Novel selection criteria for genome scans of complex traits

Due to their oligogenic inheritance, the identification of susceptibility loci for complex traits by classical selection criteria has not been very successful. One way to address this problem is to identify statistics that measure the effect of more than one locus simultaneously. In the approach described here, a p-value is assigned to a combination of loci under the null hypothesis that none of them is linked to the disease locus. In order to examine the power of this method to detect multiple loci, the Genetic Analysis Workshop 12 general population simulated data set was analyzed using variance component methods. Using the described novel selection criteria resulted in an increase of power, however, a rejection of the null hypothesis has to be interpreted with care.