WhEATBreeders_Tutorial.md
In lfmerrick21/WhEATBreeders: Wrappers for Genomic Selection
User Manual and Tutorial for WhEATBreeders
Lance F. Merrick
November 22, 2022
Wrappers for Easy Association, Tools, and Selection
for Breeders (WhEATBreeders)
Table of contents
Introduction
Description of package functions
Getting started
Load Package
Phenotypic Data
Genotypic Data
Genomic Selection Tutorial
Genome-Wide Association Tutorial using GAPIT
Cross Prediction
Frequently asked questions
Further Information
Introduction
WhEATBreeders was created to lower the bar for implementing genomic
selection models for plant breeders to utilize within their own breeding
programs. Not only does include functions for genotype quality control
and filtering, but it includes easy to use wrappers for the most
commonly use models in many scenarios with K-Fold cross-validation or
validation sets. You can also implement GWAS assisted genomic selection.
We created a full wrapper for quality control and genomci selection in
our function “WHEAT”. Additionaly we walk through the set up of
unrpelicated data using adjuste means and calculate cullis heritability.
We also go through multi-output and multi-trait wrappers for GWAS in
GAPIt. Finally we walk through cross-prediction using PopVar, rrBLUP,
and sommer.
Description of package functions
For a full list of functions within WhEATBreeders see
“Reference_Manual.pdf” this file contains not only the full list of
functions but also a description of each. The pdf also has each
functions arguments listed. And like with all R packages once
WhEATBreeders is installed and loaded you can type ?function_name and
that specific function’s full descriptions will appear in the help tab
on RStudio.
Getting started
First if you do not already have R and R studio installed on your
computer head over to https://www.r-project.org/ and install the
version appropriate for you machine. Once R and R studio are installed
you will need to install the WhEATBreeders package since this is a
working package in it’s early stages of development it’s only available
through Github. To download files off Github first download and load the
library of the package “devtools” using the code below.
Deep Dive Into Code
For a deep dive into all the code in this package and the inner working
of the functions, please review the file
WhEATBreeders_DeepDive_Into_Code.Rmd. There is a lot including the
adjusted means for single plot trials. All the quality control and GS
models, GWAS, Manhattan plots, and even popvar tutorials. There are also
GBS and heterozygote calling pipelines available in the relevant folder.
Packages needed
if (!require("pacman")) install.packages("pacman")
pacman::p_load(devtools)
library(devtools)
#Better for FDR function
devtools::install_github("jiabowang/GAPIT3",force=TRUE)
library(GAPIT3)
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("impute")
#From the source
#require(compiler) #for cmpfun
#Only if you want the source code
#source("http://zzlab.net/GAPIT/GAPIT.library.R")
#source("http://zzlab.net/GAPIT/gapit_functions.txt") #make sure compiler is running
#source("http://zzlab.net/GAPIT/emma.txt")
if (!require("pacman")) install.packages("pacman")
pacman::p_load(BGLR,
rrBLUP,
caret,
tidyr,
dplyr,
Hmisc,
WeightIt,
mpath,
glmnetUtils,
glmnet,
MASS,
Metrics,
stringr,
lsa,
keras,
tensorflow,
BMTME,
plyr,
data.table,
bigmemory,
biganalytics,
ggplot2,
tidyverse,
knitr,
cvTools,
vcfR,
compiler,
gdata,
PopVar,
BLR,
sommer,
heritability,
arm,
optimx,
purrr,
psych,
lme4,
lmerTest,
gridExtra,
grid,
readxl,
devtools)
Load Package
Next using the code below download and install the package WhEATBreeders
from Github. The bottom two line of code in the chunk below make sure
the dependencies WhEATBreeders relies on are also downloaded and
installed.
