Performs a genome-wide scan for QTL effects (Simple and Composite Interval Mapping) in multi-environment trials or multiple populations (M.P. Boer, M. Malosetti, S.J. Welham & J.T.N.M. Thissen).
Options
PRINT = string tokens |
What to print (summary, progress, model, components, effects, means, stratumvariances, monitoring, vcovariance, deviance, Waldtests, missingvalues, covariancemodels); default summ |
|---|---|
PLOT = string token |
Whether to plot the profile along the genome (profile); default prof |
POPULATIONTYPE = string token |
Type of population (BC1, DH1, F2, RIL, BCxSy, CP); must be set |
ALPHALEVEL = scalar |
Defines a genome-wide significance level to calculate the threshold; default 0.05 |
VCMODEL = string token |
Specifies the variance-covariance model for the set of environments or populations (identity, diagonal, cs, hcs, outside, fa, fa2, unstructured); default cs for multi-environment trials, and diagonal for multiple populations |
VCPARAMETERS = string token |
Whether to re-estimate the variance-covariance model parameters (estimate, fix); default esti |
QTLMODEL = string token |
Type of QTL model (q, qqe); default qqe |
COFACTORS = variate |
Index numbers of loci to be used as cofactors for the genetic background |
COFWINDOW = scalar |
Specifies a window for cofactor exclusion from the model; default 106 which means that all cofactors on the same chromosomes are excluded |
THRMETHOD = string token |
Which method to use to calculate the threshold for QTL detection (bonferroni, liji, given); default liji |
THRESHOLD = scalar |
Threshold value for test statistic when THRMETHOD=given |
DISTANCE = scalar |
Distance between loci when THRMETHOD=bonferroni; default 4 |
FIXED = formula |
Formula with extra fixed terms |
UNITFACTOR = factor |
Saves the units factor required to define the random model when UNITERROR is to be used |
STATISTICTYPE = string token |
Which test statistic to plot and save using the STATISTICS parameter (wald, minlog10p); default minl |
COLOURS = scalar, variate or text |
Colours to use for the chromosomes; default * uses the colours of pens 1, 2 up to the number of chromosomes |
TITLE = text |
General title for the plot |
YLOWERTITLE = text |
Title for the y-axis of the lower graph; default 'Environments' for multi-environment trials, and 'Populations' for multiple populations |
YUPPERTITLE = text |
Title for the y-axis of the upper graph; default uses the identifier of the STATISTICS variate or pointer |
XTITLE = string |
Title for the x-axis; default 'Chromosomes' |
MVINCLUDE = string tokens |
Whether to include units with missing values in the explanatory factors and variates and/or the y-variates (explanatory, yvariate); default expl, yvar |
MAXCYCLE = scalar |
Limit on the number of iterations; default 100 |
WORKSPACE = scalar |
Number of blocks of internal memory to be set up for use by the REML algorithm; default 100 |
Parameters
TRAIT = variates |
Quantitative trait to be analysed; must be set |
|---|---|
GENOTYPES = factors |
Genotype factor; must be set |
ENVIRONMENTS = factors |
Environment factor; must be set for a multi-environment trial |
POPULATIONS = factors |
Population factor; must be set for a multiple-population analysis |
UNITERROR = variate |
Uncertainty on trait means (derived from individual unit or plot error) to be included in QTL analysis; default * i.e. omitted |
VCINITIAL = pointers |
Initial values for the parameters ofthe variance-covariance model |
ADDITIVEPREDICTORS = pointers |
Additive genetic predictors; must be set |
ADD2PREDICTORS = pointers |
Second (paternal) set of additive genetic predictors |
DOMINANCEPREDICTORS = pointers |
Dominance genetic predictors |
CHROMOSOMES = factors |
Chromosomes corresponding to the genetic predictors; must be set |
POSITIONS = variates |
Positions on the chromosomes corresponding to the genetic predictors; must be set |
IDLOCI = texts |
Labels for the loci |
IDMGENOTYPES = texts |
Labels for the genotypes corresponding to the genetic predictors |
IDEFFECTS = texts |
Labels for the effects along the y-axis, in the frame below the profile plot |
IDPARENTS = texts |
Labels to use to identify the parents |
QSTATISTICS = variates |
