Exhaustive sliding-window scan strategy for genome-wide association study via PCA-based logistic model
In genome-wide association study (GWAS), various sliding-window scan approaches have been proposed recently. How to determine the optimal window size, which is influenced by the underlying linkage disequilibrium (LD) patterns, minor allele frequency (MAF) of the causal SNP, and others, is crucial for these methods. However, it is difficult to clarify the theoretical relationship between the optimal window size and these factors. In this regard, we proposed exhaustive strategy with ergodic window sizes along the genome matter whatever the relationship is. Simulations are conducted to assess statistical powers under different sample sizes, relative risks, MAF, LD patterns and window sizes, followed by a real data analysis to evaluate its performance. The simulation results suggested that it was difficult to determine the optimal window size because it was influenced by many factors such as MAF and LD pattern. Real data analysis indicated that the p-values with different window sizes were quite different. Furthermore, with the development of multiprocessor computational technique, the proposed exhaustive strategy combined with the cluster computer technique computationally efficient and feasible for analyzing GWAS data.So the exhaustive strategy is a powerful tool for GWAS data analysis regardless of the relationship between the window size and LD.
