A variety of disciplines including conservation (McMahon et al. 2014), animal and plant breeding (De los Campos et al. 2013) and human genetics (Yang et al. 2010) are currently making use of large volumes of genetic marker information. In particular, within animal and plant breeding programs the use of genomic information to predict the genetic merit of individuals has become a routine practice (Jonas & Koning 2015; Morrell et al. 2012). This has resulted in a significant increase in the number of individuals within a herd/population having genomic information. Nonetheless, the ability to use this information to efficiently manage agricultural populations at the genomic level, both for preserving genetic diversity and lessening in breeding depression, remains a challenge for the near future. Although the identification of lethal mutations of large effect segregating within livestock populations with genomic data is possible (VanRaden et al. 2011), optimal mating procedures that minimize the frequency of a large number of lethal and more importantly sublethal mutations across generations have not been fully implemented. As the popularity of genotyping breeding individuals increases across species, the possibility to utilize genomic information from multiple sources to manage genomic information will also increase. Methods that make effective use of information from multiple sources including performance, genome diversity and inbreeding load at the selection and/or mating step are in increasing need. The routine genotyping of individuals is a significant investment from several sectors of agriculture and the need to spread the costs across multiple avenues are of considerable practical interest.
The use of simulation is a low cost alternative to assess and validate proposed methods to predict genetic values or to compare alternative selection or mating strategies across time. A number of simulation programs have been developed that simulate breeding livestock/crops populations, but they primarily focus on testing strategies where the genetic architecture is based solely on a quantitative trait governed by additive effects (Sargolzaei & Schenkel 2009; Hickey et al. 2012; Cheng et al. 2015; Prez-Enciso & Legarra 2016). Currently there is no single self contained software that can simulate complex traits involving both quantitative and fitness components along with the ability to generate complex pedigrees and genomic information ranging from sparse marker to sequence information. Consequently, determining how various selection and management practices impact the fitness and the overall genomic variability of a population undergoing selection for a quantitative trait remains challenging. Furthermore, the precise genetic architecture of complex traits is largely unknown although in general it is assumed that complex traits are affected by variation in a large number of genes, most of which have individually minor effects (Weiss 2008). Knowledge on the demographic history of a population and the extent of linkage disequilibrium is also being generated from dense marker panels or sequence information (McKay et al. 2007; Ai et al. 2013; Porto-Neto et al. 2014). As more information is generated on the genetic architecture of complex traits and variation across the genome, simulation can be employed to more effectively predict how current selection and management practices will impact future generations.