The extent and pattern of linkage disequilibrium (LD) determine the feasibility of association studies to map genes that underlie complex traits. Here we present a statistical algorithm for constructing a joint linkage-linkage disequilibrium map by simultaneously estimating the recombination fraction and linkage disequilibrium between different molecular markers in a natural human population. This algorithm was devised with a set of random unrelated families, each including a father, a mother and a varying number of offspring, sampled from a population at Hardy-Weinberg equilibrium. A two-level hierarchical mixture model framework was built, in which the likelihood of genotype data for the parents was formulated in terms of linkage disequilibrium at an upper level, whereas the likelihood of genetic transmission from the parents to offspring formulated in terms of the recombination fraction at a lower level. The EM algorithm was implemented to obtain a closed system of maximum likelihood estimates of marker co-segregation and co-transmission. The model allows a number of testable hypotheses about population genetic parameters, opening a broad gateway to understand the genetic structure and dynamics of an outcrossing population under natural selection. The new strategy will provide a platform for studying the genetic control of inherited diseases in which genetic material is accurately copied before being passed onto the offspring from a parent.