#install package
install_github("lfmerrick21/WhEATBreeders")
library(WhEATBreeders)#package name
Genotypic Data
Read in Genotype Data and/or Phenotype Data
library(data.table)
Genotype<-fread("F:\\OneDrive\\OneDrive - Washington State University (email.wsu.edu)\\Documents\\Genomic Selection\\Genomic Selection Pipeline\\Jason_GBS\\WAC_2016-2020_production_filt.hmp.txt",fill=TRUE)
Phenotype<-fread("F:\\OneDrive\\OneDrive - Washington State University (email.wsu.edu)\\Documents\\Genomic Selection\\Genomic Selection Pipeline\\GS-Complex-Traits\\BL_EM_Pheno.csv",header=T)
Phenotypic data wide to long format
Phenotype=Phenotype[,-1]
Phenotype1=Phenotype %>%
pivot_longer(!Genotype, names_to = "Env", values_to = "EM")
Phenotype=Phenotype1
You can conduct just quality control on phenotype and genotype data using the wrapper WHEAT function without running any GS models
Make QC=TRUE and GS=FALSE
LIND_QC=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="Two-Step", #Two-Step or #One-Step
Messages=TRUE)
START HERE IF GENOTYPE DATA IS DONE
Genomic Selection Tutorial
Optional Input
if you want to use the numeric files
GDre (Numeric Matrix from output)
GT (Taxa file from output)
GIre (Map file from output)
GBS_Train (GBS RData output you want to use for training population)
GBS_Predict (GBS RData output you want to use for validation set if you’re using VS)
Multipe Trait Output
Method: “Two-Step”
Scheme: Cross-Validation (“K-Fold”) or Validation Sets (“VS”)
fold (Number of folds in K-Fold)
Training (Select trial for training population)
Prediction (Select trial if using validation sets)
Outcome: “Tested” or “Untested”
Tested allows predictions and accuracy and error comparisons
Untested for new predictions without comparisons
Packages: “rrBLUP”, “MAS”, “BGLR”, “caret”, “GLM”,
Type: “Regression” or “Classification” with caret package
Models: rrBLUP, MAS, BGLR(BayesA,BayesB,BayesC,BayesL,BayesRR,RKHS, Ordinal), caret(any model available in the caret package), GAPIT(gBLUP,cBLUP, or sBLUP)
nIter (Number of iterations for BGLR models)
burnIn (Number of burn-ins for BGLR models)
With/without Principal Components (“PC”: Number included or NULL)
With/without Covariates (“CV”: CV set (Genotypes and CVs) or NULL)
Phenotype transformation (“none”,“sqrt”,“log”,“boxcox”)
Subsampling of markers (markers: Number of markers to sample or NULL)
Kernel (“Markers”,“Linear”,Gaussian“,”Polynomial“,”Sigmoid“,”Arc-cosine with 1 layer or multiple layers:nl (“AK1”,“AKL”),“Exponential”)
Kernel (caret package allows above kernels with addition of “VanRaden”,“PC”,“Zhang”,“Endelman”(A-mat),)
nl (Number of layers for arc-cosine kernel)
degree (degree for polynomial kernel)
Sparse Matrix (Sparese=TRUE or FALSE)
m (Number of lines to keep in sparse matrix)
Replications (Number of Replications)
GWAS-Assisted GS (GAGS:TRUE or FALSE)
PCA.total (Number of PCs for GAGS and GAPIT models)
QTN (Number of top QTN or markers to include for GAGS)
GWAS (GWAS model for GAGS c("BLINK) or any GAPIT model)
threshold (“Bonferonni” or “none” for GAGS)
alpha (0.5, or any other threshold for Bonferonni for GAGS)
Messages (TRUE or FALSE)
All of the above can be done with the wrapper function WHEAT
Option shown below displays the function after QC and just GS with the GBS RData as input for validation sets using Two-step rrBLUP
Input using numeric data.
load(file="Tutorial_Filt_Imputed.RData")
#Input
F515_DH20=WHEAT(Phenotype=Phenotype,
GDre=GDre,
GT=GT,
GIre=GIre,
#GS Info
Type="Regression",
Replications=2,
Training="F5_2015",
Prediction="DH_2020",
CV=NULL,
PC=NULL,
Trait="EM",
Study="Tutorial",
Outcome="Untested",
Trial=c("F5_2015","DH_2020"),
Scheme="VS",
Method="Two-Step",
Package="rrBLUP",
model="rrBLUP",
Kernel="Markers",
markers=NULL,
folds = 5,
nIter = 1500,
burnIn = 500,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
transformation="none",
#GAGS Info
GAGS=FALSE,
PCA.total=3,
QTN=10,
GWAS=c("BLINK"),
alpha=0.05,
threshold="none",
GE=TRUE,
UN=FALSE,
GE_model="MTME",
sampling="up",
repeats=5,
method="repeatedcv",
digits=4,
nCVI=5,
Messages=TRUE)
Input using GBS RData.
