Simulating a base population in honey bee for molecular genetic studies

Our software program yielded a genomic dataset for a base population in linkage disequilibrium. In addition, information was obtained on (1) the position of markers on each chromosome, (2) allele frequency, (3) χ2 statistics for Hardy-Weinberg equilibrium, (4) a sorted list of markers with a minor allele frequency less than or equal to the input value, (5) average r2 values of linkage disequilibrium between all simulated marker loci pair for all generations and (6) average r2 value of linkage disequilibrium in the last generation for selected markers with the highest minor allele frequency.We developed a software program that takes into account the genetic and reproductive biology specific to the honey bee and that can be used to constitute a genomic dataset compatible with the simulation studies necessary to optimize breeding programs. The source code together with an instruction file is freely accessible at http://msproteomics.org/Research/Misc/honeybeepopulationsimulator.html webciteThe honey bee is an economically important species that serves as a major pollinator of wild plants and agricultural crops. Over the past years, a decline of honey bee populations has been reported [1-3] mainly caused by infestation with an ecto-parasitic mite (Varroa destructor). Selective breeding of resistant bees can help prevent losses due to the parasite, but it requires that a robust breeding program and genetic evaluation are implemented. Until now, the approach used in honey bee is a traditional breeding program based on the best linear unbiased prediction (BLUP) relying on pedigree data [4]. Recently, genomic selection strategies based on molecular marker data have emerged as a promising approach [5]. Marker-based selection has been widely tested in several species either with simulated datasets e.g. [6,7] or with real datasets e.g. [8-11] but, to date, not in honey bee. Since simulation studies require molecular genetic and pedigree datasets to ascertain selection methods,