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Type:	Package
Title:	Genome Wide Association Studies
Version:	1.0.12
Date:	2025-06-30
Description:	Fast single trait Genome Wide Association Studies (GWAS) following the method described in Kang et al. (2010), <doi:10.1038/ng.548>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris.
License:	GPL-3
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
Depends:	R (≥ 3.6)
Imports:	data.table, ggplot2 (≥ 3.0.0), LMMsolver, methods, rlang,Rcpp, sommer (≥ 4.4.1)
Suggests:	knitr, rmarkdown, officer, tinytest, snpStats, vcfR
VignetteBuilder:	knitr
LinkingTo:	Rcpp, RcppArmadillo
URL:	https://biometris.github.io/statgenGWAS/index.html,https://github.com/Biometris/statgenGWAS/
BugReports:	https://github.com/Biometris/statgenGWAS/issues
NeedsCompilation:	yes
Packaged:	2025-07-01 06:12:04 UTC; rossu027
Author:	Bart-Jan van Rossum [aut, cre], Willem Kruijer [aut], Fred van Eeuwijk [ctb], Martin Boer [ctb], Marcos Malosetti [ctb], Daniela Bustos-Korts [ctb], Emilie Millet [ctb], Joao Paulo [ctb], Maikel Verouden [ctb], Ron Wehrens [ctb], Choazhi Zheng [ctb]
Maintainer:	Bart-Jan van Rossum <bart-jan.vanrossum@wur.nl>
Repository:	CRAN
Date/Publication:	2025-07-01 06:50:03 UTC

statgenGWAS: Genome Wide Association Studies

Description

Fast single trait Genome Wide Association Studies (GWAS) following the method described in Kang et al. (2010),doi:10.1038/ng.548. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris.

Author(s)

Maintainer: Bart-Jan van Rossumbart-jan.vanrossum@wur.nl (ORCID)

Authors:

• Willem Kruijer (ORCID)

Other contributors:

• Fred van Eeuwijk (ORCID) [contributor]

• Martin Boer (ORCID) [contributor]

• Marcos Malosetti (ORCID) [contributor]

• Daniela Bustos-Korts (ORCID) [contributor]

• Emilie Millet (ORCID) [contributor]

• Joao Paulo (ORCID) [contributor]

• Maikel Verouden (ORCID) [contributor]

• Ron Wehrens (ORCID) [contributor]

• Choazhi Zheng (ORCID) [contributor]

See Also

Useful links:

• https://biometris.github.io/statgenGWAS/index.html

• https://github.com/Biometris/statgenGWAS/

• Report bugs athttps://github.com/Biometris/statgenGWAS/issues

Code and impute markers

Description

codeMarkers codes markers in agData object and optionallyperforms imputation of missing values as well.
The function performs the following steps:

1. replace strings innaStrings byNA.

2. remove genotypes with a fraction of missing values higher thannMissGeno.

3. remove SNPs with a fraction of missing values higher thannMiss.

4. recode SNPs to numerical values.

5. remove SNPs with a minor allele frequency lower thanMAF.

6. optionally remove duplicate SNPs.

7. optionally impute missing values.

8. repeat steps 5. and 6. if missing values are imputed.

Usage

codeMarkers(  gData,  refAll = "minor",  nMissGeno = 1,  nMiss = 1,  MAF = NULL,  MAC = NULL,  removeDuplicates = TRUE,  keep = NULL,  impute = TRUE,  imputeType = c("random", "fixed", "beagle"),  fixedValue = NULL,  naStrings = NA,  verbose = FALSE)

Arguments

Value

A copy of the inputgData object with markers replaced bycoded and imputed markers.

References

S R Browning and B L Browning (2007) Rapid and accurate haplotypephasing and missing data inference for whole genome association studies byuse of localized haplotype clustering. Am J Hum Genet 81:1084-1097.doi:10.1086/521987