load(file ='GBS_2_Tutorial.RData')
#Input
F515_DH20=WHEAT(Phenotype=Phenotype,
GBS_Train=GBS_2_F5_2015_EM,
GBS_Predict=GBS_2_Untested_DH_2020_EM,
#GS Info
Type="Regression",
Replications=2,
Training="F5_2015",
Prediction="DH_2020",
CV=NULL,
PC=NULL,
Trait="EM",
Study="Tutorial",
Outcome="Untested",
Trial=c("F5_2015","DH_2020"),
Scheme="VS",
Method="Two-Step",
Package="rrBLUP",
model="rrBLUP",
Kernel="Markers",
markers=NULL,
folds = 5,
nIter = 1500,
burnIn = 500,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
transformation="none",
#GAGS Info
GAGS=FALSE,
PCA.total=3,
QTN=10,
GWAS=c("BLINK"),
alpha=0.05,
threshold="none",
GE=TRUE,
UN=FALSE,
GE_model="MTME",
sampling="up",
repeats=5,
method="repeatedcv",
digits=4,
nCVI=5,
Messages=TRUE)
Full Wrapper for Quality Control and Genomic Selection
Input using Phenotype with Genotype, Environment, and Trait(s)
Use hapmap as Genotype file
Set QC and GS to TRUE
Option shown below displays the function after QC and just GS with the GBS RData as input for validation sets using Two-step rrBLUP
F515_DH20=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=TRUE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
Filter_Ind=TRUE,
Missing_Rate_Ind=0.80,
#If QC Info is FALSE
GDre=NULL,
GT=NULL,
GIre=NULL,
GBS_Train=NULL,
GBS_Predict=NULL,
Matrix=NULL,
#GS Info
Type="Regression",
Replications=2,
Training="F5_2015",
Prediction="DH_2020",
CV=NULL,
PC=NULL,
Trait="EM",
Study="Tutorial",
Outcome="Untested",
Trial=c("F5_2015","DH_2020"),
Scheme="VS",
Method="Two-Step",
Package="rrBLUP",
model="rrBLUP",
Kernel="Markers",
markers=NULL,
folds = 5,
nIter = 1500,
burnIn = 500,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
transformation="none",
#GAGS Info
GAGS=FALSE,
PCA.total=3,
QTN=10,
GWAS=c("BLINK"),
alpha=0.05,
threshold="none",
GE=TRUE,
UN=FALSE,
GE_model="MTME",
Messages=TRUE)
One-Step Genomic Selection
Input using numeric data.
Gaussian Kernel output: Kernel=“Gaussian”
load(file="Tutorial_Filt_Imputed.RData")
#Input
F515_DH20=WHEAT(Phenotype=Phenotype,
GDre=GDre,
GT=GT,
GIre=GIre,
#GS Info
Type="Regression",
Replications=2,
Training="F5_2015",
Prediction="DH_2020",
CV=NULL,
PC=NULL,
Trait="EM",
Study="Tutorial",
Outcome="Untested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",
Method="One-Step",
Package="BGLR",
Kernel="Gaussian",
markers=NULL,
folds = 5,
nIter = 1500,
burnIn = 500,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
transformation="none",
#GAGS Info
GAGS=FALSE,
PCA.total=3,
QTN=10,
GWAS=c("BLINK"),
alpha=0.05,
threshold="none",
GE=TRUE,
UN=FALSE,
GE_model="MTME",
sampling="up",
repeats=5,
method="repeatedcv",
digits=4,
nCVI=5,
Messages=TRUE)
Input using GBS RData using Matrix Input.
