A New Phylogenomic Approach to Explore Phylogenetic Position of Turtles

Apr 18, 2014     Email"> PrintText Size

Phylogenomics use genomic/transcriptomic data to resolve the phylogeny of the species. Compared with the traditional phylogenetic studies, phylogenomics can generate robust results. However, several recent studies suggested that convertional methods might be problematic for phylogenomics with large amounts of data. A potentially better alternative in the age of phylogenomics is to use genes as characters. 

Despite numerous recent attempts, the phylogenetic position of turtles remains controversial. Several potential positions have been proposed, including turtles being the sister-group to A) lepidosaurs, B) archosaurs, C) crocodilians, and D) birds. 

Dr. LU Bin and Prof. FU Jinzhong of Chengdu Institute of Biology sequenced the transctriptome of the Chinese pond turtles and obtained 4584 orthologous genes. Using genomic data, they evaluate the phylogenetic position of turtles with both conventional concatenated data analysis and a ‘‘genes as characters’’ approach. The concatenated data analysis strongly supported turtle as the sister-group to archosaurs (crocodilians, birds), similar to several recent genomic data based studies using similar methods. 

However, the incongruence analysis clearly demonstrated that there is a large amount of inconsistency among genes and most of the conflict relates to the placement of turtles. Concatenated data analysis of large and heterogeneous datasets likely suffers from systematic error and over-estimates of confidence as a consequence of a large number of characters. The “genes as characters” methods also generated the incongruence results. They conclude that the uncertain placement of turtles is a reflection of the true state of nature. Using genes as characters offers an alternative for phylogenomic analysis. It has potential to reduce systematic error, such as data heterogeneity and long-branch attraction, and it can also avoid problems associated with computation time and model selection. 

This work is supported by the National Natural Science Foundation of China and China Postdoctoral Science Foundation. The results were published on the open-access journal PLOS ONE.