The price depends on the number of licenses ordered simultaneously per software package. There are discounts for non-profit organisations universities and for upgrading from earlier versions for JoinMap from versions 4. Prices are in EURO currency, they include handling and delivery, they do not include local taxes and import duties e.
Delivery is usually with an express courier; if you make use of your own customs broker, then delivery is to this broker's office only. A window opens with in this case information on the symbols used for indicating the various levels of significance and the frequency distribution totals over all loci. Close the window with the Esc key. A chart node is created and becomes selected. On the Chart Control tabsheet you can set the data that must be plotted and various chart options.
The chart is shown using the current Page Setup paper size, orientation, margins; these can be changed using the Page Setup button. You can zoom into the chart by double clicking, and zoom out by double clicking on the other mouse button; a zoomed-in chart can be dragged with the mouse within its window to put it in another position. The tabsheet will get filled with a Groupings tree, of which in this case all branches are collapsed.
Please, take some Getting started 7 time to understand what is shown here. The tree presents how the loci fall apart in groups at increasing stringency levels of a test for linkage. Each node in the tree represents a group of loci that are concluded to be linked at a given significance threshold value of the linkage test statistic or possibly better: grouping test statistic. When you select a certain node in the groupings tree by clicking on it , the loci of that group are displayed in the table on the right-hand side of the tabsheet.
Figure 5. The results of the grouping calculations after expansion of the groupings tree; the loci present in the selected node blue "2. Here the grouping is based upon the test for independence with a LOD score as statistic. Other test statistics parameters are: P-value of the test for independence, recombination frequency and linkage LOD.
The test is done at several significance threshold values of increasing stringency. Loci determined to be significantly associated linked at the current threshold with at least one member of a group will be in the same group. In our example, at the first threshold level of the test, i. At the second threshold level of the test, i. In fact here the two groups of loci each stay associated until the most stringent level of the test, i.
You can easily see some branching if you change the grouping parameter settings in the calculation options, for instance change the Start value of the independence LOD to 0. It will show that all 22 loci are linked when the threshold is taken at 0. When you have seen this, reset the calculation options by pressing the Preset default button on the Calculation Options dialog and redo the calculations. Once you have decided which groups from the groupings tree you want to use for calculating the linkage map, you need to select their nodes by right-clicking.
This action will produce in the navigation tree a grouping node as a child node of the population node and for each group a group node as child nodes of the grouping node Figure 6.
The grouping node has a single tabsheet showing an overview of the division of loci over the groups. The Grouping tabsheet also presents the so-called Strongest Cross Link SCL information: for each locus another locus is shown with which it has the strongest linkage outside its own group.
For this so-called cross link the locus number and name, the group number and node name, as well as the value of the linkage test employed are given. This permits inspection whether the assignment of a marker to a group might be suspicious, for instance when a certain SCL-value is nearly significant this indicates that a locus has linkage outside its current group. Figure 6. The grouping node contains the overview of how loci are divided over the groups Getting started 9 Let's have a quick look at a group node, select Group 1.
The node has several tabsheets, most are empty. The information to present here are the pairwise recombination frequencies. For the sake of brevity pairwise recombination frequencies are called linkages. Press the calculate button to obtain them. After successful calculation of the linkages the Data tabsheet will show the original genotype data, but only for the loci in the group.
The Loci tabsheet shows the loci in the group and allows exclusion of them from further processing. Because the number of pairs grows dramatically in size with the number of loci L over 2 for L loci , the information on the linkages is shown from several selective angles weak, strong, maximum, suspect.
Finally there are tabsheets where you can specify a start order and one or more fixed orders for use in the map calculations of the group. After the map calculations are performed a Mapping node will appear as child of the group node, and if the calculations are successful a Map node as child of the mapping node.
The mapping node has a single tabsheet containing the Session Log of the calculations, allowing you to study the details of the procedure. The default mapping algorithm is the regression mapping algorithm, which can be changed as a calculation option.
The procedure is basically a process of building a map by adding loci one by one, starting from the most informative pair of loci. For each added locus the best position is searched and a goodness-of-fit measure is calculated. When the goodness-of-fit reduces too sharply too large a jump , or when the locus gives rise to negative distances, the locus is removed again.
