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

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.

本研究提出一种用于杂交实验设计的新型算法。该算法从大规模潜在杂交组（crossing-set）候选池中筛选出一组个体，即杂交组，以最大化目标性状的多样性——例如最大化杂交组内个体间遗传距离与地理距离的分布范围。为量化性状多样性，本研究采用性状空间中各杂交组合的平均最近邻距离作为度量指标。本研究将该算法应用于一套真实的粗糙脉孢菌（Neurospora crassa）菌株数据集，以潜在杂交组合间的遗传距离与地理距离构建二维性状空间。在模拟交配实验中，经本算法筛选得到的杂交组，相较于随机选取的杂交组，能够更精准地估计实验的潜在参数值。