Integrating high-throughput phenotyping, GWAS and prediction models reveals the genetic architecture of plant height in maize
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).
- plink v1.90b6.21
- admixture v1.3.0
- bedtools v2.30.0
- PopLDdecay v3.42
- GEC tool v0.2
- TASSEL v5.0
- LDBlockShow v1.40
- R v4.1.0
- Rstudio
- 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
- insatll conda
Download annoconda form conda (https://anaconda.org/anaconda/conda)
- create environment and install software
conda create -n GWAS plink admixture bedtools
- 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
- install GEC tool
Downlaod GEC tool from http://pmglab.top/gec/#/download
unzip GEC.zip
- insatll tassel
Download TASSEL from https://tassel.bitbucket.io/
- install LDBlockShow
Downlaod LDBlockShow-master.zip and unzip
tar -zxvf LDBlockShowXXX.tar.gz
cd LDBlockShowXXX; cd src;
sh make.sh
../bin/LDBlockShow
install.packages("ggplot2")
install.packages("caret")
install.packages("FactoMineR")
install.packages("factoextra")
install.packages("ggpubr")
install.packages("GGally")
install.packages("qqman")
# All_imputated.hmp
Download genotype data form maizeGO (http://www.maizego.org/Resources.html)
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
/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
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.
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.
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
#.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
#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
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