BGLR using MTME for
Gaussian Kernel output: Kernel=“Gaussian”
load(file ='GBS_2_Tutorial.RData')
#Input
F515_DH20=WHEAT(Phenotype=Phenotype,
Matrix=Matrix,
#GS Info
Type="Regression",
Replications=2,
Training="F5_2015",
Prediction="DH_2020",
CV=NULL,
PC=NULL,
Trait="EM",
Study="Tutorial",
Outcome="Untested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",
Method="One-Step",
Package="BGLR",
Kernel="Gaussian",
markers=NULL,
folds = 5,
nIter = 1500,
burnIn = 500,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
transformation="none",
#GAGS Info
GAGS=FALSE,
PCA.total=3,
QTN=10,
GWAS=c("BLINK"),
alpha=0.05,
threshold="none",
GE=TRUE,
UN=FALSE,
GE_model="MTME",
sampling="up",
repeats=5,
method="repeatedcv",
digits=4,
nCVI=5,
Messages=TRUE)
Individual Functions
We can get the proper matrices with the wrapper WHEAT function
Make QC=TRUE and GS=FALSE
Gaussian Kernel output: Kernel=“Gaussian”
LIND_QC_One_Step=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="One-Step", #Two-Step or #One-Step
Kernel="Gaussian",
folds = 5,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
GE=TRUE
UN=FALSE
GE_model="MTME"
Messages=TRUE)
Bayesian without GE: model=“ST”
Output with 3-Way Covariance Matrices
GE_model=“BMTME”
UN=“FALSE”
LIND_QC_One_Step=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="One-Step", #Two-Step or #One-Step
Kernel="Gaussian",
folds = 5,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
GE=TRUE
UN=FALSE
GE_model="BMTME"
Messages=TRUE)
Bayesian with 3-Way Covariance: model=“BME” and UN=FALSE
Output with Factor Analytic Matrix
GE_model=“MTME”
UN=“FALSE”
LIND_QC_One_Step=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="One-Step", #Two-Step or #One-Step
Kernel="Gaussian",
folds = 5,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
GE=TRUE
UN=FALSE
GE_model="MTME"
Messages=TRUE)
Bayesian with Factor Analytic Model: model=“BME” and UN=FALSE
Output with Marker-Environment Interaction Matrix
GE_model=“MEI”
UN=“TRUE”
LIND_QC_One_Step=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="One-Step", #Two-Step or #One-Step
Kernel="Gaussian",
folds = 5,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
GE=TRUE
UN=TRUE
GE_model="MEI"
Messages=TRUE)
Bayesian with Marker-Environment Interaction Model: model=“BME” and function is MTME_UN
One-Step With Machine Linear MLP Model
We can get the proper matrices with the wrapper WHEAT function
Make QC=TRUE and GS=FALSE
Gaussian Kernel output: Kernel=“Gaussian”
LIND_QC_One_Step=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="One-Step", #Two-Step or #One-Step
Kernel="Gaussian",
folds = 5,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
GE=TRUE
UN=FALSE
GE_model="MTME"
Messages=TRUE)
MLP without GE: model=“ST”
Output with 3-Way Covariance Matrices
GE_model=“BMTME”
UN=“FALSE”
LIND_QC_One_Step=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="One-Step", #Two-Step or #One-Step
Kernel="Gaussian",
folds = 5,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
GE=TRUE
UN=FALSE
GE_model="BMTME"
Messages=TRUE)
MLP with 3-Way Covariance: model=“BME” and UN=FALSE
Output with Factor Analytic Matrix
GE_model=“MTME”
UN=“FALSE”
LIND_QC_One_Step=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="One-Step", #Two-Step or #One-Step
Kernel="Gaussian",
folds = 5,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
GE=TRUE
UN=FALSE
GE_model="MTME"
Messages=TRUE)
MLP with Factor Analytic Model: model=“BME” and UN=FALSE
Output with Marker-Environment Interaction Matrix
GE_model=“MEI”
UN=“TRUE”
LIND_QC_One_Step=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="One-Step", #Two-Step or #One-Step
Kernel="Gaussian",
folds = 5,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
GE=TRUE,
UN=TRUE,
GE_model="MEI"
Messages=TRUE)
MLP with Marker-Environment Interaction Model: model=“BME” and function is MLP_CV_UN
Frequently asked questions
- Where can I get WhEATBreeders? WhEATBreeders is currently only
available on github via Lance F. Merrick’s public repository found
at https://github.com/lfmerrick21/WhEATBreeders.
- How to download WhEATBreeders? Please refer the “Getting started”
section above where I show how to download and install WhEATBreeders
using devtools.
- Where can I get help with issues regarding WhEATBreeders? All
questions should be directed to Lance F. Merrick at
lance.merrick21@gmail.com.
Further Information
For more information on individual functions please see the
“Reference_Manual.pdf” or type ?FUNCITON_NAME into the R console,
this will pull up specific information of each function inside
WhEATBreeders. For example typing ?manhattan_plot will pull of the help
page with details about the function that creates the Manhattan plots.
lfmerrick21/WhEATBreeders documentation built on Jan. 1, 2023, 7:12 a.m.
