In knausb/GAPIT3documentation: Documentation for GAPIT3
knitr::opts_chunk$set(echo = TRUE)
knitr::include_graphics("../inst/GAPITflow.png")
GAPIT()
Object: To perform GWAS and GPS (Genomic Prediction/Selection)
Designed by Zhiwu Zhang
Writen by Jiabo Wang
'
' perform GWAS and GPS
'
'
'
'
’ @param Y=NULL a sample (row) by phenotype (column) data.frame
’ @param G=NULL genotypes in HapMap format
’ @param GD=NULL genotypes in numeric format
’ @param GM=NULL genotype map for genotypes in numeric format (GD)
’ @param KI=NULL kinship data.frame (sample by sample matrix)
’ @param Z=NULL
’ @param CV = NULL covariate valiables
' @param CV.Inheritance = NULL
' @param GP = NULL
' @param GK = NULL
' @param testY = NULL
’ @param group.from=1000000
’ @param group.to=1000000
’ @param group.by=20
’ @param DPP=100000
’ @param kinship.cluster="average"
’ @param kinship.group='Mean'
’ @param kinship.algorithm = “Zhang”
’ @param buspred=FALSE
’ @param lmpred=FALSE
’ @param FDRcut=FALSE
’ @param bin.from=10000
’ @param bin.to=10000
’ @param bin.by=10000
’ @param inclosure.from=10
’ @param inclosure.to=10
’ @param inclosure.by=10
’ @param SNP.P3D=TRUE
’ @param SNP.effect="Add"
’ @param SNP.impute="Middle"
’ @param PCA.total=0
’ @param SNP.fraction = 1
’ @param seed = NULL
’ @param BINS = 20
’ @param SNP.test=TRUE
’ @param SNP.MAF=0
’ @param FDR.Rate = 1
’ @param SNP.FDR=1
’ @param SNP.permutation=FALSE
’ @param SNP.CV=NULL
’ @param SNP.robust="GLM"
’ @param file.from=1
’ @param file.to=1
’ @param file.total=NULL
’ @param file.fragment = 99999
’ @param file.path=NULL
’ @param file.G=NULL
’ @param file.Ext.G=NULL
’ @param file.GD=NULL
’ @param file.GM=NULL
’ @param file.Ext.GD=NULL
’ @param file.Ext.GM=NULL
’ @param ngrid = 100
’ @param llim = -10
’ @param ulim = 10
’ @param esp = 1e-10
’ @param LD.chromosome=NULL
’ @param LD.location=NULL
’ @param LD.range=NULL
’ @param PCA.col=NULL
’ @param PCA.3d=FALSE
’ @param NJtree.group=NULL
’ @param NJtree.type=c("fan","unrooted")
’ @param sangwich.top=NULL
’ @param sangwich.bottom=NULL
’ @param QC=TRUE
’ @param GTindex=NULL
’ @param LD=0.1
’ @param plot.bin=10^5
’ @param file.output=TRUE
’ @param cutOff=0.05
’ @param Model.selection = FALSE
’ @param output.numerical = FALSE
’ @param output.hapmap = FALSE
’ @param Create.indicator = FALSE
’ @param Multi_iter=FALSE
’ @param num_regwas=10
’ @param opt="extBIC"
’ @param QTN=NULL
’ @param QTN.round=1
’ @param QTN.limit=0
’ @param QTN.update=TRUE
’ @param QTN.method="Penalty"
’ @param Major.allele.zero = FALSE
’ @param Random.model=FALSE
’ @param method.GLM="FarmCPU.LM"
’ @param method.sub="reward"
’ @param method.sub.final="reward"
’ @param method.bin="static"
’ @param bin.size=c(1000000)
’ @param bin.selection=c(10,20,50,100,200,500,1000)
’ @param memo=NULL
’ @param Prior=NULL
’ @param ncpus=1
’ @param maxLoop=3
’ @param threshold.output=.01
’ @param Inter.Plot=FALSE
’ @param Inter.type=c("m","q")
’ @param WS=c(1e0,1e3,1e4,1e5,1e6,1e7)
’ @param alpha=c(.01,.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,1)
’ @param maxOut=100
’ @param QTN.position=NULL
’ @param CG=NULL
’ @param converge=1
’ @param iteration.output=FALSE
’ @param acceleration=0
’ @param iteration.method="accum"
’ @param PCA.View.output=TRUE
’ @param Geno.View.output=TRUE
’ @param plot.style="Oceanic"
’ @param SUPER_GD=NULL
’ @param SUPER_GS=FALSE
’ @param h2=NULL
’ @param NQTN=NULL
’ @param QTNDist="normal"
’ @param effectunit=1
’ @param category=1
’ @param r=0.25
’ @param cveff=NULL
’ @param a2=0
’ @param adim=2
’ @param Multiple_analysis=FALSE
’ @param model="MLM"
’ @param Para=NULL
'
'
' @return out
'
' A list containing the following elements.
'
' GD a data.frame containing genotypes in numeric format
' GM a data.frame containing a genotype map
' G a data.frame containing genotypes in hapmap format
' kinship a data.frame containing a kinship matrix
' chor_taxa a character vector
'
' or
'
' $GWAS "data.frame"
' $Pred "data.frame"
' $mc "numeric"
' $bc "matrix" "array"
' $mp "matrix" "array"
' $h2 "numeric"
' $PCA "data.frame"
" $GD "data.frame"
' $GM "data.frame"
' $KI "data.frame"
' $Compression "matrix" "array"
'
'
'
`GAPIT` <- function(
Y = NULL,
G = NULL,
GD = NULL,
GM = NULL,
KI = NULL,
Z = NULL,
CV = NULL,
CV.Inheritance = NULL,
GP = NULL,
GK = NULL,
testY = NULL,
group.from = 1000000,
group.to = 1000000,
group.by = 20,
DPP = 100000,
kinship.cluster = "average",
kinship.group = 'Mean',
kinship.algorithm = "VanRaden",
buspred = FALSE,
lmpred = FALSE,
FDRcut = FALSE,
bin.from = 10000,
bin.to = 10000,
bin.by = 10000,
inclosure.from = 10,
inclosure.to = 10,
inclosure.by = 10,
SNP.P3D = TRUE,
SNP.effect = "Add",
SNP.impute = "Middle",
PCA.total = 0,
SNP.fraction = 1,
seed = NULL,
BINS = 20,
SNP.test = TRUE,
SNP.MAF = 0,
FDR.Rate = 1,
SNP.FDR = 1,
SNP.permutation = FALSE,
SNP.CV = NULL,
SNP.robust = "GLM",
file.from = 1,
file.to = 1,
file.total = NULL,
file.fragment = 99999,
file.path = NULL,
file.G = NULL,
file.Ext.G = NULL,
file.GD = NULL,
file.GM = NULL,
file.Ext.GD = NULL,
file.Ext.GM = NULL,
ngrid = 100,
llim = -10,
ulim = 10,
esp = 1e-10,
LD.chromosome = NULL,
LD.location = NULL,
LD.range = NULL,
PCA.col = NULL,
PCA.3d = FALSE,
NJtree.group = NULL,
NJtree.type = c("fan","unrooted"),
sangwich.top = NULL,
sangwich.bottom = NULL,
QC = TRUE,
GTindex = NULL,
LD = 0.1,
plot.bin = 10^5,
file.output = TRUE,
cutOff = 0.05,
Model.selection = FALSE,
output.numerical = FALSE,
output.hapmap = FALSE,
Create.indicator = FALSE,
Multi_iter = FALSE,
num_regwas = 10,
opt = "extBIC",
QTN = NULL,
QTN.round = 1,
QTN.limit = 0,
QTN.update = TRUE,
QTN.method = "Penalty",
Major.allele.zero = FALSE,
Random.model = FALSE,
method.GLM = "FarmCPU.LM",
method.sub = "reward",
method.sub.final = "reward",
method.bin = "static",
