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QTL Data Submission Guidelines
This document guides QTL data submitters on the process of loading the data, setting statistical parameters and finally the QTL mapping.
The uploading of QTL data needs to be done in one session. Therefore, prepare your data files in the required format before starting the process. Please read the submission guidelines first. If you would like to have the formatting of your dataset checked beforehand, send your data to: iyt2@cornell.edu.
Required data:
- Some basic information about the population,
- Traits data file (tab delimited),
- Phenotype data file (tab delimited),
- Genotype data file (tab delimited) and
- Statistical parameters.
- Markers with their sequences need to be already in SGN. Read here on how to submit markers to SGN.
Detailed guide:
 Step 1: Population details
 Step 1: Population details
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- Select organism: fill in the common name of the species the population belongs to. Press the 'associate organism' button. Then proceed with the rest of the form.
- Cross type: at this stage only inter cross (F2), backcross, RIL (from both sibling mating and selfing) diploid 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: Include the species name and cultivar name. Eg. Solanum lycopersicum cv. moneymaker.
- Recurrent and donor parents: In the same format as the female and male parents but required only 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.
| Step 2: Traits list
Step 2: Traits list
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The traits list file needs to be in a text file and should look like the following format.
Use of the Solanaceae Phenotype Ontology is highly encouraged to allow cross-study comparative analysis but not required.
traits definition unit fruit perimeter the maximum perimeter of the fruit cm fruit area total area of the fruit sq. cm fruit weightThe 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.
| Step 3: Phenotype dataset
Step 3: Phenotype dataset
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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
| Step 4: Genotype dataset
Step 4: Genotype dataset
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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.
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Third row: first column is empty. The rest of the columns are marker positions in their respective chromosomes.
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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
| Step 5: Statistical parameters
Step 5: Statistical parameters
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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.
| Step 6: QTL analysis
Step 6: QTL analysis
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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.
| Further reading
Further reading
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You can find detailed documentation of the tool here: solQTL: a tool for QTL analysis, visualization and linking to genomes at SGN database.
| Contact us
Contact us
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If you have any questions or feedback, please contact the development team.
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