Pedigrees provide a fine resolution of the genealogical relationships among individuals and serve an important function in many areas of genetic studies. One such use of pedigree information is in the estimation of short-term effective population size (Ne), which is of great relevance in fields such as conservation genetics. Despite the usefulness of pedigrees, however, they are often an unknown parameter and must be inferred from genetic data. In this study, we present a Bayesian method to jointly estimate pedigrees and Ne from genetic markers using Markov Chain Monte Carlo. Our method supports analysis of a large number of markers and individuals with the use of composite likelihood, which significantly increases computational efficiency. We show on simulated data that our method is able to jointly estimate relationships up to first cousins and Ne with high accuracy. We also apply the method on a real dataset of house sparrows to reconstruct their previously unreported pedigree.
Joint Estimation of Sequential Labor Force Participation and Fertility Decisions Using Markov Chain Monte Carlo Techniques
