University of Cambridge, Department of Genetics, UK.
Brief Funct Genomics. 2011 Sep;10(5):258-65. doi: 10.1093/bfgp/elr031.
The systematic investigation of the phenotypes associated with genotypes in model organisms holds the promise of revealing genotype-phenotype relations directly and without additional, intermediate inferences. Large-scale projects are now underway to catalog the complete phenome of a species, notably the mouse. With the increasing amount of phenotype information becoming available, a major challenge that biology faces today is the systematic analysis of this information and the translation of research results across species and into an improved understanding of human disease. The challenge is to integrate and combine phenotype descriptions within a species and to systematically relate them to phenotype descriptions in other species, in order to form a comprehensive understanding of the relations between those phenotypes and the genotypes involved in human disease. We distinguish between two major approaches for comparative phenotype analyses: the first relies on evolutionary relations to bridge the species gap, while the other approach compares phenotypes directly. In particular, the direct comparison of phenotypes relies heavily on the quality and coherence of phenotype and disease databases. We discuss major achievements and future challenges for these databases in light of their potential to contribute to the understanding of the molecular mechanisms underlying human disease. In particular, we discuss how the use of ontologies and automated reasoning can significantly contribute to the analysis of phenotypes and demonstrate their potential for enabling translational research.
在模式生物中系统地研究与基因型相关的表型有望直接揭示基因型-表型关系，而无需进行其他额外的中间推断。目前正在进行大规模的项目，以对一个物种的完整表型进行编目，特别是对老鼠。随着越来越多的表型信息可用，生物学今天面临的一个主要挑战是对这些信息进行系统分析，并将研究结果在不同物种之间进行转化，以提高对人类疾病的理解。挑战在于整合和组合一个物种内的表型描述，并将其与其他物种的表型描述进行系统地关联，以形成对涉及人类疾病的那些表型和基因型之间关系的全面理解。我们区分了用于比较表型分析的两种主要方法：第一种方法依赖于进化关系来弥合物种差距，而另一种方法则直接比较表型。特别是，表型的直接比较在很大程度上依赖于表型和疾病数据库的质量和一致性。我们根据这些数据库在帮助理解人类疾病潜在分子机制方面的潜力，讨论了这些数据库的主要成就和未来挑战。特别是，我们讨论了如何使用本体和自动推理可以显著促进表型分析，并展示它们在实现转化研究方面的潜力。
