🌾 Genome-Wide Association Study (GWAS) Pipeline Using rMVP

Overview

This repository provides a complete and reproducible GWAS pipeline using the rMVP package.

The workflow includes:

• HapMap genotype import
• Phenotype preprocessing
• Population structure analysis (PCA)
• Kinship estimation
• GWAS using
  • GLM
  • MLM
  • FarmCPU
• Manhattan plots
• QQ plots
• Significant SNP detection
• FDR correction
• Automatic export of GWAS results

The pipeline has been tested using a 122 genotype × 18,525 SNP cereal dataset.

---

Pipeline

HapMap
      │
      ▼
Data QC
      │
      ▼
Phenotype preprocessing
      │
      ▼
Population Structure (PCA)
      │
      ▼
Kinship Matrix
      │
      ▼
GWAS
 ├── GLM
 ├── MLM
 └── FarmCPU
      │
      ▼
Visualization
 ├── Manhattan Plot
 ├── QQ Plot
 └── SNP Density
      │
      ▼
Significant SNP Detection
      │
      ▼
Excel Report

---

Repository Structure

GWAS-rMVP/

│
├── Data/
│   ├── 122G18K.hmp.txt
│   ├── Traits.txt
│   └── Traits_clean.txt
│
├── Scripts/
│   ├── 01_Data_Preparation.R
│   ├── 02_PCA.R
│   ├── 03_Kinship.R
│   ├── 04_GLM.R
│   ├── 05_MLM.R
│   ├── 06_FarmCPU.R
│   ├── 07_Visualization.R
│   ├── 08_Post_GWAS.R
│   └── GWAS_Pipeline.R
│
├── Results/
│   ├── Manhattan/
│   ├── QQ/
│   ├── Tables/
│   └── Figures/
│
├── README.md
├── LICENSE
└── requirements.R

---

Installation

Install required packages

install.packages(c(
"rMVP",
"data.table",
"ggplot2",
"bigmemory",
"openxlsx"
))

Load packages

library(rMVP)
library(data.table)
library(bigmemory)
library(openxlsx)
library(ggplot2)

---

Input Files

Genotype

HapMap format

122G18K.hmp.txt

Phenotype

Traits.txt

Example

Taxa	Ph21	Ph2022	SW21
Geno001	90	90.67	1.89

---

Running the Pipeline

Run

source("Scripts/GWAS_Pipeline.R")

The pipeline automatically performs

• Data preparation
• PCA
• Kinship
• GLM
• MLM
• FarmCPU
• Manhattan plots
• QQ plots
• Significant SNP export

---

Output

The following files are generated

Ph21.GLM.csv

Ph21.MLM.csv

Ph21.FarmCPU.csv

Manhattan plots

QQ plots

SNP density plots

Significant_SNPs.xlsx

---

Statistical Models

GLM

General Linear Model

Population structure only.

---

MLM

Mixed Linear Model

Population structure + Kinship matrix.

---

FarmCPU

Fixed and random model iterations.

Recommended for reducing false positives.

---

Multiple Testing

Bonferroni threshold

0.05 / Number_of_SNPs

False Discovery Rate

p.adjust(method="BH")

---

Example Results

Pipeline produces

• Manhattan plots

• QQ plots

• PCA

• Kinship matrix

• Significant SNP tables

---

Citation

If you use this repository, please cite

Yin L., et al.

rMVP: Memory-efficient, Visualization-enhanced and Parallel-accelerated GWAS Package.

Bioinformatics.

---

Future Development

Planned additions

• BLINK

• Genomic Prediction

• LD Decay

• Candidate Gene Annotation

• GO Enrichment

• KEGG Enrichment

• Interactive Shiny Dashboard

• Docker Support

• Nextflow Workflow

• GitHub Actions

---

Author

Dr.Ali Hassan Anany

Researcher

National Gene Bank

Agricultural Research Center (ARC) Mob:+201123108813 Email:ali.anany@post.ag.cu.edu.eg Egypt

Research Interests

• GWAS
• Plant Genetics
• Bioinformatics
• NGS data analysis
• Genomic Prediction
• AI-assisted Plant Breeding

---

License

MIT License