Record added by Jurg Ott (the original Rockefeller list)


GWASelect implements a novel variable selection method for GWAS data and is able to handle more than half million SNPs. Extensive simulation studies and real data analysis show that this method enjoys high power and low false discovery rate compared to existing variable selection methods. The variables selected by GWASelect can be readily placed into a logistic regression model for disease prediction. The current release is designed for binary outcome under the additive mode of inheritance.


  • Qianchuan He

  • Dan-Yu Lin (Univ North Carolina)



He, Lin (2010), “A variable selection method for genome-wide association studies”, Bioinformatics, 27:1-8.