bin.size = c(1000000),
bin.selection = c(10,20,50,100,200,500,1000),
memo = NULL,
Prior = NULL,
ncpus = 1,
maxLoop = 3,
threshold.output = .01,
Inter.Plot = FALSE,
Inter.type = c("m","q"),
WS = c(1e0,1e3,1e4,1e5,1e6,1e7),
alpha = c(.01,.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,1),
maxOut = 100,
QTN.position = NULL,CG = NULL,
converge = 1,
iteration.output = FALSE,
acceleration = 0,
iteration.method = "accum",
PCA.View.output = TRUE,
Geno.View.output = TRUE,
plot.style = "Oceanic",
SUPER_GD = NULL,
SUPER_GS = FALSE,
h2 = NULL,
NQTN = NULL,
QTNDist = "normal",
effectunit = 1,
category = 1,
r = 0.25,
cveff = NULL,
a2 = 0,
adim = 2,
Multiple_analysis = FALSE,
model = "MLM",
Para = NULL
){
#Object: To perform GWAS and GPS (Genomic Prediction/Selection)
#Designed by Zhiwu Zhang
#Writen by Jiabo Wang
#Last update: Novenber 3, 2016
##############################################################################################
#print("--------------------- Welcome to GAPIT ----------------------------")
#echo = TRUE
#all.memo = NULL
}
GAPIT.DP()
`GAPIT.DP` <- function(
G = NULL,
GD = NULL,
GM = NULL,
KI = NULL,
Z = NULL,
CV = NULL,
CV.Inheritance = NULL,
GP = NULL,
GK = NULL,
group.from = 30,
group.to = 1000000,
group.by = 10,
DPP = 100000,
kinship.cluster = "average",
kinship.group = 'Mean',
kinship.algorithm = "VanRaden",
bin.from = 10000,bin.to = 10000,bin.by = 10000,
inclosure.from = 10,inclosure.to = 10,inclosure.by = 10,
SNP.P3D = TRUE,
SNP.effect = "Add",
SNP.impute = "Middle",
PCA.total = 0,
SNP.fraction = 1,
seed = 123,
BINS = 20,
SNP.test = TRUE,
SNP.MAF = 0,
FDR.Rate = 1,
SNP.FDR = 1,
SNP.permutation = FALSE,
SNP.CV = NULL,
SNP.robust = "GLM",
NJtree.group = NULL,
NJtree.type = c("fan","unrooted"),
plot.bin = 10^6,
PCA.col = NULL, PCA.3d = FALSE,
file.from = 1, file.to = 1, file.total = NULL,
file.fragment = 99999,file.path = NULL,
Inter.Plot = FALSE,Inter.type = c("m","q"),
file.G = NULL, file.Ext.G = NULL,file.GD = NULL,
file.GM = NULL, file.Ext.GD = NULL,file.Ext.GM = NULL,
ngrid = 100, llim = -10, ulim = 10, esp = 1e-10,
Multi_iter = FALSE,num_regwas = 10,FDRcut = FALSE,
LD.chromosome = NULL,LD.location = NULL,LD.range = NULL,
p.threshold = NA,QTN.threshold = 0.01,maf.threshold = 0.03,
sangwich.top = NULL,sangwich.bottom = NULL,QC = TRUE,
GTindex = NULL,LD = 0.1,opt = "extBIC",
file.output = FALSE,cutOff = 0.01,
Model.selection = FALSE,output.numerical = FALSE,
Random.model = FALSE, output.hapmap = FALSE,
Create.indicator = FALSE,
QTN = NULL, QTN.round = 1, QTN.limit = 0, QTN.update = TRUE,
QTN.method = "Penalty", Major.allele.zero = FALSE,
method.GLM = "fast.lm", method.sub = "reward",
method.sub.final = "reward",
method.bin = "static",
bin.size = c(1000000),
bin.selection = c(10,20,50,100,200,500,1000),
memo = "", Prior = NULL, ncpus = 1, maxLoop = 3,
threshold.output = .01,
WS = c(1e0,1e3,1e4,1e5,1e6,1e7),
alpha = c(.01,.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,1),
maxOut = 100,
QTN.position = NULL,
converge = 1,
iteration.output = FALSE,
acceleration = 0,
iteration.method = "accum",
PCA.View.output = TRUE,
Geno.View.output = TRUE,
plot.style = "Oceanic",
SUPER_GD = NULL,SUPER_GS = FALSE,
CG = NULL,model = "MLM"){
#Object: To Data and Parameters
#Designed by Zhiwu Zhang
#Writen by Jiabo Wang
}
# return(
# list(
# Y = NULL,
# G = G,GD = GD,GM = GI,KI = KI,Z = Z,CV = CV,
# CV.Inheritance = CV.Inheritance,
# GP = GP,GK = GK,PC = PC,GI = GI,
# group.from = group.from ,group.to = group.to,group.by = group.by,
# DPP = DPP, name.of.trait = NULL,
# kinship.cluster = kinship.cluster,
# kinship.group = kinship.group,
# kinship.algorithm = kinship.algorithm,
# NJtree.group = NJtree.group,
# NJtree.type = NJtree.type,
# PCA.col = PCA.col, PCA.3d = PCA.3d,
# bin.from = bin.from,bin.to = bin.to,bin.by = bin.by,
# inclosure.from = inclosure.from,
# inclosure.to = inclosure.to,
# inclosure.by = inclosure.by,
# opt = opt,
# SNP.P3D = SNP.P3D, SNP.effect = SNP.effect,
# SNP.impute = SNP.impute,
# PCA.total = PCA.total,
# SNP.fraction = SNP.fraction,
# seed = seed, BINS = BINS,SNP.test = SNP.test,
# SNP.MAF = SNP.MAF, FDR.Rate = FDR.Rate,
# SNP.FDR = SNP.FDR,
# SNP.permutation = SNP.permutation,
# SNP.CV = SNP.CV, SNP.robust = SNP.robust, file.from = file.from,
# file.to = file.to, file.total = file.total,
# file.fragment = file.fragment, file.path = file.path,
# file.G = file.G, file.Ext.G = file.Ext.G,
# file.GD = file.GD,
# file.GM = file.GM,
# file.Ext.GD = file.Ext.GD,
# file.Ext.GM = file.Ext.GM,
# ngrid = ngrid,
# llim = llim, ulim = ulim,
# esp = esp, Inter.Plot = Inter.Plot,
# Inter.type = Inter.type,
# LD.chromosome = LD.chromosome, LD.location = LD.location,
# LD.range = LD.range,
# Multi_iter = Multi_iter,
# sangwich.top = sangwich.top,
# sangwich.bottom = sangwich.bottom,
# QC = QC,GTindex = GTindex, LD = LD, GT = GT,
# file.output = file.output,cutOff = cutOff,
# Model.selection = Model.selection,
# output.numerical = output.numerical,
# output.hapmap = output.hapmap,
# Create.indicator = Create.indicator,
# Random.model = Random.model,
# QTN = QTN, QTN.round = QTN.round, QTN.limit = QTN.limit,
# QTN.update = QTN.update, QTN.method = QTN.method,
# Major.allele.zero = Major.allele.zero,
# method.GLM = method.GLM, method.sub = method.sub,
# method.sub.final = method.sub.final,
# method.bin = method.bin, bin.size = bin.size,
# bin.selection = bin.selection, FDRcut = FDRcut,
# memo = memo, Prior = Prior, ncpus = 1,
# maxLoop = maxLoop,
# threshold.output = threshold.output,
# WS = WS, alpha = alpha, maxOut = maxOut,
# QTN.position = QTN.position, converge = 1,
# iteration.output = iteration.output,
# acceleration = 0,
# iteration.method = iteration.method,
# PCA.View.output = PCA.View.output,
# p.threshold = p.threshold,
# QTN.threshold = QTN.threshold,
# maf.threshold = maf.threshold, chor_taxa = chor_taxa,
# num_regwas = num_regwas, Geno.View.output = Geno.View.output,
# plot.style = plot.style,
# SUPER_GD = SUPER_GD, SUPER_GS = SUPER_GS,
# CG = CG, plot.bin = plot.bin
# )
# )
GAPIT.Genotype()
