qgg (version 1.0.3)

gsolve: Genomic prediction based on a linear mixed model

Description

The gsolve function is used for solving of linear mixed model equations. The algorithm used to solve the equation system is based on a Gauss-Seidel (GS) method (matrix-free with residual updates) that handles large data sets.

The linear mixed model fitted can account for multiple traits, multiple genetic factors (fixed or random genetic marker effects), adjust for complex family relationships or population stratification, and adjust for other non-genetic factors including lifestyle characteristics. Different genetic architectures (infinitesimal, few large and many small effects) is accounted for by modeling genetic markers in different sets as fixed or random effects and by specifying individual genetic marker weights.

Usage

gsolve(y = NULL, X = NULL, Glist = NULL, W = NULL, ids = NULL,
  rsids = NULL, sets = NULL, validate = NULL, scale = TRUE,
  lambda = NULL, weights = FALSE, maxit = 500, tol = 1e-05,
  method = "gsru", ncores = 1)

Arguments

y

vector or matrix of phenotypes

X

design matrix of fixed effects

Glist

list of information about centered and scaled genotype matrix

W

matrix of centered and scaled genotypes

ids

vector of individuals used in the analysis

rsids

vector of marker rsids used in the analysis

sets

list containing marker sets rsids

validate

dataframe or list of individuals used in cross-validation (one column for each set)

scale

logical if TRUE the genotypes in Glist has been scaled to mean zero and variance one

lambda

overall shrinkage factor

weights

vector of single marker weights used in BLUP

maxit

maximum number of iterations used in the Gauss-Seidel procedure

tol

tolerance, i.e. the maximum allowed difference between two consecutive iterations of reml to declare convergence

method

used in solver (currently only methods="gsru": gauss-seidel with resiudal update)

ncores

number of cores used in the analysis

Examples

# NOT RUN {
# Simulate data
W <- matrix(rnorm(1000000), ncol = 1000)
	colnames(W) <- as.character(1:ncol(W))
	rownames(W) <- as.character(1:nrow(W))
m <- ncol(W)
causal <- sample(1:ncol(W),50)
y <- rowSums(W[,causal]) + rnorm(nrow(W),sd=sqrt(50))

X <- model.matrix(y~1)

Sg <- 50
Se <- 50
h2 <- Sg/(Sg+Se)
lambda <- Se/(Sg/m)
lambda <- m*(1-h2)/h2

# BLUP of single marker effects and total genomic effects based on Gauss-Seidel procedure
fit <- gsolve( y=y, X=X, W=W, lambda=lambda)

# }