A major issue plaguing genome-wide studies is multiple testing that arises from testing hundreds of thousands (if not millions) of SNP markers for association with the disease or trait of interest. In response to this issue, many investigators have advocated the use of a Bonferroni-correction to limit the probability of committing type-I errors. However, this comes at a cost of simultaneously increasing the probability of committing type-II errors, thereby diminishing the opportunity of detecting true association signals. This is particularly true of smaller genome-wide association datasets such as the sibling pairs samples. One solution is to utilize prior information into the association scan. In this study, we use a weighted association approach as implemented by Roeder et al., 2006 to accomplish this. While there are a variety of ways to construct weights, there are only two criteria that must be met. First, each weight must be greater than 0 and the mean of the weights must be 1. There are numerous sources of prior information that can motivate the weighting scheme including linkage scans, bioinformatics information, as well as previously