'
' Description
'
' @param G = NULL
' @param GD = NULL
' @param GM = NULL
' @param KI = NULL
' @param kinship.algorithm = "Zhang"
' @param SNP.effect = "Add"
' @param SNP.impute = "Middle"
' @param PCA.total = 0
' @param PCA.col = NULL
' @param PCA.3d = PCA.3d
' @param seed = 123
' @param SNP.fraction = 1
' @param file.path = NULL
' @param file.from = NULL
' @param file.to = NULL
' @param file.total = NULL
' @param file.fragment = 1000
' @param SNP.test = TRUE
' @param file.G = NULL
' @param file.Ext.G = NULL
' @param file.GD = NULL
' @param file.Ext.GD = NULL
' @param file.GM = NULL
' @param file.Ext.GM = NULL
' @param SNP.MAF = 0.05
' @param FDR.Rate = 0.05
' @param SNP.FDR = 1
' @param Timmer = NULL
' @param Memory = NULL
' @param LD.chromosome = NULL
' @param LD.location = NULL
' @param LD.range = NULL
' @param SNP.CV = NULL
' @param GP = NULL
' @param GK = NULL
' @param GTindex = NULL
' @param bin.size = 1000
' @param inclosure.size = 100
' @param sangwich.top = NULL
' @param sangwich.bottom = NULL
' @param file.output = TRUE
' @param kinship.cluster = "average"
' @param NJtree.group = NULL
' @param NJtree.type = c("fan","unrooted")
' @param Create.indicator = FALSE
' @param Major.allele.zero = FALSE
' @param Geno.View.output = TRUE
'
'
' @return a list consisting of the following elements.
"
'
'
'
G = G
GD = GD
GI = GI
GT = GT
hasGenotype = hasGenotype
genoFormat = genoFormat KI = KI
PC = PC
byFile = byFile
fullGD = fullGD
Timmer = Timmer
Memory = Memory
SNP.QTN = SNP.QTN
chor_taxa = chor_taxa
Details
#Object: To unify genotype and calculate kinship and PC if required:
# 1.For G data, convert it to GD and GI
# 2.For GD and GM data, nothing change
# 3.Samling GD and create KI and PC
# 4.Go through multiple files
# 5.In any case, GD must be returned (for QC)
#Output: GD, GI, GT, KI and PC
#'
#'
#' return (list(G = G,GD = GD,GI = GI,GT = GT,hasGenotype = hasGenotype, genoFormat = genoFormat, KI = KI,PC = PC,byFile = byFile,fullGD = fullGD,Timmer = Timmer,Memory = Memory,SNP.QTN = SNP.QTN,chor_taxa = chor_taxa))
#'
#'
GAPIT.Genotype <- function(x){
}
GAPIT.Phenotype.View()
'
' Distribution of density,Accumulation,result:a pdf of the scree plot
'
' myY = NULL
' traitname = "_"
' memo = "_"
'
'
' @return
' a string (function creates graphics)
'
'
`GAPIT.Phenotype.View` <- function(myY = NULL,traitname = "_",memo = "_"){
# Object: Analysis for Phenotype data:Distribution of density,Accumulation,result:a pdf of the scree plot
# myY:Phenotype data
}
GAPIT.Judge()
'
' @param DP=NULL
'
'
' @return
' A list of
' group.to = group.to
' group.from = group.from
'
'
`GAPIT.Judge`<-
function(Y = Y,G = NULL,GD = NULL,KI = NULL,GM = NULL,group.to = group.to,group.from = group.from,sangwich.top = sangwich.top,sangwich.bottom = sangwich.bottom,kinship.algorithm = kinship.algorithm,PCA.total = PCA.total,model = "MLM",SNP.test = TRUE){
#Object: To judge Pheno and Geno data practicability
}
GAPIT.IC()
'
' Intermediate Components
'
'
' @param DP=NULL
'
'
'
' @return
` a list of either
'
' Y = comY
' GT = GT
' PCA = comCV
' K = DP\$KI
' GD = comGD
' GM = DP\$GM
' myallCV = CV
' myallGD = GD
'
' or
'
'
' Y = comY
' GT = GT
' PCA = comCV
' K = DP\$KI
' GD = comGD
' GM = DP\$GM
' myallCV = CV
' myallGD = GD
' myallY = Y
'
'
'
`GAPIT.IC` <-
function(DP = NULL){
#Object: To Intermediate Components
}
GAPIT.SS()
'
' Sufficient Statistics (SS) for GWAS and GS
'
'
' @param DP = NULL
' @param IC=NULL
' @param buspred=FALSE
' @param lmpred=TRUE
'
'
' @return
'
` a list containing
'
' GWAS=GWAS
' Pred=Pred
' FDR=NULL
' Power=NULL
' Power.Alpha=NULL
' alpha=NULL
' h2=h2
' va=va
' ve=ve
' Compression=Compression
' mc=mc
' bc=bc
' mp=mp
' TV=gapitMain$TV
' Timmer=Timmer
' Memory=Memory
'
'
'
`GAPIT.SS` <-
function(DP = NULL,IC = NULL,buspred = FALSE,lmpred = TRUE){
#Object: To Sufficient Statistics (SS) for GWAS and GS
}
GAPIT.Bus()
function is used to run multiple method, Thanks MLMM FarmCPU Blink to share program and code
' @param Y=NULL
' @param CV=NULL
' @param Z=NULL
' @param GT=NULL
' @param KI=NULL
' @param GK=NULL
' @param GD=NULL
' @param GM=NULL
' @param WS=c(1e0,1e3,1e4,1e5,1e6,1e7)
' @param alpha=c(.01,.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,1)
' @param method=NULL
' @param delta=NULL
' @param vg=NULL
' @param ve=NULL
' @param LD=0.01
' @param GTindex=NULL
' @param cutOff=0.01
' @param Multi_iter=FALSE
' @param num_regwas=10
' @param Random.model=FALSE
' @param FDRcut=FALSE
' @param p.threshold=NA
' @param QTN.threshold=0.01
' @param maf.threshold=0.03
' @param method.GLM="FarmCPU.LM"
' @param method.sub="reward"
' @param method.sub.final="reward"
' @param method.bin="static"
' @param DPP=1000000
' @param bin.size=c(5e5, 5e6,5e7)
' @param bin.selection=seq(10,100,10)
' @param file.output=TRUE
' @param opt="extBIC"
'
'
'
' @return
' a list consisting of the following elements
'
' GWAS=GWAS
' GPS=GPS
' REMLs=REMLs
' vg=vg
' ve=ve
' delta=delta
' GVs=GR$GVs
' seqQTN=seqQTN
'
'
`GAPIT.Bus`<-
function(Y = NULL,CV = NULL,Z = NULL,GT = NULL,KI = NULL,GK = NULL,GD = NULL,GM = NULL,
WS = c(1e0,1e3,1e4,1e5,1e6,1e7),alpha = c(.01,.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,1),
method = NULL,delta = NULL,vg = NULL,ve = NULL,LD = 0.01,GTindex = NULL,
cutOff = 0.01,Multi_iter = FALSE,num_regwas = 10,Random.model = FALSE,FDRcut = FALSE,
p.threshold = NA,QTN.threshold = 0.01,maf.threshold = 0.03,
method.GLM = "FarmCPU.LM",method.sub = "reward",method.sub.final = "reward",method.bin = "static",
