run.gwa: GBLUP and GWA computation

In steibelj/gwaR: Functions for performing GWA from GBLUP

Description

function for fitting a gblup model followed by gwas analysis.

Usage

run.gwa(rsp, data, design, G, vdata = NULL, wt = NULL, x, LRT = F,
  threshold = 0.01, returnz = T, saveblup = F, basename = "", ...)

Arguments

rsp	a string with the name of the response variable if a vector, first element is used.
data	either a gpData object from synbreed or a dataframe containing records for response variable and classification factors used as fixed or random effects
design	a list or a vector with formulas for fixed and random effects
G	a proportional covariance for genetics effects being predicted.
vdata	a list with proportional covariance matrices of additional random effects (not included in data)
wt	a vector of weigths (proportional to diagonals of residual covariance matrix), or a matrix of weights with anmimals in rows and response variables in columns
x	A matrix of standardized genotypes with animals in columns and markers in rows
LRT	logical indicating if a LRT is performed to test for h2 before performing GWA
threshold	the significance threshold of LRT
returnz	a logical value. If T the t-statistics for each SNP is returned, if F estimated SNP effects and variances are returnes
...	additional parameters for regress function
vdata	a list with proportional covariance matrices of additional random effects (not included in data)

Value

if returnz=F, an object of the class GWAS: a list with two vectors

• ghat estimated SNP effects

• varg estimated SNP effect variances

if returnz=F the t-statistic t=ghat/sqrt(varg)

See Also

gblup gwas lrt

steibelj/gwaR documentation built on May 30, 2019, 10:45 a.m.