Saves test statistics for QTL effects along the genome |
QEFFECTS = pointers |
Saves QTL effects along the genome |
QSE = pointers |
Saves standard errors of the QTL effects |
OUTFILENAME = texts |
Name of the Genstat workbook file (*.gwb) to be created |
DFILENAME = texts |
Name of the graphics file for the plots |
Description
QMQTLSCAN performs a genome-wide QTL scan in multi-environment trials as described by Malosetti et al. (2004) and Boer et al. (2007). Alternatively, it can analyse data from multiple populations. It uses means per genotype-environment or genotype-population combinations as phenotypic data, but weights can be attached to the means (see the UNITERROR parameter and the UNITFACTOR option below). The response variable must be specified by the TRAIT parameter, and the corresponding environment and genotype factors must be specified by the ENVIRONMENTS and GENOTYPES parameters, respectively. The POPULATIONTYPE option must be set to specify the population type. For a multiple-population analysis, the POPULATIONS parameter should be set (to a factor) instead of ENVIRONMENTS.
Molecular information must be provided in the form of additive genetic predictors stored in variates and supplied, in a pointer, by the ADDITIVEPREDICTORS parameter. Non-additive effects can be included in the model by specifying dominance genetic predictors using the DOMINANCEPREDICTORS parameter (e.g. in a F2 population). In the case of segregating F1 populations (outbreeders) two sets of additive genetic predictors must be specified, the maternal ones by the ADDITIVEPREDICTORS parameter, and the paternal ones by the ADD2PREDICTORS parameter. The corresponding map information for the genetic predictors must be given by the CHROMOSOMES and POSITIONS parameters. The labels for the loci can be supplied by the IDLOCI parameter, and the labels for the genotypes in the marker data can be supplied by the IDMGENOTYPES parameter. If IDMGENOTYPES is set, the match between the genotypes in the phenotypic and in the marker data will be checked.
The QTL detection model assumes ENVIRONMENTS (or POPULATIONS) as a fixed term, and GENOTYPES as a random term. Extra fixed effects can be specified using the FIXED option. For the random genetic effects in the different environments (or populations) a multi-Normal distribution is assumed with mean vector 0 and variance-covariance matrix Σ. The VCMODEL option defines the model to use for Σ; the default for a multi-environment trial is to take compound symmetry, while for a multiple-population analysis the default is to take a diagonal variance matrix (the best model can be selected using the VGESELECT procedure). Initial values for the parameters in the variance-covariance model can be defined by the VCINITIAL parameter. The VCPARAMETERS option controls whether variance-covariance parameters are re-estimated at each iteration (VCPARAMETERS=estimate), or whether they are fixed at the initial values (VCPARAMETERS=fix). The fix setting can be useful to save computation time with large data sets or with more complex models.
By default the QTL model includes a separate QTL effect in every environment (or population), but it is possible to search for QTLs based only on QTL main effects by setting option QTLMODEL=q. The QTL search can be performed with cofactors to control for genetic background effects (Composite Interval Mapping) or without cofactors (Simple Interval Mapping). For Composite Interval Mapping, the COFACTORS option must be set to a variate containing the index numbers of the loci designated as cofactors. The COFWINDOW option defines a window around a tested position within which cofactors are temporarily excluded from the model.
The MVINCLUDE, MAXCYCLE and WORKSPACE options operate in the same way as these options of the REML directive. The UNITERROR parameter allows uncertainty on the trait means (derived from individual unit or plot error) to be specified to include in the random model; by default this is omitted. The UNITFACTOR option allows the factor that is needed to define the unit-error term to be saved (this would be needed, for example, to save information later about the term using VKEEP).
