocus association test for the study of multivariate traits. The test is derived from a partially linear tree-based regression model for multiple outcomes. This novel tree-based model provides a formal statistical testing framework for the evaluation of the association between a multivariate outcome and a set of candidate predictors, such as markers within a gene or pathway, while accommodating adjustment for other covariates. Through simulation studies we show that the proposed method has an acceptable type I error rate and improved power over the univariate outcome analysis, which studies each component of the complex trait separately with multiple-comparison adjustment. A candidate gene association study of multiple smoking-related phenotypes is used to demonstrate the application and advantages of this new method. The proposed method is general enough to be used for the assessment of the joint effect of a set of multiple risk factors on a multivariate outcome in other biomedical research settings. [source] | ||||||||||