BioMERCATOR version 2.0

Transcription

1 BioMERCATOR version 2.0 Software for genetic maps display, maps projection and QTL meta-analysis Anne Arcade, Aymeric Labourdette, Fabien Chardon, Matthieu Falque, Alain Charcosset, Johann Joets

2 Contacts: Address: UMR Génétique végétale Ferme du Moulon Gif-sur-Yvette France BioMercator was supported by Génoplante ( BioMercator 2.0 was supported by the Generation Challenge Programme ( 1

3 Contents Introduction... 3 Installation... 3 Interface overview... 3 Concepts... 5 Map projection... 5 QTL meta-analysis... 7 Using BioMercator... 9 Creating a workspace... 9 Creating a project... 9 Loading a map file... 9 Loading a QTL file... 9 Display options Whole map / zoom on one chromosome Informations about QTLs Displaying markers and EST QTL display Markers display Performing map projection Computing QTL meta-analysis Selecting the QTL set Viewing results Data files Map file Text map file QTL file MC-QTL XML map file BioMercator XML map file References

4 Introduction A new challenge in breeding programs is to integrate information from genomics and that from quantitative trait loci (QTL) analysis, in order to identify sequences controlling the variation of important traits. Thus, discovering co-locations between candidate genes and QTLs is an essential step. Despite the outburst of genomic databases, constructing an integrative genetic map compiling genes, QTLs and other loci gathered from multiple maps remains a manual and tedious task. Those QTLs detected in independent experiments and located in a same region of a chromosome might be in fact several estimations of the position of one single QTL. This assertion can be verified by means of appropriate statistical tools such as meta-analysis, which consists in combining data from diverse sources in a single study. Meta-analysis is a useful tool to synthesize dense QTL information and to refine QTL position. BioMercator is a stand-alone application which automatically performs these tasks. (1) BioMercator offers a user-friendly graphical map browser. Genetic map and QTL data are loaded from text files. (2) BioMercator performs automatic compilations of several genetic maps. A consensus genetic map can be built from multiple individual maps by iterative projection of QTLs, genes and other loci. (3) BioMercator computes meta-analysis of QTLs (Goffinet and Gerber, 2000). This statistical method determines the most likely number of "real" QTLs represented by N QTLs detected in independent experiments for the same trait or related ones. Finally, the graphical interface allows to visualize co-locations between consensus QTLs and genes. Installation BioMercator is a software written in Java 1.4. It can run under Windows, Unix, Linux and Mac OS X platforms supporting at least Java 1.4. If you already have a previous version of the Java virtual machine, you will have to uninstall it before installing Java 1.4. You can install Java 1.4 for free by connecting to Sun MicroSystems web site. Once connected to this site, the installation can be automatically performed through a Java Plugin. If you choose a manual installation, select the "Java 2 Runtime Environment" (J2RE) (at least version 1.4). You must have 40 Mo of hard disk space available to install the J2RE on your computer. Once the JRE installed on your computer, you the application. Interface overview When starting BioMercator, you will see the general interface of the software, divided in multiple parts : on the top of the window, a menu bar. In the upper left corner, a navigation panel containing an arborescence of nodes corresponding to projects, maps, meta-analysis experiments, Loaded maps are displayed in the main central panel. BioMercator also provides many display options : zoom on each chromosome of the map, informations about each QTL by passing mouse over it, adaptable scale for the genetic unit, export of map display to JPEG image format. 3

5 Menu bar Navigation panel Map display panel Scale Information field Display options buttons Map projection dialog Meta-analysis dialog QTL consensus positions given by meta-analysis Figure 1 : BioMercator general interface 4

6 Concepts Map projection The objective is to project QTLs, genes and other loci from a genetic map to another one, in order to pool all information on one single map. This computation is only based on loci position data. For each pair of homologous chromosomes (Fig. 2), common loci (sharing a same name) are listed (Fig. 3). Inverted common loci can be automatically discarded from the analysis. Then, a specific distance ratio is computed for each interval between two common loci (Fig. 4). A global ratio is computed for projecting loci located above the first interval of common markers and below the last interval of common markers. At last, QTLs and/or remaining loci position on the target map are computed by application of the appropriate distance ratio (homothetic projection) (Fig.5). As no criteria are provided to assess the quality of projection process, users are encouraged to examine the resulting map. Individual maps can be iteratively projected on the compiled map. In this case, carefully deciding the order of maps in projection process is crucial. We suggest to begin projection with maps showing highest quality in loci order, in order to limit error propagation. Figure 2 : A correspondence ( ) between homologous chromosomes of the two maps is established, based on chromosomes names. 5

