qtlMapTwoPart {MetaNetwork} | R Documentation |
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.
qtlMapTwoPart(genotypes, traits, spike, filename=NULL)
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. |
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.
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.
The individual columns of genotypes
and traits
must have the same order.
The names of individuals must be consistent over genotypes
and traits
.
Jingyuan Fu <j.fu@rug.nl>, Morris Swertz <m.a.swertz@rug.nl>, Ritsert Jansen <r.c.jansen@rug.nl>
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.
Fu J, Swertz MA, Keurentjes JJB, Jansen RC. MetaNetwork: a computational tool for the genetic study of metabolism. Nature Protocols (2007).
http://gbic.biol.rug.nl/supplementary/2007/MetaNetwork
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.
## 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]