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bioRxiv · 10.1101/009720

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

Kolea Zimmerman·Daniel Levitis·Ethan Addicott·Anne Pringle
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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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Kolea Zimmerman, Daniel Levitis, Ethan Addicott, Anne Pringle. 2014-09-27. Selection of Pairings Reaching Evenly Across the Data (SPREAD): A simple algorithm to design maximally informative fully crossed mating experiments. https://doi.org/10.1101/009720

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