7 Figure 3 : For each pair of homologous chromosomes, common loci (sharing the same name) are listed. Figure 4 : Inverted common loci are detected, and those showing the most intersections with others can be automatically discarded from the analysis (case of m3). A specific distance ratio R is computed for each interval bounded by common loci. 6

8 Figure 5 : QTLs and/or remaining loci are projected ( ) from one map on the other one, by application of the appropriate distance ratio. QTL meta-analysis QTL meta-analysis is a useful tool to synthesize QTL information from independent experiments and to refine the chromosomal region involved in trait variation control. The QTL meta-analysis algorithm developed by Goffinet and Gerber (2000) can help to determine if N QTLs linked to a same trait or related ones, detected in independent experiments and located in a same region, are consistent with 1-, 2-, 3-, 4- or N- QTL models (N-QTLs model being the case where there are as much "real" QTLs as input QTLs). For each of those five models, the most likely QTL arrangement, assuming a Gaussian distribution, is determined by means of the maximum likelihood method (Fig. 6). Then, an Akaike-type statistical criterion indicates the best model among the five ones. For each model, consensus QTL positions are determined as the mean of QTL distribution maximizing the likelihood. The variance of QTL consensus position was estimated in the following manner (Charcosset, unpublished results) : var(qtl) = 1 Σ 1 σ i 2 where σ i 2 = variance of position of the i th QTL of the distribution 7

9 The 95% confidence interval of the consensus QTL position can be approximately deduced from the previous variance by : C.I. = 3.92 * var(qtl) Meta-analysis computing is based on the position of each input QTL, and on the variance of this position, assessed through confidence interval values. Goffinet and Gerber jumped directly from a model with 4 QTLs to a model with N QTLs and did not extend their algorithm to intermediary models (5, 6, 7, ). The reason for this is that Goffinet and Gerbers's study was based on a model chromosome with a length of 200 cm, and that 4 hypothetical non-overlapping QTLs of 50 cm-long can be found at most on this chromosome. The Akaike-type criteria values were determined using simulations processes. These values are optimized for use under precise conditions: - QTLs linked to a same trait and located on a same chromosome should all come from strictly independent experiments. Including several QTLs detected in a same experiment for a given trait and located on the same chromosome may introduce biases in the analysis. - the QTL dataset should include 10 to 40 QTLs - this set of QTLs should lye within a genome region no longer than 200 cm. Users working on larger chromosomes, or on a larger set of QTLs, should segment their working set to fit the above conditions, and repeat meta-analysis experiment on each subset. Figure 6 : Computation of global likelihood for one of the possible QTLs arrangements in a 2-QTLs model 8

10 Using BioMercator Creating a workspace A workspace is a directory containing the files corresponding to the data (maps, QTL, metaanalysis). At the first time run, you will be asked to choose a new workspace. Then each time you launch the application, you may either select an existing workspace or create a new one. You can work with one workspace at a time only. Once a workspace is selected, all the data (maps, QTL, meta-analysis) stored in it are automatically loaded. Creating a project All map and QTL data in BioMercator are organized in projects. Projects are stored in a workspace and thus may be loaded each time you run BioMercator. Many projects can be created in a same session. After launching BioMercator, you will first have to create a new project through the menu File/New project, and precise its name in the dialog window. A new project node will appear in the navigator. A project can be removed (right-click on the project name) Loading a map file All maps to be loaded in BioMercator are described in text files. Three different formats are currently accepted as input: - A text file closely formatted to the output of the widely-used mapping tool MapMaker (Lander and Green, 1987). See the "Data files" chapter for details about this format. - A XML file corresponding to the output of MC-QTL (Jourjon et al., 2003), a software that allows the detection of QTL in multiple parental lines. This XML file describes map data as well as QTL data. - A XML file corresponding to the output of BioMercator when the map projection module is used. This XML files describes the results of map projection, and contains markers, QTL and other loci data. To load a map file, select File/Load text map file, File/Load MC-QTL map file, or File/Load XML map file according to the concerned map format. Choose the project to load the map in, and browse the file chooser for the file to open. A new node "MAP" is created in the navigator, and the map is displayed in the central panel. A map can be removed (right-click on the map name) Loading a QTL file One or several QTL files relative to a given genetic map can be loaded. QTLs are described in tabulated files (see "Data files" chapter for more details about this format). 9

