Zimmerman K, Levitis D, Addicott E, Pringle A
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
Bates College, Department of Biology, Lewiston, ME, USA.
Heredity (Edinb). 2016 Feb;116(2):182-9. doi: 10.1038/hdy.2015.88. Epub 2015 Sep 30.
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.
我们提出了一种用于设计杂交实验的新算法。该算法通过最大化感兴趣性状的多样性，从更大的潜在杂交组池中识别出一组个体（一个“杂交组”），例如，最大化杂交组中个体之间的遗传和地理距离范围。为了计算多样性，我们使用在性状空间中绘制的杂交的平均最近邻距离。我们在粗糙脉孢菌菌株的真实数据集上实现了我们的算法，将潜在杂交之间的遗传和地理距离用作二维性状空间。在模拟交配实验中，我们的算法选择的杂交组比随机选择的杂交组能更好地估计潜在参数值。