DPP = 1000000,bin.size = c(5e5,5e6,5e7),bin.selection = seq(10,100,10),
file.output = TRUE,opt = "extBIC"){
#Object: To license data by method
#Output: Coresponding numerical value
# This function is used to run multiple method, Thanks MLMM FarmCPU Blink to share program and code.
}
GAPIT.Main()
Object: To perform GWAS and GPS (Genomic Prediction or Selection)
Output: GWAS table (text file), QQ plot (PDF), Manhattan plot (PDF), genomic prediction (text file), and
genetic and residual variance components
Authors: Zhiwu Zhang
'
' perform GWAS and GP
'
' @param Y a sample (row) by phenotype (column) data.frame
' @param G = NULL genotypes in HapMap format
' @param GD = NULL genotypes in numeric format
' @param GM = NULL genotype map for genotypes in numeric format (GD)
' @param KI = NULL kinship matrix (sample by sample)
' @param Z = NULL
' @param CV = NULL covariate valiables
' @param CV.Inheritance = NULL
' @param SNP.P3D = TRUE
' @param GP = NULL
' @param GK = NULL
' ...
'
'
' @return a list consisting of the following elements.
'
' Timmer = Timmer,
' Compression = Compression,
' kinship.optimum = theK.back,
' kinship = KI,
' PC = PC,
' GWAS = PWI.Filtered,
' GPS = GPS,
' Pred = Pred,
' REMLs = Compression[count,4],
' Timmer = Timmer,
' Memory = Memory,
' SUPER_GD = SUPER_GD,
' P = ps,
' effect.snp = DTS[,7],
' effect.cv = p3d$effect.cv,
' h2 = h2.opt,
' TV = TV
'
'
'
`GAPIT.Main` <- function(
Y,
G = NULL,
GD = NULL,
GM = NULL,
KI = NULL,
Z = NULL,
CV = NULL, CV.Inheritance = NULL,
SNP.P3D = TRUE,
GP = NULL,
GK = NULL,
group.from = 1000000, group.to = 1, group.by = 10,
kinship.cluster = "average",
kinship.group = 'Mean',
kinship.algorithm = NULL,
DPP = 50000,
ngrid = 100, llin = -10, ulim = 10, esp = 1e-10,
GAPIT3.output = TRUE,
file.path = NULL, file.from = NULL, file.to = NULL,
file.total = NULL, file.fragment = 512,
file.G = NULL, file.Ext.G = NULL,
file.GD = NULL,
file.GM = NULL,
file.Ext.GD = NULL,
file.Ext.GM = NULL,
SNP.MAF = 0, FDR.Rate = 1, SNP.FDR = 1, SNP.effect = "Add",
SNP.impute = "Middle", PCA.total = 0,
GAPIT.Version = GAPIT.Version,
name.of.trait,
GT = NULL,
SNP.fraction = 1,
seed = 123,
BINS = 20,
SNP.test = TRUE, SNP.robust = "FaST",
LD.chromosome = NULL, LD.location = NULL, LD.range = NULL,
model = model,
bin.from = 10000, bin.to = 5000000, bin.by = 1000,
inclosure.from = 10, inclosure.to = 1000, inclosure.by = 10,
SNP.permutation = FALSE,
SNP.CV = NULL,
NJtree.group = NJtree.group,
NJtree.type = NJtree.type,
plot.bin = plot.bin,
genoFormat = NULL,
hasGenotype = NULL, byFile = NULL, fullGD = NULL,
PC = NULL,
GI = NULL,
Timmer = NULL,
Memory = NULL,
sangwich.top = NULL, sangwich.bottom = NULL,
QC = TRUE,
GTindex = NULL, LD = 0.05,
file.output = TRUE, cutOff = 0.05,
Model.selection = FALSE, Create.indicator = FALSE,
QTN = NULL, QTN.round = 1, QTN.limit = 0,
QTN.update = TRUE, QTN.method = "Penalty",
Major.allele.zero = FALSE,
QTN.position = NULL,
SUPER_GD = NULL,
SUPER_GS = SUPER_GS,
plot.style = "Beach",
CG = CG,
chor_taxa = chor_taxa){
#Object: To perform GWAS and GPS (Genomic Prediction or Selection)
#Output: GWAS table (text file), QQ plot (PDF), Manhattan plot (PDF), genomic prediction (text file), and
# genetic and residual variance components
#Authors: Zhiwu Zhang
}
# return(
# list(
# Timmer = Timmer,
# Compression = Compression,
# kinship.optimum = theK.back,
# kinship = KI,
# PC = PC,
# GWAS = PWI.Filtered,
# GPS = GPS,
# Pred = Pred,
# REMLs = Compression[count,4],
# Timmer = Timmer,
# Memory = Memory,
# SUPER_GD = SUPER_GD,
# P = ps,
# effect.snp = DTS[,7],
# effect.cv = p3d$effect.cv,
# h2 = h2.opt,
# TV = TV
# )
# )
GAPIT.ID()
Description
To Interpretation and Diagnoses
' @param G = NULL
' @param GD = NULL
' @param GM = NULL
' @param KI = NULL
' @param kinship.algorithm = "Zhang"
' @param SNP.effect = "Add"
' @param SNP.impute = "Middle"
' @param PCA.total = 0
' @param PCA.col = NULL
' @param PCA.3d = PCA.3d
' @param seed = 123
' @param SNP.fraction = 1
' @param file.path = NULL
' @param file.from = NULL
' @param file.to = NULL
' @param file.total = NULL
' @param file.fragment = 1000
' @param SNP.test = TRUE
' @param file.G = NULL
' @param file.Ext.G = NULL
' @param file.GD = NULL
' @param file.Ext.GD = NULL
' @param file.GM = NULL
' @param file.Ext.GM = NULL
' @param SNP.MAF = 0.05,
' @param FDR.Rate = 0.05
' @param SNP.FDR = 1
' @param Timmer = NULL
' @param Memory = NULL
' @param LD.chromosome = NULL
' @param LD.location = NULL
' @param LD.range = NULL
' @param SNP.CV = NULL
' @param GP = NULL
' @param GK = NULL
' @param GTindex = NULL
' @param bin.size = 1000
' @param inclosure.size = 100
' @param sangwich.top = NULL
' @param sangwich.bottom = NULL
' @param file.output = TRUE
' @param kinship.cluster = "average"
' @param NJtree.group = NULL
' @param NJtree.type = c("fan","unrooted")
' @param Create.indicator = FALSE
' @param Major.allele.zero = FALSE
' @param Geno.View.output = TRUE
'
'
'
'
' @return a string
'
'
' DP = NULL,
' IC = NULL,
' SS = NULL,
' RS = NULL,
' ...
'
'
' Details
'
' @author Jiabo Wang.
