These software packages take into account all available information from each individual dataset (e.g., population structure and size) and estimate the marker order and genetic distances of common (anchor or bridge) markers using regression mapping (JoinMap) or multiple 2-point maximum likelihood (Carthagène).
Since this is a single-marker regression QTL mapping, we estimated the physical interval size of a QTL based on most of the original drought QTLs to be around a 1-MB region.
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.
QTL mapping was carried out by interval mapping using marker regression on GeneNetwork's WebQTL module [ 57].
The positions of 225 markers, as obtained by regression mapping, are presented here.
Likelihood ratio statistics (LRS) for association of phenotypic variation with genotypic inheritance were estimated using the interval mapping and marker regression functions of MapManagerQTXb19 (Meer et al, 2004).
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).
Markers were ordered with the regression mapping algorithm and were classified into LGs using the grouping module at LOD thresholds of 8.0 10.0 at an increment of 0.5.
For QTL detection, the following different methods were compared: single-marker regression; simple interval mapping; and composite interval mapping.
These QTL may correspond to our Hort5-3 group QTL for yield, Brix, and days to first ripe fruit, although the low resolution of their map and single marker regression analysis precluded more precise QTL localization.
