Use of Pleiotropy to Model Genetic Interactions in a Population

Systems-level genetic studies in humans and model systems increasingly involve both high-resolution genotyping and multi-dimensional quantitative phenotyping. We present a novel method to infer and interpret genetic interactions that exploits the complementary information in multiple phenotypes. We applied this approach to a population of yeast strains with randomly assorted perturbations of five genes involved in mating. We quantified pheromone response at the molecular level and overall mating efficiency. These phenotypes were jointly analyzed to derive a network of genetic interactions that mapped mating-pathway relationships. To determine the distinct biological processes driving the phenotypic complementarity, we analyzed patterns of gene expression to find that the pheromone response phenotype is specific to cellular fusion, whereas mating efficiency was a combined measure of cellular fusion, cell cycle arrest, and modifications in cellular metabolism. We applied our novel method to global gene expression patterns to derive an expression-specific interaction network and demonstrate applicability to global transcript data. Our approach provides a basis for interpretation of genetic interactions and the generation of specific hypotheses from populations assayed for multiple phenotypes.

针对人类及模式生物的系统级遗传学研究，如今愈发兼具高分辨率基因分型与多维度定量表型分析两大特征。本研究提出一种全新方法，可利用多表型间的互补信息对遗传互作进行推断与阐释。我们将该方法应用于一组酵母菌株群体，这些菌株的5个与交配相关的基因均经过随机组合的扰动处理。我们从分子层面定量检测了信息素响应水平，并评估了整体交配效率。对这些表型进行联合分析后，我们构建出一套遗传互作网络，清晰呈现了交配通路内的各类关联。为明确驱动表型互补性的独特生物学过程，我们通过分析基因表达模式发现：信息素响应表型仅特异性对应细胞融合过程，而交配效率则是细胞融合、细胞周期阻滞与细胞代谢修饰三者的综合体现。我们将该全新方法应用于全局基因表达模式数据，构建出了表达特异性的遗传互作网络，证实了其在全局转录组数据中的适用性。本研究方法可为遗传互作的阐释，以及从多表型检测群体中生成针对性假说提供理论基础。