`scan_multi_onechr` calculates log likelihood for d-variate phenotype model fits. Inputted parameter `start_snp` indicates where in the `probs` object to start the scan.
scan_multi_onechr(
probs,
pheno,
kinship = NULL,
addcovar = NULL,
start_snp = 1,
n_snp = dim(probs)[3],
max_iter = 10000,
max_prec = 1/1e+08,
cores = 1
)
an array of founder allele probabilities for a single chromosome
a matrix of phenotypes
a kinship matrix for one chromosome
a matrix, n subjects by c additive covariates
index of where to start the scan within probs
the number of (consecutive) markers to include in the scan
maximum number of iterations for EM algorithm
stepwise precision for EM algorithm. EM stops once incremental difference in log likelihood is less than max_prec
number of cores for parallelization
a tibble with d + 1 columns. First d columns indicate the genetic data (by listing the marker ids) used in the design matrix; last is log10 likelihood
Knott SA, Haley CS (2000) Multitrait least squares for quantitative trait loci detection. Genetics 156: 899<U+2013>911.
Jiang C, Zeng ZB (1995) Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140: 1111-1127.
Zhou X, Stephens M (2014) Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nature methods 11:407-409.
Broman KW, Gatti DM, Simecek P, Furlotte NA, Prins P, Sen S, Yandell BS, Churchill GA (2019) R/qtl2: software for mapping quantitative trait loci with high-dimensional data and multi-parent populations. GENETICS https://www.genetics.org/content/211/2/495.
# NOT RUN {
# read data
n <- 50
pheno <- matrix(rnorm(2 * n), ncol = 2)
rownames(pheno) <- paste0("s", 1:n)
colnames(pheno) <- paste0("tr", 1:2)
probs <- array(dim = c(n, 2, 5))
probs[ , 1, ] <- rbinom(n * 5, size = 1, prob = 0.2)
probs[ , 2, ] <- 1 - probs[ , 1, ]
rownames(probs) <- paste0("s", 1:n)
colnames(probs) <- LETTERS[1:2]
dimnames(probs)[[3]] <- paste0("m", 1:5)
scan_multi_onechr(probs = probs, pheno = pheno, kinship = NULL, cores = 1)
# }
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