y9c / y10k-prediction

Predicting quantitative traits from genome and phenome with near perfect accuracy

Python code and IPython notebooks accompanying our paper.biorxiv ncomms

How to use

.
├── figures
│   └── figure2a.png
├── input_data
│   └── y10k_hybrids_Yield.hdf5
├── notebooks
│   ├── results1.ipynb
│   ├── results2ab.ipynb
│   ├── results2c.ipynb
│   ├── results2d.ipynb
│   ├── results2e.ipynb
│   └── results2f.ipynb
├── output
│   └── README.md
├── README.md
├── REQUIREMENT.txt
└── y10k_prediction_methods
    ├── BLUP.py
    ├── data_import.py
    ├── dependence.py
    ├── helper_functions.py
    ├── __init__.py
    ├── LMM.py
    ├── midparent.py
    ├── MRF.py
    ├── MTLMM.py
    ├── QTL_fitting.py
    └── train_and_test_sets.py

wrapped script for different prediction methods

• train_and_test_sets.py - code for partitioning individuals into four sets, as shown in Figure 3a
• BLUP.py - fitting the BLUP model
• QTL_fitting.py - constructing and fitting the QTL model
• LMM.py - constructing and fitting the LMM and LMM+P models
• MTLMM.py - fitting the multi-trait LMM
• MRF.py - fitting the mixed random forest

jupyter notebooks

This notebook was used to produce the results for Figure 2. All models were fitted using Limix.