runSingleTraitGwas: Perform single-trait GWAS

Description

runSingleTraitGwas performs a single-trait Genome Wide Association Study (GWAS) on phenotypic and genotypic data contained in a gData object. A covariance matrix is computed using the EMMA algorithm (Kang et al., 2008), the Newton-Raphson algorithm (Tunnicliffe, 1989) in the sommer package, or the modified Henderson algorithm in the LMMsolver package. Then a Generalized Least Squares (GLS) method is used for estimating the marker effects and corresponding p-values. This is done using either one kinship matrix for all chromosomes or different chromosome-specific kinship matrices for each chromosome. Significant SNPs are selected based on a user defined threshold.

Usage

runSingleTraitGwas(
  gData,
  traits = NULL,
  trials = NULL,
  covar = NULL,
  snpCov = NULL,
  kin = NULL,
  kinshipMethod = c("astle", "IBS", "vanRaden"),
  remlAlgo = c("EMMA", "NR", "Hend"),
  GLSMethod = c("single", "multi"),
  useMAF = TRUE,
  MAF = 0.01,
  MAC = 10,
  genomicControl = FALSE,
  thrType = c("bonf", "fixed", "small", "fdr"),
  alpha = 0.05,
  LODThr = 4,
  nSnpLOD = 10,
  pThr = 0.05,
  rho = 0.5,
  sizeInclRegion = 0,
  minR2 = 0.5,
  nCores = NULL
)

Value

An object of class GWAS.

Arguments

gData: An object of class gData containing at least map, markers and pheno.
traits: A vector of traits on which to run GWAS. These can be either numeric indices or character names of columns in pheno. If NULL, GWAS is run on all traits.
trials: A vector of trials on which to run GWAS. These can be either numeric indices or character names of list items in pheno. If NULL, GWAS is run for all trials. GWAS is run for the selected trials in sequential order.
covar: An optional vector of covariates taken into account when running GWAS. These can be either numeric indices or character names of columns in covar in gData. If NULL no covariates are used.
snpCov: An optional character vector of snps to be included as covariates.
kin: An optional kinship matrix or list of kinship matrices. These matrices can be from the matrix class as defined in the base package or from the dsyMatrix class, the class of symmetric matrices in the Matrix package.
If GLSMethod = "single" then one matrix should be provided, if GLSMethod = "multi", a list of chromosome specific matrices of length equal to the number of chromosomes in map in gData.
If NULL then matrix kinship in gData is used.
If both kin is provided and gData contains a matrix kinship then kin is used.
kinshipMethod: An optional character indicating the method used for calculating the kinship matrix(ces). Currently "astle" (Astle and Balding, 2009), "IBS" and "vanRaden" (VanRaden, 2008) are supported. If a kinship matrix is supplied either in gData or in parameter kin, kinshipMethod is ignored.
remlAlgo: A character string indicating the algorithm used to estimate the variance components. Either EMMA, for the EMMA algorithm, NR, for the Newton-Raphson algorithm (using sommer::mmes), or Hend for the modified Henderson algorithm (using LMMsolver::LMMsolve).
GLSMethod: A character string indicating the method used to estimate the marker effects. Either single for using a single kinship matrix, or multi for using chromosome specific kinship matrices.
useMAF: Should the minor allele frequency be used for selecting SNPs for the analysis. If FALSE, the minor allele count is used instead.
MAF: The minor allele frequency (MAF) threshold used in GWAS. A numerical value between 0 and 1. SNPs with MAF below this value are not taken into account in the analysis, i.e. p-values and effect sizes are put to missing (NA). Ignored if useMAF is FALSE.
MAC: A numerical value. SNPs with minor allele count below this value are not taken into account for the analysis, i.e. p-values and effect sizes are set to missing (NA). Ignored if useMAF is TRUE.
genomicControl: Should genomic control correction as in Devlin and Roeder (1999) be applied?
thrType: A character string indicating the type of threshold used for the selection of candidate loci. See Details.
alpha: A numerical value used for calculating the LOD-threshold for thrType = "bonf" and the significant p-Values for thrType = "fdr".
LODThr: A numerical value used as a LOD-threshold when thrType = "fixed".
nSnpLOD: A numerical value indicating the number of SNPs with the smallest p-values that are selected when thrType = "small".
pThr: A numerical value just as the cut off value for p-Values for thrType = "fdr".
rho: A numerical value used a the minimum value for SNPs to be considered correlated when using thrType = "fdr".
sizeInclRegion: An integer. Should the results for SNPs close to significant SNPs be included? If so, the size of the region in centimorgan or base pairs. Otherwise 0.
minR2: A numerical value between 0 and 1. Restricts the SNPs included in the region close to significant SNPs to only those SNPs that are in sufficient Linkage Disequilibrium (LD) with the significant snp, where LD is measured in terms of \(R^2\). If for example sizeInclRegion = 200000 and minR2 = 0.5, then for every significant SNP also those SNPs whose LD (\(R^2\)) with the significant SNP is at least 0.5 AND which are at most 200000 away from this significant snp are included. Ignored if sizeInclRegion = 0.
nCores: A numerical value indicating the number of cores to be used by the parallel part of the algorithm. If NULL the number of cores used will be equal to the number of cores available on the machine - 1.