The method to define the threshold value is defined by the THRMETHOD option and uses a genome-wide error rate defined by the option ALPHALEVEL (default 0.05). If THRMETHOD=given, a user-defined threshold value must be specified using the THRESHOLD option. If THRMETHOD=bonferroni, an effective number of tests is calculated using the value specified by the DISTANCE option as the step size (default 4). Alternatively the liji setting uses the method described by Li & Ji (2005). See procedure QTHRESHOLD for details.
The PRINT option specifies the output to be displayed. The summary setting prints the information about the QTLs retained in the model, and the progress setting shows how the scan is progressing. The other settings correspond to those in the PRINT option of the REML directive.
By default QMQTLSCAN produces a pair of graphs: the upper one plots the test statistic associated with the effects of the genetic predictors against their position on the chromosomes, and the lower one is a heat plot showing how the statistic changes over the environments (or populations). You can suppress the plotting by setting option PLOT=*. The STATISTICTYPE option specifies what to plot along the y-axis of the upper plot, either the test statistic or the associated probability value (on a -log10 scale), and also defines what is saved in the variates specified by the QSTATISTICS parameter. The IDEFFECTS parameter can be used to label the effects, and the IDPARENTS parameter can supply labels to identify the parents.
The effects of each genetic predictor and their standard errors can be saved, in pointers, by the QEFFECTS and QSE parameters, respectively. These pointers have 2 levels of suffixes: the first level has 1, 2 or 3 values depending on the setting of the 3 possible predictors ADDITIVEPREDICTORS, ADD2PREDICTORS and DOMINANCEPREDICTORS; the second level has as many levels as the number of levels of the ENVIRONMENTS (or POPULATIONS) factor.
The TITLE, YLOWERTITLE, YUPPERTITLE and XTITLE options can specify the general title of the graph, the title of the y-axis on the lower graph(s), the title of the y-axis on the upper graph, and the title of the x-axis, respectively. The colours to use for the chromosomes in the upper graph are specified by the COLOURS option using either a text of colour names or a variate of RGB values (see the PEN directive for details). If COLOURS is not set, the default is to use the default colours of the pens 1, 2, onwards, up to the number of chromosomes. By default, the plot is sent to the screen. However, you can supply a file for the plot, using the DFILENAME parameter. You can discover the types of graphics file that are supported by running the command DHELP possible.
The OUTFILENAME parameter can be used to write the QSTATISTICS, QEFFECTS and QSE structures to a Genstat work book file in a sheet named STATISTICS. This parameter should not contain an extension as the extension is defined automatically given as .gwb.
Options: PRINT, PLOT, POPULATIONTYPE, ALPHALEVEL, VCMODEL, VCPARAMETERS, QTLMODEL, COFACTORS, COFWINDOW, THRMETHOD, THRESHOLD, DISTANCE, FIXED, UNITFACTOR, STATISTICTYPE, COLOURS, TITLE, YLOWERTITLE, YUPPERTITLE, XTITLE, YLABEL, MVINCLUDE, MAXCYCLE, WORKSPACE.
Parameters: TRAIT, GENOTYPES, ENVIRONMENTS, POPULATIONS, UNITERROR, VCINITIAL, ADDITIVEPREDICTORS, ADD2PREDICTORS, DOMINANCEPREDICTORS, CHROMOSOMES, POSITIONS, IDLOCI, IDMGENOTYPES, IDEFFECTS, IDPARENTS, QSTATISTICS, QEFFECTS, QSE, OUTFILENAME, DFILENAME.
