gwas.analysis: Performs GWAS analysis with five optional models and...

In kbroman/lmem.gwaser: Linear Mixed Effects Models for Genome-Wide Association Studies

Description

GWAS analysis with models: naive: y= x + e, fixed: y = x + q + e, kinship: y=x+z+e (Pariseaux and Bernardo, 2004), QK: y = x + q + z + e (Yu et al., 2006), and eigenstrat: y = x + q + e (Price et al., 2006; Malosetti et al., 2007).

Usage

gwas.analysis (crossobj, method, provide.K,
covariates, trait, threshold, p,out.file)

Arguments

crossobj	An object of class = cross obtained from the gwas.cross function from this package, or the read.cross function from r/qtl package (Broman and Sen, 2009). This file contains phenotypic means, genotypic marker score, and genetic map.
method	Methods to perform GWAS analysis. Options are naive, fixed, kinship, QK and egeinstrat. The general Mixed Model equation used is: Y = X β + Q ν + Zu + e , where Y is the phenotypic vector, X is the molecular marker matrix, β is the unknown vector of allelic effects to be estimated, Q is the population structure, ν is the vector of population effects (parameters), Z is a matrix that relates each measurement to the individual from which it was obtained, u is the vector of random background polygenic effects, and e is the residual errors. Random effects are underlined. The following mainstream models are available with the package: 1) naive; a simple test of association (Kruskal-Wallis) with no correction for population structure Y = X β + e , 2) fixed; a fixed-effects model using populations structure as fixed covariate Y = X β + Q ν + e , 3) kinship; a mixed model including the coancestry matrix among genotypes as a random effect following Parisseaux and Bernardo 2004 Y = X β + Zu + e 4) eigenstrat; a mixed-effects model including population structure but as a random effect following Price et al. 2006 and Malosetti et al. 2007 Y = X β + Q ν + e 5) QK; a mixed-effects model including both population structure and coancestry among genotypes following Yu et al. 2006. Y = X β + Qν + Zu + e Principal component analysis (PCA) is used as a random effect in the Price model including all significant axes, following Patterson et al. (2006). When used in the Fixed or QK model, PCA, or another population structure is included as a fixed effect.
provide.K	K is the kinship matrix. If pedigree kinship is available, or a specific kinship matrix is desired, set provide.k=TRUE. Otherwise, a realized kinship matrix is estimated if needed for the model. Indicates whether a qqplot shouldo be performed. TRUE/FALSE term. FALSE is set as default.
covariates	A vector of structure covariates. Can be pca$scores for eigenstrat or any group for the fixed model. Indicates whether a scatterplot should be performed.
trait	Indicates the trait to be analyzed.
threshold	Thresholds options are: Li&Ji (Li and Ji, 2005), FDR (Benjamini and Hochberg, 1995), and set alpha levels (p.values)
p	Alpha level (numeric) for test of marker-trait hypothesis.
out.file	Name of the file to be written. Example: 'GWAS fixed Groups model'.

Details

This analysis is performed with adjusted means of the field.

Value

The function return p.values tested on the GWAS analyses saved to gwas_reports, and Manhattan plots.

Note

For multi-trait or multi-environment see GWAS.MEMQ

Author(s)

Lucia Gutierrez

References

Benjamini and Hochberg (1995) Controlling the false discovery rate: a b practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57, 289-300.

Comadran J, Thomas W, van Eeuwijk F, Ceccarelli S, Grando S, Stanca A, Pecchioni N, Akar T, Al-Yassin A, Benbelkacem A, Ouabbou H, Bort J, Romagosa I, Hackett C, Russell J (2009) Patterns of genetic diversity and linkage disequilibrium in a highly structured Hordeum vulgare associatio-mapping population for the Mediterranean basin. Theor Appl Genet 119:175-187

Li J, Ji L (2005) Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity:1-7.

Yu et al. (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Genetics 38:203-208.

Malosetti et al. (2007) A mixed-model approach to association mapping using pedigree information with an illustration of resistance to Phytophthora infestans in potato. Genetics 175:879-889.

Parisseaux B, Bernardo R (2004) Insilico mapping of quantitative trait loci in maize. Theor. Appl. Genet. 109:08-514.

Peterson RF, Campbell AB, Han_nah AE, 1948. A diagrammatic scale for estimating rust intensity on leaves and stems of cereals. Canadian Journal of Genetics and Cytology C. 26:496-500

Price et al. (2006) Principal components analysis corrects for stratificat on in genome-wide association studies, Nat. Genet. 38:904-909

Turner, S. (2014). qqman: Q-Q and manhattan plots for GWAS data R package 0.1.2 https://CRAN.R-project.org/package=qqman

See Also

gwas.cross mq.g.diagnostics

Examples

## Not run: 
data (QA_geno)
data (QA_map)
data (QA_pheno)

P.data <- QA_pheno
G.data <- QA_geno
map.data <- QA_map

cross.data <- gwas.cross (P.data, G.data, map.data,
cross='gwas', heterozygotes=FALSE)
summary (cross.data)


#PCA
pca <- pca.analysis (crossobj=cross.data, p.val=0.05)

#LD.plots
linkdis.plots(crossobj = cross.data, heterozygotes = FALSE, chr = c('1'))

#Mixed model: Q+K
(qk.GWAS <- gwas.analysis (crossobj=cross.data4, method="QK", provide.K=FALSE,
covariates=pca$scores, trait="yield", threshold="Li&Ji", p=0.05,
out.file="GWAS Q + K model"))$selected

#Mixed model: Eigenanalysis (PCA as random component)
(pcaR.GWAS <- gwas.analysis(crossobj=cross.data4, method="eigenstrat",
provide.K=FALSE, covariates=pca$scores, trait="yield", threshold="Li&Ji",
 p=0.05, out.file="GWAS PCA as Random model"))$selected

#Mixed model: Kinship model
 (k.GWAS <- gwas.analysis(crossobj=cross.data4, method="kinship",
 provide.K=FALSE, covariates=FALSE, trait="yield",
 threshold="Li&Ji", p=0.05, out.file =" GWAS K as Random model "))$selected

#Fixed effects: Groups
 data (QA_pheno2)
 P.data.1 <- QA_pheno2
 covariate <- P.data.1 [,2]

 (g.GWAS <- gwas.analysis (crossobj=cross.data4,
 method="fixed", provide.K=FALSE, covariates=covariate,
 trait="yield", threshold="Li&Ji", p=0.05,
 out.file="GWAS fixed Groups model"))$selected

# Naive
 (naive.GWAS <- gwas.analysis(crossobj=cross.data4, method="naive",
  provide.K=FALSE, covariates=FALSE, trait="yield", threshold="Li&Ji",
  p=0.05, out.file="GWAS naive model"))$selected

## End(Not run)

kbroman/lmem.gwaser documentation built on May 30, 2019, 3:10 p.m.