4.6 Article

Mining transcriptomic data to study the origins and evolution of a plant allopolyploid complex

期刊

PEERJ
卷 2, 期 -, 页码 -

出版社

PEERJ INC
DOI: 10.7717/peerj.391

关键词

Polyploidy; Phylogenetics; Population genomics; NGS

资金

  1. US National Science Foundation [0822258, 0939423]
  2. Division Of Integrative Organismal Systems
  3. Direct For Biological Sciences [0822258, 1229956, 0939423] Funding Source: National Science Foundation

向作者/读者索取更多资源

Allopolyploidy combines two progenitor genomes in the same nucleus. It is a common speciation process, especially in plants. Deciphering the origins of polyploid species is a complex problem due to, among other things, extinct progenitors, multiple origins, gene flow between different polyploid populations, and loss of parental contributions through gene or chromosome loss. Among the perennial species of Glycine, the plant genus that includes the cultivated soybean ( G. max), are eight allopolyploid species, three of which are studied here. Previous crossing studies and molecular systematic results from two nuclear gene sequences led to hypotheses of origin for these species from among extant diploid species. We use several phylogenetic and population genomics approaches to clarify the origins of the genomes of three of these allopolyploid species using single nucleotide polymorphism data and a guided transcriptome assembly. The results support the hypothesis that all three polyploid species are fixed hybrids combining the genomes of the two putative parents hypothesized on the basis of previous work. Based on mapping to the soybean reference genome, there appear to be no large regions for which one homoeologous contribution is missing. Phylogenetic analyses of 27 selected transcripts using a coalescent approach also are consistent with multiple origins for these allopolyploid species, and suggest that origins occurred within the last several hundred thousand years.

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。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据