'
'
`GAPIT.ID` <- function(
DP = NULL,
IC = NULL,
SS = NULL,
RS = NULL,
cutOff = 0.01,
DPP = 100000,
Create.indicator = FALSE,
FDR.Rate = 1,QTN.position = NULL,
plot.style = "Oceanic",
file.output = TRUE,
SNP.MAF = 0,
CG = NULL,
plot.bin = 10^9 ){
#Object: To Interpretation and Diagnoses
#Designed by Zhiwu Zhang
#Writen by Jiabo Wang
}
knausb/GAPIT3documentation documentation built on Sept. 24, 2022, 11:38 a.m.
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knitr::opts_chunk$set(echo = TRUE)
knitr::include_graphics("../inst/GAPITflow.png")
GAPIT()
Object: To perform GWAS and GPS (Genomic Prediction/Selection)
Designed by Zhiwu Zhang
Writen by Jiabo Wang
'
' perform GWAS and GPS
'
'
'
'
’ @param Y=NULL a sample (row) by phenotype (column) data.frame
’ @param G=NULL genotypes in HapMap format
’ @param GD=NULL genotypes in numeric format
’ @param GM=NULL genotype map for genotypes in numeric format (GD)
’ @param KI=NULL kinship data.frame (sample by sample matrix)
’ @param Z=NULL
’ @param CV = NULL covariate valiables
' @param CV.Inheritance = NULL
' @param GP = NULL
' @param GK = NULL
' @param testY = NULL
’ @param group.from=1000000
’ @param group.to=1000000
’ @param group.by=20
’ @param DPP=100000
’ @param kinship.cluster="average"
’ @param kinship.group='Mean'
’ @param kinship.algorithm = “Zhang”
’ @param buspred=FALSE
’ @param lmpred=FALSE
’ @param FDRcut=FALSE
’ @param bin.from=10000
’ @param bin.to=10000
’ @param bin.by=10000
’ @param inclosure.from=10
’ @param inclosure.to=10
’ @param inclosure.by=10
’ @param SNP.P3D=TRUE
’ @param SNP.effect="Add"
’ @param SNP.impute="Middle"
’ @param PCA.total=0
’ @param SNP.fraction = 1
’ @param seed = NULL
’ @param BINS = 20
’ @param SNP.test=TRUE
’ @param SNP.MAF=0
’ @param FDR.Rate = 1
’ @param SNP.FDR=1
’ @param SNP.permutation=FALSE
’ @param SNP.CV=NULL
’ @param SNP.robust="GLM"
’ @param file.from=1
’ @param file.to=1
’ @param file.total=NULL
’ @param file.fragment = 99999
’ @param file.path=NULL
’ @param file.G=NULL
’ @param file.Ext.G=NULL
’ @param file.GD=NULL
’ @param file.GM=NULL
’ @param file.Ext.GD=NULL
’ @param file.Ext.GM=NULL
’ @param ngrid = 100
’ @param llim = -10
’ @param ulim = 10
’ @param esp = 1e-10
’ @param LD.chromosome=NULL
’ @param LD.location=NULL
’ @param LD.range=NULL
’ @param PCA.col=NULL
’ @param PCA.3d=FALSE
’ @param NJtree.group=NULL
’ @param NJtree.type=c("fan","unrooted")
’ @param sangwich.top=NULL
’ @param sangwich.bottom=NULL
’ @param QC=TRUE
’ @param GTindex=NULL
’ @param LD=0.1
’ @param plot.bin=10^5
’ @param file.output=TRUE
’ @param cutOff=0.05
’ @param Model.selection = FALSE
’ @param output.numerical = FALSE
’ @param output.hapmap = FALSE
’ @param Create.indicator = FALSE
’ @param Multi_iter=FALSE
’ @param num_regwas=10
’ @param opt="extBIC"
’ @param QTN=NULL
’ @param QTN.round=1
’ @param QTN.limit=0
’ @param QTN.update=TRUE
’ @param QTN.method="Penalty"
’ @param Major.allele.zero = FALSE
’ @param Random.model=FALSE
’ @param method.GLM="FarmCPU.LM"
’ @param method.sub="reward"
’ @param method.sub.final="reward"
’ @param method.bin="static"
’ @param bin.size=c(1000000)
’ @param bin.selection=c(10,20,50,100,200,500,1000)
’ @param memo=NULL
’ @param Prior=NULL
’ @param ncpus=1
’ @param maxLoop=3
’ @param threshold.output=.01
’ @param Inter.Plot=FALSE
’ @param Inter.type=c("m","q")
’ @param WS=c(1e0,1e3,1e4,1e5,1e6,1e7)
’ @param alpha=c(.01,.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,1)
’ @param maxOut=100
’ @param QTN.position=NULL
’ @param CG=NULL
’ @param converge=1
’ @param iteration.output=FALSE
’ @param acceleration=0
’ @param iteration.method="accum"
’ @param PCA.View.output=TRUE
’ @param Geno.View.output=TRUE
’ @param plot.style="Oceanic"
’ @param SUPER_GD=NULL
’ @param SUPER_GS=FALSE
’ @param h2=NULL
’ @param NQTN=NULL
’ @param QTNDist="normal"
’ @param effectunit=1
’ @param category=1
’ @param r=0.25
’ @param cveff=NULL
’ @param a2=0
’ @param adim=2
’ @param Multiple_analysis=FALSE
’ @param model="MLM"
’ @param Para=NULL
'
'
' @return out
'
' A list containing the following elements.
