In this thesis, we develop some novel methods for univariate and multivariate analyses of additive genetic factors including heritability and genetic correlation. For the univariate heritability analysis, we present 3 newly proposed estimation methods—Frequentist ReML, LR-SD and LR-SD ReML. The comparison of these novel and those currently available approaches demonstrates the non-iterative LRSD method is extremely fast and free of any convergence issues. The properties of this LR-SD method motivate the use of the non-parametric permutation and bootstrapping inference approaches. The permutation framework also allows the utilization of spatial statistics, which we find increases the statistical sensitivity of the test. For the bivariate genetic analysis, we generalize the univariate LR-SD method to the bivariate case, where the integration of univariate and bivariate LR-SD provides a new estimation method for genetic correlation. Although simulation studies show that our measure of genetic correlation is not ideal, we propose a closely related test statistic based on the ERV, which we show to be a valid hypothesis test for zero genetic correlation. The rapid implementation of this ERV estimator makes it feasible to use with permutation as well. Finally, we consider a method for high-dimensional multivariate genetic analysis based on pair-wise correlations of different subject pairs. While traditional genetic analysis models the correlation over subjects to produce an estimate of heritability, this approach estimates correlation over a (high-dimensional) phenotype for pairs of subjects, and then estimates heritability based on the difference in MZ-pair and DZ-pair correlations. A significant two-sample t-test comparing MZ and DZ correlations implies the existence of heritable elements. The resulting summary measure of aggregate heritability, defined as twice the difference of MZ and DZ mean correlations, can be treated as a quick screening estimate of whole-phenotype heritability that is closely related to the average of traditional heritability.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:644922 |
| Date | January 2014 |
| Creators | Chen, Xu |
| Publisher | University of Warwick |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://wrap.warwick.ac.uk/67103/ |
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