Given a matrix of genotypes, this function estimates the genetic relatedness matrix (GRM, also known as the RRM, see Hayes et al. 2009, doi:10.1017/S0016672308009981 ) among the subjects: XX'/p, where X is standardized.
Arguments
- X
An n x p numeric matrix of genotypes (from fully-imputed data). Note: This matrix should not include non-genetic features.
- std
Logical: should X be standardized? If you set this to FALSE (which can only be done if data are stored in memory), you should have a good reason for doing so, as standardization is a best practice.
- fbm
Logical: is X stored as an FBM? Defaults to FALSE
- ns
Optional vector of values indicating the indices of nonsingular features
- ...
Other optional arguments to
bigstatsr::bigapply()(likencores = ...)
Value
An n x n numeric matrix capturing the genomic relatedness of the
samples represented in X. In our notation, we call this matrix K for 'kinship';
this is also known as the GRM or RRM.
Examples
RRM <- relatedness_mat(X = admix$X)
RRM[1:5, 1:5]
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.81268908 -0.09098097 -0.07888910 0.06770613 0.08311777
#> [2,] -0.09098097 0.81764801 0.20480021 0.02112812 -0.02640295
#> [3,] -0.07888910 0.20480021 0.82177986 -0.02864226 0.18693970
#> [4,] 0.06770613 0.02112812 -0.02864226 0.89327266 -0.03541470
#> [5,] 0.08311777 -0.02640295 0.18693970 -0.03541470 0.79589686