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User Manual and Tutorial for WhEATBreeders
Lance F. Merrick November 22, 2022
Wrappers for Easy Association, Tools, and Selection for Breeders (WhEATBreeders)
Table of contents
Introduction
Description of package functions
Getting started
Load Package
Phenotypic Data
Genotypic Data
Genomic Selection Tutorial
Genome-Wide Association Tutorial using GAPIT
Cross Prediction
Frequently asked questions
Further Information
Introduction
WhEATBreeders was created to lower the bar for implementing genomic selection models for plant breeders to utilize within their own breeding programs. Not only does include functions for genotype quality control and filtering, but it includes easy to use wrappers for the most commonly use models in many scenarios with K-Fold cross-validation or validation sets. You can also implement GWAS assisted genomic selection. We created a full wrapper for quality control and genomci selection in our function “WHEAT”. Additionaly we walk through the set up of unrpelicated data using adjuste means and calculate cullis heritability. We also go through multi-output and multi-trait wrappers for GWAS in GAPIt. Finally we walk through cross-prediction using PopVar, rrBLUP, and sommer.
Description of package functions
For a full list of functions within WhEATBreeders see “Reference_Manual.pdf” this file contains not only the full list of functions but also a description of each. The pdf also has each functions arguments listed. And like with all R packages once WhEATBreeders is installed and loaded you can type ?function_name and that specific function’s full descriptions will appear in the help tab on RStudio.
Getting started
First if you do not already have R and R studio installed on your computer head over to https://www.r-project.org/ and install the version appropriate for you machine. Once R and R studio are installed you will need to install the WhEATBreeders package since this is a working package in it’s early stages of development it’s only available through Github. To download files off Github first download and load the library of the package “devtools” using the code below.
Deep Dive Into Code
For a deep dive into all the code in this package and the inner working of the functions, please review the file WhEATBreeders_DeepDive_Into_Code.Rmd. There is a lot including the adjusted means for single plot trials. All the quality control and GS models, GWAS, Manhattan plots, and even popvar tutorials. There are also GBS and heterozygote calling pipelines available in the relevant folder.
Packages needed
if (!require("pacman")) install.packages("pacman")
pacman::p_load(devtools)
library(devtools)
#Better for FDR function
devtools::install_github("jiabowang/GAPIT3",force=TRUE)
library(GAPIT3)
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("impute")
#From the source
#require(compiler) #for cmpfun
#Only if you want the source code
#source("http://zzlab.net/GAPIT/GAPIT.library.R")
#source("http://zzlab.net/GAPIT/gapit_functions.txt") #make sure compiler is running
#source("http://zzlab.net/GAPIT/emma.txt")
if (!require("pacman")) install.packages("pacman")
pacman::p_load(BGLR,
rrBLUP,
caret,
tidyr,
dplyr,
Hmisc,
WeightIt,
mpath,
glmnetUtils,
glmnet,
MASS,
Metrics,
stringr,
lsa,
keras,
tensorflow,
BMTME,
plyr,
data.table,
bigmemory,
biganalytics,
ggplot2,
tidyverse,
knitr,
cvTools,
vcfR,
compiler,
gdata,
PopVar,
BLR,
sommer,
heritability,
arm,
optimx,
purrr,
psych,
lme4,
lmerTest,
gridExtra,
grid,
readxl,
devtools)
Load Package
Next using the code below download and install the package WhEATBreeders from Github. The bottom two line of code in the chunk below make sure the dependencies WhEATBreeders relies on are also downloaded and installed.
