Quantitative Genetics Analysis of Maize Using G2F Data

Overview

This repository demonstrates complementary approaches to quantitative genetics using the Genomes to Fields maize dataset: genetic discovery through GWAS and phenotype prediction through machine learning.

Dataset

Genomes to Fields (G2F) Initiative: 5,899 hybrid genotypes, 2,425 SNPs, phenotypic data from 272 environments (2014-2023).

Projects

1. GWAS: Genetic Architecture of Flowering Time

• Goal: Identify loci associated with flowering time variation
• Methods: GWAS using GAPIT, 2,425 SNPs, 2022-2023 environments
• Results: [Summary findings]
• View detailed analysis →

2. Machine Learning: Yield Prediction

• Goal: Predict grain yield from genomic and environmental data
• Methods: Random Forest and Neural Network, 673 environmental covariates
• Results: [Model performance]
• View detailed analysis →

Why Both Approaches?

In modern breeding programs, GWAS identifies causal biology while ML models optimize selection. This portfolio demonstrates capability in both discovery and application.

Setup Instructions

Prerequisites

• R (>= 4.3.0)
• Python (>= 3.12.0)
• Git

Installation

1. Clone Repository

git clone https://github.com/YOUR_USERNAME/g2f-maize-quantitative-genetics.git
cd g2f-maize-quantitative-genetics

2. Download Data

See data/README.md for data download instructions.

3. R Environment Setup

Open RStudio in this directory:

# Install renv if needed
install.packages("renv")

# Restore R packages from lockfile
renv::restore()

This installs all R packages listed in renv.lock.

4. Python Environment Setup

# Create virtual environment
python -m venv venv

# Activate environment
source venv/Scripts/activate  # Git Bash on Windows
# OR
venv\Scripts\activate  # PowerShell on Windows

# Install packages
pip install -r requirements.txt