Determining trait locus position from multipoint analysis: accuracy and power of three different statistics

Abstract

Previous work using two-point linkage analysis showed that performing a lod score (LOD) analysis twice, once assuming dominant and once assuming recessive inheritance, and then taking the larger of the two values (designated MMLS) usually has more power to detect linkage than any other method tested. Using computer simulation for a variety of complex inheritance models, we demonstrated power for the MMLS comparable with analysis assuming the true model. However, reports in the literature suggested that the MMLS approach might fail to detect linkage using multipoint analysis due to genetic model misspecification. Here, we tested the robustness of the MMLS approach under multipoint analysis. We simulated data under complex inheritance models, including heterogeneity, epistatic, and additive models. We examined the expected maximum LOD, LOD assuming heterogeneity (HLOD), and nonparametric linkage statistics and the corresponding estimated position in a chromosomal interval of 10 markers with 10% recombination between markers. The mean estimates of position were generally good for all three statistics except when heterogeneity existed, where the LOD and the NPL did not perform as well as the HLOD. The MMLS approach was as robust using multipoint as using two-point linkage analysis. LOD and/or the HLOD generally had more power to detect linkage than NPL across a variety of generating models, even after correcting for the multiple tests. For finding linkage to one locus of several contributing to disease expression, assuming the dominant and recessive models with reduced penetrance is a good approximation of the mode of inheritance at that locus.