#install package
install_github("lfmerrick21/WhEATBreeders")
library(WhEATBreeders)#package name
Genotypic Data
Read in Genotype Data and/or Phenotype Data
library(data.table)
Genotype<-fread("F:\\OneDrive\\OneDrive - Washington State University (email.wsu.edu)\\Documents\\Genomic Selection\\Genomic Selection Pipeline\\Jason_GBS\\WAC_2016-2020_production_filt.hmp.txt",fill=TRUE)
Phenotype<-fread("F:\\OneDrive\\OneDrive - Washington State University (email.wsu.edu)\\Documents\\Genomic Selection\\Genomic Selection Pipeline\\GS-Complex-Traits\\BL_EM_Pheno.csv",header=T)
Phenotypic data wide to long format
Phenotype=Phenotype[,-1]
Phenotype1=Phenotype %>%
pivot_longer(!Genotype, names_to = "Env", values_to = "EM")
Phenotype=Phenotype1
You can conduct just quality control on phenotype and genotype data using the wrapper WHEAT function without running any GS models
Make QC=TRUE and GS=FALSE
LIND_QC=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="Two-Step", #Two-Step or #One-Step
Messages=TRUE)
START HERE IF GENOTYPE DATA IS DONE
Genomic Selection Tutorial
Optional Input
if you want to use the numeric files
GDre (Numeric Matrix from output)
GT (Taxa file from output)
GIre (Map file from output)
GBS_Train (GBS RData output you want to use for training population)
GBS_Predict (GBS RData output you want to use for validation set if you’re using VS)
Multipe Trait Output
Method: “Two-Step”
Scheme: Cross-Validation (“K-Fold”) or Validation Sets (“VS”)
fold (Number of folds in K-Fold)
Training (Select trial for training population)
Prediction (Select trial if using validation sets)
Outcome: “Tested” or “Untested”
Tested allows predictions and accuracy and error comparisons
Untested for new predictions without comparisons
Packages: “rrBLUP”, “MAS”, “BGLR”, “caret”, “GLM”,
Type: “Regression” or “Classification” with caret package
Models: rrBLUP, MAS, BGLR(BayesA,BayesB,BayesC,BayesL,BayesRR,RKHS, Ordinal), caret(any model available in the caret package), GAPIT(gBLUP,cBLUP, or sBLUP)
nIter (Number of iterations for BGLR models)
burnIn (Number of burn-ins for BGLR models)
With/without Principal Components (“PC”: Number included or NULL)
With/without Covariates (“CV”: CV set (Genotypes and CVs) or NULL)
Phenotype transformation (“none”,“sqrt”,“log”,“boxcox”)
Subsampling of markers (markers: Number of markers to sample or NULL)
Kernel (“Markers”,“Linear”,Gaussian“,”Polynomial“,”Sigmoid“,”Arc-cosine with 1 layer or multiple layers:nl (“AK1”,“AKL”),“Exponential”)
Kernel (caret package allows above kernels with addition of “VanRaden”,“PC”,“Zhang”,“Endelman”(A-mat),)
nl (Number of layers for arc-cosine kernel)
degree (degree for polynomial kernel)
Sparse Matrix (Sparese=TRUE or FALSE)
m (Number of lines to keep in sparse matrix)
Replications (Number of Replications)
GWAS-Assisted GS (GAGS:TRUE or FALSE)
PCA.total (Number of PCs for GAGS and GAPIT models)
QTN (Number of top QTN or markers to include for GAGS)
GWAS (GWAS model for GAGS c("BLINK) or any GAPIT model)
threshold (“Bonferonni” or “none” for GAGS)
alpha (0.5, or any other threshold for Bonferonni for GAGS)
Messages (TRUE or FALSE)
All of the above can be done with the wrapper function WHEAT
Option shown below displays the function after QC and just GS with the GBS RData as input for validation sets using Two-step rrBLUP
Input using numeric data.
load(file="Tutorial_Filt_Imputed.RData")
#Input
F515_DH20=WHEAT(Phenotype=Phenotype,
GDre=GDre,
GT=GT,
GIre=GIre,
#GS Info
Type="Regression",
Replications=2,
Training="F5_2015",
Prediction="DH_2020",
CV=NULL,
PC=NULL,
Trait="EM",
Study="Tutorial",
Outcome="Untested",
Trial=c("F5_2015","DH_2020"),
Scheme="VS",
Method="Two-Step",
Package="rrBLUP",
model="rrBLUP",
Kernel="Markers",
markers=NULL,
folds = 5,
nIter = 1500,
burnIn = 500,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
transformation="none",
#GAGS Info
GAGS=FALSE,
PCA.total=3,
QTN=10,
GWAS=c("BLINK"),
alpha=0.05,
threshold="none",
GE=TRUE,
UN=FALSE,
GE_model="MTME",
sampling="up",
repeats=5,
method="repeatedcv",
digits=4,
nCVI=5,
Messages=TRUE)
Input using GBS RData.