This is continued until all loci have been handled once. This is the end of the socalled first round. The present data are quite perfect data that only require a first round, otherwise subsequent rounds would be needed. The results at the end of each round are represented by a map node. A map node has several tabsheets, the first three are different representations of the map itself: as a chart, as a table and in plain text format.
The Data tabsheet is similar to this tabsheet of the group node, but here the loci are ordered according to the map while excluded loci are not shown. Figure 7.
The colorized view of the Data tabsheet allows a visual inspection of the estimated order 10 Getting started These graphical genotypes allow a visual inspection of the ordered genotype data, enabling you to see for instance whether the recombination breakpoints are reasonably well distributed over the estimated map Figure 7. The Mean Chisquare Contribs. The Genotype Probabilities tabsheet is for the detection of unlikely genotype scores.
After the calculations are done a mapping node and a map node will be created. The maximum likelihood mapping algorithm is implemented as a combination of several numerical methods: spatial sampling, simulated annealing and Gibbs sampling.
Simulated annealing is a general optimization method used here for estimating the best map order by minimizing the sum of recombination frequencies in adjacent segments. Gibbs sampling is employed to obtain multipoint recombination frequency estimates, given the current map order. In order to reduce the influence of errors and unknown or dominant genotypes in the dataset the map is built gradually by taking spatial samples of loci, i. The algorithm is very fast for high density maps.
Again, the mapping node will contain the details of the procedure in the session log. The map node will show similar information as the previous map node, as well as information specific for the maximum likelihood algorithm: the expected number of recombinations per individual Expected Rec.
Finally, an adapted maximum likelihood algorithm can be used to calculate Plausible positions of all loci starting from the current map order as best position. As a final exercise you will compare the maps of both algorithms. Upon success a map node will appear as child of the group node, displaying the combined map. The result will look like Figure 8 Figure 8. Map orders can be visually compared in a combined map using the Show Homologs option The guide for getting you started with JoinMap will stop here.
There is a lot more that the program can do, you can read about all possibilities in the next chapter Using JoinMap. If you are working under a full license, you are encouraged to continue with the Tutorial chapter after reading the Using JoinMap chapter. If you are working under an evaluation license, you are encouraged to try out some of the possibilities by yourself. The various. You can even load your own data using a dataset node or directly with the Load Data function if they have the proper format.
The data format is described extensively in the Data files chapter. If you are working under an evaluation license, you may need to remove nodes from your project because the program limits the number of populations, etcetera; 12 Getting started removing nodes in the navigation tree can be done by selecting the node and applying the Delete Node function of the Edit menu, or pressing ctrl-F The latter way is established only after running the program a first time.
When the program runs you will see a window that is divided into several main parts: on the top the menu and the tool bar with buttons, on the left side there is the navigation panel, on the right side the contents-and-results panel, and on the bottom the status bar Figure 1.
Once data are loaded the navigation panel will contain a tree view like the Folders panel in Windows Explorer, in which each node will represent an element in a mapping project, such as a population, a linkage group or a map. The formats of data files used by JoinMap are described thoroughly in the Data files chapter. Projects of JoinMap 3. Keyboard shortcuts Because JoinMap is an MS-Windows program, you can expect the many features to be controlled in the normal MS-Windows way with the mouse and the keyboard.
The sorting also works on the Exclude column with checkboxes. Multiple checkboxes in the Exclude column can be un set simultaneously by first selecting their rows by clicking outside the checkboxes while holding the control or shift key and subsequently un setting one of the checkboxes in the selection; if that checkbox is un set while holding the control key the selection remains visible.
When tables become larger than their window, standard scrollbars will enable navigation through the table. In such cases the top most row s and left most column s stay frozen, i. These frozen rows and columns are behind thin black lines in the table; these lines can be dragged to change the set of frozen rows or columns.
Columns in the tables can be moved to other positions in the table by dragging the header, they cannot be dragged before the by default frozen column s and the by default frozen column s themselves cannot be moved.
