Introduction

QTL Reaper is software, written in C and compiled as a Python module, for rapidly scanning microarray expression data for QTLs. It is essentially the batch-oriented version of WebQTL. It requires, as input, expression data from members of a set of recombinant inbred lines and genotype information for the same lines. It searches for an association between each expression trait and all genotypes and evaluates that association by a permutation test. For the permutation test, it performs only as many permutations as are necessary to define the empirical P-value to a reasonable precision. It also performs bootstrap resampling to estimate the confidence region for the location of a putative QTL.

Updates

2005-01-27 : The first version of QTL Reaper, version 1.0.0, is released on SourceForge.

2005-07-14 : The second version of QTL Reaper, version 1.1.0, is released on SourceForge.

Update since version 1.0.0
1. Add intercross support
2. Add Mb position for each marker
3. Domiance regression
4. interval values

Download

QTL Reaper can be download from SourceForge at http://sourceforge.net/projects/qtlreaper/.

Installation

First gunzip and untar the package file. Enter the qtlreaper directory
To compile qtlreaper module, execute:

	python setup.py build  
	

you may also modify the setup.py script if your system doesn't have blas and lapack library installed and want to use the ones included in this package.
To install qtlreaper module, execute:

	python setup.py install   
	

Examples

Run the following command in python interpreter to test the module:

#Import the reaper module
import reaper

#Initiate a python Dataset object
bxdGeno = reaper.Dataset()
#read genotype information from a file
#argument is a string containing the filename and location
#file is a tab-delimited text file
#check sample BXD.txt for file format
#make sure the file location is correct
bxdGeno.read("../../Example/BXD.txt")

################
# Dataset Object
################
#dataset type (RI Set or Intercross)
bxdGeno.type
#dataset name
bxdGeno.name
#progeny
bxdGeno.prgy
#number of progeny
bxdGeno.nprgy
#parent 1
bxdGeno.pat
#parent 2
bxdGeno.mat
#Number of Loci
bxdGeno.nloci
#Number of Chromosome
len(bxdGeno[0])
#1st Chromosome
bxdGeno[0].name
#last Chromosome
bxdGeno[-1].name
#1st Locus
bxdGeno[0][0].name
#last Locus
bxdGeno[-1][-1].name
#Locus cM
bxdGeno[-1][-1].cM
#Whether physical info (Mb) are available
bxdGeno[-1][-1].Mbmap
#Locus Mb
bxdGeno[-1][-1].Mb, if physical info are available
#Locus genotype in numbers
bxdGeno[-1][-1].genotype
#Locus genotype in abbrevs
bxdGeno[-1][-1].genotext

################
# Add Parents and F1
################
#Add Parents and F1 and generate a new Dataset Object
bxdNewGeno = bxdGeno.add(Mat='C57BL/6J', F1='B6D2F1', Pat='DBA/2J')
#New Progeny list
bxdNewGeno.prgy

################
# Interval Mapp
################
#Calculate inervals at 1cM, generate a new dataset object 
bxdIntervalGeno = bxdGeno.addinterval()
#Number of Loci
bxdIntervalGeno.nloci


################
# individual chromosome
################
#You can create a new dataset object which contains
only one chromosome for individual chromosome analysis 
chr_1_bxdGeno = reaper.Dataset()
chr_1_bxdGeno.chromosome = [bxdGeno[0]]
#Number of Loci
chr_1_bxdGeno.nloci


################
# Member functions
################
#Most reaper functions require two lists as inputs
#the first list is the strain list, the sceond is the trait values
#the two lists should have the same number of memebers
#the strain list should have the same strains as the header of the genotype file or should be a subset of those
strains =['BXD1', 'BXD2', 'BXD5', 'BXD6', 'BXD8', 'BXD9', 'BXD11', 'BXD12', 'BXD13', 'BXD14', 'BXD15', 'BXD16', 'BXD18', 'BXD19', 'BXD20', 'BXD21', 'BXD22', 'BXD23', 'BXD24', 'BXD25', 'BXD27', 'BXD42']
trait =  [53.570 ,63.885 ,56.700 ,61.750 ,66.325 ,65.150 ,60.400 ,57.920 ,51.925 ,62.350 ,67.175 ,65.850 ,52.425 ,60.925 ,65.350 ,56.750 ,59.750 ,57.888 ,60.250 ,64.433 ,57.125 ,63.600]

#vaiance list will be used in weighted regression (using 1/variance as the weights)
variance = [0.777, 0.108, 1.78, 1.18, 0.370, 0.808, 1.549, 0.710, 0.257, 1.482, 1.816, 0.711, 1.204, 0.059, 0.182, 0.591, 0.357, 0.072, 0.490, 0.239, 0.905, 1.327]
#genotypes of a control locus as a cofactor in the regression
control = "D1Mit1"

################
# Regression
################
#the result is a list of QTL Object
#simple regression (No variance and no control cofactor)
qtl1 = bxdGeno.regression(strains = strains, trait = trait)
#weighted regression (variance weighing but no control cofactor)
qtl2 = bxdGeno.regression(strains = strains, trait = trait, variance = variance)
#composite regression (control cofactor with or without variance weighting)
qtl3 = bxdGeno.regression(strains = strains, trait = trait, control = "D1Mit1")
qtl4 = bxdGeno.regression(strains = strains, trait = trait, variance = variance, control = "D1Mit1")


#maximum QTL
max(qtl1)
max(qtl2)
max(qtl3)
max(qtl4)

#maximum LRS
max(qtl1).lrs
max(qtl2).lrs
max(qtl3).lrs
max(qtl4).lrs

#Locus with maximum LRS
max(qtl1).locus
max(qtl1).locus.name

#sort the qtl list
qtl1.sort()
qtl2.sort()
qtl3.sort()
qtl4.sort()

################
# Permutation
################
#returns a list of highest LRS from each permutation in ascending order
#fixed number permutation
permu1 = bxdGeno.permutation(strains = strains, trait = trait,nperm=1000)
permu2 = bxdGeno.permutation(strains = strains, trait = trait, variance = variance,nperm=1000)
#progressive permutation
#keep on doing permutation until the thresh LRS is not in the top 10 
#or the total number of permutations reaches 1,000,000 
permu3 = bxdGeno.permutation(strains = strains, trait = trait, thresh = 23)

#calculate p-value
pv1 = reaper.pvalue(max(qtl1).lrs, permu1)

################
# Bootstrap
################
#returns a list of counts of times while a single locus has the highest LRS score
#the length of the list equal to the total number of markers
boot1 = bxdGeno.bootstrap(strains = strains, trait = trait, nboot=1000)
boot2 = bxdGeno.bootstrap(strains = strains, trait = trait, variance = variance, nboot=1000)



################
# Anova 
################
# A simple ANOVA (ANalysis Of VAriance between groups) function
list = [1, 2, 4.1, 2, 4, 3.2, 1.1, 5, 5.6, 7.1, 2.3, 4.3, 3.6]
mean, median, variance, stdev, stderr, N = reaper.anova(list)

#######################
# Read from Input file
#######################
#It is easy to read from tab-delimited input file to generate the strain and trait value lists
#use the included trait.txt as example
import string
fp = open("../../Example/trait.txt")
header = fp.readline()
header = string.split(header)
header = map(string.strip, header)
#strip any blank characters
strains = header[1:]
#the header here is the strain list


trait = fp.readline()
trait = string.split(trait)
trait = map(string.strip, trait[1:])
#strip any blank characters
trait = map(float, trait)
#the trait here is the trait valuelist

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