load(file ='GBS_2_Tutorial.RData')
#Input
F515_DH20=WHEAT(Phenotype=Phenotype,
GBS_Train=GBS_2_F5_2015_EM,
GBS_Predict=GBS_2_Untested_DH_2020_EM,
#GS Info
Type="Regression",
Replications=2,
Training="F5_2015",
Prediction="DH_2020",
CV=NULL,
PC=NULL,
Trait="EM",
Study="Tutorial",
Outcome="Untested",
Trial=c("F5_2015","DH_2020"),
Scheme="VS",
Method="Two-Step",
Package="rrBLUP",
model="rrBLUP",
Kernel="Markers",
markers=NULL,
folds = 5,
nIter = 1500,
burnIn = 500,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
transformation="none",
#GAGS Info
GAGS=FALSE,
PCA.total=3,
QTN=10,
GWAS=c("BLINK"),
alpha=0.05,
threshold="none",
GE=TRUE,
UN=FALSE,
GE_model="MTME",
sampling="up",
repeats=5,
method="repeatedcv",
digits=4,
nCVI=5,
Messages=TRUE)
Full Wrapper for Quality Control and Genomic Selection
Input using Phenotype with Genotype, Environment, and Trait(s)
Use hapmap as Genotype file
Set QC and GS to TRUE
Option shown below displays the function after QC and just GS with the GBS RData as input for validation sets using Two-step rrBLUP
F515_DH20=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=TRUE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
Filter_Ind=TRUE,
Missing_Rate_Ind=0.80,
#If QC Info is FALSE
GDre=NULL,
GT=NULL,
GIre=NULL,
GBS_Train=NULL,
GBS_Predict=NULL,
Matrix=NULL,
#GS Info
Type="Regression",
Replications=2,
Training="F5_2015",
Prediction="DH_2020",
CV=NULL,
PC=NULL,
Trait="EM",
Study="Tutorial",
Outcome="Untested",
Trial=c("F5_2015","DH_2020"),
Scheme="VS",
Method="Two-Step",
Package="rrBLUP",
model="rrBLUP",
Kernel="Markers",
markers=NULL,
folds = 5,
nIter = 1500,
burnIn = 500,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
transformation="none",
#GAGS Info
GAGS=FALSE,
PCA.total=3,
QTN=10,
GWAS=c("BLINK"),
alpha=0.05,
threshold="none",
GE=TRUE,
UN=FALSE,
GE_model="MTME",
Messages=TRUE)
One-Step Genomic Selection
Input using numeric data.
Gaussian Kernel output: Kernel=“Gaussian”
load(file="Tutorial_Filt_Imputed.RData")
#Input
F515_DH20=WHEAT(Phenotype=Phenotype,
GDre=GDre,
GT=GT,
GIre=GIre,
#GS Info
Type="Regression",
Replications=2,
Training="F5_2015",
Prediction="DH_2020",
CV=NULL,
PC=NULL,
Trait="EM",
Study="Tutorial",
Outcome="Untested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",
Method="One-Step",
Package="BGLR",
Kernel="Gaussian",
markers=NULL,
folds = 5,
nIter = 1500,
burnIn = 500,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
transformation="none",
#GAGS Info
GAGS=FALSE,
PCA.total=3,
QTN=10,
GWAS=c("BLINK"),
alpha=0.05,
threshold="none",
GE=TRUE,
UN=FALSE,
GE_model="MTME",
sampling="up",
repeats=5,
method="repeatedcv",
digits=4,
nCVI=5,
Messages=TRUE)
Input using GBS RData using Matrix Input.