Column widths can be resized by dragging the right border of the header, double clicking there results in resizing such that all cells are completely visible. In the data matrix of a dataset node the same methods for moving and resizing as for columns can be applied to rows. The Edit menu function Reset Tabsheet sets the table in original order and sizes. Printing and exporting The tabsheet on display in the contents-and-results panel except the chart control and the groupings tree tabsheets can be printed, exported to file and copied to the MS-Windows clipboard to enable the pasting into for instance an MS-Word document.
The tool bar has buttons to perform these functions: , , , respectively. When one or more rows in a table are selected, or when there is a text selection in a plain text view, the print, export and copy functions are performed on the selection only; pressing ctrl-A will select all of the current view.
Charts can be exported in the enhanced meta file format, which as an MS-Windows standard can be used in many other applications. Tables with genotype data dataset and data tabsheets can be exported to loc-files, all tables can be exported to tab separated text format and comma separated text format. Tables, plain text and charts can all be exported to Adobe pdf format. Prior to printing, a preview of the print-out can be obtained through the Print Preview function of the File menu or the tool bar button.
From within the Print Preview the pages to be printed can be selected. Special selection of nodes in tree views For the purposes of combining maps or groups, or for obtaining a grouping for a population based on a map or other grouping in the project, the map, group or grouping nodes in the navigation tree can be specially selected for these purposes by right-clicking on the nodes, after which they become red or magenta for the current node , and subsequently applying the corresponding menu function.
If these menu functions are applied without nodes being specially selected, then an appropriate Using JoinMap 15 dialog will appear with the necessary instructions.
Nodes in the groupings tree must be specially selected by right-clicking, in order to create group nodes in the navigation tree that are needed to calculate the maps.
Both trees can also be controlled with the keyboard after clicking in the tree window ; the up and down arrows let you move up and down in the tree, the right and left arrows expand and collapse branches, the space bar toggles the special selection of nodes. Various In some instances there is some extra information available on a displayed tabsheet.
In such cases the i-button in the tool bar is highlighted. Clicking this button or selecting the Info on Tabsheet Contents function from the File menu will show this information.
The program has preset i. Every project has its own set of environment and calculation options, every map chart has its own set of map chart options. This user manual is accessible as an Adobe pdf document though the Help menu.
JoinMap project In JoinMap 4 your work is organised into a project. You create a new project or open an existing project using the File menu. A JoinMap project consists physically of 1 the project file with extension.
The project data directory resides in the same directory as the project file; it will contain all many internal data files. When backing up a JoinMap project, always take the project file as well as the project directory with all its files.
Every project has a project node that can be used to make notes that will be stored with the project. Once a project is opened, you can load data into the project. This must be done with the Load Data function in the File menu or with the corresponding tool bar button. With this function you can load three types of data files into the project, and you can load more than one data file. The most important one is the locus genotype file also called loc-file , which contains the genotype codes for the loci of a single segregating population.
Such a dataset is referred to as a genotype data population. As an important new alternative to loading genotype data through loc-files, JoinMap offers the possibility to load locus genotype observations stored in MS-Excel spreadsheets by copying from the spreadsheet and pasting into the data matrix of a dataset node. For the case in which the population type is not handled directly by JoinMap, or if you only have the recombination frequencies between pairs of loci with their LOD scores e.
Such a dataset is referred to as a pairwise data population. When such population datasets are loaded successfully, they will be represented by a population node in the root of the navigation tree, the icon of the pairwise data population in different colours than that of a genotype data population. The third type of data file that you can load into a project, is a map file.
A map file can contain more than one 16 Using JoinMap linkage group. This will allow you to compare an external map with a map calculated for a segregating population in the project and it may allow you to use the map for determining the linkage groups of a new genotype data population.
Loaded maps are represented as map nodes in the root of the navigation tree. The dataset node provides a data matrix in which it is possible to enter genotype observations for a population. The matrix holds space for genotype observations for each locus and each individual, for locus names, for individual names codes , and if applicable for segregation, phase and classification types.