'
' GD a data.frame containing genotypes in numeric format
' GM a data.frame containing a genotype map
' G a data.frame containing genotypes in hapmap format
' kinship a data.frame containing a kinship matrix
' chor_taxa a character vector
'
' or
'
' $GWAS "data.frame"
' $Pred "data.frame"
' $mc "numeric"
' $bc "matrix" "array"
' $mp "matrix" "array"
' $h2 "numeric"
' $PCA "data.frame"
" $GD "data.frame"
' $GM "data.frame"
' $KI "data.frame"
' $Compression "matrix" "array"
'
'
'
`GAPIT` <- function( Y = NULL, G = NULL, GD = NULL, GM = NULL, KI = NULL, Z = NULL, CV = NULL, CV.Inheritance = NULL, GP = NULL, GK = NULL, testY = NULL, group.from = 1000000, group.to = 1000000, group.by = 20, DPP = 100000, kinship.cluster = "average", kinship.group = 'Mean', kinship.algorithm = "VanRaden", buspred = FALSE, lmpred = FALSE, FDRcut = FALSE, bin.from = 10000, bin.to = 10000, bin.by = 10000, inclosure.from = 10, inclosure.to = 10, inclosure.by = 10, SNP.P3D = TRUE, SNP.effect = "Add", SNP.impute = "Middle", PCA.total = 0, SNP.fraction = 1, seed = NULL, BINS = 20, SNP.test = TRUE, SNP.MAF = 0, FDR.Rate = 1, SNP.FDR = 1, SNP.permutation = FALSE, SNP.CV = NULL, SNP.robust = "GLM", file.from = 1, file.to = 1, file.total = NULL, file.fragment = 99999, file.path = NULL, file.G = NULL, file.Ext.G = NULL, file.GD = NULL, file.GM = NULL, file.Ext.GD = NULL, file.Ext.GM = NULL, ngrid = 100, llim = -10, ulim = 10, esp = 1e-10, LD.chromosome = NULL, LD.location = NULL, LD.range = NULL, PCA.col = NULL, PCA.3d = FALSE, NJtree.group = NULL, NJtree.type = c("fan","unrooted"), sangwich.top = NULL, sangwich.bottom = NULL, QC = TRUE, GTindex = NULL, LD = 0.1, plot.bin = 10^5, file.output = TRUE, cutOff = 0.05, Model.selection = FALSE, output.numerical = FALSE, output.hapmap = FALSE, Create.indicator = FALSE, Multi_iter = FALSE, num_regwas = 10, opt = "extBIC", QTN = NULL, QTN.round = 1, QTN.limit = 0, QTN.update = TRUE, QTN.method = "Penalty", Major.allele.zero = FALSE, Random.model = FALSE, method.GLM = "FarmCPU.LM", method.sub = "reward", method.sub.final = "reward", method.bin = "static", bin.size = c(1000000), bin.selection = c(10,20,50,100,200,500,1000), memo = NULL, Prior = NULL, ncpus = 1, maxLoop = 3, threshold.output = .01, Inter.Plot = FALSE, Inter.type = c("m","q"), WS = c(1e0,1e3,1e4,1e5,1e6,1e7), alpha = c(.01,.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,1), maxOut = 100, QTN.position = NULL,CG = NULL, converge = 1, iteration.output = FALSE, acceleration = 0, iteration.method = "accum", PCA.View.output = TRUE, Geno.View.output = TRUE, plot.style = "Oceanic", SUPER_GD = NULL, SUPER_GS = FALSE, h2 = NULL, NQTN = NULL, QTNDist = "normal", effectunit = 1, category = 1, r = 0.25, cveff = NULL, a2 = 0, adim = 2, Multiple_analysis = FALSE, model = "MLM", Para = NULL ){ #Object: To perform GWAS and GPS (Genomic Prediction/Selection) #Designed by Zhiwu Zhang #Writen by Jiabo Wang #Last update: Novenber 3, 2016 ############################################################################################## #print("--------------------- Welcome to GAPIT ----------------------------") #echo = TRUE #all.memo = NULL }
GAPIT.DP()
`GAPIT.DP` <- function( G = NULL, GD = NULL, GM = NULL, KI = NULL, Z = NULL, CV = NULL, CV.Inheritance = NULL, GP = NULL, GK = NULL, group.from = 30, group.to = 1000000, group.by = 10, DPP = 100000, kinship.cluster = "average", kinship.group = 'Mean', kinship.algorithm = "VanRaden", bin.from = 10000,bin.to = 10000,bin.by = 10000, inclosure.from = 10,inclosure.to = 10,inclosure.by = 10, SNP.P3D = TRUE, SNP.effect = "Add", SNP.impute = "Middle", PCA.total = 0, SNP.fraction = 1, seed = 123, BINS = 20, SNP.test = TRUE, SNP.MAF = 0, FDR.Rate = 1, SNP.FDR = 1, SNP.permutation = FALSE, SNP.CV = NULL, SNP.robust = "GLM", NJtree.group = NULL, NJtree.type = c("fan","unrooted"), plot.bin = 10^6, PCA.col = NULL, PCA.3d = FALSE, file.from = 1, file.to = 1, file.total = NULL, file.fragment = 99999,file.path = NULL, Inter.Plot = FALSE,Inter.type = c("m","q"), file.G = NULL, file.Ext.G = NULL,file.GD = NULL, file.GM = NULL, file.Ext.GD = NULL,file.Ext.GM = NULL, ngrid = 100, llim = -10, ulim = 10, esp = 1e-10, Multi_iter = FALSE,num_regwas = 10,FDRcut = FALSE, LD.chromosome = NULL,LD.location = NULL,LD.range = NULL, p.threshold = NA,QTN.threshold = 0.01,maf.threshold = 0.03, sangwich.top = NULL,sangwich.bottom = NULL,QC = TRUE, GTindex = NULL,LD = 0.1,opt = "extBIC", file.output = FALSE,cutOff = 0.01, Model.selection = FALSE,output.numerical = FALSE, Random.model = FALSE, output.hapmap = FALSE, Create.indicator = FALSE, QTN = NULL, QTN.round = 1, QTN.limit = 0, QTN.update = TRUE, QTN.method = "Penalty", Major.allele.zero = FALSE, method.GLM = "fast.lm", method.sub = "reward", method.sub.final = "reward", method.bin = "static", bin.size = c(1000000), bin.selection = c(10,20,50,100,200,500,1000), memo = "", Prior = NULL, ncpus = 1, maxLoop = 3, threshold.output = .01, WS = c(1e0,1e3,1e4,1e5,1e6,1e7), alpha = c(.01,.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,1), maxOut = 100, QTN.position = NULL, converge = 1, iteration.output = FALSE, acceleration = 0, iteration.method = "accum", PCA.View.output = TRUE, Geno.View.output = TRUE, plot.style = "Oceanic", SUPER_GD = NULL,SUPER_GS = FALSE, CG = NULL,model = "MLM"){ #Object: To Data and Parameters #Designed by Zhiwu Zhang #Writen by Jiabo Wang } # return( # list( # Y = NULL, # G = G,GD = GD,GM = GI,KI = KI,Z = Z,CV = CV, # CV.Inheritance = CV.Inheritance, # GP = GP,GK = GK,PC = PC,GI = GI, # group.from = group.from ,group.to = group.to,group.by = group.by, # DPP = DPP, name.of.trait = NULL, # kinship.cluster = kinship.cluster, # kinship.group = kinship.group, # kinship.algorithm = kinship.algorithm, # NJtree.group = NJtree.group, # NJtree.type = NJtree.type, # PCA.col = PCA.col, PCA.3d = PCA.3d, # bin.from = bin.from,bin.to = bin.to,bin.by = bin.by, # inclosure.from = inclosure.from, # inclosure.to = inclosure.to, # inclosure.by = inclosure.by, # opt = opt, # SNP.P3D = SNP.P3D, SNP.effect = SNP.effect, # SNP.impute = SNP.impute, # PCA.total = PCA.total, # SNP.fraction = SNP.fraction, # seed = seed, BINS = BINS,SNP.test = SNP.test, # SNP.MAF = SNP.MAF, FDR.Rate = FDR.Rate, # SNP.FDR = SNP.FDR, # SNP.permutation = SNP.permutation, # SNP.CV = SNP.CV, SNP.robust = SNP.robust, file.from = file.from, # file.to = file.to, file.total = file.total, # file.fragment = file.fragment, file.path = file.path, # file.G = file.G, file.Ext.G = file.Ext.G, # file.GD = file.GD, # file.GM = file.GM, # file.Ext.GD = file.Ext.GD, # file.Ext.GM = file.Ext.GM, # ngrid = ngrid, # llim = llim, ulim = ulim, # esp = esp, Inter.Plot = Inter.Plot, # Inter.type = Inter.type, # LD.chromosome = LD.chromosome, LD.location = LD.location, # LD.range = LD.range, # Multi_iter = Multi_iter, # sangwich.top = sangwich.top, # sangwich.bottom = sangwich.bottom, # QC = QC,GTindex = GTindex, LD = LD, GT = GT, # file.output = file.output,cutOff = cutOff, # Model.selection = Model.selection, # output.numerical = output.numerical, # output.hapmap = output.hapmap, # Create.indicator = Create.indicator, # Random.model = Random.model, # QTN = QTN, QTN.round = QTN.round, QTN.limit = QTN.limit, # QTN.update = QTN.update, QTN.method = QTN.method, # Major.allele.zero = Major.allele.zero, # method.GLM = method.GLM, method.sub = method.sub, # method.sub.final = method.sub.final, # method.bin = method.bin, bin.size = bin.size, # bin.selection = bin.selection, FDRcut = FDRcut, # memo = memo, Prior = Prior, ncpus = 1, # maxLoop = maxLoop, # threshold.output = threshold.output, # WS = WS, alpha = alpha, maxOut = maxOut, # QTN.position = QTN.position, converge = 1, # iteration.output = iteration.output, # acceleration = 0, # iteration.method = iteration.method, # PCA.View.output = PCA.View.output, # p.threshold = p.threshold, # QTN.threshold = QTN.threshold, # maf.threshold = maf.threshold, chor_taxa = chor_taxa, # num_regwas = num_regwas, Geno.View.output = Geno.View.output, # plot.style = plot.style, # SUPER_GD = SUPER_GD, SUPER_GS = SUPER_GS, # CG = CG, plot.bin = plot.bin # ) # )
