Description Usage Arguments Details Value References See Also Examples
Fit a singleQTL model at a single putative QTL position and get detailed results about estimated coefficients and individuals contributions to the LOD score.
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genoprobs 
A matrix of genotype probabilities, individuals x genotypes.
If NULL, we create a single intercept column, matching the individual IDs in 
pheno 
A numeric vector of phenotype values (just one phenotype, not a matrix of them) 
kinship 
Optional kinship matrix. 
addcovar 
An optional numeric matrix of additive covariates. 
nullcovar 
An optional numeric matrix of additional additive covariates that are used under the null hypothesis (of no QTL) but not under the alternative (with a QTL). This is needed for the X chromosome, where we might need sex as a additive covariate under the null hypothesis, but we wouldn't want to include it under the alternative as it would be collinear with the QTL effects. 
intcovar 
An optional numeric matrix of interactive covariates. 
weights 
An optional numeric vector of positive weights for the
individuals. As with the other inputs, it must have 
contrasts 
An optional numeric matrix of genotype contrasts, size
genotypes x genotypes. For an intercross, you might use

model 
Indicates whether to use a normal model (least
squares) or binary model (logistic regression) for the phenotype.
If 
zerosum 
If TRUE, force the genotype or allele coefficients
sum to 0 by subtracting their mean and add another column with
the mean. Ignored if 
se 
If TRUE, calculate the standard errors. 
hsq 
(Optional) residual heritability; used only if

reml 
If 
blup 
If TRUE, fit a model with QTL effects being random, as in 
... 
Additional control parameters; see Details; 
For each of the inputs, the row names are used as individual identifiers, to align individuals.
If kinship
is absent, HaleyKnott regression is performed.
If kinship
is provided, a linear mixed model is used, with a
polygenic effect estimated under the null hypothesis of no (major)
QTL, and then taken as fixed as known in the genome scan.
If contrasts
is provided, the genotype probability matrix,
P, is postmultiplied by the contrasts matrix, A, prior
to fitting the model. So we use P A as the X
matrix in the model. One might view the rows of
A^{1}
as the set of contrasts, as the estimated effects are the estimated
genotype effects premultiplied by
A^{1}.
The ...
argument can contain several additional control
parameters; suspended for simplicity (or confusion, depending on
your point of view). tol
is used as a tolerance value for linear
regression by QR decomposition (in determining whether columns are
linearly dependent on others and should be omitted); default
1e12
. maxit
is the maximum number of iterations for
converence of the iterative algorithm used when model=binary
.
bintol
is used as a tolerance for converence for the iterative
algorithm used when model=binary
. eta_max
is the maximum value
for the "linear predictor" in the case model="binary"
(a bit of a
technicality to avoid fitted values exactly at 0 or 1).
A list containing
coef
 Vector of estimated coefficients.
SE
 Vector of estimated standard errors (included if se=TRUE
).
lod
 The overall lod score.
ind_lod
 Vector of individual contributions to the LOD score (not provided if kinship
is used).
fitted
 Fitted values.
resid
 Residuals.
If blup==TRUE
, only coef
and SE
are included at present.
Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324.
Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723.
pull_genoprobpos()
, find_marker()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26  # read data
iron < read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
# insert pseudomarkers into map
map < insert_pseudomarkers(iron$gmap, step=5)
# calculate genotype probabilities
probs < calc_genoprob(iron, map, error_prob=0.002)
# grab phenotypes and covariates; ensure that covariates have names attribute
pheno < iron$pheno[,1]
covar < match(iron$covar$sex, c("f", "m")) # make numeric
names(covar) < rownames(iron$covar)
# scan chromosome 7 to find peak
out < scan1(probs[,"7"], pheno, addcovar=covar)
# find peak position
max_pos < max(out, map)
# genoprobs at max position
pr_max < pull_genoprobpos(probs, map, max_pos$chr, max_pos$pos)
# fit QTL model just at that position
out_fit1 < fit1(pr_max, pheno, addcovar=covar)

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