Center for Human Genetics Research and Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232, USA.
Trends Genet. 2013 Nov;29(11):659-68. doi: 10.1016/j.tig.2013.07.001. Epub 2013 Aug 1.
Gene functions, interactions, disease associations, and ecological distributions are all correlated with gene age. However, it is challenging to estimate the intricate series of evolutionary events leading to a modern-day gene and then to reduce this history to a single age estimate. Focusing on eukaryotic gene families, we introduce a framework that can be used to compare current strategies for quantifying gene age, discuss key differences between these methods, and highlight several common problems. We argue that genes with complex evolutionary histories do not have a single well-defined age. As a result, care must be taken to articulate the goals and assumptions of any analysis that uses gene age estimates. Recent algorithmic advances offer the promise of gene age estimates that are fast, accurate, and consistent across gene families. This will enable a shift to integrated genome-wide analyses of all events in gene evolutionary histories in the near future.
基因的功能、相互作用、疾病关联和生态分布都与基因年龄相关。然而,要估计导致现代基因的一系列复杂进化事件,并将这段历史简化为一个单一的年龄估计值,是具有挑战性的。本文聚焦于真核生物基因家族,提出了一个可以用来比较当前量化基因年龄的策略的框架,讨论了这些方法之间的关键差异,并强调了几个常见的问题。我们认为,具有复杂进化历史的基因没有一个明确的定义年龄。因此,在使用基因年龄估计值进行任何分析时,都必须注意阐明分析的目标和假设。最近的算法进展有望提供快速、准确且在基因家族中一致的基因年龄估计值。这将使我们能够在不久的将来转向对基因进化历史中所有事件进行综合的全基因组分析。