11 To add a QTL file to a map, select File/Load text QTL file. In the dialog window, select the map to add QTLs to, and choose how positions of QTL confidence intervals will be represented: - Confidence intervals values can be described by the user in fields "From" and "To" from the QTL file. Confidence intervals are often defined as the interval delimited by a standard decrease in the LOD score value (support interval). This support interval can be very biased for QTL having small effect (Mangin et al., 1994). For this reason, the following method for estimating confidence interval values was implemented. - Confidence interval values can be deduced from population size and proportion of variance (R²) explained by the QTL ("Resolving power", as described by Darvasi and Soller (1997)). Resolving power can help to better estimate the 95% confidence interval of the QTL location than the classical method of LOD support interval. Resolving power is calculated on the basis of an infinite number of markers, however it can be applied for quite a moderate marker spacing (10 to 20 cm), as long as marker spacing is equal to or less than resolving power. The expected confidence interval (resolving power) can be expressed as: CI = 530 / (N * v) where N = population size v = proportion of variance explained by the QTL In the latter case, you will be prompted to enter a value for population size in the dialog window. Then, select the QTL file to load. A new node "QTL data" is created with leaves corresponding to the different traits described, and added to the map node. QTLs are displayed on the map. The vertical bar corresponds to the confidence interval of the QTL. The horizontal bar represents the position of the QTL, while bar's length is proportional to R² value. Display options Whole map / zoom on one chromosome After loading a new map, all chromosomes are displayed in the panel. It is possible to display a single chromosome by clicking on its main rectangle. Click on the button "Whole map" to display all chromosomes of the map. By default, the scale representation for the map is set to 1. You can change this through the Scale selector. This option is useful in markers- or QTL-dense regions of the chromosome. Informations about QTLs By passing the mouse over a QTL, informations concerning it can be displayed in the information field (name of the QTL, trait, R², Ftest, LOD, position, allelic and cross effects, original map if this QTL was projected). Displaying markers and EST By default, markers and ESTs (if described) are displayed on the screen. It is possible to filter markers display and to allow only EST vizualisation by deselecting the Display/Show markers 10

12 option. This is useful when markers density is high, and that the user needs to display only ESTs in order to vizualise colocations between ESTs and QTLs. The quality of "EST" is described in a given field of map text file (see "Data files" chapter for more details). QTL display Several parameters of QTL display can be modified through the Display/QTL settings menu. - Filtering of QTL based on R² value: only QTL whose R² value exceeds the defined threshold will be displayed on the screen. - Filtering of QTL based on LOD value: only QTL whose LOD value exceeds the defined threshold will be displayed on the screen. - Default colors attributed to QTL traits can be modified through the color chooser. Markers display Specific types of markers can be represented in defined colors. These markers can be defined by specifying a type in the corresponding field of the map text file (see "Data files" chapter for more details). Markers projected from one map on another one will be displayed in blue by default. The default colors can be changed through the Display/Markers settings menu. Saving map display as a JPEG image Map display can be saved in JPEG format. Select menu File/Save as JPG image, choose a directory and precise a file name for the image. Performing map projection To project elements from a loaded map on another loaded map, open the projection window dialog through Tools/Maps projection menu. The resulting map will correpond to the original map, carrying the projected loci. - First select in the list of loaded maps which map to project on which one. - Then, precise which elements to project: non-est markers, QTLs, EST. Several elements can be selected. - Precise the name of the new map (ex: myprojectedmap). This name will be used for the new MAP node, and for names of output files describing the new projected map. - Precise if inversions of common markers must be automatically resolved (i.e. these markers will automatically be detected and will not be used as common markers) or if you prefer to manually resolve these inversions (in this case, to help you in this task, BioMercator will display the list of inverted markers in a dialog window and in the report file, and will abort the projection process). - Select the project of the navigation panel to store the new projected map in. - Precise a directory in which BioMercator will store projection output files. BioMercator creates 4 output files: myprojectedmapreport.out: this file describes the main steps of projection process. It enumerates the list of common markers for each chromosome, describes the 11

