What is the GeneNetwork and WebQTL

The GeneNetwork consists of a set of linked resources for systems genetics. It has been designed for multiscale integration of networks of genes, transcripts, and traits such as toxicity, cancer susceptibility, and behavior. This open resource combines more than 25 years of legacy data generated by hundreds of scientists with full genome sequence and deep transcriptome data sets. 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.

What can I do with WebQTL?

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 (n < 32), a correlation matrix and new prinicipal component phenotypes can be generated.

Cluster Tree: For larger sets, (n<64 traits), 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. Numerous links to the UCSC and Ensembl Genome Browsers, PubMed, Entrez Gene, GNF Expression Atlas, ABI Panther, and WebGestalt provide users with rapid interpretive information about genomic regions, published phenotypes and genes highlighted in WebQTL.

How to Use WebQTL?

  1. Choose RI set and data source:

	First select the genetic reference population from the menu. Then you have the options to import the trait data from a file or simply enter trait data by pasting or typing multiple values into the text box assigned. You can also leave both blank and input the values during next step. Check the trait variance checkbox if you want to use your trait variance data in WebQTL.
OR
	You can map loci controlling traits for phenotypes in recombinant inbred sets by searching our database.

  2. Check data and set thresholds:

During this step, you may check your data for accuracy and edit it, if necessary, before analysis. If you haven't entered data, you can now input data into corresponding boxes individually. You can manually set the minimal LRS for display and for significance, otherwise default values will be assigned if both are left blank. If you want your result to be returned in an email, enter your email address in the assigned box. WebQTL will repeat step one and two to let user enter trait variance data if you select that option.

  3. Mapping:

	Once all you data have been entered and checked, you now can do various mapping analyses using your data against the genotypes of the cross or recombinant inbred set you have chosen. The result of each analysis will be returned in a separate window.

• NIAAA Integrative Neuroscience Initiative on Alcoholism (U01AA13499, U24AA13513)
• A Human Brain Project funded jointly by the NIDA , NIMH, and NIAAA (P20-DA 21131)
• NCI MMHCC (U01CA105417)
• Biomedical Informatics Research Network (BIRN), NCRR (U24 RR021760)