BGLR using MTME for
Gaussian Kernel output: Kernel=“Gaussian”
load(file ='GBS_2_Tutorial.RData')
#Input
F515_DH20=WHEAT(Phenotype=Phenotype,
Matrix=Matrix,
#GS Info
Type="Regression",
Replications=2,
Training="F5_2015",
Prediction="DH_2020",
CV=NULL,
PC=NULL,
Trait="EM",
Study="Tutorial",
Outcome="Untested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",
Method="One-Step",
Package="BGLR",
Kernel="Gaussian",
markers=NULL,
folds = 5,
nIter = 1500,
burnIn = 500,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
transformation="none",
#GAGS Info
GAGS=FALSE,
PCA.total=3,
QTN=10,
GWAS=c("BLINK"),
alpha=0.05,
threshold="none",
GE=TRUE,
UN=FALSE,
GE_model="MTME",
sampling="up",
repeats=5,
method="repeatedcv",
digits=4,
nCVI=5,
Messages=TRUE)
Individual Functions
We can get the proper matrices with the wrapper WHEAT function
Make QC=TRUE and GS=FALSE
Gaussian Kernel output: Kernel=“Gaussian”
LIND_QC_One_Step=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="One-Step", #Two-Step or #One-Step
Kernel="Gaussian",
folds = 5,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
GE=TRUE
UN=FALSE
GE_model="MTME"
Messages=TRUE)
Bayesian without GE: model=“ST”
Output with 3-Way Covariance Matrices
GE_model=“BMTME”
UN=“FALSE”
LIND_QC_One_Step=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="One-Step", #Two-Step or #One-Step
Kernel="Gaussian",
folds = 5,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
GE=TRUE
UN=FALSE
GE_model="BMTME"
Messages=TRUE)
Bayesian with 3-Way Covariance: model=“BME” and UN=FALSE
Output with Factor Analytic Matrix
GE_model=“MTME”
UN=“FALSE”
LIND_QC_One_Step=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="One-Step", #Two-Step or #One-Step
Kernel="Gaussian",
folds = 5,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
GE=TRUE
UN=FALSE
GE_model="MTME"
Messages=TRUE)
Bayesian with Factor Analytic Model: model=“BME” and UN=FALSE
Output with Marker-Environment Interaction Matrix
GE_model=“MEI”
UN=“TRUE”
LIND_QC_One_Step=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="One-Step", #Two-Step or #One-Step
Kernel="Gaussian",
folds = 5,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
GE=TRUE
UN=TRUE
GE_model="MEI"
Messages=TRUE)
Bayesian with Marker-Environment Interaction Model: model=“BME” and function is MTME_UN
One-Step With Machine Linear MLP Model
We can get the proper matrices with the wrapper WHEAT function
Make QC=TRUE and GS=FALSE
Gaussian Kernel output: Kernel=“Gaussian”
LIND_QC_One_Step=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="One-Step", #Two-Step or #One-Step
Kernel="Gaussian",
folds = 5,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
GE=TRUE
UN=FALSE
GE_model="MTME"
Messages=TRUE)
MLP without GE: model=“ST”
Output with 3-Way Covariance Matrices
GE_model=“BMTME”
UN=“FALSE”
LIND_QC_One_Step=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="One-Step", #Two-Step or #One-Step
Kernel="Gaussian",
folds = 5,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
GE=TRUE
UN=FALSE
GE_model="BMTME"
Messages=TRUE)
MLP with 3-Way Covariance: model=“BME” and UN=FALSE
Output with Factor Analytic Matrix
GE_model=“MTME”
UN=“FALSE”
LIND_QC_One_Step=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="One-Step", #Two-Step or #One-Step
Kernel="Gaussian",
folds = 5,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
GE=TRUE
UN=FALSE
GE_model="MTME"
Messages=TRUE)
MLP with Factor Analytic Model: model=“BME” and UN=FALSE
Output with Marker-Environment Interaction Matrix
GE_model=“MEI”
UN=“TRUE”
LIND_QC_One_Step=WHEAT(Phenotype=Phenotype,
Genotype=Genotype,
QC=TRUE,
GS=FALSE,
#QC Info
Geno_Type="Hapmap",
Imputation="Beagle",
Filter=TRUE,
Missing_Rate=0.20,
MAF=0.05,
#Do not remove individuals
Filter_Ind=FALSE,
Missing_Rate_Ind=0.80,
Trait=c("EM"),
Study="Tutorial",
Outcome="Tested",
Trial=c("F5_2015","DH_2020"),
Scheme="K-Fold",#K-Fold or VS
Method="One-Step", #Two-Step or #One-Step
Kernel="Gaussian",
folds = 5,
Sparse=FALSE,
m=NULL,
degree=NULL,
nL=NULL,
GE=TRUE,
UN=TRUE,
GE_model="MEI"
Messages=TRUE)
MLP with Marker-Environment Interaction Model: model=“BME” and function is MLP_CV_UN
Frequently asked questions
- Where can I get WhEATBreeders? WhEATBreeders is currently only available on github via Lance F. Merrick’s public repository found at https://github.com/lfmerrick21/WhEATBreeders.
- How to download WhEATBreeders? Please refer the “Getting started” section above where I show how to download and install WhEATBreeders using devtools.
- Where can I get help with issues regarding WhEATBreeders? All questions should be directed to Lance F. Merrick at lance.merrick21@gmail.com.
Further Information
For more information on individual functions please see the “Reference_Manual.pdf” or type ?FUNCITON_NAME into the R console, this will pull up specific information of each function inside WhEATBreeders. For example typing ?manhattan_plot will pull of the help page with details about the function that creates the Manhattan plots.
R Package Documentation
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