13 resolution of each inversion and the markers discarded from the analysis, and gives the new position for each projected locus. myprojectedmap.txt: this file describes the new projected map (chromosome, locus name, positions, ) in tabulated format myprojectedmap.xml: this file describes the new projected map in XML format. It can be loaded in next sessions of BioMercator through the File/Load XML map file menu. Note: This format can sometimes be not generated in case of huge and dense genetic maps. myprojectedmapcommonmarkers.txt: this file describes the common markers used for computing R ratios and project loci. No criteria assessing the quality of projection process are delivered. Users are thus encouraged to carefully examine the resulting map. Moreover, in case of iterative map projection, caution in designing the order of maps in projection process is crucial. We suggest to begin projection with maps showing highest quality in loci order, in order to limit error propagation. Computing QTL meta-analysis A meta-analysis experiment must be carried out on a chromosome with a set of QTLs. The number of QTLs should lie between 10 and 40 to optimize analysis conditions. If you are working on a larger set of QTLs, or if meta-analysis selects the N-QTLs model as the most likely one, you can try to split up the analysis, by working on a subset of these QTLs, and launching a new meta-analysis experiment. Selecting the QTL set First, zoom on the desired chromosome carrying QTLs by clicking on its main rectangle. Then, you can choose to discard some of these QTLs from the analysis, by simply rightclicking on their R² or CI bar. These QTLs will not be permanently deleted from the map, but only discarded from the present analysis. Clicking on the Default display button will restore them (as well as QTLs that were discarded by R² or LOD threshold filter). Caution: QTLs that are not displayed, either because their R² or LOD value is below a given threshold (defined in Display/QTL settings), or because they were discarded by mouse-click, will NOT be included in the meta-analysis experiment. Select menu Tools/Meta-analysis to launch meta-analysis. Viewing results A new "Meta-Analysis" node is added to the present MAP node. The meta-analysis node is composed of subnodes detailing meta-analysis results: - Input node: clicking on this node opens a table recapitulating the set of QTLs used as input data. - Results node: clicking on this node opens a table describing results of the meta-analysis experiment. For each model (1, 2, 3, 4, N QTLs), the value of the Akaike criterion (AIC value) is given. The most likely model is the one minimizing the Akaike criterion (the lower the AIC value, the more likely the model). For each model, the most likely position(s) of consensus QTL(s) and confidence intervals are given. In some cases, the global 12

14 likelihood for a model, whatever the QTL distribution, is null. Then, consensus positions cannot be determined, which is indicated by the "NA" (non-available) value. - MAP k=1 MAP k=n : for each of these five models, consensus positions of QTLs can be displayed (unless they are "not available"). Consensus QTLs are represented in red, while input QTLs are represented in grey. A meta-analysis node can be removed (right-click on the meta-analysis name) To launch a new meta-analysis experiment, you can come back to the original map by clicking on the concerned MAP node, display the whole map to see all chromosomes, and, if necessary, click the Default display button to display all QTLs of the map. 13

15 Data files Map file Text map file Mapmaker software (Lander and Green, 1987) is widely used for constructing genetic maps. BioMercator input map file is therefore closely formatted to the output of Mapmaker software (Fig. 7). >map ======================================================================== Map: Markers Distance 607 psr119c 1.9 cm 608 hp20075a 6.7 cm 609 csu25b 22.0 cm 610 npi cm 611 npi386cz 24.3 cm 612 rz900b 3.1 cm 613 phi cm 614 isu85b 2.9 cm 615 isu54b 5.4 cm 616 umc cm 617 bcd1072b 2.6 cm 618 isu53b cm 12 markers log-likelihood= ======================================================================== Figure 7 : Mapmaker output format This format was a little simplified and modified, in order to integrate additional information. Figure 8 describes the minimal output format accepted by BioMercator. Fields separators in Mapmaker are SPACE characters. However, BioMercator accepts SPACE characters as well as TABULATIONS as fields separators. If you modify the output of a Mapmaker file with your favorite editor or table spreadsheet, save it as TXT file (for tabulations separators) or PRN file (for SPACE separators). Please avoid Unicode encoding, as BioMercator will not be able to further read the file. As space characters are considered as separators, please avoid to insert space characters in markers names. 14

