# Providing the path to the bed file required for analysis
# bed_file = "/birl2/users/cbe453/arabidopsis-association/PLINK_manipulation/Seed_Oil_Composition_maf_ge_05_Fully_Merged_391_Subset_Final"
#pheno_file = "/birl2/data/P2IRC/GE2P/GWAS/arabidopsis/arabidopsis-pheno-files/BC16_0/bioBC_FA-BC16_0_plink.pheno"
# Perform the single_snp GWAS analysis.
# By default, FaST-LMM does not generate a proper output file so the output_file_name option
# is required. An arbitrary RAM cap of 10G was set based on previous tests.
results_df = single_snp(args.bed_file,
args.pheno_file,
GB_goal=10,
count_A1=True,
output_file_name=args.out_file)
# Tools for visualization if you're equipped with Xquartz (my Desktop machine is not...)
import matplotlib.pyplot as plt
import fastlmm.util.util as flutil
#draw manhattan plot
flutil.manhattan_plot(results_df.as_matrix(["Chr", "ChrPos", "PValue"]),
pvalue_line=1e-5,
xaxis_unit_bp=False)
plt.title(args.plot_title)
plt.savefig(args.out_file + '.png')
#draw qqplot
from fastlmm.util.stats.plotp import qqplot
qqplot(results_df["PValue"].values,
fileout='qq_' + args.out_file + '.png',
title=args.plot_title.replace('Manhattan', 'Quantile-quantile'))
results_df.head()
figure(num=None, figsize=(10, 7), dpi=80,)
manhattan(results_df["PValue"], results_df["ChrPos"], results_df["Chr"], OUTPUT_NAME,
lines=[5], colors=['r', 'b'], cut=1)
plt.savefig('Figures/' + OUTPUT_NAME + '_Manhattan_Plot.png')
plt.close()
# # QQ Plot
# In[11]:
from fastlmm.util.stats import plotp
plotp.qqplot(results_df["PValue"].values, xlim=[0,5], ylim=[0,5])
pylab.savefig('Figures/' + OUTPUT_NAME + 'QQ_Plot.png')
pylab.close()
print 'Time after QQ plot: ' + str(time.clock()-start)
# # The End
# In[12]:
print "\nDone!\n\n"