Detection and localization of a single binary trait locus in experimental populations

The advancements made in molecular technology coupled with statistical meth odology have led to the successful detection and location of genomic region s (quantitative trait loci; QTL) associated with quantitative traits. Binar y traits (e.g. susceptibility/resistance), while not quantitative in nature , are equally important for the purpose of detecting and locating significa nt associations with genomic regions. Existing interval regression methods used in binary trait analysis are adapted from quantitative trait analysis and the tests for regression coefficients are tests of effect, not detectio n. Additionally, estimates of recombination that fail to take into account varying penetrance perform poorly when penetrance is incomplete. In this wo rk a complete probability model for binary trait data is developed allowing for unbiased estimation of both penetrance and recombination between a gen etic marker locus and a binary trait locus for backcross and F-2 experiment al designs. The regression model is reparameterized. allowing for tests of detection. Extensive simulations were conducted to assess the performance o f estimation and testing in the proposed parameterization. The proposed par ameterization was compared with interval regression via simulation. The res ults indicate that our parameterization shows equivalent estimation capabil ities, requires less computational effort and works well with only a single marker.