qtlMapTwoPart          package:MetaNetwork          R Documentation

_Q_T_L _t_e_s_t _s_t_a_t_i_s_t_i_c _u_s_i_n_g _t_w_o-_p_a_r_t _m_o_d_e_l

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

     QTL model for a mixed phenotype with qualitative and quantitative
     variance.

     Part one tests whether the presence/absence of metabolites has a
     genetic  basis, i.e. whether different genotype classes at a given
     marker differ in  their number of non-zero observations. 

     Part two tests whether  quantitative variation in non-zero
     abundance has a genetic basis: whether  the non-zero observations
     for each of these genotype classes at a given  marker differ in
     mean abundance. 

     The 'p-value' of the QTL is computed  as the product of the two
     'p-values' in the two parts.

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

     qtlMapTwoPart(genotypes, traits, spike, filename=NULL) 

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

genotypes: matrix of genotypes for each marker (rownames) and
          individual  (columnnames), as numeric values 1, 2 or NA when
          missing.
           See 'genotypes' example data.

  traits: matrix of phenotypes for each trait (rownames) and individual
           (columnnames), as numeric or NA when missing.
           See 'traits' or 'traits2' example data. 

   spike: numeric cut-off value to separate absent (qualitative) from
          available (quantitative) trait abundance. 

filename: (optional) path of the file where the QTLs are to be stored.
          Default is NULL. 

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

     The analysis model for 'traits' data with both qualitative and
     quantitative variance.  If the distribution of 'traits' is mixed,
     a two part model is used. 

     The first part describes a binominal model which tests  for
     association of markers with observed noise value. For each trait: 
     Let y_i denote the mass intensity for i^{th} RIL and let z_i=0 if
     y_i <=q 'spike' and z_i=1 if y_i >'spike'. We then test each
     marker for significant differences between the two genotypes  for
     the probability of presence of the trait (mass) peak: 
      H_0: p lbrace z=1arrowvert g=1 rbrace = p lbrace z=1 arrowvert
     g=2 rbrace
        versus the alternative hypothesis
      H_1: p lbrace z=1 arrowvert g=1 rbrace neq p lbrace z=1 arrowvert
     g=2 rbrace,
      where g is the genotype (1 or 2) of a marker under analysis. 

     The second part describes a parametric model which tests for
     association of  markers with quantitative variances for those
     individuals where y_i >'spike'. Under the assumption of normal
     distribution, we test each marker for  significant differences in
     the mean values between two genotypes:
       H_0: u lbrace g=1 rbrace = u lbrace g=2 rbrace 
      versus the alternative hypothesis
       H_1: u lbrace g=1 neq u lbrace g=2 rbrace . 

     When there is binary data only (no quantitative data) or
     quantitative data only (no spike), the 'p-value' of the missing
     part is set to one.  The p value of the two-part model is then
     determined by the multiple of the p  values from the two parts.

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

     Returns a matrix of QTL mapping of traits (rownames)  to markers
     (columnnames), as -log_{10}(p) values.  The -log_{10}(p) values
     are assigned negative if traits for individuals carrying genotype
     "1"  are larger on average than traits for individuals carrying
     genotype "2".  Otherwise, the -log_{10}(p) value is positive.

_N_o_t_e:

     The individual columns of 'genotypes' and 'traits' must have the
     same order. The names of individuals must be consistent over
     'genotypes' and 'traits'.

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

     Jingyuan Fu <j.fu@rug.nl>,  Morris Swertz <m.a.swertz@rug.nl>, 
     Ritsert Jansen <r.c.jansen@rug.nl>

_S_o_u_r_c_e:

     Keurentjes JJB, FU J, de vos CHR, Lommen A, Hall RD, Bino RJ, van
     der Plas LHW, Jansen RC, Vreugdenhil D and Koornneef M. The
     genetics of plant metabolism. Nature Genetics (2006) 7: 842-849.

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

     Fu J, Swertz MA, Keurentjes JJB, Jansen RC. MetaNetwork: a
     computational tool for the  genetic study of metabolism. Nature
     Protocols (2007). 

     <URL: http://gbic.biol.rug.nl/supplementary/2007/MetaNetwork>

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

     Use 'genotypes' and 'traits' as example data sets or  use
     'loadData' to load your own data.
       Use 'MetaNetwork' for automated application of this function as 
     part a genetic analysis protocol on metabolites.

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

     ## load the example data provided with this package
     data(genotypes)
     data(traits)

     ##OR: load your own data
     #genotypes  <- loadData("genotypes.csv")
     #traits     <- loadData("traits.csv")

     ##calculate the two part qtl using a cutoff spike of 4
     qtlProfiles <- qtlMapTwoPart(genotypes=genotypes, traits=traits, spike=4) 

     #show part of the results
     qtlProfiles[1:5,1:5]