Threshold types for the selection of candidate loci

For the selection of candidate loci from the GWAS output four different methods can be used. The method used can be specified in the function parameter thrType. Further parameters can be used to fine tune the method.

bonf: The Bonferroni threshold, a LOD-threshold of \(-log10(alpha / m)\), where m is the number of SNPs and alpha can be specified by the function parameter alpha
fixed: A fixed LOD-threshold, specified by the function parameter LODThr
small: The n SNPs with with the smallest p-Values are selected. n can be specified in nSnpLOD
fdr: Following the algorithm proposed by Brzyski D. et al. (2017), SNPs are selected in such a way that the False Discovery Rate (fdr) is minimized. To do this, first the SNPs are restricted to the SNPs with a p-Value below pThr. Then clusters of SNPs are created using a two step iterative process in which first the SNP with the lowest p-Value is selected as cluster representative. This SNP and all SNPs that have a correlation with this SNP of \(\rho\) or higher will form a cluster. \(\rho\) can be specified as an argument in the function and has a default value of 0.5, which is a recommended starting value in practice. The selected SNPs are removed from the list and the procedure repeated until no SNPs are left. Finally to determine the number of significant clusters the first cluster is determined for which the p-Value of the cluster representative is not smaller than \(cluster_{number} * \alpha / m\) where m is the number of SNPs and alpha can be specified by the function parameter alpha. All previous clusters are selected as significant.

References

Astle, William, and David J. Balding. 2009. Population Structure and Cryptic Relatedness in Genetic Association Studies. Statistical Science 24 (4): 451–71. tools:::Rd_expr_doi("10.1214/09-sts307").

Brzyski D. et al. (2017) Controlling the Rate of GWAS False Discoveries. Genetics 205 (1): 61-75. tools:::Rd_expr_doi("10.1534/genetics.116.193987")

Devlin, B., and Kathryn Roeder. 1999. Genomic Control for Association Studies. Biometrics 55 (4): 997–1004. tools:::Rd_expr_doi("10.1111/j.0006-341x.1999.00997.x").

Kang et al. (2008) Efficient Control of Population Structure in Model Organism Association Mapping. Genetics 178 (3): 1709–23. tools:::Rd_expr_doi("10.1534/genetics.107.080101").

Millet, E. J., Pommier, C., et al. (2019). A multi-site experiment in a network of European fields for assessing the maize yield response to environmental scenarios - Data set. tools:::Rd_expr_doi("10.15454/IASSTN")

Rincent et al. (2014) Recovering power in association mapping panels with variable levels of linkage disequilibrium. Genetics 197 (1): 375–87. tools:::Rd_expr_doi("10.1534/genetics.113.159731").

Segura et al. (2012) An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nature Genetics 44 (7): 825–30. tools:::Rd_expr_doi("10.1038/ng.2314").

Sun et al. (2010) Variation explained in mixed-model association mapping. Heredity 105 (4): 333–40. tools:::Rd_expr_doi("10.1038/hdy.2010.11").

Tunnicliffe W. (1989) On the use of marginal likelihood in time series model estimation. JRSS 51 (1): 15–27.

VanRaden P.M. (2008) Efficient methods to compute genomic predictions. Journal of Dairy Science 91 (11): 4414–23. tools:::Rd_expr_doi("10.3168/jds.2007-0980").

Examples

Run this code

## Create a gData object Using the data from the DROPS project.
## See the included vignette for a more extensive description on the steps.
data(dropsMarkers)
data(dropsMap)
data(dropsPheno)

## Add genotypes as row names of dropsMarkers and drop Ind column.
rownames(dropsMarkers) <- dropsMarkers[["Ind"]]
dropsMarkers <- dropsMarkers[colnames(dropsMarkers) != "Ind"]

## Add genotypes as row names of dropsMap.
rownames(dropsMap) <- dropsMap[["SNP.names"]]

## Rename Chomosome and Position columns.
colnames(dropsMap)[match(c("Chromosome", "Position"), 
                   colnames(dropsMap))] <- c("chr", "pos")
                   
## Convert phenotypic data to a list.
dropsPhenoList <- split(x = dropsPheno, f = dropsPheno[["Experiment"]])

## Rename Variety_ID to genotype and select relevant columns.
dropsPhenoList <- lapply(X = dropsPhenoList, FUN = function(trial) {
  colnames(trial)[colnames(trial) == "Variety_ID"] <- "genotype"
  trial <- trial[c("genotype", "grain.yield", "grain.number", "seed.size",
                 "anthesis", "silking", "plant.height", "tassel.height",
                 "ear.height")]
return(trial)
}) 

## Create gData object.
gDataDrops <- createGData(geno = dropsMarkers, map = dropsMap, 
                          pheno = dropsPhenoList)
                          
## Run single trait GWAS for trait 'grain.yield' for trial Mur13W.
# \donttest{
GWASDrops <- runSingleTraitGwas(gData = gDataDrops,
                               trials = "Mur13W",
                               traits = "grain.yield")
# }
                               
## Run single trait GWAS for trait 'grain.yield' for trial Mur13W.
## Use chromosome specific kinship matrices calculated using vanRaden method.
# \donttest{
GWASDropsMult <- runSingleTraitGwas(gData = gDataDrops,
                                    trials = "Mur13W",
                                    traits = "grain.yield",
                                    kinshipMethod = "vanRaden",
                                    GLSMethod = "multi")  
# }

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