The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
PLoS Comput Biol. 2012;8(6):e1002559. doi: 10.1371/journal.pcbi.1002559. Epub 2012 Jun 21.
Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens.
遗传相互作用有助于绘制生物过程及其功能关系图。遗传相互作用的定义是在组合多个基因突变时，与预期表型的偏差。在酿酒酵母中，大多数遗传相互作用是在单个表型（标准实验室条件下的生长速率）下测量的。最近，已经在不同的表型读数和实验条件下收集了遗传相互作用。这些网络有何不同，我们可以从它们的差异中学到什么？我们对酵母中在不同实验条件下进行的定量遗传相互作用网络进行了系统分析。我们发现，使用不同表型读数、在不同条件下和来自不同实验室获得的网络重叠程度低于预期，并且提供了重要的独特信息。为了利用这些信息，我们开发了一种新的方法来组合单个遗传相互作用数据集，并表明由此产生的网络提高了基因功能预测性能，证明了单个网络提供了互补信息。我们的结果支持这样一种观点，即使用不同的表型读数和实验条件将大大增加遗传相互作用筛选产生的基因功能信息的数量。