Method
QMQTLSCAN fits the following mixed models repeatedly along the genome:
1) yij = μ + Ej + Σf∈F xiladd cjfadd + xiadd αjadd + GEij
if only ADDITIVEPREDICTORS are specified
2) yij = μ + Ej + Σf∈F ( xifadd cjfadd + xifdom cjfdom ) + ( xiadd αjadd + xidom αjdom ) + GEij
if DOMINANCEPREDICTORS are also specified
3) yij = μ + Ej + Σf∈F ( xifadd cjfadd + xifadd2 cjfadd2 + xifdom cjfdom )
+ ( xiadd αjadd + xiadd2 αjadd2 + xidom αjdom ) + GEij
if both ADD2PREDICTORS and DOMINANCEPREDICTORS are specified (for population type CP)
where yij is the trait value of genotype i in environment (or population) j, Ej is the environmental (or population) main effect, F is a set of cofactors (if cofactors are included in the model), and xifadd and xiadd are the additive genetic predictors of genotype i at the cofactor positions and at the tested position, respectively. The associated effects are denoted by cjfadd and αjadd for cofactors and tested position respectively. In model 2 and 3, xifdom and xidom are dominance genetic predictors of genotype i at the cofactor positions and at the tested position, respectively, with associated effects cjfdom, and αjdom. In model 3, xifadd and xiadd are the additive genetic predictors for the maternal genotype, for cofactors and tested position, respectively, and xifadd2 and xiadd2 are the equivalent additive genetic predictors for the paternal genotype. Finally xifdom and xidom are the dominance genetic predictors for the cofactors and tested position, respectively. The associated effects are given by cjfadd, cjfadd2 and cjfdom for cofactors, and αjadd, αjadd2 and αjdom for tested positions. Genetic predictors are genotypic covariables that reflect the genotypic composition of a genotype at a specific chromosome location (Lynch & Walsh 1998). The residual unexplained genetic and environmental (or population) effects are modelled by the GEij term, which is assumed to follow a multi-Normal distribution with mean vector 0, and a variance covariance matrix Σ. The matrix Σ can either be modelled explicitly (with an unstructured model) or by some parsimonious models (defined by option VCMODEL) as described in the VGESELECT procedure.
The procedure uses the REML directive iteratively to fit the model at each chromosome position, storing the Wald statistic for hypothesis testing. The resulting Wald statistic or the associated probability value (on a -log10 scale) can be plotted to produce the well-known profile plots along the chromosomes.
Action with RESTRICT
Restrictions are not allowed.
References
Boer, M.P., Wright, D., Feng, L., Podlich, D.W., Luo, L., Cooper, M. & van Eeuwijk, F.A. (2007). A mixed-model quantitative trait loci (QTL) analysis for multiple-environment trial data using environmental covariables for QTL-by-environment interactions, with an example in maize. Genetics, 177, 1801-1813.
Malosetti, M., Voltas, J., Romagosa, I., Ullrich, S.E. & van Eeuwijk, F.A. (2004). Mixed models including environmental covariables for studying QTL by environment interaction. Euphytica, 137, 139-145.
Lynch, M. & Walsh, B. (1998). Genetics and Analysis of Quantitative Traits. Sinauer Associates, Sunderland, MA.
See also
Procedures: QMBACKSELECT, QMESTIMATE, QMVAF, VGESELECT.
Commands for: Statistical genetics and QTL estimation.
Example
CAPTION 'QMQTLSCAN example'; STYLE=meta
SPLOAD [PRINT=*] '%GENDIR%/Examples/F2maize_traits.gsh'
& '%GENDIR%/Examples/F2maizemarkers.GWB'; SHEET='LOCI'
& '%GENDIR%/Examples/F2maizemarkers.GWB'; SHEET='ADDPREDICTORS'
& '%GENDIR%/Examples/F2maizemarkers.GWB'; SHEET='DOMPREDICTORS'
" best variance-covariance model from VGESELECT "
POINTER [MODIFY=yes; NVAL=idlocus] addpred
POINTER [MODIFY=yes; NVAL=idlocus] dompred
QMQTLSCAN [PRINT=summary,progress; PLOT=profile; POPULATIONTYPE=F2;\
VCMODEL=fa; QTLMODEL=QQE;\
THRESHOLD=th; STAT=minlog; THRMETHOD=liji]\
TRAIT=yld; GENOTYPES=G; ENVIRONMENTS=E;\
CHROMOSOMES=mkchr; POSITIONS=mkpos; IDLOCI=idlocus;\
ADDITIVEPREDICTORS=addpred; DOMINANCEPREDICTORS=dompred;\
QSTATISTICS=minlog10p; QEFFECTS=Eff2; QSE=Se2;\
OUTFILE='F2maize_multi_out'