Using the yield deviations of the raw scores adjusted for fixed factors as phenotypic measurements, a quantitative trait loci (QTL) analysis was performed on the basis of a multi-marker regression method.
Using the G matrix derived from markers by the regression method, the estimated genetic merits of trees were obtained as: GEBV (u ^ ) = [ R - 1 + G - 1 λ] - 1 R - 1 (y - X b ^ ) (6) We denoted the genomic estimated breeding values using this method as GEBVb.
The scantwo function (R/qtl, method marker regression) was used to examine all pairwise combinations of markers by regression (Broman et al. 2003).
xQTL workbench wraps R/qtl (Arends et al., 2010; Broman et al., 2003) in a web-based analysis framework offering all important QTL mapping routines, such as the EM algorithm, imputation, Haley-Knott regression, the extended Haley-Knott method, marker regression and Multiple QTL mapping.
HaploMax is a marker-haplotype-regression method adapted to the following two hypotheses: the QTL is bi-allelic, and QTL alleles and marker alleles are in complete linkage.
A window size of 10 cM, 5 control markers and the forward regression method were used.
All QTL analyses of albinism were conducted using R/qtl (Broman et al. 2003) run for each of three scan-one mapping methods: marker regression (MR), expect maximum (EM), and Haley-Knott (HK), according to the methodology in Gross et al. (2014).
A single marker regression and an ensemble learning method, GBM, were utilized in the current analysis to characterize regions that have an impact on inbreeding depression based on a ROH metric.
Below, we extend the standard lasso and propose a new penalized regression method for detecting markers with pleiotropic effect on correlated quantitative traits.
