maizeHTP

Integrating high-throughput phenotyping, GWAS and prediction models reveals the genetic architecture of plant height in maize

Introduction

Here, we developed an automated high-throughput phenotyping platform (HTP) and used it to quantify image-based traits (i-traits) for 228 maize inbred lines across all developmental stages. We upload all code used in the paper entitled "Integrating high-throughput phenotyping, GWAS and prediction models reveals the genetic architecture of plant height in maize".

First, the i-Traits were extracted from maize images (code: maize image analysis.iac) on the HTP platform software system, and then all i-Traits data were analyzed, such as Principal component analysis (PCA) in R (Folder: PCA_example, scatter_example);

Next, we performed genome-wide association analysis (GWAS) and showed the distribution of significant association SNP at a genome-wide scale (Folder: GWAS_example_data, Manhattan_and_qq_example, Candidate_gene_example, CMplot_example);

Finally, we implemented maize final plant height (PH) prediction using early stage i-Traits based on Random forests (RF) (Folder: Random_Forest_example).

System requirement

For GWAS

1. plink v1.90b6.21
2. admixture v1.3.0
3. bedtools v2.30.0
4. PopLDdecay v3.42
5. GEC tool v0.2
6. TASSEL v5.0
7. LDBlockShow v1.40

For data visualization

1. R v4.1.0
2. Rstudio
3. R package
   • ggplot2 v3.3.6
   • caret v6.0-93
   • FactoMineR v2.6
   • factoextra v1.0.7
   • ggpubr v0.4.0
   • GGally v2.1.2
   • qqman v0.1.8

Quick Start to install the required program

Linux

1. insatll conda

Download annoconda form conda (https://anaconda.org/anaconda/conda)  

2. create environment and install software

conda create -n GWAS plink  admixture bedtools   

3. install PopLDdecay form github

Download PopLDdecay-master.zip and unzip 
cd PopLDdecaymaster 
cd src  
make  
../bin/PopLDdecay

Note: More detail please see https://github.com/BGI-shenzhen/PopLDdecay

4. install GEC tool

Downlaod GEC tool from http://pmglab.top/gec/#/download  
unzip GEC.zip  

5. insatll tassel

Download TASSEL from https://tassel.bitbucket.io/  

6. install LDBlockShow

Downlaod LDBlockShow-master.zip and unzip  
tar -zxvf  LDBlockShowXXX.tar.gz  
cd LDBlockShowXXX;  cd src;  
sh  make.sh                         
../bin/LDBlockShow  

R package

install.packages("ggplot2")
install.packages("caret") 
install.packages("FactoMineR")
install.packages("factoextra")  
install.packages("ggpubr")  
install.packages("GGally")  
install.packages("qqman")  

GWAS

1. data preparation

# All_imputated.hmp
Download genotype data form maizeGO (http://www.maizego.org/Resources.html)  

2. Format conversion and filter with MAF (minor allelic frequency)

 run_pipeline.pl  -Xmx100g   -SortGenotypeFilePlugin -inputFile All_imputated.hmp -outputFile All_imputated.sort.hmp.txt -fileType Hapmap  
 run_pipeline.pl  -Xms10g   -Xmx100g  -fork1 -h   All_imputated.sort.hmp.txt -export -exportType VCF  
 plink --vcf  All_imputated.vcf    --maf  0.05  --recode vcf-iid --out  All.SNP.filter.maf0.05 --allow-extra-chr  
 
 #2649413 All_imputated.vcf
 #1253830 All.SNP.filter.maf0.05.vcf

3. Linkagedisequilibrium (LD) calculation and visualization

 /home/guoweijun/soft/LD/PopLDdecay-master/bin/PopLDdecay     -InVCF   All_imputated.vcf  -OutStat  LDdecay  -MaxDist  300  -Miss 0.1  -MAF 0.05  
 perl  /home/guoweijun/soft/LD/PopLDdecay-master/bin/Plot_OnePop.pl  -inFile   LDdecay.stat.gz  -output  Fig-all  

