Predicting quantitative traits from genome and phenome with near perfect accuracy

Python code and IPython notebooks accompanying our paper.

Specifically,

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

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

Name		Name	Last commit message	Last commit date
Latest commit History 21 Commits
.gitignore		.gitignore
BLUP.py		BLUP.py
LMM.py		LMM.py
MRF.py		MRF.py
MTLMM.py		MTLMM.py
QTL_fitting.py		QTL_fitting.py
README.md		README.md
__init__.py		__init__.py
data_import.py		data_import.py
figure2a.png		figure2a.png
helper_functions.py		helper_functions.py
imports.py		imports.py
midparent.py		midparent.py
results1.ipynb		results1.ipynb
results2ab.ipynb		results2ab.ipynb
results2c.ipynb		results2c.ipynb
results2d.ipynb		results2d.ipynb
results2e.ipynb		results2e.ipynb
results2f.ipynb		results2f.ipynb
train_and_test_sets.py		train_and_test_sets.py

zhzheng92/y10k-prediction