Houle David
Department of Biological Science, Florida State University, Tallahassee, FL 32306-4295, USA.
Proc Natl Acad Sci U S A. 2010 Jan 26;107 Suppl 1(Suppl 1):1793-9. doi: 10.1073/pnas.0906195106. Epub 2009 Oct 26.
Evolution and medicine share a dependence on the genotype-phenotype map. Although genotypes exist and are inherited in a discrete space convenient for many sorts of analyses, the causation of key phenomena such as natural selection and disease takes place in a continuous phenotype space whose relationship to the genotype space is only dimly grasped. Direct study of genotypes with minimal reference to phenotypes is clearly insufficient to elucidate these phenomena. Phenomics, the comprehensive study of phenotypes, is therefore essential to understanding biology. For all of the advances in knowledge that a genomic approach to biology has brought, awareness is growing that many phenotypes are highly polygenic and susceptible to genetic interactions. Prime examples are common human diseases. Phenomic thinking is starting to take hold and yield results that reveal why it is so critical. The dimensionality of phenotypic data are often extremely high, suggesting that attempts to characterize phenotypes with a few key measurements are unlikely to be completely successful. However, once phenotypic data are obtained, causation can turn out to be unexpectedly simple. Phenotypic data can be informative about the past history of selection and unexpectedly predictive of long-term evolution. Comprehensive efforts to increase the throughput and range of phenotyping are an urgent priority.
进化生物学和医学都依赖于基因型-表型图谱。尽管基因型存在并在便于多种分析的离散空间中遗传，但诸如自然选择和疾病等关键现象的因果关系发生在连续的表型空间中，而我们对其与基因型空间的关系还知之甚少。仅以最少的表型参考直接研究基因型显然不足以阐明这些现象。因此，表型组学，即对表型的全面研究，对于理解生物学至关重要。尽管基因组学方法给生物学知识带来了诸多进展，但人们越来越意识到，许多表型是高度多基因的，并且容易受到基因相互作用的影响。常见的人类疾病就是典型例子。表型组学思维开始占据主导并产生成果，揭示了其为何如此关键。表型数据的维度通常极高，这表明试图通过一些关键测量来表征表型不太可能完全成功。然而，一旦获得表型数据，因果关系可能会出人意料地简单。表型数据可以提供有关过去选择历史的信息，并意外地预测长期进化。提高表型分析的通量和范围的全面努力是当务之急。