4. Admixture

 plink --vcf All.SNP.filter.maf0.05.vcf --indep-pairwise 50 10 0.1 --out demo.admixture --allow-extra-chr  
 plink --vcf All.SNP.filter.maf0.05.vcf --recode vcf-iid --extract demo.admixture.prune.in --out demo.admixture.in  
 plink --vcf demo.admixture.in.vcf  --make-bed  --out imputed  
 for K in {1..10}; do admixture --cv  imputed.bed $K | tee log${K}.out; done  
 grep -h CV log*.out  
 #CV error (K=10): 0.85957  
 #CV error (K=1): 0.92360  
 #CV error (K=2): 0.89921  
 #CV error (K=3): 0.88652  
 #CV error (K=4): 0.87761  
 #CV error (K=5): 0.87044  
 #CV error (K=6): 0.86636  
 #CV error (K=7): 0.86133  
 #CV error (K=8): 0.86028  
 #CV error (K=9): 0.86086  

Note: K=10, Q was selected for next analysis.

5. Calculating the effective number (EN) of independent SNP

 java -Xmx10g -jar /home/00.soft/kggsee.jar --var-gec --nt 8 --filter-maf-le 0.05 --vcf-ref All.SNP.filter.maf0.05.vcf --out All.SNP.filter.maf0.05.efficent    

 #1828 variants are ignored due to their minor allele frequency (MAF) in sample <=0.05000000074505806
 #1253814 variant-lines (0 indels) are scanned in /mnt/d/guowj/share/MP/response/00.SNP_info/all_data/All.SNP.filter.maf0.05.vcf; and 1251986 variants of 540 individual(s) are retained.

6. GWAS using TASSEL

 1. Open TASSEL  
 2. Import data  
     Genotype data: All.SNP.filter.maf0.05.vcf  
     Phenotype data  
     Q: imputed.10.Q 
     Kinship:kinship.txt (Tassel - Analysis -Relatedness - Kinship)  
 3. Data - InsertJoin - Analysis - Association - MLM  
 4.Export results  

7. Candidate gene analysis

 #.Zm00001d035008	i-TL

 ### 1. Extracting upstream- and down-stream 50kb SNP of significant SNP associated with i-TL  

 zcat  All_imputated.vcf.gz   |awk  -v OFS="\t"  '($1=="6"&&$2>=1487642&&$2<=1591643){print$0}'   >Zm035008.vcf  

 ### 2. Extracting GWAS result  
 awk  -v OFS="\t"  '($3=="6"&&$4>=1487642&&$4<=1591643){print$3,$4,$6}'  GLM_Stats_5.dMPAR_S7.txt  >i-MPAR_S7_Zm035008.GLM.txt  
 awk  -v OFS="\t"  '($3=="6"&&$4>=1487642&&$4<=1591643){print$3,$4,$7}'  MLM_statistics_5.dMPAR_S7.txt  >i-MPAR_S7_Zm035008.MLM.txt  

 ### 3. Extracting significant GWAS result
 
 awk  -v OFS="\t"  '($3=="6"&&$4>=1487642&&$4<=1591643&&$7<=7.98731E-07){print$3,$4,$2}'  MLM_statistics_i-TL.txt   >i-MPAR_S7_Zm035008.MLM.snp  
 
 ### 4.Visualization
 
 ./LDBlockShow-master/bin/LDBlockShow   -InVCF  Zm035008.vcf -OutPut  Zm035008     -InGWAS  i-MPAR_S7_Zm035008.GLM.txt  -Region   6:1487642:1591643    -OutPng    -SeleVar 2  
 ./LDBlockShow-master/bin/ShowLDSVG   -InPreFix  Zm035008  -OutPut   Zm035008.1     -InGWAS   i-MPAR_S7_Zm035008.GLM.txt   -Cutline  6.097599473  -PointSize 15   -OutPng   -SpeSNPName  i-MPAR_S7_Zm035008.GLM.snp  -ShowGWASSpeSNP  

Data visualization and model training

#These analysis were performed in R software, for details, please see folder:

• CMplot_example
• Manhattan_and_qq_example
• PCA_example
• Random_Forest_example
• scatter_example