European Molecular Biology Lab, 69122 Heidelberg, Germany.
BMC Bioinformatics. 2011 Oct 20;12:406. doi: 10.1186/1471-2105-12-406.
With the advance of genome sequencing technologies, phenotyping, rather than genotyping, is becoming the most expensive task when mapping genetic traits. The need for efficient selective phenotyping strategies, i.e. methods to select a subset of genotyped individuals for phenotyping, therefore increases. Current methods have focused either on improving the detection of causative genetic variants or their precise genomic location separately.
Here we recognize selective phenotyping as a Bayesian model discrimination problem and introduce SPARE (Selective Phenotyping Approach by Reduction of Entropy). Unlike previous methods, SPARE can integrate the information of previously phenotyped individuals, thereby enabling an efficient incremental strategy. The effective performance of SPARE is demonstrated on simulated data as well as on an experimental yeast dataset.
Using entropy reduction as an objective criterion gives a natural way to tackle both issues of detection and localization simultaneously and to integrate intermediate phenotypic data. We foresee entropy-based strategies as a fruitful research direction for selective phenotyping.
随着基因组测序技术的进步，表型分析（phenotyping）而非基因分型（genotyping）正成为遗传特征映射中最昂贵的任务。因此，需要有效的选择性表型分析策略，即选择一组已基因分型的个体进行表型分析的方法。目前的方法要么侧重于分别提高致病变异的检测能力，要么侧重于提高其精确的基因组位置定位能力。
在这里，我们将选择性表型分析视为贝叶斯模型判别问题，并引入 SPARE（通过降低熵的选择性表型分析方法）。与以前的方法不同，SPARE 可以整合先前表型分析个体的信息，从而实现有效的增量策略。SPARE 在模拟数据和实验酵母数据集上的有效性能得到了验证。
使用熵减少作为目标标准，为同时解决检测和定位问题以及整合中间表型数据提供了一种自然的方法。我们预见基于熵的策略将成为选择性表型分析的一个富有成效的研究方向。