Examples

## Create markersmarkers <- matrix(c("AA",   "AB",   "AA",   "BB",   "BA",   "AB",   "AA",   "AA",   NA,  "AA","AA",   "AA",   "BB",   "BB",   "AA",   "AA",   "BB",   "AA",   NA,  "AA","AA",   "BA",   "AB",   "BB",   "AB",   "AB",   "AA",   "BB",   NA,  "AA","AA",   "AA",   "BB",   "BB",   "AA",   "AA",   "AA",   "AA",   NA,  "AA","AA",   "AA",   "BB",   "BB",   "AA",   "BB",   "BB",   "BB",  "AB", "AA","AA",   "AA",   "BB",   "BB",   "AA",    NA,    "BB",   "AA",   NA,  "AA","AB",   "AB",   "BB",   "BB",   "BB",   "AA",   "BB",   "BB",   NA,  "AB","AA",   "AA",    NA,    "BB",    NA,    "AA",   "AA",   "AA",  "AA", "AA","AA",    NA,     NA,    "BB",   "BB",   "BB",   "BB",   "BB",  "AA", "AA","AA",    NA,    "AA",   "BB",   "BB",   "BB",   "AA",   "AA",   NA,  "AA"),ncol = 10, byrow = TRUE, dimnames = list(paste0("IND", 1:10),paste0("SNP", 1:10)))## create object of class 'gData'.gData <- createGData(geno = markers)## Code markers by minor allele, no imputation.gDataCoded1 <- codeMarkers(gData = gData, impute = FALSE)## Code markers by reference alleles, impute missings by fixed value.gDataCoded2 <- codeMarkers(gData = gData,                           refAll = rep(x = c("A", "B"), times =  5),                           impute = TRUE, imputeType = "fixed",                           fixedValue = 1)## Code markers by minor allele, impute by random value.gDataCoded3 <- codeMarkers(gData = gData, impute = TRUE,                           imputeType = "random")

DROPS data sets

Description

This dataset comes from the European Union project DROPS (DROught-tolerantyielding PlantS). A panel of 256 maize hybrids was grown with two waterregimes (irrigated or rainfed), in seven fields in 2012 and 2013,respectively, spread along a climatic transect from western to easternEurope, plus one site in Chile in 2013. This resulted in 28 experimentsdefined as the combination of one year, one site and one water regime, withtwo and three repetitions for rainfed and irrigated treatments, respectively.A detailed environmental characterisation was carried out, with hourlyrecords of micrometeorological data and soil water status, and associatedwith precise measurement of phenology. Grain yield and its components weremeasured at the end of the experiment.
10 experiments have been selected from the full data set, two for each ofthe five main environmental scenarios that were identified in the data. Thescenarios have been added to the data as well as a classification of thegenotypes in four genetic groups.
The main purpose of this datasetconsists in using the environmental characterization to quantify the geneticvariability of maize grain yield in response to the environmental driversfor genotype-by-environment interaction. For instance, allelic effects atQTLs identified over the field network are consistent within a scenario butlargely differ between scenarios.

The data is split in three separate data.frames.

dropsMarkers

This data.frame contains the 50K genotypingmatrix coded in allelic dose (012) filtered and imputed. Genotyping of41,722 loci on 246 parental lines were obtained using 50K Illumina InfiniumHD arrays (Ganal et al., 2011). Genotype were coded in allelic dose with 0for the minor allele, 1 for the heterozygote, and 2 for the major allele.Genotype were filtered (MAF > 1%) and missing data imputed using Beagle v3.
A data.frame with 246 rows and 41723 columns.

Ind

name of the genotype

SYMN83 to PZE-110111485

coded QTLs

dropsMap

This data.frame contains the description of the41,722 loci genotyped by 50K Illumina Infinium Array on the 246 lines.
A data.frame with 41722 rows and 5 columns.

SNP.names

name of the SNP

Chromosome

number of the B73 reference genome V2

Position

position on the B73 reference genome V2 in basepairs

allele1

first original allele (A, T, G or C)

allele2

second original allele (A, T, G or C)

dropsPheno

This data.frame contains the genotypic means(Best Linear Unbiased Estimators, BLUEs), with one value per experiment(Location × year × water regime) per genotype.
A data.frame with 2460 rows and 19 columns.

