WebQTL is the leading GeneNetwork module, and has been optimized for on-line analysis of traits that are controlled by combinations of allelic variants and environmental factors. WebQTL exploits several permanent genetic reference populations (GRP) of mouse (BXD, LXS, etc.), rat (HXB), and Arabidopsis (BayXSha). Each GRP is accompanied by dense genetic maps used to locate modifiers that cause downstream differences in expression and higher-order phenotypes, including disease susceptibility.

Users can also enter their own private data directly into WebQTL to exploit the full range of analytic tools and to map upstream modulators in a powerful environment. Numerous statistical tools are combined with a database consisting of three million mouse SNPs. This combination allows relatively efficient analysis of possible relations between sequence variants and sets of functional variants.

• consists of set of linked resources for systems genetics
  
• designed for multiscale integration of networks of genes, transcripts, and traits
  
• combines data generated with full genome sequence and deep transcriptome data sets
  
• has been optimized for online analysis of traits controlled by combinations of allelic variants and environmental factors
  
• exploits several permanent mouse and rat genetic reference populations
  
• includes genetic correlations between transcripts
  
• includes genetic correlations between transcript abundance values and alcohol traits
  

QTL Mapping:
Interval Mapping: Statistical tests of association between trait values and the genotypes of marker loci through the genome. A significant association is interpreted as indicating the presence of a QTL linked to the marker that shows the association.

Simple interval mapping: This method evaluates the association between the trait values and the expected genotype of a hypothetical QTL (the target QTL) at multiple analysis points between each pair of adjacent marker loci. The analysis point that yields the most significant associations may be taken as the location of a putative QTL. Bootstrap methods may be performed for estimating confidence intervals on QTL location.

Composite interval mapping: Like simple interval mapping, this method evaluates the possibility of a target QTL at multiple analysis points across each interlocus interval. However, at each point it also includes in the analysis the effect of one or more markers elsewhere in the genome. These markers, also called background markers, have previously been shown to be associated with the trait and therefore are each presumably close to another QTL (a background QTL).

Pair-scan: This method evaluates all marker pairs in two-locus models including main effects of each locus and their interaction. These allow discovery of multiple QTL models for complex phenotypes. For all mapping methods Permutation tests may also be selected to establish empirical significance thresholds.

Genetic Correlation Analysis:
For sets of phenotypes, particularly those in GeneNetwork's databases, a variety of correlation analyses can be performed. Trait values entered by users or retrieved from the databases can be correlated with any other database of phenotypes from the same mapping genetic reference panel.

Correlation Matrix / Principal Components Analysis: For a small set of traits, a correlation matrix and new principal component phenotypes can be generated.

Cluster Tree: For larger sets, a cluster analysis can be performed to define sets of correlated traits and identify common genetic determinants of the phenotypes. QTL mapping results for all traits are presented in a parallel thermogram display below the cluster dendrogram.
Compare Correlates: Allows users to find shared genetic correlates among a group of traits by correlating them with all records from any database.

Network Graph: Allows users to examine the network of associations among large groups of phenotypes. Most graphical displays are interactive and allow users to define interesting trait sets which can be temporarily stored for further analysis in WebQTL.
Systems Genetics and Complex Trait Analysis: GeneNetwork pages are extensively connected to external resources.

Contributors to The GeneNework and WebQTL are at the vanguard in terms of data sharing and have made data sets available within days of acquisition, transformation, and error-checking. "Sharing" however is not equivalent to "free distribution." We anticipate reciprocal contributions from you in the form of one or more of the following: acknowledgement of data sources and use of The GeneNetwork and WebQTL, communication with and possible collaboration with our colleagues who have provided data, suggestions for improvements, and best of all, contributions of new data sets. We hope that you will contribute to the annotation, use, and extension of these data sets in ways that are rewarding to all who are involved.