Rank transformation in Haseman-Elston regression using scores for location-scale alternatives

The Haseman-Elston method is a simple regression approach for detecting genetic linkage to quantitative traits in sib-pair studies. Although this method and especially the new extended Haseman-Elston approach are quite robust, there might be some loss of power for non-normally distributed traits. We propose using rank transformation techniques, which either combine the information on a trend in locations and in scales or detect a trend only for a subset of the trait variables for genetically different sibs under linkage. As this rank transformation is based on linear regression, no exact grouping of identity by descent proportions has to be assumed. Simulation results indicate a gain in power compared to recently suggested nonparametric methods.