qtlSummary            package:MetaNetwork            R Documentation

_S_u_m_m_a_r_i_z_e _Q_T_L _e_f_f_e_c_t_s

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

     Summarize QTL profiles including peak position (chromosome,
     centi-Morgan),  support interval (calculated via the function
     'qtlSupportInterval'),  proportion of QTL variation explained by
     part 1 and 2 of the 'qtlMapTwoPart' model, and allele substitution
     effect.

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

       qtlSummary(markers, genotypes, traits, qtlProfiles, spike, qtlThres, 
                  interval.dropoff = 1.5, filename = NULL)

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

 markers: matrix of markers (rownames) and their chromosome numbers 
          (column 1) and centi-Morgan positions (cM, column 2), ordered
          by position.
           See 'markers' example data. 

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' example data. 

qtlProfiles: matrix of QTL mapping of traits (rownames)  to markers
          (columnnames), as -log_{10}(p) values.
           See 'qtlProfiles' example data.

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

qtlThres: numeric -log_{10}(p) threshold value for significant QTLs. 

interval.dropoff: (optional) drop-off value for QTL support  intervals.
          Default is 1.5. 

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

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

     Returns a data frame with a QTL summary which contains the
     following headers: 

traitName: name of trait.

  QTLchr: the chromosome number where a QTL locates.

   QTLmk: the marker where the trait maps to.

QTLleftcm: the cM position of left border of the QTL support interval.

QTLpeakcm: the cM position of the QTL peak.

QTLrightcm: the cM of right border of the QTL support interval.

    logp: the -log_{10}(p) value of a QTL.

   VarP1: the percentage of qualitative variance explained by a QTL.

   VarP2: the percentage of quantitative variance explained by a QTL.

additive: the allele substitution effect (=half the difference of 
          metabolite abundance between genotype 1 and 2).

     See 'qtlSumm' example data.

_N_o_t_e:

     The individual columns of genotypes and traits must have the same
     order.  The markers should be ordered sequentially. The names of
     markers, traits and individuals should be consistent over
     'markers',  'genotypes', 'traits' and 'qtlProfiles'.

_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>

_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 'markers', 'genotypes' and 'traits'  as example data sets or
     use 'loadData' to load your own data.
       Use 'qtlMapTwoPart' to calculate 'qtlProfiles'.
      Use 'qtlThreshold' to estimate 'qtlThres' QTL threshold for 
     significance.
      Use 'MetaNetwork' for automated application of this function as 
     part of a genetic analysis protocol on metabolites.

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

     ## load the example data provided with this package                         
     data(markers)   
     data(genotypes)
     data(traits)                                                       
                                                  
     ##OR: load your own data                     
     #markers    <- loadData("markers.csv")
     #genotypes  <- loadData("genotypes.csv")
     #traits     <- loadData("traits.csv")  
                                                  
     ##calculate the two part qtl
     qtlProfiles <- qtlMapTwoPart(genotypes=genotypes, traits=traits, spike=4)

     ##set the qtl threshold
     qtlThres    <- 3.79

     ##OR: estimate the threshold yourself
     #qtlThres   <- qtlThreshold(genotypes, traits, spike=4)
                                                                                                                            
     ##summarize the qtlProfiles
     qtlSumm     <- qtlSummary(markers, genotypes, traits, qtlProfiles, spike=4, 
                               qtlThres=qtlThres)

     ##show the summary
     qtlSumm[1:5,]