Experiment

experiments ID described by the three first letters of thecity’s name followed by the year of experiment and the water regime with Wfor watered and R for rain-fed.

parent1

identifier of donor dent line

Code_ID, Variety_ID, Accession_ID

identifier of the genotype

geno.panel

project in which the genetic material was generated

grain.yield

genotypic mean for yield adjusted at 15% grain moisture,in ton per hectare (t ha^-1)

grain.number

genotypic mean for number of grain per square meter

grain.weight

genotypic mean for individual grain weight in milligram(mg)

anthesis

genotypic mean for male flowering (pollen shed), in thermaltime cumulated since emergence (d_20°C)

silking

genotypic mean for female flowering (silking emergence), inthermal time cumulated since emergence (d_20°C)

plant.height

genotypic mean for plant height, from ground level tothe base of the flag leaf (highest) leaf in centimeter (cm)

tassel.height

genotypic mean for plant height including tassel, fromground level to the highest point of the tassel in centimeter (cm)

ear.height

genotypic mean for ear insertion height, from ground levelto ligule of the highest ear leaf in centimeter (cm)

year

year in which the experiment was performed

loc

location where the experiment was performed, a three letterabbreviation

scenarioWater

water scenario for the experiment, well watered (WW) orwater deficit (WD)

scenarioTemp

temperature scenario for the experiment, Cool, Hot orHot(Day)

scenarioFull

the full scenario for the experiment, a combination ofscenarioWater and scenarioTemp

geneticGroup

the genetic group to which the genotype belongs

Note

From the source data, the experiments from 2011 have been removed sincethese do not contain all genotypes. Also the experiment Gra13W has beenremoved.

Source

doi:10.15454/IASSTN

References

Millet, E. J., Pommier, C., et al. (2019). A multi-siteexperiment in a network of European fields for assessing the maize yieldresponse to environmental scenarios - Data set.doi:10.15454/IASSTN

Ganal MW, et al. (2011) A Large Maize (Zea mays L.) SNPGenotyping Array: Development and Germplasm Genotyping, and Genetic Mappingto Compare with the B73 Reference Genome. PLoS ONE 6(12): e28334.doi:10.1371/journal.pone.0028334

S3 Class gData

Description

createGData creates an object of S3 class gData with genotypic andphenotypic data for usage in further analysis. All input to the function isoptional, however at least one input should be provided. It is possible toprovide an existinggData object as additional input in which casedata is added to this object. Existing data will be overwritten with awarning.

Usage

createGData(  gData = NULL,  geno = NULL,  map = NULL,  kin = NULL,  pheno = NULL,  covar = NULL)

Arguments

Value

An object of classgData with the following components:

Author(s)

Bart-Jan van Rossum

See Also

summary.gData

Examples

set.seed(1234)## Create genotypic data.geno <- matrix(sample(x = c(0, 1, 2), size = 15, replace = TRUE), nrow = 3)dimnames(geno) <- list(paste0("G", 1:3), paste0("M", 1:5))## Construct map.map <- data.frame(chr = c(1, 1, 2, 2, 2), pos = 1:5,                  row.names = paste0("M", 1:5))## Compute kinship matrix.kin <- kinship(X = geno, method = "IBS")## Create phenotypic data.pheno <- data.frame(paste0("G", 1:3),                    matrix(rnorm(n = 12, mean = 50, sd = 5), nrow = 3),                    stringsAsFactors = FALSE)dimnames(pheno) = list(paste0("G", 1:3), c("genotype", paste0("T", 1:4)))## Combine all data in gData object.gData <- createGData(geno = geno, map = map, kin = kin, pheno = pheno)summary(gData)## Construct covariate.covar <- data.frame(C1 = c("a", "a", "b"), row.names = paste0("G", 1:3))## Compute alternative kinship matrix.kin2 <- kinship(X = geno, method = "astle")## Add covariates to previously created gData object and overwrite## current kinship matrix by newly computed one.gData2 <- createGData(gData = gData, kin = kin2, covar = covar)

Functions for calculating kinship matrices

Description

A collection of functions for calculating kinship matrices using differentalgorithms. The following algorithms are included: astle (Astle and Balding,2009), Identity By State (IBS) and VanRaden (VanRaden, 2008) formarker matrices. For method identity an identity kinship matrix is returned.

Usage

kinship(  X,  method = c("astle", "IBS", "vanRaden", "identity"),  MAF = NULL,  denominator = NULL)

Arguments

Value

An n x n kinship matrix.

Marker matrices

In all algorithms the input matrixX is first cleaned, i.e. markerswith a variance of 0 are excluded from the calculation of the kinship matrix.Then some form of scaling is done which differs per algorithm. This gives ascaled matrixZ. The matrixZZ^t / denominator is returned.By default the denominator is equal to the number of columns inZ forastle andIBS and2 * p * (1-p) wherep = colSums(X) / (2 * nrow(X)) forvanRaden. This denominatorcan be overwritten by the user, e.g. when computing kinship matrices bysplittingX in smaller matrices and then adding the results togetherin the end.

