Marker-QTL-Association Analysis Incorporating Diversification of QTL Variance and its Application

The Analysis of QTL and QTL x Treatment Experiments using Spatial Models for Marker Effects

Detecting dominant QTL with variance component analysis in simulated pedigrees.

Dominance is an important source of variation in complex traits. Here, we have carried out the first thorough investigation of quantitative trait locus (QTL) detection using variance component (VC) models extended to incorporate both additive and dominant QTL effects. Simulation results showed that the empirical distribution of the test statistic when testing for dominant QTL effects did not be...

Non-iterative variance component estimation in QTL analysis.

In variance component quantitative trait loci (QTL) analysis, a mixed model is used to detect the most likely chromosome position of a QTL. The putative QTL is included as a random effect and a method is needed to estimate the QTL variance. The standard estimation method used is an iterative method based on the restricted maximum likelihood (REML). In this paper, we present a novel non-iterativ...

Effective QTL Discovery Incorporating Genomic Annotations

Mapping molecular QTLs has emerged as an important tool for understanding the genetic basis of cell functions. With the increasing availability of functional genomic data, it is natural to incorporate genomic annotations into QTL discovery. In this paper, we describe a novel method, named TORUS, for integrative QTL discovery. Using hierarchical modeling, our approach embeds a rigorous enrichmen...

Methods for QTL analysis

SINGLE VERSUS MULTIPLE MARKERS ........................................................................................................ 45 DETERMINING ASSOCIATIONS BETWEEN GENETIC MARKERS AND QTL WITH TWO MARKERS ................... 45 INTERVAL MAPPING .................................................................................................................................. 50 Maximum Li...