The matrix has rows for the loci and columns for the individuals, but this can be exchanged easily by using the Transpose function of the Edit menu or the Transpose button.
The data matrix is defined by several fields at the bottom of the Dataset tabsheet: the population name and type including the generation numbers if applicable , the numbers of loci and individuals.
The numbers of rows and columns is not limited other than by available RAM memory of the computer. Increasing the numbers of loci or individuals creates extra empty cells, decreasing will cause the right most columns or bottom most rows to be removed, but this must be confirmed with a warning dialog. Often it can be handy to create some extra cells to provide some workspace within the data matrix, that should be removed when ready.
As mentioned, there is space for names of the individuals, in fact these names are required later; if you do not have these you can use the Re- Number all Individuals function from the Dataset menu to let JoinMap create some basic names.
The standard editing functions, copy , cut and paste , work on groups of cells of the matrix, not within a cell. Each cell can be edited after pressing the F2 or the Enter key. Although it is possible, it is not the intention to enter all data in the data matrix by hand.
The intention is to use a more flexible MS-Excel or similar spreadsheet for data entry, and subsequently copy the data from the spreadsheet and paste them into the data matrix of JoinMap. You can even drag an area from MS-Excel and drop it on the data matrix. Dragging an area is also possible within the data matrix, but the original area will keep its original values and stays selected so that a cut action is needed to remove the original values.
For the spreadsheet it is not important if you use rows for loci and columns for individuals or the other way around, as long as you make sure the prepared JoinMap data matrix is oriented in the same direction using the transpose function prior to the pasting.
Genotype observations should use a coding scheme conform with the scheme described in the Data files chapter. Applying the Highlight Errors function of the Dataset menu will verify whether the data in the data matrix complies to the JoinMap coding scheme. Any cell in error will be highlighted with a red color the colors are environment options , the first cell in error will become selected blue and the corresponding error will be reported on the status bar.
When the whole dataset is in compliance the status bar will report no coding errors detected. When the dataset is ready, you can proceed towards the further process of genetic mapping by creating a population node based on the dataset. This can be done with the Dataset menu function Create Population Node.
This function first checks if there are any coding errors in the data and if there are has the same result as the Highlight Errors function. In the copying of the genotypes of the data matrix to the population node, the empty genotype cells will be coded as unknown genotypes Using JoinMap 17 "-".
For populations of type CP outbreeder full-sib family it is often useful to study the genetic mapping per parental meiosis prior to the simultaneous analysis. Population node When a genotype data population is loaded successfully through a loc-file or from a dataset node a population node will appear in the root level of the navigation tree and the contents-and-results panel will contain several tabsheets e.
Figure 3. The Info tabsheet will display a summary on the data in the population. The Data tabsheet will show a non-editable copy of the genotype data. The tabsheets shows the assigned sequential numbers that will be used for the loci and individuals in all child nodes of the population node. The other tabsheets are initially empty; they will be filled with results of corresponding calculations. Clicking on the Calculate button on the tool bar, or pressing F9, will start the calculations, and after completion the tabsheet will be filled with the results.
The segregation is tested against the normal Mendelian expectation ratios with a normal classification of genotypes using the chisquare test Tables 6, 7. For some situations you can change the classification for which the test must be done, for instance with dominance in an F2 you may wish to test against a ratio rather than a ratio. To do this you must first select the rows in the table that you want to modify, and then apply the Set X2-Test Classification for Selected Loci function from the Population menu and pick the appropriate choice from the dialog.
Tip: for easy selection you can sort the table on an appropriate column, for instance sorting on the genotype c column in an F2 will pool the loci that have c scores. The Individual Genot. It is normal that some individuals will resemble the one parent, some the other, while many will be intermediate, so there is no chisquare test here. But you may use it for instance to detect individuals that have many missing values.
Based upon the chisquare values or the numbers of missing genotypes you can make a selection of records in these tabsheets, and by subsequently applying the Population menu function Exclude Selected Items the corresponding loci or individuals will be checked as excluded in the Loci or Individuals tabsheet, respectively; subsequently you should use the Calculate function again to renew the current tabsheet.
