期刊
GENOME BIOLOGY AND EVOLUTION
卷 2, 期 -, 页码 393-409出版社
OXFORD UNIV PRESS
DOI: 10.1093/gbe/evq019
关键词
lineage-specific genes; nonsynonymous polymorphism; evolutionary age; adaptive selection
资金
- Microsoft Corporation
- National Science Foundation [CNS-0619926]
Genes in the same organism vary in the time since their evolutionary origin. Without horizontal gene transfer, young genes are necessarily restricted to a few closely related species, whereas old genes can be broadly distributed across the phylogeny. It has been shown that young genes evolve faster than old genes; however, the evolutionary forces responsible for this pattern remain obscure. Here, we classify human-chimp protein-coding genes into different age classes, according to the breath of their phylogenetic distribution. We estimate the strength of purifying selection and the rate of adaptive selection for genes in different age classes. We find that older genes carry fewer and less frequent nonsynonymous single-nucleotide polymorphisms than younger genes suggesting that older genes experience a stronger purifying selection at the protein-coding level. We infer the distribution of fitness effects of new deleterious mutations and find that older genes have proportionally more slightly deleterious mutations and fewer nearly neutral mutations than younger genes. To investigate the role of adaptive selection of genes in different age classes, we determine the selection coefficient (gamma = 2N(e)S) of genes using the MKPRF approach and estimate the ratio of the rate of adaptive nonsynonymous substitution to synonymous substitution (omega(A)) using the DoFE method. Although the proportion of positively selected genes (gamma > 0) is significantly higher in younger genes, we find no correlation between omega(A) and gene age. Collectively, these results provide strong evidence that younger genes are subject to weaker purifying selection and more tenuous evidence that they also undergo adaptive evolution more frequently.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据