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Title: A second generation integrated map of the rainbow trout (Oncorhynchus mykiss) genome: analysis of synteny with model fish genomes

item Palti, Yniv
item GENET, CARINE - Institut National De La Recherche Agronomique (INRA)
item Gao, Guangtu
item HU, YUGIN - University Of California
item YOU, FRANK - University Of California
item BOUSSAHA, MEKKI - Institut National De La Recherche Agronomique (INRA)
item Rexroad, Caird
item LUO, MING-CHENG - University Of California

Submitted to: Marine Biotechnology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/18/2011
Publication Date: 3/30/2012
Citation: Palti, Y., Genet, C., Gao, G., Hu, Y., You, F., Boussaha, M., Rexroad III, C.E., Luo, M. 2012. A second generation integrated map of the rainbow trout (Oncorhynchus mykiss) genome: analysis of synteny with model fish genomes. Marine Biotechnology. 14:343-357.

Interpretive Summary: Rainbow trout is one of the most important aquaculture species in the United States and around the world, but little is known about its genetic makeup. To this end we report on the expansion and improvement of the integrated map of the rainbow trout genome which combines genetic information on the inheritance of chromosomes throughout generations with the physical DNA sequences that contain genes that control biological processes. This resource will facilitate the identification of genes affecting important aquaculture production traits and enhance strategies targeting the genetic improvement of this species for production efficiency.

Technical Abstract: In this paper we generated DNA fingerprints and end sequences from bacterial artificial chromosomes (BACs) from two new libraries to improve the first generation integrated physical and genetic map of the rainbow trout (Oncorhynchus mykiss) genome. The current version of the physical map is composed of 167,989 clones of which 158,670 are assembled into contigs and 9,319 remained singletons. It includes 13,550 clones from the two new libraries and the number of contigs was reduced from 4,173 to 3,220. End sequencing of clones from the new libraries generated a total of 11,958 high quality sequence reads longer than 100 bp. The end sequences were used to develop 238 new microsatellites of which 42 were added to the genetic map. Overall, 27 physical map contigs were added to the integrated genome map through genetic mapping of 29 new microsatellite loci, which represents an estimated addition of at least 15,660 Kb to the integrated map. In addition, the integration of 12 contigs that were anchored in the first generation integrated map was expanded by genetic mapping of an additional 14 new markers. We analyzed synteny between the rainbow trout genome and model fish genomes using a combined BAC end sequence (BES) database of 188,443 reads. The fractions of trout BES reads that had significant BLASTN hits against the zebrafish, medaka and stickleback genome databases were 8.8%, 9.7% and 10.5%, respectively, while the fractions of BES reads that had significant BLASTX hits against the zebrafish, medaka, and stickleback protein databases were 6.2%, 5.8% and 5.5%, respectively. The overall number of unique regions of synteny we identified through grouping of the rainbow trout BES into fingerprinting contigs and by using both BlastN and BlastX was 2,259, 2,229 and 2,203 for stickleback, medaka and zebrafish, respectively. These numbers are approximately 3-5 times greater than those we have previously identified using individual BAC paired-ends. The second generation integrated map of the rainbow trout genome provides a frame work for a robust composite genome map and a minimal tiling path for a draft genome sequence assembly. The results of our comparative genome analyses show that the rainbow trout genome is currently limited to a large number of small and non-continuous blocks of synteny caused by the large evolutionary distances that exist between rainbow trout and the currently available reference fish genomes, but also illustrate that for many regions in the genome comparative mapping might serve as a useful resource for identifying candidate genes in QTL detection studies.