The Similarity of Loci and Similarity of Individuals tabsheets will show the fraction of identical genotypes the calculations include the missing genotypes for fractions above 0. The 0. By using the Population menu function Exclude Identicals the second locus column Locus2 or individual column Individual2 in pairs with a similarity of exactly 1 will be checked as excluded in the Loci or Individuals tabsheet, respectively.
Doing this for loci will result in faster calculations, while you can be certain that identical loci will map at the identical position. For individuals this is not a normal action, though it is available.
For individuals this tabsheet is intended to reveal identical individuals which should be very rare under high density maps and thus indicate possible errors. The Groupings text and Groupings tree tabsheets will show the grouping of loci using the 18 Using JoinMap genotypes of the currently selected i. Both tabsheets are different views of the same analysis, but the text view is more suitable for printing, while the tree view e. Figure 5 is used for creating group nodes in the navigation tree necessary for calculating linkage maps.
Each node in the tree represents a group of linked loci. The grouping is based upon one of the four available test statistics for grouping and will be done at several significance levels thresholds of increasing stringency. The four test statistics parameters can be chosen from the Calculation Options dialog: LOD-value of the test for independence, P-value of the test for independence, recombination frequency and linkage LOD.
Each test parameter has a start value, an end value and a step size that determine the ranges and steps of significance levels that are used for the grouping. Loci determined to be significantly associated at the current threshold value with at least one member of a group will be in the same group. The tree structure arises because at increasing LOD thresholds, groups of loci fall apart branch into unlinked subgroups.
Because the tree can become very large, the branches in the tree that do not branch any further below a certain node will automatically be shown collapsed at this node. Under the null hypothesis the statistic has a chisquare distribution with as degrees of freedom df the number of rows minus one multiplied by the number of columns minus one. The test for independence is not affected by segregation distortion like the LOD score employed normally in linkage analysis, which is called here the linkage LOD, thus leading to less incidence of spurious linkage.
Because pairs can differ in numbers of cells in the contingency table the degrees of freedom will differ as well. Therefore the G2 statistic with more than one df is transformed into a G2 statistic with one df, using an approximation based on equality of P-values.
Finally the value is multiplied by 0. This property is used in JoinMap to calculate from a recombination frequency and its LOD score the virtual numbers of recombinant and nonrecombinant gametes. The above mentioned not transformed G2 statistic for independence in a two-way contingency table can be compared to the chisquare distribution with its corresponding degrees of freedom to obtain the P-value, which is termed here the independence P-value.
The pairwise recombination frequency is estimated with maximum likelihood, either using explicit formulas or using numerical methods iterative EM or Brent's numerical method; cf.
Maliepaard et al, ; Press et al, For situations where the linkage phases are not known DH, HAP, CP , the linkage phases are determined prior to selecting the appropriate estimate of the recombination Using JoinMap 19 frequency. The linkage LOD is the log likelihood ratio comparing the estimated value of the pairwise recombination frequency with 0. This is the nearly final version of the program. The rewriting of the manual is the only remaining task. First the individual populations are analysed, subsequently the combined LOD score or deviance is calculated.
Charts can be made showing the combined LODs as well as the LODs and other parameters of the separate analysis results. Automatic cofactor selection and permutation test for traits combined over populations will follow later. The results for the marker cofactors as used to be shown in the cofactor monitor file are added to this table, this cofactor monitor file is not used anymore.
The table is in the extra tabsheet called "Results cont'd ". The preceding two releases contain errors in analyses using covariates or design cofactors; these are corrected. Interval mapping of multiple quantitative trait loci. Kao, On the differences between maximum likelihood and regression interval mapping in the analysis of quantitative trait loci. Methods for multiple-marker mapping of quantitative trait loci in half-sib populations. Multiple marker mapping of quantitative trait loci in a cross between outbred wild boar and large white pigs.
Genomics 1: QTL mapping in a full-sib family of an outcrossing species. Estimating the locations and the sizes of the effects of quantitative trait loci using flanking markers. Van Ooijen, Accuracy of mapping quantitative trait loci in autogamous species. Kyazma B.
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