Statistical Methods for Latent Class Quantitative Trait Loci Mapping.

ABSTRACT: Identifying the genetic basis of complex traits is an important problem with the potential to impact a broad range of biological endeavors. A number of effective statistical methods are available for quantitative trait loci (QTL) mapping that allow for the efficient identification of multiple, potentially interacting, loci under a variety of experimental conditions. Although proven useful in hundreds of studies, the majority of these methods assumes a single model common to each subject, which may reduce power and accuracy when genetically distinct subclasses exist. To address this, we have developed an approach to enable latent class QTL mapping. The approach combines latent class regression with stepwise variable selection and traditional QTL mapping to estimate the number of subclasses

SUBMITTER: Ye S 

PROVIDER: S-EPMC5500132 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature