Abstract:
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Genome-wide association studies (GWAS) have played an important role in identifying genetic variants underlying human complex traits. However, its success is hindered by weak effect at causal variants and noise at non-causal variants. In an effort to overcome these difficulties, Liu et al. (2010) proposed a regularized regression method that penalizes on the difference of signal strength between two consecutive single-nucleotide polymorphisms (SNPs). We provides a generalization to this method so that more adjacent SNPs can be considered. The choice of optimal number of markers is studied. Simulation studies indicate that this penalized moving window regression method provides improved true positive findings. The practical utility of the proposed method is demonstrated by applying it to Genetic Analysis Workshop 16 rheumatoid arthritis GWAS data.
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