|Title||GWASelect: A variable selection method for genomewide association studies (C++).|
|Year of Publication||2011|
|Authors||He, Qianchuan, and Dan-Yu Lin|
GWASelect, a statistically powerful and computationally efficient variable selection method designed to tackle the unique challenges of GWAS data, introduced in the Bioinformatics article "A variable selection method for genome-wide association studies" by He, Q. and Lin, DY.
This method searches iteratively over the potential SNPs conditional on previously selected SNPs and is thus capable of capturing causal SNPs that are marginally correlated with disease as well as those that are marginally uncorrelated with disease. A special resampling mechanism is built into the method to reduce false positive findings. Simulation studies demonstrate that the GWASelect performs well under a wide spectrum of linkage disequilibrium patterns and can be substantially more powerful than existing methods in capturing causal variants while having a lower FDR. In addition, the regression models based on the GWASelect tend to yield more accurate prediction of disease risk than existing methods. The advantages of the GWASelect are illustrated with the Wellcome Trust Case-Control Consortium (WTCCC) data.
|Original Publication||GWASelect: A variable selection method for genomewide association studies (C++).|
GWASelect: A variable selection method for genomewide association studies (C++).