Franco-Duarte Ricardo, Mendes Inês, Umek Lan, Drumonde-Neves João, Zupan Blaz, Schuller Dorit
Centre of Molecular and Environmental Biology (CBMA), Department of Biology, University of Minho, Braga, Portugal.
Yeast. 2014 Jul;31(7):265-77. doi: 10.1002/yea.3016. Epub 2014 May 26.
Genome sequencing is essential to understand individual variation and to study the mechanisms that explain relations between genotype and phenotype. The accumulated knowledge from large-scale genome sequencing projects of Saccharomyces cerevisiae isolates is being used to study the mechanisms that explain such relations. Our objective was to undertake genetic characterization of 172 S. cerevisiae strains from different geographical origins and technological groups, using 11 polymorphic microsatellites, and computationally relate these data with the results of 30 phenotypic tests. Genetic characterization revealed 280 alleles, with the microsatellite ScAAT1 contributing most to intrastrain variability, together with alleles 20, 9 and 16 from the microsatellites ScAAT4, ScAAT5 and ScAAT6. These microsatellite allelic profiles are characteristic for both the phenotype and origin of yeast strains. We confirm the strength of these associations by construction and cross-validation of computational models that can predict the technological application and origin of a strain from the microsatellite allelic profile. Associations between microsatellites and specific phenotypes were scored using information gain ratios, and significant findings were confirmed by permutation tests and estimation of false discovery rates. The phenotypes associated with higher number of alleles were the capacity to resist to sulphur dioxide (tested by the capacity to grow in the presence of potassium bisulphite) and the presence of galactosidase activity. Our study demonstrates the utility of computational modelling to estimate a strain technological group and phenotype from microsatellite allelic combinations as tools for preliminary yeast strain selection.
基因组测序对于理解个体变异以及研究解释基因型与表型之间关系的机制至关重要。从酿酒酵母分离株的大规模基因组测序项目中积累的知识正被用于研究解释此类关系的机制。我们的目标是使用11个多态性微卫星对来自不同地理来源和技术组的172株酿酒酵母菌株进行遗传特征分析，并将这些数据与30项表型测试的结果进行计算关联。遗传特征分析揭示了280个等位基因，微卫星ScAAT1对菌株内变异性的贡献最大，同时还有来自微卫星ScAAT4、ScAAT5和ScAAT6的等位基因20、9和16。这些微卫星等位基因谱对于酵母菌株的表型和来源都具有特征性。我们通过构建和交叉验证计算模型来确认这些关联的强度，这些模型可以根据微卫星等位基因谱预测菌株的技术应用和来源。使用信息增益比来评估微卫星与特定表型之间的关联，并通过置换检验和错误发现率估计来确认显著结果。与较多等位基因相关的表型是抵抗二氧化硫的能力（通过在亚硫酸氢钾存在下的生长能力测试）和半乳糖苷酶活性的存在。我们的研究证明了计算建模在从微卫星等位基因组合估计菌株技术组和表型方面的实用性，可作为初步酵母菌株选择的工具。
