inbr_gcta | R Documentation |
This function calculates the biased GCTA inbreeding estimator III described in Yang et al. (2011).
Though these estimates (MOR version) were the basis of the GRM diagonal according to that paper, the GCTA software history shows that this exact estimator was abandoned in version 0.93.0 (8 Jul 2011) in favor of kinship_std()
(also MOR version), which remains in use as of writing (2022).
inbr_gcta( X, n = NA, mean_of_ratios = FALSE, loci_on_cols = FALSE, mem_factor = 0.7, mem_lim = NA, m_chunk_max = 1000 )
X |
The genotype matrix (BEDMatrix, regular R matrix, or function, same as |
n |
The number of individuals.
Required if |
mean_of_ratios |
The estimator can be computed in two broad forms.
If |
loci_on_cols |
Determines the orientation of the genotype matrix (by default, |
mem_factor |
Proportion of available memory to use loading and processing genotypes.
Ignored if |
mem_lim |
Memory limit in GB, used to break up genotype data into chunks for very large datasets.
Note memory usage is somewhat underestimated and is not controlled strictly.
Default in Linux and Windows is |
m_chunk_max |
Sets the maximum number of loci to process at the time. Actual number of loci loaded may be lower if memory is limiting. |
Inbreeding estimates
GCTA 2011 GRM estimator ROM limit kinship_gcta_limit()
.
The limit of inbr_gcta
with mean_of_ratios = FALSE
is given by popkin::inbr(kinship_gcta_limit(true_kinship))
.
Standard kinship estimator kinship_std()
and the limit of the ROM version kinship_std_limit()
.
GCTA software, including history/update log. https://yanglab.westlake.edu.cn/software/gcta/#Download
# dimensions of simulated data n_ind <- 100 m_loci <- 1000 n_data <- n_ind * m_loci # missingness rate miss <- 0.1 # simulate ancestral allele frequencies # uniform (0,1) # it'll be ok if some of these are zero p_anc <- runif(m_loci) # simulate some binomial data X <- rbinom(n_data, 2, p_anc) # sprinkle random missingness X[ sample(X, n_data * miss) ] <- NA # turn into a matrix X <- matrix(X, nrow = m_loci, ncol = n_ind) # estimate inbreeding # ... ROM version (see Ochoa and Storey (2021)). inbr_gcta_rom <- inbr_gcta(X) # ... MOR version (from Yang et al. (2011)). inbr_gcta_mor <- inbr_gcta(X, mean_of_ratios = TRUE)
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