TY - JOUR T1 - Selection of Pairings Reaching Evenly Across the Data (SPREAD): A simple algorithm to design maximally informative fully crossed mating experiments JF - bioRxiv DO - 10.1101/009720 SP - 009720 AU - Kolea Zimmerman AU - Daniel Levitis AU - Ethan Addicott AU - Anne Pringle Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/06/25/009720.abstract N2 - 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. ER -