A novel targeted learning method for quantitative trait loci mapping.

ABSTRACT: We present a novel semiparametric method for quantitative trait loci (QTL) mapping in experimental crosses. Conventional genetic mapping methods typically assume parametric models with Gaussian errors and obtain parameter estimates through maximum-likelihood estimation. In contrast with univariate regression and interval-mapping methods, our model requires fewer assumptions and also accommodates various machine-learning algorithms. Estimation is performed with targeted maximum-likelihood learning methods. We demonstrate our semiparametric targeted learning approach in a simulation study and a well-studied barley data set.

SUBMITTER: Wang H 

PROVIDER: S-EPMC4256757 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature