Genetic maps analyses

Previous BioMercator version allows you to directly use command line analyses from the metaQTL toolbox. The standard workflow is the following: * Verify connectivity between input maps * Create a consensus map * Launch a meta-analysis on all QTLs from the consensus map

Connectivity (InfoMap)

This method creates text files about connectivity (where dynamic comparison is only used in a visual way), in order to determine if the can be used for creating a consensus map.

• Click on “Analysis”, “Statistics”, “InfoMap”

• Create a project “Statistics” and validate

• Select all maps from the “Data” project

• Set the result name as “connectivity”

• Click ‘Next’ for launching the analysis

Select dataset

Once the analysis done, you’ll see 2 created files in the Statistics folder; they contain informations about markers and their connectivity.

Connectivity files (1/2)

Connectivity files (2/2)

Consensus map creation (ConsMap)

This analysis is done in single one step, avoiding the iteration part. * Click on “Analysis”, “Map compilations”, “MetaQTL Cons” * Create a project “Consensus” and validate * Select all maps from the “data” project * Set the resulting map name to “pre_consensus” * Launch the analysis

Select dataset

When the analysis finishes, a information dialog is shown; the analysis should be a success, and the resulting consensus map should be visible in the explorer. Drag it to display it; you notice that no QTL is present; the next analysis is needed.

Display consensus map

QTLs projection (QTLProj)

Once the consensus map is created, use this analysis in order to project QTLs from all the maps used. * Click on “Analysis”, “Map compilations”, “MetaQTL QTLProj” * Choose the “Consensus” project and validate * In the next window, in the left explorer, select all maps from the “data” project * In the right explorer, select the “pre_consensus” map from the “Consensus” project * Set the resulting map name to “consensus” * Launch the analysis

Select dataset

QTLs projection

QTLs Meta-analysis (QTLClust)

This meta-analysis is a two steps analysis; the first one calculates and estimates the best models (ie number of meta QTLs), so you can choose the one to show in the second.

First step

• Click on ‘Analyses’, ‘QTL Meta analyses’, ‘MetaQTL Meta analysis 1/2’

• Choose ‘meta_v1’ for the meta analysis’ name

• Select the ‘Consensus’ project

• Select the ‘consensus’ map

• Select the ‘1’ chromosome

• Select the ‘1’ linkage group

• Choose to regroup the traits into a single meta trait named ‘FT’ (for Flowering Time)

• Click ‘Next’ to launch the analysis

QTLs meta-analysis parameters

Once the analysis is done, browse the explorer down to the created meta analysis situated inside the previously selected linkage group (project ‘Consensus’, map ‘consensus’, chromosome ‘1’, linkage group ‘1’). You’ll see 3 created files; drag the one named ‘meta_v1_model.txt’; it corresponds to the most probable model given different criterions.(for more explications, please refer to the ‘MetaQTL’ software documentation)

First step analysis

Second step

• Click on ‘Analyses’, ‘QTL Meta analyses’, ‘MetaQTL Meta analysis 2/2’

• Select the ‘Consensus’ project

• Select the ‘consensus’ map

• Select the ‘1’ chromosome

• Select the ‘1’ linkage group

• Choose ‘meta_v1’ for the meta analysis’ name

• Choose the meta trait named ‘FT’

• Click ‘Next’ to launch the analysis

• Set ‘5’ for the ‘best’ parameter

Second step parameters

Once the analysis is done, browse the explorer down to the created meta analysis situated inside the previously selected linkage group (project ‘Consensus’, map ‘consensus’, chromosome ‘1’, linkage group ‘1’). Inside the folder, you’ll see a linkage group named ‘1’, drag it to see the meta-QTLs along with the QTLs and their percentage of belonging.

Display meta-analysis results

Next step