Variance Genome-wide Association (parallel)

Variance Genome-wide association for using nonparametric variance test and other

Usage

vGWASparallel(phenotype, geno.matrix, marker.map = NULL,
chr.index = NULL, geno.snp = "row", method = "bfmedian", test.alpha = 0.05,
test.na.rm = TRUE, p.adjust.method = "none", include.het = FALSE, pB = TRUE,
ncores = 1)

Arguments

phenotype: a numeric or logical vector of the phenotyic values.
geno.matrix: a matrix or data.frame or sparseMatrix with individuals as columns and markers as rows (geno.snp = "row") or individuals as rows and markers as columns (geno.snp = "col").
marker.map: a numeric vector giving the marker map positions for each chromosome.
chr.index: a numeric vector giving the chromosome index for each marker.
geno.snp: if individuals at columns and markers at rows use "row" else if individuals at rows and markers at columns use "col"
method: the test method to use (default = bfmedian). Default is set to the Brown-Forsythe's Test of Equality of Variances using group medians. There are 31 other tests available via the onewaytests package: Alvandi's F test ("af"), Alexander-Govern test ("ag"), Alvandi's generalized p-value ("agp"), One-way analysis of variance ("aov"), Approximate F test ("ap"), Adjusted Welch's heteroscedastic F test ("aw"), B square test ("b2"), Brown-Forsythe test ("bf"), Box F test ("box"), Cochran test ("cochran"), Generalized tests equivalent to Parametric Bootstrap ("gtb"), Generalized tests equivalent to Fiducial tests ("gtf"), Variance homogeneity tests ("homog"), James second order test ("james"), Johansen F test ("johansen"), Kruskal-Wallis test ("kw"), Modified Brown-Forsythe test ("mbf"), Mann-Whitney U test ("mw"), Anderson-Darling normility test ("nor_ad"), Cramer-vin Mises normility test ("nor_cvm"), Kolmogorov-Smirnov normility test ("nor_ks"), Pearson Chi-square normility test ("nor_pct"), Shapiro-Wilk normility test ("nor_sw"), Shapiro-Francia normility test ("nor_sf"), Permutation F test ("pf"), Scott-Smith test ("ss"), Student's t-test ("st"), Welch-Aspin test ("wa"), Welch's heteroscedastic F test with trimmed means and Winsorized variances ("welch"), Weerahandi's generalized F test ("wgf"), Welch's t-test ("wt").
test.alpha: the level of significance to assess the statistical difference. Default is set to alpha = 0.05.
test.na.rm: a logical value indicating whether NA values should be stripped before the computation proceeds. Default us set to TRUE.
p.adjust.method: correction method (default = "none"). There are 8 p-value correction methods available via the p.adjust function: "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"
include.het: specify if heterozygous calls should be split and added equally to homozygous ref and alt counts (default = FALSE)
pB: show progress bar
ncores: number of cores to parallelize (default = 1)

Value

a data.frame containing columns of marker names, chromosome indices, marker.map positions, test statistic values, and p.value for each position.

References

Shen, X., Pettersson, M., Ronnegard, L. and Carlborg, O. (2011): Inheritance beyond plain heritability: variance-controlling genes in Arabidopsis thaliana. PLoS Genetics, 8, e1002839.

Ronnegard, L., Shen, X. and Alam, M. (2010): hglm: A Package for Fitting Hierarchical Generalized Linear Models. The R Journal, 2(2), 20-28.

Author

Xia Shen

Kristian Ullrich

Examples

# ----- load data ----- #
data(pheno)
data(geno)
data(chr)
data(map)
# ----- variance GWA scan ----- #
vgwa <- vGWASparallel(phenotype = pheno, geno.matrix = geno,
marker.map = map, chr.index = chr,
geno.snp = "col", pB = FALSE)
# ----- other test GWA scan ----- #
vgwa.mw <- vGWASparallel(phenotype = pheno, geno.matrix = geno,
marker.map = map, chr.index = chr,
geno.snp = "col", method = "mw", pB = FALSE)
# ----- multiple cores ----- #
vgwa.st <- vGWASparallel(phenotype = pheno, geno.matrix = geno,
marker.map = map, chr.index = chr,
geno.snp = "col", method = "st", ncores = 2, pB = FALSE)