fit1: Fit single-QTL model at a single position
In rqtl/qtl2scan: Genome Scans for QTL Experiments

Description Usage Arguments Details Value References Examples

Fit a single-QTL model at a single putative QTL position and get detailed results about estimated coefficients and individuals contributions to the LOD score.

fit1(genoprobs, pheno, kinship = NULL, addcovar = NULL, nullcovar = NULL,
  intcovar = NULL, weights = NULL, contrasts = NULL, model = c("normal",
  "binary"), se = TRUE, hsq = NULL, reml = TRUE, tol = 0.000000000001,
  maxit = 100)

`genoprobs`	A matrix of genotype probabilities, individuals x genotypes
`pheno`	A numeric vector of phenotype values (just one phenotype, not a matrix of them)
`kinship`	Optional kinship matrix.
`addcovar`	An optional matrix of additive covariates.
`nullcovar`	An optional 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 matrix of interactive covariates.
`weights`	An optional vector of positive weights for the individuals. As with the other inputs, it must have `names` for individual identifiers. Ignored if `kinship` is provided.
`contrasts`	An optional matrix of genotype contrasts, size genotypes x genotypes. For an intercross, you might use `cbind(c(1,1,1), c(-1, 0, 1), c(-0.5, 1, -0.5))` to get mean, additive effect, and dominance effect. The default is the identity matrix.
`model`	Indicates whether to use a normal model (least squares) or binary model (logistic regression) for the phenotype. If `model="binary"`, the phenotypes must have values in [0, 1].
`se`	If TRUE, calculate the standard errors.
`hsq`	(Optional) residual heritability; used only if `kinship` provided.
`reml`	If `kinship` provided: if `reml=TRUE`, use REML; otherwise maximum likelihood.
`tol`	Tolerance value for linear regression by QR decomposition (in determining whether columns are linearly dependent on others and should be omitted)
`maxit`	Maximum number of iterations in logistic regression fit (when `model="binary"`).

For each of the inputs, the row names are used as individual identifiers, to align individuals.

If kinship is absent, Haley-Knott 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.

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.

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.

# load qtl2geno package for data and genoprob calculation
library(qtl2geno)

# read data
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2geno"))

# insert pseudomarkers into map
map <- insert_pseudomarkers(iron$gmap, step=1)

# 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)

# leave-one-chromosome-out kinship matrix for chr 7
kinship7 <- calc_kinship(probs, "loco")[[7]]

# scan chromosome 7 to find peak
out <- scan1(probs[,7], pheno, kinship7, addcovar=covar)

# find peak position
max_pos <- rownames(max(out, map[7]))

# fit QTL model just at that position
out_fit1 <- fit1(probs[[7]][,,max_pos], pheno, addcovar=covar)

# fit QTL model just at that position, with polygenic effect
out_fit1_pg <- fit1(probs[[7]][,,max_pos], pheno, kinship7, addcovar=covar)