Kyle Gardiner ¹ Xuekui Zhang ² Li Xing ^1*

• ¹ University of Saskatchewan, Saskatoon, Saskatchewan, Canada
• ² University of Victoria, Victoria, British Columbia, Canada

The traditional single nucleotide polymorphism (SNP)-wise approach in genome-wide association studies is focused on examining the marginal association between each SNP with the outcome separately and applying multiple testing adjustments to the resulting p-values to reduce false positives. However, the approach suffers a lack of power in identifying biomarkers.We design an ensemble machine learning approach to aggregate results from logistic regression models based on multiple sub-samples, which helps to identify biomarkers from high-dimensional genomic data. We employ different methods to analyze a genome-wide association study from the Alzheimer's Disease Neuroimaging Initiative. The SNP-wise approach does not identify any significant signal, while our novel approach provides a list of ranked SNPs associated with the cognitive functions of interests.