• Select organism: fill in the common name of the species the population belongs to.
• Cross type: at this stage only inter cross (F2) and backcross populations are supported.
• Population name: provide a concise and precise name for the population. This will also serve as the genetic map name of the population.
• Population description: provide an abstract style characterization of the population including experimental design highlights.
• Female and male parents: provide the common name or cultivar name of the parents.
• Recurrent and donor parents: necessary in case of backcross population.
• Do you want to make the data public: Choose 'Yes' or 'No' depending on whether you want to share the QTL data with the community or not.
• Parents need to be submitted in scientific name format eg. Solanum lycopersicum cv. moneymaker

The traits list file needs to be in a text file and should look like the following format.

  traits             definition                            unit
  fruit perimeter    the maximum perimeter of the fruit    cm
  fruit area         total area of the fruit               sq. cm
  fruit weight

The definition and unit are optional but would make the phenotype data more informative to users of the database.
Use of the Solanaceae Phenotype Ontology is highly encouraged to allow cross-study comparative analysis but not required.

The phenotype dataset should be in a text file and in the following format. The traits names in the phenotype dataset must match exactly with the traits in the traits file, submitted in the previous step.

                plant    fruit       fruit     fruit
                         perimeter   area      weigh

                p-1      76.71       420.72    21.89
                p-2      83.24       482.62    26.29
                p-3      57.71       232.42    16.29
                p-4      71.08       360.42    20.7  
                p-5      85.26       500.51    26.32
                p-6      67.60       315.39    18.48
                p-7      66.84       312.99    18.91

The genotype dataset should be in a text file and in the following format. The plant names in the genotype dataset must be exactly the same as the ones submitted in the phenotype dataset file in the previous step.

• First row: first column refers to the label of the rows of lines. The rest of the columns are marker names.
• Second row: first column is empty. The rest of the columns are chromosome names for the corresponding markers.

• Third row: first column is empty. The rest of the columns are marker positions in their respective chromosomes.

• The rest of the rows: first column is the genotype name and the rest are the genotype values for each marker.

 plant        M_1   M_2    M_3    M_4   M_5     M_6     M_7     M_8    M_9    M_10    M_11    M_12   
              1     2      3      4     5       6       7       8      9      10      11      12   
              0.99  24.84  40.41  49.99 52.80   70.11   70.80   80.62  81.39  84.93   92.68   93.64   
 p-1          B      B      B      B    H       H       H       B      B      H       H       B  
 p-2          A      B      B      B    B       H       H       H      H      H       H       B 
 p-3          B      B      H      H    H       H       H       H      H      B       B       A  
 p-4          B      B      B      H    H       H       H       B      B      B       B       H
 p-5          H      H      H      H    H       B       H       H      H      H       H       H  
 p-6          H      H      H      B    B       B       B       B      B      B       B       A
 p-7          H      H      H      H    H       A       A       A      A      H       H       B

The qtl analysis uses R/QTL package and implements an interval mapping with the following parameters.

• QTL model: Select the one dimension single QTL genome scan  (the two-dimension two genome QTL scan is under development)
• QTL mapping method: You can select  'Single Marker Analysis'  or 'Maximum Likelihood' for standard interval mapping or for an approximation of it 'Haley-Knot Regression' or 'Multiple Imputation'. Use 'Multiple Imputation' only if you want to estimate the QTL genotype based on random imputation.
• QTL genotype probability method: Select 'Calculate' if you want the QTL genotype to be estimated based the observed marker data. This method must be selected if you selected 'Maximum Likelihood' or 'Haley-Knot Regression'. If you selected 'Multiple Imputation' then choose the 'Simulate' method.
• Genotype probability significance level: select the genotyping error rate you want to allow.
• Genome scan size (cM): select the genome size (interval) you want the QTL genotype probabilities to be calculated.
• No. of imputations: applicable only for 'Multiple Imputation' mapping method and 'Simulate' QTL genotype probability method.
• No. of permutations and significance level: select the number of permutations to perform and the error rate for the calculation of the LOD threshold.

Now that the QTL data is uploaded to the database, you can proceed to perform the on-the-fly QTL analysis for one trait at a time. On the population page, click the graph icon under the 'QTL(s)' column and in a couple of minutes you will be taken to a page with the genome wide QTL mapping output. Further help can be found here.

You can find detailed documentation of the tool here: solQTL: a tool for QTL analysis, visualization and linking to genomes at SGN database.

If you have any questions or feedback, please contact us at: sgn-feedback@solgenomics.net.