16 mapname=mymap poptype=f2 chromosome psr119c 1.9 cm 608 hp20075a 6.7 cm EST 609 csu25b 22.0 cm 613 phi cm 614 isu85b 2.9 cm EST 615 isu54b 5.4 cm 616 umc cm 617 bcd1072b 2.6 cm 618 isu53b chromosome umc cm 557 bnlg cm 558 bnlg cm 559 umc cm 560 isu136b 9.1 cm 561 dupssr cm EST 562 isu111b 16.1 cm 563 bnlg cm EST 568 rz273c 5.2 cm 569 psr160d 2.0 cm 570 rz Figure 8 : BioMercator minimal input file - REQUIRED: A first line describing the name of the map must be given, with the format: mapname=mymap - OPTIONAL: A second line describing the population type, with the format: poptype=f2 - REQUIRED: Each chromosome description must begin with the line: chromosome mychromosome1 - REQUIRED: Chromosome description is a succession of lines composed of a defined number of fields separated either by spaces or tabulations: All lines excepted the last line of the chromosome are composed of at least 4 obligatory fields, and a 5 th optional field: 561 dupssr cm EST Required Number for the marker. For convenience, can be given any value Required Name of marker locus. Avoid space characters Required Relative distance in cm between this locus and the next one Required cm unit Optional Quality of the marker (EST, SSR, RFLP, ) 15

17 The last line of a chromosome is composed of at least 3 obligatory fields, and a 4 th optional field: 570 rz EST Required Number for the marker. For convenience, can be given any value Required Name of marker locus. Avoid space characters Required At least four '-' characters to mark the end of the chromosome Optional Quality of the marker (EST, SSR, RFLP, ) Additional lines created by Mapmaker (such as "Map: " or "Markers Distance" or "81.5 cm 12 markers log-likelihood= " can be left in the file, and will not be interpreted by BioMercator). QTL file A QTL file is relative to a given map that must be already loaded in BioMercator. The QTL file is a tabulated (TXT) file (fields separators are tabulations) and can be created with any table spreadsheet. The QTL file is composed of 10 fields, and missing values, if optional, must be replaced by "NA". The order of fields must be respected. An example of QTL file is given in Fig. 9. mapnam e mymap mymap name FLORM 1 FLORF 1 chromosom trai e t 1 FLOR M 2 FLOR F lodscor r2 e SI M positio n fro m N Y to Figure 9 : Example of QTL file The first line describes the titles of the 10 fields. Each following line corresponds to the description of a distinct QTL and its parameters: - mapname: name of the map as described in the line "mapname=" of the map file. REQUIRED - name: name of the QTL. It can be the name of the trait suffixed with a number. REQUIRED - chromosome: chromosome on which the QTL is located. This name must correspond to the names of chromosomes described in the map file. REQUIRED - trait: name of the trait. REQUIRED - lodscore: value for LOD score. Please use '.' as decimal. OPTIONAL (indicate "NA" if not available) - r2: value for R². Please use '.' as decimal. REQUIRED - SIM: indicate whether this QTL was detected by means of simple interval mapping (in this case indicate Y) or composite interval mapping (indicate N). OPTIONAL (indicate "NA" if not available) 16

18 - position: position of the QTL on the chromosome. Please use '.' as decimal. REQUIRED - from: position for the beginning of confidence interval. Please use '.' as decimal. REQUIRED - to: position for the end of confidence interval. Please use '.' as decimal. REQUIRED 17

19 MC-QTL XML map file BioMercator can read XML output files generated by MC-QTL software. BioMercator XML map file BioMercator can read XML files created during previous projection processes and saved on the hard disk. 18

20 References Darvasi, A. and Soller, M. (1997) A simple method to calculate resolving power and confidence interval of QTL map location. Behav Genet, 27, Goffinet, B. and Gerber, S. (2000) Quantitative Trait Loci: a meta-analysis. Genetics, 155, Jourjon, M.-F., Mangin, B., Marcel, J., Ngom, B. (2003) Improving QTL detection with MCQTL software. Plant & Animal Genome XI Conference, San Diego, January 11-15, Lander, E.S., Green, P., Abrahamson, J., Barlow, A., Daly, M.J., Lincoln, S.E. and Newburg, L. (1987) MAPMAKER : an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics, 2, Mangin, B., Goffinet, B., Rebai, A. (1994) Constructing confidence intervals for QTL location. Genetics, 138,