Genome Association Predict Integrate Tools
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README.md

GAPIT3

Genome Association Predict Integrate Tools

GAPIT

Citation

If you use GAPIT and publish your analysis, please report the program version and cite the appropriate article:

The citation for GAPIT3 is: Wang J.,Zhang Z., 2018 GAPIT Version 3:An Interactive Analytical Tool for Genomic Association and Prediction. preprint.

The citation for GAPIT2 is: Tang Y., Liu X., Wang J., Li M., Wang Q., et al., 2016 GAPIT Version 2: An Enhanced Integrated Tool for Genomic Association and Prediction. Plant J. 9.

The citation for GAPIT1 is: Lipka A. E., Tian F., Wang Q., Peiffer J., Li M., et al., 2012 GAPIT: genome association and prediction integrated tool. Bioinformatics 28: 2397–2399.

The citation for SUPER method is: Wang Q., Tian F., Pan Y., Buckler E. S., Zhang Z., 2014 A SUPER Powerful Method for Genome Wide Association Study (Y Li, Ed.). PLoS One 9: e107684.

The citation for cBLUP and sBLUP is: Wang J., Zhou Z., Zhang Z., Li H., Liu D., et al., 2018 Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits. Heredity

The citation for Farm-CPU is: Liu X., Huang M., Fan B., Buckler E. S., Zhang Z., 2016 Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies. PLoS Genet. 12: e1005767.

The citation for P3D is: Zhang Z., Ersoz E., Lai C. Q., Todhunter R. J., Tiwari H. K., et al., 2010 Mixed linear model approach adapted for genome-wide association studies. Nat. Genet. 42: 355–360.

Authors:

Jiabo Wang and Zhiwu Zhang

Contact:

wangjiaboyifeng@163.com (Jiabo)

Source:

User manual

Demo Data

Source code

Contents:

Start

GAPIT is a package that is run in the R software environment, which can be freely downloaded from http://www.r-project.org or http://www.rstudio.com.

Function Loading

Now GAPIT can load library by only one funciton.

source("http://www.zzlab.net/GAPIT/GAPIT.library.R")

After loading library, we need to source GAPIT function.

source("http://www.zzlab.net/GAPIT/gapit_functions.txt")

Data Preparing

Phenotype Data

The user has the option of performing GWAS on multiple phenotypes in GAPIT. This is achieved by including all phenotypes in the text file of phenotypic data. Taxa names should be in the first column of the phenotypic data file and the remaining columns should contain the observed phenotype from each individual. Missing data should be indicated by either “NaN” or “NA”.

Genotype Data

Hapmap Format

Hapmap is a commonly used format for storing sequence data where SNP information is stored in the rows and taxa information is stored in the columns. This format allows the SNP information (chromosome and position) and genotype of each taxa to be stored in one file.

Numeric Format

GAPIT also accepts the numeric format. Homozygotes are denoted by “0” and “2” and heterozygotes are denoted by “1” in the “GD” file. Any numeric value between “0” and “2” can represent imputed SNP genotypes. The first row is a header file with SNP names, and the first column is the taxa name. The “GM” file contains the name and location of each SNP. The first column is the SNP id, the second column is the chromosome, and the third column is the base pair position. As seen in the example, the first row is a header file.

Anlysis

GWAS

  • GLM

The GAPIT use Least Squares to solve the modle. The code of GAPIT running GLM is:

  myGAPIT_GLM <- GAPIT(
  Y=myY[,c(1,2)],
  GD=myGD,
  GM=myGM,
  model="GLM",
  PCA.total=5,
  file.output=T
  )
  • MLM

EMMA method is used in GAPIT, the code of MLM is:

  myGAPIT_MLM <- GAPIT(
  Y=myY[,c(1,2)],
  GD=myGD,
  GM=myGM,
  model="MLM",
  PCA.total=5,
  file.output=T
  )
  • CMLM

