Fast computation for genome-wide association studies using boosted one-step statistics

Motivation: Statistical analyses of genome-wide association studies (GWAS) require fitting large numbers of very similar regression models, each with low statistical power. Taking advantage of repeated observations or correlated phenotypes can increase this statistical power, but fitting the more complicated models required can make computation impractical. Results: In this article, we present simple methods that capitalize on the structure inherent in GWAS studies to dramatically speed up computation for a wide variety of problems, with a special focus on methods for correlated phenotypes.