QTL Resources
Quantitative Trait Loci Maps:
-
Atherosclerosis QTL Map with linked resource tables Placed in 2005
-
Asthma Human/Mouse homology QTL Map Placed in 2007
-
Cholesterol Gallstones Placed in 2002
-
HDL Cholesterol QTL Map with linked resource table Placed in 2005
-
HDL Human/Mouse homology QTL Map Placed in 2007
-
LDL Cholesterol QTL Map with linked resource tables Placed in 2005
-
Triglycerides QTL Maps with linked resource tables Placed in 2005
-
Hypertension Placed in 2002
-
Kidney Disease QTL map with linked resource table Placed in 2005
QTL for Baseline White Blood Cell count, Platelet Count, and Mean Platelet Volume
QTL mapping of PLTP (Phospholipid transfer protein) - 5 significant loci involved in PLTP activity.
QTL mapping gender and diet covariants - HDL cholesterol in SM/J x NZB/BINJ.
Links
Links
New publications
- Quantitative trait loci for baseline erythroid traits
- Quantitative trait loci for baseline white blood cell count, platelet count, and mean platelet volume
- Bioinformatics toolbox for narrowing rodent quantitative trait loci
- Identifying novel genes for atherosclerosis through mouse-human comparative genetics
- Gene expression analysis of mouse chromosome substitution strains
- Quantitative trait locus analysis for obesity reveals multiple networks of interacting
Other data
- Dr. Gary Churchill's QTL Archive of raw data from various QTL (quantitative trait loci) studies using rodent inbred line crosses. Most data are available in .csv fromat.
- Dr. Churchill's QTL data submission form for rodent (mouse and rat) QTL mapping data. - Submitted QTL data will be archived in a publicly accessible repository. The submitter is assumed to hold ownership of the data.
- More information on Dr. Churchill's QTL mapping projects
Tools
Updated! - J/QTL Software - Quantitative Trait Loci, or QTL, is a statistical technique used to find links between certain expressed traits and regions in a genetic map. J/QTL implements the necessary statistical calculations in an easy to use software package. It supports several different methods for viewing experimental data and also has features to detect possible errors in that data. We'd like to thank Dr. Hao Wu and Dr. Gary Churchill for updating this software tool.