Compress Mixed Linear Model is published by Zhang in 2010. The code of CMLM is:

  myGAPIT_CMLM <- GAPIT(
  Y=myY[,c(1,2)],
  GD=myGD,
  GM=myGM,
  model="CMLM",
  PCA.total=5,
  file.output=T
  )
  • MLMM

Multiple Loci Mixied linear Model is published by Segura in 2012. The code of MLMM in GAPIT is:

  myGAPIT_MLMM <- GAPIT(
  Y=myY[,c(1,2)],
  GD=myGD,
  GM=myGM,
  model="MLMM",
  PCA.total=5,
  file.output=T
  )
  • SUPER

Settlement of MLM Under Progressively Exclusive Relation- ship is published by Qishan in 2014. The code of SUPER is:

  myGAPIT_SUPER <- GAPIT(
  Y=myY[,c(1,2)],
  GD=myGD,
  GM=myGM,
  model="SUPER",
  PCA.total=5,
  file.output=T
  )
  • Farm-CPU

Fixed and random model Circulating Probability Unification (FarmCPU) is published by Xiaolei in 2016. The code of Farm-CPU in GAPIT is:

  myGAPIT_FarmCPU <- GAPIT(
  Y=myY[,c(1,2)],
  GD=myGD,
  GM=myGM,
  model="FarmCPU",
  PCA.total=5,
  file.output=T
  )

GS

  • gBLUP

gBLUP used marker kinship to replace the pedgree relationship matrix. The code is:

  myGAPIT_gBLUP <- GAPIT(
  Y=myY[,c(1,2)],
  GD=myGD,
  GM=myGM,
  model="gBLUP",
  PCA.total=5,
  file.output=T
  )
  • cBLUP

cBLUP used group kinship to replace the individual matrix. The code is:

  myGAPIT_cBLUP <- GAPIT(
  Y=myY[,c(1,2)],
  GD=myGD,
  GM=myGM,
  model="cBLUP",
  PCA.total=5,
  file.output=T
  )
  • sBLUP

sBLUP used SUPER method to build psedue QTN kinship matrix. The code is:

  myGAPIT_sBLUP <- GAPIT(
  Y=myY[,c(1,2)],
  GD=myGD,
  GM=myGM,
  model="sBLUP",
  PCA.total=5,
  file.output=T
  )

Result

Interactive Plots:

Interactive.Manhattan Interactive.QQ Interactive.3D.PCAs Interactive.GS

Example

  rm(list=ls())
  # loading packages for GAPIT and GAPIT functions
  source("http://www.zzlab.net/GAPIT/GAPIT.library.R")
  source("http://www.zzlab.net/GAPIT/gapit_functions.txt")
  # loading data set
  myY=read.table(file="http://zzlab.net/GAPIT/data/mdp_traits.txt", head = TRUE)
  myGD=read.table("http://zzlab.net/GAPIT/data/mdp_numeric.txt",head=T)
  myGM=read.table("http://zzlab.net/GAPIT/data/mdp_SNP_information.txt",head=T)
  #myG=read.table(file="http://zzlab.net/GAPIT/data/mdp_genotype_test.hmp.txt", head = FALSE)
  # performing simulation phenotype
  set.seed(198521)
  Para=list(h2=0.7,NQTN=20)
  mysimulation<-GAPIT(Para=Para,GD=myGD,GM=myGM)
  myY=mysimulation$Y


  myGAPIT <- GAPIT(
  Y=myY[,c(1,2)],
  GD=myGD,
  GM=myGM,
  model=c("GLM","MLM","SUPER","MLMM","FarmCPU","Blink"),# choose model
  #model=c("FarmCPU"),
  PCA.total=3,                                          # set total PCAs
  NJtree.group=4,                                       # set the number of clusting group in Njtree plot
  QTN.position=mysimulation$QTN.position,
  Inter.Plot=TRUE,                                      # perform interactive plot
  Multiple_analysis=TRUE,                               # perform multiple analysis
  PCA.3d=TRUE,                                          # plot 3d interactive PCA
  file.output=T
  )