References

Astle, William, and David J. Balding. 2009. “Population Structureand Cryptic Relatedness in Genetic Association Studies.” Statistical Science24 (4): 451–71.doi:10.1214/09-sts307.

VanRaden P.M. (2008) Efficient methods to compute genomicpredictions. Journal of Dairy Science 91 (11): 4414–23.doi:10.3168/jds.2007-0980.

Examples

## Create example matrix.M <- matrix(c(1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1), nrow = 4)## Compute kinship matrices using different methods.kinship(M, method = "astle")kinship(M, method = "IBS")kinship(M, method = "vanRaden")## Only use markers with a Minor Allele Frequency of 0.3 or more.kinship(M, method = "astle", MAF = 0.3)## Compute kinship matrix using astle and balding method with denominator 2.kinship(M, method = "astle", denominator = 2)

Plot function for the classGWAS

Description

Creates a plot of an object of S3 classGWAS. The following types ofplot can be made:

• a manhattan plot, i.e. a plot of LOD-scores per SNP

• a QQ plot of observed LOD-scores versus expected LOD-scores

• a qtl plot of effect sizes and directions for multiple traits

Manhattan plots and QQ plots are made for a single trait whichshould be indicated using the parametertrait. If the analysis wasdone for only one trait, it is detected automatically. The qtl plot will plotall traits analyzed.
See details for a detailed description of the plots and the plot optionsspecific to the different plots.

Usage

## S3 method for class 'GWAS'plot(  x,  ...,  plotType = c("manhattan", "qq", "qtl"),  trial = NULL,  trait = NULL,  title = NULL,  output = TRUE)

Arguments

Manhattan Plot

A LOD-profile of all marker positions and corresponding LOD-scores isplotted. Significant markers are highlighted with red dots. By default theseare taken from the result of the GWAS analysis however the LOD-threshold forsignificant parameters may be modified using the parameteryThr. Thethreshold is plotted as a horizontal line. If there are previously knownmarker effect, false positives and true negatives can also be marked.
Extra parameter options:

xLab

A character string, the x-axis label. Default ="Chromosomes"

yLab

A character string, the y-axis label. Default =-log10(p)

effects

A character vector, indicating which SNPs correspondto a real (known) effect. Used for determining true/false positives andfalse negatives. True positives are colored green, false positives orange andfalse negatives yellow.

colPalette

A color palette used for plotting. Defaultcoloring is done by chromosome, using black and grey.

yThr

A numerical value for the LOD-threshold. The value fromthe GWAS analysis is used as default.

signLwd

A numerical value giving the thickness of thepoints that are false/true positives/negatives. Default = 0.6

lod

A positive numerical value. For the SNPs with a LOD-valuebelow this value, only 5% is plotted. The chance of a SNP being plotted isproportional to its LOD-score. This option can be useful when plotting alarge number of SNPs. The 5% of SNPs plotted is selected randomly. Forreproducible results use set.seed before calling the function.

chr

A vector of chromosomes to be plotted. By default, allchromosomes are plotted. Using this option allows restricting the plot to asubset of chromosomes.

startPos

A numerical value indicating the start position forthe plot. Using this option allows restricting the plot to a part of aselected chromosome. Only used if exactly one chromosome is specified inchr.

endPos

A numerical value indicating the end position forthe plot. Using this option allows restricting the plot to a part of aselected chromosome. Only used if exactly one chromosome is specified inchr.

QQ-Plot

From the LOD-scores calculated in the GWAS analysis, a QQ-plot is generatedwith observed LOD-scores versus expected LOD-scores. Code is adapted fromSegura et al. (2012).

