Nonparametric Risk and Nonparametric Odds in Quantitative Genetic Association Studies.

ABSTRACT: The coefficient in a linear regression model is commonly employed to evaluate the genetic effect of a single nucleotide polymorphism associated with a quantitative trait under the assumption that the trait value follows a normal distribution or is appropriately normally distributed after a certain transformation. When this assumption is violated, the distribution-free tests are preferred. In this work, we propose the nonparametric risk (NR) and nonparametric odds (NO), obtain the asymptotic normal distribution of estimated NR and then construct the confidence intervals. We also define the genetic models using NR, construct the test statistic under a given genetic model and a robust test, which are free of the genetic uncertainty. Simulation studies show that the proposed confidence interva

SUBMITTER: Zhang W 

PROVIDER: S-EPMC5378889 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature