title: Selection of pairings reaching evenly across the data (SPREAD): A simple algorithm to design maximally informative fully crossed mating experiments.
abstract: We present a novel algorithm for the design of crossing experiments. The algorithm identifies a set of individuals (a 'crossing-set') from a larger pool of potential crossing-sets by maximizing the diversity of traits of interest, for example, maximizing the range of genetic and geographic distances between individuals included in the crossing-set. To calculate diversity, we use the mean nearest neighbor distance of crosses plotted in trait space. We implement our algorithm on a real dataset of Neurospora crassa strains, using the genetic and geographic distances between potential crosses as a two-dimensional trait space. In simulated mating experiments, crossing-sets selected by our algorithm provide better estimates of underlying parameter values than randomly chosen crossing-sets.
authors.affiliation: Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
authors.forename: K
authors.identifier: http://orcid.org/0000-0001-6224-3364
authors.initials: K
authors.lastname: Zimmerman
authors.affiliation: Bates College, Department of Biology, Lewiston, ME, USA.
authors.forename: D
authors.initials: D
authors.lastname: Levitis
authors.affiliation: Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
authors.forename: E
authors.initials: E
authors.lastname: Addicott
authors.affiliation: Department of Botany, University of Wisconsin, Madison, WI, USA.|Department of Bacteriology, University of Wisconsin, Madison, WI, USA.
authors.forename: A
authors.initials: A
authors.lastname: Pringle
mesh_terms: D000465:Algorithms; D003433:Crosses, Genetic; D049770:Genes, Mating Type, Fungal; D005838:Genotype; D008957:Models, Genetic; D009491:Neurospora / Q000235:genetics*