Cytological evidence indicates that genetic interference can be partitioned into two empirical components: nonrandomness in the number of chiasmata that occur and nonrandomness in the locations of multiple chiasmata. Previous studies have incorporated the first effect into genetic models for analyzing multipoint data. An extension to this approach is presented which allows for the second component of interference by modeling the probability density function of the locations of multiple crossovers. Results of reanalyses of multilocus data for the Drosophila X chromosome show that models that incorporate only the first effect give a better fit to these data than do standard mapping functions and that the extended model significantly improved the fit by decreasing the predicted frequency of multiple crossovers in nearby regions. Our results demonstrate that chiasma-based models of multilocus recombination, which are unique in incorporating direct estimates of the frequency of multiple crossovers for a chromosome region, can provide a powerful and realistic means of accounting for genetic interference when applied to the problems of gene localization, locus ordering, and exclusion mapping.