Influence of aberrant observations on high-resolution linkage analysis outcomes

Abstract

Because of the availability of efficient, user-friendly computer analysis programs, the construction of multilocus human genetic maps has become commonplace. At the level of resolution at which most of these maps have been developed, the methods have proved to be robust. This may not be true in the construction of high-resolution linkage maps (3-cM interlocus resolution or less). High-resolution meiotic maps, by definition, have a low probability of recombination occurring in an interval. As such, even low frequencies of errors in typing (1.5% or less) may influence mapping outcomes. To investigate the influence of aberrant observations on high-resolution maps, a Monte Carlo simulation analysis of multipoint linkage data was performed. Introduction of error was observed to reduce power to discriminate orders, dramatically inflate map length, and provide significant support for incorrect over correct orders. These results appear to be due to the misclassification of nonrecombinant gametes as multiple recombinants. Chi 2-Like goodness-of-fit analysis appears to be quite sensitive to the appearance of misclassified gametes, providing a simple test for aberrant data sets. Multiple pairwise likelihood analysis appears to be less sensitive than does multipoint analysis and may serve as a check for map validity.