In lfmerrick21/WhEATBreeders: Wrappers for Genomic Selection

 

 

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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.

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

In order to have an effective tutorial you’ll need some data to play with, this code below downloads and loads into your environment data used for this tutorial.

Phenotypic Data

Read in Phenotypic Data

#Read in phenotypic files
em_trials=read.csv("C:\\Users\\lance\\OneDrive - Washington State University (email.wsu.edu)\\Documents\\Genomic Selection\\Genomic Selection Pipeline\\Selected Trials_C\\Selected Trials_Emergence\\Emergence_Trials.csv",header=TRUE)
str(em_trials)
em_trials$bloc=as.factor(em_trials$bloc)
em_trials$checks=as.factor(em_trials$checks)
em_trials$prow=as.factor(em_trials$prow)
em_trials$pcol=as.factor(em_trials$pcol)
em_trials$ibloc=as.factor(em_trials$ibloc)
em_trials$year1=as.factor(em_trials$year1)
em_trials$r_expt=as.factor(em_trials$r_expt)
colnames(em_trials)[8]<-c("Name")

#We have to create new identifiers according to the model indicated in the review paper I sent you.
em_trials$new.ind=em_trials$checks
em_trials=em_trials %>% mutate(check.ind = recode(checks,"1"="0", "0"="1"))
em_trials$check.ind=as.factor(em_trials$check.ind)
em_trials$new.ind=as.factor(em_trials$new.ind)
str(em_trials)
View(em_trials)

#I just subset the different trials I needed
lq15=subset(em_trials,em_trials$r_expt==levels(em_trials$r_expt)[2])
lq17=subset(em_trials,em_trials$r_expt==levels(em_trials$r_expt)[3])
lq18=subset(em_trials,em_trials$r_expt==levels(em_trials$r_expt)[4])
lq19=subset(em_trials,em_trials$r_expt==levels(em_trials$r_expt)[5])
lq15_17=em_trials[em_trials$r_expt == "2015 QAM Plots Lind" | em_trials$r_expt == "2017 QAM Plots Lind", ]

lq15_18=em_trials[em_trials$r_expt == "2015 QAM Plots Lind" | em_trials$r_expt == "2017 QAM Plots Lind" |em_trials$r_expt == "2018 QAM Rows Lind", ]
lq15_19=em_trials[em_trials$r_expt == "2015 QAM Plots Lind" | em_trials$r_expt == "2017 QAM Plots Lind" |em_trials$r_expt == "2018 QAM Rows Lind" |em_trials$r_expt == "2019 QAM Rows Lind", ]

Cullis Heritability (From Shantel Martinez Github)

Single Environment Including genotype

lq15_bp <- lmer(emer~check.ind + (1|Name:new.ind) + (1|ibloc), data=lq15)
Cullis_H2(lq15_bp)
summary(lq15_bp)
anova(lq15_bp)
ranova(lq15_bp)

Multi Environment including genotype

lq15_17_bp <- lmer(emer~check.ind + (1|Name:new.ind) + (1|ibloc)+(1|r_expt)+check.ind:r_expt + (1|Name:new.ind:r_expt) + (1|ibloc:r_expt), data=lq15_17)
Cullis_H2(lq15_17_bp)
summary(lq15_17_bp)
anova(lq15_17_bp)
ranova(lq15_17_bp)

Singularity Problems

If you run into singularity or convergence problems you can always add in an lmerControl and then mess around with the maxfun number to get rid of the problem.

lb1_2_bp <- lmer(emer~check.ind + (1|Name:new.ind) + (1|ibloc)+(1|r_expt)+check.ind:r_expt + (1|Name:new.ind:r_expt) + (1|ibloc:r_expt), data=lbl1_2,
            control=lmerControl(optimizer="Nelder_Mead",
                                 optCtrl=list(maxfun=1e3)))

Adjusted Means

Single Environment

#Adjustments
lq15_r <- lm(emer~ibloc+checks, data=lq15)
#residuals(lq15_r)
lq15=lq15 %>% mutate(ap_adj = emer+lq15_r$residuals)
lq15_ap=aggregate(lq15[,c(21)],list(lq15$Name),mean)
colnames(lq15_ap)<-c("Name","emer_f15_ap")

Multi Environment

lq15_17_r <- lm(emer~ibloc+checks+r_expt+ibloc*r_expt+checks*r_expt, data=lq15_17)
#lq15_17_r$residuals
lq15_17=lq15_17 %>% mutate(ap_adj = emer+lq15_17_r$residuals)
lq15_17_ap=aggregate(lq15_17[,c(21)],list(lq15_17$Name),mean)
colnames(lq15_17_ap)<-c("Name","emer_15_17_ap")

Combine Environments

d1=left_join(lq15_r,lq15_17_r,by="Name")
d2=left_join(d1,lq18_raw,by="Name")

True version of Cullis heritability code that I'm working on which has been adapted from the Comparison of heritability papers and the github "https://github.com/PaulSchmidtGit/Heritability".

Frequently asked questions

1. 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.
2. How to download WhEATBreeders? Please refer the “Getting started” section above where I show how to download and install WhEATBreeders using devtools.
3. 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.