GAPIT.Genotype()
'
' Description
'
' @param G = NULL
' @param GD = NULL
' @param GM = NULL
' @param KI = NULL
' @param kinship.algorithm = "Zhang"
' @param SNP.effect = "Add"
' @param SNP.impute = "Middle"
' @param PCA.total = 0
' @param PCA.col = NULL
' @param PCA.3d = PCA.3d
' @param seed = 123
' @param SNP.fraction = 1
' @param file.path = NULL
' @param file.from = NULL
' @param file.to = NULL
' @param file.total = NULL
' @param file.fragment = 1000
' @param SNP.test = TRUE
' @param file.G = NULL
' @param file.Ext.G = NULL
' @param file.GD = NULL
' @param file.Ext.GD = NULL
' @param file.GM = NULL
' @param file.Ext.GM = NULL
' @param SNP.MAF = 0.05
' @param FDR.Rate = 0.05
' @param SNP.FDR = 1
' @param Timmer = NULL
' @param Memory = NULL
' @param LD.chromosome = NULL
' @param LD.location = NULL
' @param LD.range = NULL
' @param SNP.CV = NULL
' @param GP = NULL
' @param GK = NULL
' @param GTindex = NULL
' @param bin.size = 1000
' @param inclosure.size = 100
' @param sangwich.top = NULL
' @param sangwich.bottom = NULL
' @param file.output = TRUE
' @param kinship.cluster = "average"
' @param NJtree.group = NULL
' @param NJtree.type = c("fan","unrooted")
' @param Create.indicator = FALSE
' @param Major.allele.zero = FALSE
' @param Geno.View.output = TRUE
'
'
' @return a list consisting of the following elements.
"
'
'
'
G = G
GD = GD
GI = GI
GT = GT
hasGenotype = hasGenotype
genoFormat = genoFormat KI = KI
PC = PC
byFile = byFile
fullGD = fullGD
Timmer = Timmer
Memory = Memory
SNP.QTN = SNP.QTN
chor_taxa = chor_taxa
Details
#Object: To unify genotype and calculate kinship and PC if required: # 1.For G data, convert it to GD and GI # 2.For GD and GM data, nothing change # 3.Samling GD and create KI and PC # 4.Go through multiple files # 5.In any case, GD must be returned (for QC) #Output: GD, GI, GT, KI and PC #' #' #' return (list(G = G,GD = GD,GI = GI,GT = GT,hasGenotype = hasGenotype, genoFormat = genoFormat, KI = KI,PC = PC,byFile = byFile,fullGD = fullGD,Timmer = Timmer,Memory = Memory,SNP.QTN = SNP.QTN,chor_taxa = chor_taxa)) #' #' GAPIT.Genotype <- function(x){ }
GAPIT.Phenotype.View()
'
' Distribution of density,Accumulation,result:a pdf of the scree plot
'
' myY = NULL
' traitname = "_"
' memo = "_"
'
'
' @return
' a string (function creates graphics)
'
'
`GAPIT.Phenotype.View` <- function(myY = NULL,traitname = "_",memo = "_"){ # Object: Analysis for Phenotype data:Distribution of density,Accumulation,result:a pdf of the scree plot # myY:Phenotype data }
GAPIT.Judge()
'
' @param DP=NULL
'
'
' @return
' A list of
' group.to = group.to
' group.from = group.from
'
'
`GAPIT.Judge`<- function(Y = Y,G = NULL,GD = NULL,KI = NULL,GM = NULL,group.to = group.to,group.from = group.from,sangwich.top = sangwich.top,sangwich.bottom = sangwich.bottom,kinship.algorithm = kinship.algorithm,PCA.total = PCA.total,model = "MLM",SNP.test = TRUE){ #Object: To judge Pheno and Geno data practicability }
GAPIT.IC()
'
' Intermediate Components
'
'
' @param DP=NULL
'
'
'
' @return
` a list of either
'
' Y = comY
' GT = GT
' PCA = comCV
' K = DP\$KI
' GD = comGD
' GM = DP\$GM
' myallCV = CV
' myallGD = GD
'
' or
'
'
' Y = comY
' GT = GT
' PCA = comCV
' K = DP\$KI
' GD = comGD
' GM = DP\$GM
' myallCV = CV
' myallGD = GD
' myallY = Y
'
'
'
`GAPIT.IC` <- function(DP = NULL){ #Object: To Intermediate Components }
GAPIT.SS()
'
' Sufficient Statistics (SS) for GWAS and GS
'
'
' @param DP = NULL
' @param IC=NULL
' @param buspred=FALSE
' @param lmpred=TRUE
'
'
' @return
'
` a list containing
'
' GWAS=GWAS
' Pred=Pred
' FDR=NULL
' Power=NULL
' Power.Alpha=NULL
' alpha=NULL
' h2=h2
' va=va
' ve=ve
' Compression=Compression
' mc=mc
' bc=bc
' mp=mp
' TV=gapitMain$TV
' Timmer=Timmer
' Memory=Memory
'
'
'
`GAPIT.SS` <- function(DP = NULL,IC = NULL,buspred = FALSE,lmpred = TRUE){ #Object: To Sufficient Statistics (SS) for GWAS and GS }
GAPIT.Bus()
function is used to run multiple method, Thanks MLMM FarmCPU Blink to share program and code
' @param Y=NULL
' @param CV=NULL
' @param Z=NULL
' @param GT=NULL
' @param KI=NULL
' @param GK=NULL
' @param GD=NULL
' @param GM=NULL
' @param WS=c(1e0,1e3,1e4,1e5,1e6,1e7)
' @param alpha=c(.01,.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,1)
' @param method=NULL
' @param delta=NULL
' @param vg=NULL
' @param ve=NULL
' @param LD=0.01
' @param GTindex=NULL
' @param cutOff=0.01
' @param Multi_iter=FALSE
' @param num_regwas=10
' @param Random.model=FALSE
' @param FDRcut=FALSE
' @param p.threshold=NA
' @param QTN.threshold=0.01
' @param maf.threshold=0.03
' @param method.GLM="FarmCPU.LM"
' @param method.sub="reward"
' @param method.sub.final="reward"
' @param method.bin="static"
' @param DPP=1000000
' @param bin.size=c(5e5, 5e6,5e7)
' @param bin.selection=seq(10,100,10)
' @param file.output=TRUE
' @param opt="extBIC"
'
'
'
' @return
' a list consisting of the following elements
'
' GWAS=GWAS
' GPS=GPS
' REMLs=REMLs
' vg=vg
' ve=ve
' delta=delta
' GVs=GR$GVs
' seqQTN=seqQTN
'
'
`GAPIT.Bus`<- function(Y = NULL,CV = NULL,Z = NULL,GT = NULL,KI = NULL,GK = NULL,GD = NULL,GM = NULL, WS = c(1e0,1e3,1e4,1e5,1e6,1e7),alpha = c(.01,.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,1), method = NULL,delta = NULL,vg = NULL,ve = NULL,LD = 0.01,GTindex = NULL, cutOff = 0.01,Multi_iter = FALSE,num_regwas = 10,Random.model = FALSE,FDRcut = FALSE, p.threshold = NA,QTN.threshold = 0.01,maf.threshold = 0.03, method.GLM = "FarmCPU.LM",method.sub = "reward",method.sub.final = "reward",method.bin = "static", DPP = 1000000,bin.size = c(5e5,5e6,5e7),bin.selection = seq(10,100,10), file.output = TRUE,opt = "extBIC"){ #Object: To license data by method #Output: Coresponding numerical value # This function is used to run multiple method, Thanks MLMM FarmCPU Blink to share program and code. }
GAPIT.Main()
Object: To perform GWAS and GPS (Genomic Prediction or Selection)
Output: GWAS table (text file), QQ plot (PDF), Manhattan plot (PDF), genomic prediction (text file), and
genetic and residual variance components
Authors: Zhiwu Zhang
'
' perform GWAS and GP
'
' @param Y a sample (row) by phenotype (column) data.frame
' @param G = NULL genotypes in HapMap format
' @param GD = NULL genotypes in numeric format
' @param GM = NULL genotype map for genotypes in numeric format (GD)
' @param KI = NULL kinship matrix (sample by sample)
' @param Z = NULL
' @param CV = NULL covariate valiables
' @param CV.Inheritance = NULL
' @param SNP.P3D = TRUE
' @param GP = NULL
' @param GK = NULL
' ...