QTL Plot

A plot of effect sizes for the significant SNPs found in the GWAS analysisis created. Each horizontal line contains QTLs of one trait,phenotypic trait or trial. Optionally, vertical white lines can indicatechromosome subdivision, genes of interest, known QTL, etc. Circle diametersare proportional to the absolute value of allelic effect. Colors indicate thedirection of the effect: green when the allele increases the trait value,and blue when it decreases the value.
Extra parameter options:

normalize

Should the snpEffect be normalized? Default =FALSE

sortData

Should the data be sorted before plotting? EitherFALSE, if no sorting should be done, or a character string indicatingthe data column to use for sorting. This should be a numerical column.Default =FALSE

binPositions

An optional data.frame containing at leasts twocolumns, chr(omosome) and pos(ition). Vertical lines are plotted at thosepositions. Default =NULL

printVertGrid

Should default vertical grid lines be plotted.Default =TRUE

yLab

A character string, the y-axis label. Default ="Traits"

yThr

A numerical value for the LOD-threshold. The value fromthe GWAS analysis is used as default.

chr

A vector of chromosomes to be plotted. By default allchromosomes are plotted. Using this option this can be restricted to asubset of chromosomes.

exportPptx

Should the plot be exported to a .pptx file?Default =FALSE

pptxName

A character string, the name of the .pptx file towhich the plot is exported. Ignored if exportPptx =FALSE.

References

Millet et al. (2016) Genome-wide analysis of yield in Europe:Allelic effects vary with drought and heat scenarios. Plant Physiology,October 2016, Vol. 172, p. 749–764

Segura et al. (2012) An efficient multi-locus mixed-modelapproach for genome-wide association studies in structured populations.Nature Genetics, June 2012, Vol. 44, p. 825–830.

Examples

## 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.GWASDrops <- runSingleTraitGwas(gData = gDataDrops,                               trials = "Mur13W",                               traits = "grain.yield")                               ## Create a manhattan plot.plot(GWASDrops)## Manually set a threshold for significant snps and add a title.plot(GWASDrops,     yThr = 3.5,     title = "Manhattan plot for Mur13W")    ## Restrict plot to part of chr 6.plot(GWASDrops,     yThr = 3.5,     chr = 6,    startPos = 0,    endPos = 110000000)    ## Create a qq plot.plot(GWASDrops,     plotType = "qq",    title = "QQ plot for Mur13W")    ## Create a QTL plot.plot(GWASDrops,    plotType = "qtl",    title = "QTL plot for Mur13W")    ## Manually set a threshold and don't show vertical lines.plot(GWASDrops,         plotType = "qtl",    yThr = 3.5,    printVertGrid = FALSE,    title = "QTL plot for Mur13W")

Plot function for the classgData

Description

Creates a plot of the genetic map in an object of S3 classgData. Aplot of the genetic map showing the length of the chromosomes and thepositions of the markers in the genetic map is created.

Usage

## S3 method for class 'gData'plot(x, ..., highlight = NULL, title = NULL, output = TRUE)

Arguments

Value

An object of classggplot is invisibly returned.

Examples

set.seed(1234)## Create genotypic data.geno <- matrix(sample(x = c(0, 1, 2), size = 15, replace = TRUE), nrow = 3)dimnames(geno) <- list(paste0("G", 1:3), paste0("M", 1:5))## Construct map.map <- data.frame(chr = c(1, 1, 2, 2, 2), pos = 1:5,                  row.names = paste0("M", 1:5))## Compute kinship matrix.kin <- kinship(X = geno, method = "IBS")## Create phenotypic data.pheno <- data.frame(paste0("G", 1:3),                    matrix(rnorm(n = 12, mean = 50, sd = 5), nrow = 3),                    stringsAsFactors = FALSE)dimnames(pheno) = list(paste0("G", 1:3), c("genotype", paste0("T", 1:4)))## Combine all data in gData object.gData <- createGData(geno = geno, map = map, kin = kin, pheno = pheno)## Plot genetic map.plot(gData)## Plot genetic map. Highlight first marker in map.plot(gData, highlight = map[1, ])

Read PLINK binary data

Description

Read PLINK binary data and save in gData format. This is a wrapper aroundsnpStats::read.plink in the Bioconductor packagesnpStats. This package needs to be installed for the function towork.

Usage

readPLINK(bed, bim, fam, ...)

Arguments

Value

An object of classgData.

Read variant call format data

Description

Read variant call format (VCF) data and save in gData format. This is awrapper aroundvcfR::read.vcfR in the packagevcfR. This packageneeds to be installed for the function to work.

Usage

readVcf(vcfFile, ...)

Arguments

Value

An object of classgData.

