The advancements made in molecular technology coupled with statistical methodology have led to the successful detection and location of genomic regions (quantitative trait loci; QTL) associated with quantitative traits. Binary traits (e.g., susceptibility/resistance), while not quantitative in nature are equally important in associating with genomic regions for the purpose of detecting and locating significant associations. Prior methods used in binary trait analysis are adapted from quantitative trait analysis and the interpretation of the model parameterizations in the binary trait setting is unclear. In addition, commonly used chi-square tests of association between the marker and trait fail to take into account varying penetrance and perform poorly when penetrance is incomplete. In this work a probability model for binary trait data is developed, allowing detection of binary trait locus (BTL) and unbiased estimation of both penetrance and recombination between a genetic marker locus and a BTL for backcross and F2 experimental designs. The method is easily expanded to incorporate multiple unlinked markers.