'
'
' @return a list consisting of the following elements.
'
' Timmer = Timmer,
' Compression = Compression,
' kinship.optimum = theK.back,
' kinship = KI,
' PC = PC,
' GWAS = PWI.Filtered,
' GPS = GPS,
' Pred = Pred,
' REMLs = Compression[count,4],
' Timmer = Timmer,
' Memory = Memory,
' SUPER_GD = SUPER_GD,
' P = ps,
' effect.snp = DTS[,7],
' effect.cv = p3d$effect.cv,
' h2 = h2.opt,
' TV = TV
'
'
'
`GAPIT.Main` <- function( Y, G = NULL, GD = NULL, GM = NULL, KI = NULL, Z = NULL, CV = NULL, CV.Inheritance = NULL, SNP.P3D = TRUE, GP = NULL, GK = NULL, group.from = 1000000, group.to = 1, group.by = 10, kinship.cluster = "average", kinship.group = 'Mean', kinship.algorithm = NULL, DPP = 50000, ngrid = 100, llin = -10, ulim = 10, esp = 1e-10, GAPIT3.output = TRUE, file.path = NULL, file.from = NULL, file.to = NULL, file.total = NULL, file.fragment = 512, file.G = NULL, file.Ext.G = NULL, file.GD = NULL, file.GM = NULL, file.Ext.GD = NULL, file.Ext.GM = NULL, SNP.MAF = 0, FDR.Rate = 1, SNP.FDR = 1, SNP.effect = "Add", SNP.impute = "Middle", PCA.total = 0, GAPIT.Version = GAPIT.Version, name.of.trait, GT = NULL, SNP.fraction = 1, seed = 123, BINS = 20, SNP.test = TRUE, SNP.robust = "FaST", LD.chromosome = NULL, LD.location = NULL, LD.range = NULL, model = model, bin.from = 10000, bin.to = 5000000, bin.by = 1000, inclosure.from = 10, inclosure.to = 1000, inclosure.by = 10, SNP.permutation = FALSE, SNP.CV = NULL, NJtree.group = NJtree.group, NJtree.type = NJtree.type, plot.bin = plot.bin, genoFormat = NULL, hasGenotype = NULL, byFile = NULL, fullGD = NULL, PC = NULL, GI = NULL, Timmer = NULL, Memory = NULL, sangwich.top = NULL, sangwich.bottom = NULL, QC = TRUE, GTindex = NULL, LD = 0.05, file.output = TRUE, cutOff = 0.05, Model.selection = FALSE, Create.indicator = FALSE, QTN = NULL, QTN.round = 1, QTN.limit = 0, QTN.update = TRUE, QTN.method = "Penalty", Major.allele.zero = FALSE, QTN.position = NULL, SUPER_GD = NULL, SUPER_GS = SUPER_GS, plot.style = "Beach", CG = CG, chor_taxa = chor_taxa){ #Object: To perform GWAS and GPS (Genomic Prediction or Selection) #Output: GWAS table (text file), QQ plot (PDF), Manhattan plot (PDF), genomic prediction (text file), and # genetic and residual variance components #Authors: Zhiwu Zhang } # return( # list( # Timmer = Timmer, # Compression = Compression, # kinship.optimum = theK.back, # kinship = KI, # PC = PC, # GWAS = PWI.Filtered, # GPS = GPS, # Pred = Pred, # REMLs = Compression[count,4], # Timmer = Timmer, # Memory = Memory, # SUPER_GD = SUPER_GD, # P = ps, # effect.snp = DTS[,7], # effect.cv = p3d$effect.cv, # h2 = h2.opt, # TV = TV # ) # )
GAPIT.ID()
Description To Interpretation and Diagnoses
' @param G = NULL
' @param GD = NULL
' @param GM = NULL
' @param KI = NULL
' @param kinship.algorithm = "Zhang"
' @param SNP.effect = "Add"
' @param SNP.impute = "Middle"
' @param PCA.total = 0
' @param PCA.col = NULL
' @param PCA.3d = PCA.3d
' @param seed = 123
' @param SNP.fraction = 1
' @param file.path = NULL
' @param file.from = NULL
' @param file.to = NULL
' @param file.total = NULL
' @param file.fragment = 1000
' @param SNP.test = TRUE
' @param file.G = NULL
' @param file.Ext.G = NULL
' @param file.GD = NULL
' @param file.Ext.GD = NULL
' @param file.GM = NULL
' @param file.Ext.GM = NULL
' @param SNP.MAF = 0.05,
' @param FDR.Rate = 0.05
' @param SNP.FDR = 1
' @param Timmer = NULL
' @param Memory = NULL
' @param LD.chromosome = NULL
' @param LD.location = NULL
' @param LD.range = NULL
' @param SNP.CV = NULL
' @param GP = NULL
' @param GK = NULL
' @param GTindex = NULL
' @param bin.size = 1000
' @param inclosure.size = 100
' @param sangwich.top = NULL
' @param sangwich.bottom = NULL
' @param file.output = TRUE
' @param kinship.cluster = "average"
' @param NJtree.group = NULL
' @param NJtree.type = c("fan","unrooted")
' @param Create.indicator = FALSE
' @param Major.allele.zero = FALSE
' @param Geno.View.output = TRUE
'
'
'
'
' @return a string
'
'
' DP = NULL,
' IC = NULL,
' SS = NULL,
' RS = NULL,
' ...
'
'
' Details
'
' @author Jiabo Wang.
'
'
`GAPIT.ID` <- function( DP = NULL, IC = NULL, SS = NULL, RS = NULL, cutOff = 0.01, DPP = 100000, Create.indicator = FALSE, FDR.Rate = 1,QTN.position = NULL, plot.style = "Oceanic", file.output = TRUE, SNP.MAF = 0, CG = NULL, plot.bin = 10^9 ){ #Object: To Interpretation and Diagnoses #Designed by Zhiwu Zhang #Writen by Jiabo Wang }
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