References

Knaus BJ, Grünwald NJ (2017). “VCFR: a package to manipulate andvisualizevariant call format data in R.”Molecular Ecology Resources,17(1), 44-53. ISSN 757,doi:10.1111/1755-0998.12549.

Perform single-trait GWAS

Description

runSingleTraitGwas performs a single-trait Genome Wide AssociationStudy (GWAS) on phenotypic and genotypic data contained in agDataobject. A covariance matrix is computed using the EMMA algorithm (Kang etal., 2008), the Newton-Raphson algorithm (Tunnicliffe, 1989) in thesommer package, or the modified Henderson algorithm in theLMMsolver 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 differentchromosome-specific kinship matrices for each chromosome. Significant SNPsare 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)

Arguments

Value

An object of classGWAS.

Threshold types for the selection of candidate loci

For the selection of candidate loci from the GWAS output four differentmethods can be used. The method used can be specified in the functionparameterthrType. Further parameters can be used to fine tune themethod.

bonf

The Bonferroni threshold, a LOD-threshold of-log10(alpha / m), where m is the number of SNPs and alpha can bespecified by the function parameteralpha

fixed

A fixed LOD-threshold, specified by the function parameterLODThr

small

The n SNPs with with the smallest p-Values are selected. n canbe specified innSnpLOD

fdr

Following the algorithm proposed by Brzyski D. et al. (2017),SNPs are selected in such a way that the False Discovery Rate (fdr) isminimized. To do this, first the SNPs are restricted to the SNPs with ap-Value belowpThr. Then clusters of SNPs are created using atwo step iterative process in which first the SNP with the lowest p-Value isselected as cluster representative. This SNP and all SNPs that have acorrelation with this SNP of\rho or higher will form a cluster.\rho can be specified as an argument in the function and has a defaultvalue of 0.5, which is a recommended starting value in practice.The selected SNPs are removed from the list and the procedure repeated untilno SNPs are left. Finally to determine the number of significant clustersthe first cluster is determined for which the p-Value of the clusterrepresentative is not smaller thancluster_{number} * \alpha / m wherem is the number of SNPs and alpha can be specified by the function parameteralpha. All previous clusters are selected as significant.

References

Astle, William, and David J. Balding. 2009. Population Structureand Cryptic Relatedness in Genetic Association Studies. Statistical Science24 (4): 451–71.doi:10.1214/09-sts307.

Brzyski D. et al. (2017) Controlling the Rate of GWAS FalseDiscoveries. Genetics 205 (1): 61-75.doi:10.1534/genetics.116.193987

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

Kang et al. (2008) Efficient Control of Population Structure inModel Organism Association Mapping. Genetics 178 (3): 1709–23.doi:10.1534/genetics.107.080101.

Millet, E. J., Pommier, C., et al. (2019). A multi-siteexperiment in a network of European fields for assessing the maize yieldresponse to environmental scenarios - Data set.doi:10.15454/IASSTN

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

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

Sun et al. (2010) Variation explained in mixed-model associationmapping. Heredity 105 (4): 333–40.doi:10.1038/hdy.2010.11.

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

VanRaden P.M. (2008) Efficient methods to compute genomicpredictions. Journal of Dairy Science 91 (11): 4414–23.doi:10.3168/jds.2007-0980.

See Also

summary.GWAS,plot.GWAS

Examples

## 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.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.GWASDropsMult <- runSingleTraitGwas(gData = gDataDrops,                                    trials = "Mur13W",                                    traits = "grain.yield",                                    kinshipMethod = "vanRaden",                                    GLSMethod = "multi")

Summary function for the classGWAS

Description

Gives a summary for an object of S3 classGWAS.

Usage

## S3 method for class 'GWAS'summary(object, ..., trials = NULL, traits = NULL)

Arguments

Summary function for the classgData

Description

Gives a summary for an object of S3 classgData.

Usage

## S3 method for class 'gData'summary(object, ..., trials = NULL)

Arguments

Value

A list with a most four components:

mapSum

A list with number of markers and number of chromosomes inthe map.

markerSum

A list with number of markers, number of genotypes andthe distribution of the values within the markers.

phenoSum

A list of data.frames, one per trial with a summary of alltraits within the trial.

covarSum

A list of data.frames, one per trial with a summary of allcovariates within the trial.

All components are only present in the output if the corresponding content ispresent in the gData object.