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Data from: Selection of Pairings Reaching Evenly Across the Data (SPREAD): a simple algorithm to design maximally informative fully crossed mating experiments

Citation

Zimmerman, Kolea; Levitis, Daniel; Addicott, Ethan; Pringle, Anne (2015), Data from: Selection of Pairings Reaching Evenly Across the Data (SPREAD): a simple algorithm to design maximally informative fully crossed mating experiments, Dryad, Dataset, https://doi.org/10.5061/dryad.8br20

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.

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