qb.sim.cross             package:qtlbim             R Documentation

_S_i_m_u_l_a_t_e_s _Q_T_L _r_e_l_a_t_e_d _d_a_t_a _f_o_r _a_n _F_2 _o_r _B_C _c_r_o_s_s.

_D_e_s_c_r_i_p_t_i_o_n:

     This function is used to simulate genotypic, phenotypic and
     covariate data for BC and F2 populations. The underlying genetic
     model is Cockerham's model and data for both continuous (normally
     distributed only) and ordinal traits can be generated.

_U_s_a_g_e:

     qb.sim.cross(len = rep(100,20), n.mar = 11, eq.spacing = TRUE,
       n.ind = 400, type = c("f2","bc"), missing.geno = 0.0,
       missing.pheno = 0.0, ordinal = c(0.5,0.5),
       qtl.pos = NULL, qtl.main = NULL, qtl.epis = NULL,
       covariate = NULL, gbye = NULL, seed = NULL )
     ## S3 method for class 'qb.sim':
     summary(object, ...)

_A_r_g_u_m_e_n_t_s:

     len: defines the length (in cM) of each chromosome and number of
          chromosomes.  Thus 'len = c(80,90,44)' would represent a
          model with three chromosomes of lengths 80, 90, and 44
          respectively.

   n.mar: The number of markers per chromosome. This can be specified
          as a single number or as a vector. If a single number is
          specified, all the chromosomes will have the same number of
          markers.  If n.mark is a vector then it must have the same
          number of entries as there are chromosomes.  For example, if
          n.mar = c(10,11,9) then we have a three chromosome model in
          which the first chromosome has 10 markers, the second has 11
          and the third has 9.a vector specifying the number of markers
          per chromosome.

eq.spacing: if TRUE, markers will be equally spaced. Default is TRUE. 
          If FALSE, markers are generated uniformly over the
          chromosome.

   n.ind: specifies the number of individuals.

    type: indicates whether to simulate an intercross ("f2") or a
          backcross ("bc").

missing.geno: the frequency of missing genotypes.

missing.pheno: the frequency of missing phenotypic values.

 ordinal: define the probabilities of each ordinal category and the
          number of elements in the vector will determine the number of
          categories. The elements must be positive and the should sum
          up to 1

 qtl.pos: This parameter specifies the positions of qtl as a matrix
          with dimensions (number of qtl) x 2.  Note that the row
          dimension is the number of qtl and is not the number of
          chromosomes.  Each row identifies a qtl, the first column
          entries represent the chromosome's index, the second column
          entries represent the location on the chromosome of the qtl. 
           The (row) order in which qtl are listed in this parameter is
          the index by which they are identified later on in the
          parameters 'qtl.main' and 'qtl.epi'.

qtl.main: The parameter 'qtl.main' is a matrix specifying the main
          effects of QTLs.  The first column gives the qtl-index (the
          row index of the qtl in the 'qtl.pos' parameter.), the second
          and third column gives the additive and dominance effects,
          respectively. There are two or three columns depending on
          'type' being "bc" or "f2".

qtl.epis: It is a matrix specifying epistatic effects. There are 3 or 6
          columns  depending on 'type' being "bc" or "f2". Each row
          gives an epistatic pair.  The first entry in a row gives the
          first qtl index, the second entry represents the index of the
          second qtl. The other entries give the value of the epistatic
          effects (additive-additive, additive-dominance,
          dominance-additive and dominance-dominance)  of the two qtls.
           The indices used to represent the qtl are the row indices of
          the 'qtl.pos' matrix which correspond to the first and second
          qtl in each epistatic pair.

covariate: A vector of two elements, the first being the true value of
          the coffecient  for the fixed covariate and the second the
          true value for the standard deviation  of the random
          covariate. 

    gbye: A matrix specifying the interaction between the fixed
          covariate and QTL main effect. The first column is the index
          of the QTL, the other column(s) is(are) the value(s) of
          interaction(s). 

    seed: Set pseudo-random number seed with 'set.seed' if not 'NULL'.

  object: An object of class 'qb.sim', typically the 'qtl' element of a
          'cross' object created by 'qb.sim.cross'. 

     ...: Not used here.

_D_e_t_a_i_l_s:

     The most important difference of this simulation function from
     others is that it computes phenotype values with full genetic
     model. i.e. both additive,  dominance, and espistatic effects are
     considerred. Furthermore, environmental  effects and
     gene-environment interactions can be included in the model to 
     simulate phenotypes. 

