Selection of pairings reaching evenly across the data (SPREAD): A simple algorithm to design maximally informative fully crossed mating experiments

K Zimmerman; D Levitis; E Addicott; A Pringle

doi:10.1038/hdy.2015.88

Selection of pairings reaching evenly across the data (SPREAD): A simple algorithm to design maximally informative fully crossed mating experiments

Heredity (Edinb). 2016 Feb;116(2):182-9. doi: 10.1038/hdy.2015.88. Epub 2015 Sep 30.

Authors

K Zimmerman¹, D Levitis², E Addicott¹, A Pringle^{3

4}

Affiliations

¹ Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
² Bates College, Department of Biology, Lewiston, ME, USA.
³ Department of Botany, University of Wisconsin, Madison, WI, USA.
⁴ Department of Bacteriology, University of Wisconsin, Madison, WI, USA.

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.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms*
Crosses, Genetic*
Genes, Mating Type, Fungal
Genotype
Models, Genetic*
Neurospora / genetics*