Saitoh, K, T Sado, RL Mayden, N Hanzawa, K Nakamura, M Nishida & M Miya, 2006. Mitogenomic Evolution and Interrelationships of the Cypriniformes (Actinopterygii: Ostariophysi): The First Evidence Toward Resolution of Higher-Level Relationships of the World’s Largest Freshwater Fish Clade Based on 59 Whole Mitogenome Sequences. Journal of Molecular Evolution 63: 826–841.

Abstract

Fishes of the order Cypriniformes are almost completely restricted to freshwater bodies and number > 3400 species placed in 5 families, each with poorly defined subfamilies and/or tribes. The present study represents the first attempt toward resolution of the higher-level relationships of the world’s largest freshwater-fish clade based on whole mitochondrial (mt) genome sequences from 53 cypriniforms (including 46 newly determined sequences) plus 6 outgroups. Unambiguously aligned, concatenated mt genome sequences (14,563 bp) were divided into 5 partitions (first, second, and third codon positions of the protein-coding genes, rRNA genes, and tRNA genes), and partitioned Bayesian analyses were conducted, with protein-coding genes being treated in 3 different manners (all positions included; third codon positions converted into purine [R] and pyrimidine [Y] [RY-coding]; third codon positions excluded). The resultant phylogenies strongly supported monophyly of the Cypriniformes as well as that of the families Cyprinidae, Catostomidae, and a clade comprising Balitoridae + Cobitidae, with the 2 latter loach families being reciprocally paraphyletic. Although all of the data sets yielded nearly identical tree topologies with regard to the shallower relationships, deeper relationships among the 4 major clades (the above 3 major clades plus Gyrinocheilidae, represented by a single species Gyrinocheilus aymonieri in this study), were incongruent depending on the data sets. Treatment of the rapidly saturated third codon–position transitions appeared to be a source of such incongruities, and we advocate that RY-coding, which takes only transversions into account, effectively removes this likely “noise” from the data set and avoids the apparent lack of signal by retaining all available positions in the data set.

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