     The outputted genotypes for markers and qtls will be coded as 1
     and 2 for BC and  1,2, and 3 for F2. Missing data will be coded as
     NA.

_V_a_l_u_e:

     'qb.sim.cross' will returns an object of class 'cross'. See
     'read.cross' for details. In addition, a component 'qtl' of class
     'qb.sim' is added which is a list of atmost 6 components depending
     on the options specified.

    geno: is a matrix of true QTL genotypes for every individual and
          each locus. The genotypes are defined following 'read.cross'.

     pos: is a matrix of true QTL position. Same as 'qtl.pos'.

herit.main: is a matrix of the heritability of main effects.
          'nrow($qtl$herit.main)'=no. of QTLs and 
          'ncol($qtl$herit.main)'=2 or 3 depending on the type of
          genetic cross ("bc" or "f2"). The first column being the QTL
          index and the others being additive and dominant heritability
          respectively.

herit.epis: is a matrix of the heritability of epistatic effects.
          'nrow($qtl$herit.epis)'=no. of QTLs pairs interacting and 
          'ncol($qtl$herit.main)'=3 or 6 depending on the type of
          genetic cross ("bc" or "f2"). The first column being the QTL
          index and the others being additive-additive, 
          additive-dominant, dominant-additive and dominant-dominant
          heritability respectively.

herit.cov: is a vector of length 2 containing the heritability of the
          fixed and random covariate.

herit.gbye: is a matrix of heritability of GxE interactions.
          'nrow($qtl$herit.gbye)'= no. of GxE interactions and
          'ncol($qtl$herit.gbye)'= 2 or 3 depending on the type of
          genetic cross ("bc" or "f2"). The first column being the GxE
          index and the others being additive and dominant GxE 
          interaction heritability.

_A_u_t_h_o_r(_s):

     Dr. Nengjun Yi, et al.,  nyi@ms.ssg.uab.edu

_R_e_f_e_r_e_n_c_e_s:

     <URL: http://www.qtlbim.org>

_S_e_e _A_l_s_o:

     'qb.genoprob', 'qb.data'  'qb.model', 'qb.mcmc', 'sim.cross'

_E_x_a_m_p_l_e_s:

     ## Not run: 
     ## Simulate large intercross.
     cross <- qb.sim.cross(len = rep(100,20), n.mar = 11, eq.spacing =FALSE,
       n.ind = 500, type = "f2", ordinal = c(0.3,0.3,0.2,0.2),
       missing.geno = 0.03, missing.pheno = 0.03,
       qtl.pos = rbind(qtl.1=c(chr=1,pos=15),qtl.2=c(1,45),qtl.3=c(3,12),
         qtl.4=c(5,15),qtl.5=c(7,15),qtl.6=c(10,15),qtl.7=c(12,35),qtl.8=c(19,15)),
       qtl.main = rbind(main.1=c(qtl=1,add=0.5,dom=0),main.2=c(2,0,0.7),
         main3=c(3,-0.5,0),main4=c(4,0.5,-0.5)),
       qtl.epis = rbind(epis1=c(qtl.a=4,qtl.b=5,aa=-0.7,ad=0,da=0,dd=0),
         epis2=c(6,8,0,1.2,0,0)),
       covariate = c(fix.cov=0.5,ran.cov=0.07),
       gbye = rbind(GxE.1=c(qtl=7,add=0.8,dom=0)) ) 

     ## Examine simulation information.
     summary(cross$qtl)
     ## End(Not run)

     ## Simulate small backcross.
     cross <- qb.sim.cross(len = rep(60,3), n.mar = 7, eq.spacing =FALSE,
       n.ind = 100, type = "bc", ordinal = c(0.3,0.3,0.2,0.2),
       missing.geno = 0.03, missing.pheno = 0.03,
       qtl.pos = rbind(qtl.1=c(chr=1,pos=15), qtl.2=c(1,45),
                       qtl.3=c(2,12), qtl.4=c(3,15)),
       qtl.main = rbind(main.1=c(qtl=1,add=1.5), main.2=c(2,0),
                        main3=c(3,-1), main4=c(4,0)),
       qtl.epis = rbind(epis1=c(qtl.a=2,qtl.b=3,aa=-2), epis2=c(2,4,3)),
       covariate = c(fix.cov=0.5,ran.cov=0.07),
       gbye = rbind(GxE.1=c(qtl=3,add=2)))
     summary(cross$qtl)

