- Research article
- Open Access
A first generation integrated map of the rainbow trout genome
- Yniv Palti†1Email author,
- Carine Genet†2,
- Ming-Cheng Luo3,
- Aurélie Charlet2,
- Guangtu Gao1,
- Yuqin Hu3,
- Cecilia Castaño-Sánchez1, 4,
- Kamila Tabet-Canale2, 5,
- Francine Krieg2,
- Jianbo Yao4,
- Roger L Vallejo1 and
- Caird E RexroadIII1
© Palti et al; licensee BioMed Central Ltd. 2011
- Received: 22 December 2010
- Accepted: 7 April 2011
- Published: 7 April 2011
Rainbow trout (Oncorhynchus mykiss) are the most-widely cultivated cold freshwater fish in the world and an important model species for many research areas. Coupling great interest in this species as a research model with the need for genetic improvement of aquaculture production efficiency traits justifies the continued development of genomics research resources. Many quantitative trait loci (QTL) have been identified for production and life-history traits in rainbow trout. An integrated physical and genetic map is needed to facilitate fine mapping of QTL and the selection of positional candidate genes for incorporation in marker-assisted selection (MAS) programs for improving rainbow trout aquaculture production.
The first generation integrated map of the rainbow trout genome is composed of 238 BAC contigs anchored to chromosomes of the genetic map. It covers more than 10% of the genome across segments from all 29 chromosomes. Anchoring of 203 contigs to chromosomes of the National Center for Cool and Cold Water Aquaculture (NCCCWA) genetic map was achieved through mapping of 288 genetic markers derived from BAC end sequences (BES), screening of the BAC library with previously mapped markers and matching of SNPs with BES reads. In addition, 35 contigs were anchored to linkage groups of the INRA (French National Institute of Agricultural Research) genetic map through markers that were not informative for linkage analysis in the NCCCWA mapping panel. The ratio of physical to genetic linkage distances varied substantially among chromosomes and BAC contigs with an average of 3,033 Kb/cM.
The integrated map described here provides a framework for a robust composite genome map for rainbow trout. This resource is needed for genomic analyses in this research model and economically important species and will facilitate comparative genome mapping with other salmonids and with model fish species. This resource will also facilitate efforts to assemble a whole-genome reference sequence for rainbow trout.
- Linkage Group
- Rainbow Trout
- Bacterial Artificial Chromosome
- Doubled Haploid
- Bacterial Artificial Chromosome Library
Rainbow trout (Oncorhynchus mykiss) are the most-widely cultivated cold freshwater fish in the world and are considered by many to be the "aquatic lab-rat". Interests in the utilization of rainbow trout as a model species for genome-related research activities focusing on carcinogenesis, toxicology, comparative immunology, disease ecology, physiology, transgenics, evolutionary genetics, and nutrition have been well documented . Rainbow trout are cultured on every continent except Antarctica, with 2008 global production estimated at 576,289 metric tons and valued at $2.39 billion . Coupling great interest in this species as a research model with the need for genetic improvement for aquaculture production efficiency and product quality justifies the continued development of genome resources facilitating selective breeding.
The rainbow trout genome is large and complex. Genome size estimates derived from determining the molecular weight of DNA per cell for rainbow trout and other salmonids vary from 2.4 to 3.0 × 109 bp [3, 4]. As with most salmonids, rainbow trout experienced a recent genome duplication event resulting in a semi-tetraploid state (i.e. after an autotetraploid event in the salmonids, their genome is undergoing reversion to a diploid state) . All ray-finned fishes share an additional (3R) round of ancestral genome duplication in their evolutionary history compared to mammals and birds, but the salmonids' common ancestor underwent an additional recent (4R) whole genome duplication event and more than half of the loci are still duplicated . In addition, it is estimated that 50% to 60% of the rainbow trout genome contains interspersed repeat sequences (Genet et al.: Analysis of BAC-end sequences in rainbow trout: content characterization and assessment of synteny between trout and other fish genomes, submitted).
Current genomic resources available for rainbow trout research include multiple bacterial artificial chromosome (BAC) libraries and a BAC fingerprinting physical map [6–8]; a database of ~200,000 BAC end sequences (BES) (Genet et al.: Analysis of BAC-end sequences in rainbow trout: content characterization and assessment of synteny between trout and other fish genomes, submitted); doubled haploid (DH) clonal lines [9–12]; multiple genetic maps based on clonal lines and outbred populations [4, 13–16]; large expressed sequence tag (EST) databases and a reference transcriptome [17–19]; a microRNAs database  and high density DNA microarrays [21, 22].
Two microsatellite-based genetic maps with medium to high marker densities were recently developed for rainbow trout by INRA  and the NCCCWA . The INRA map is based on a panel of two DH gynogenetic lines. It has more than 900 microsatellites over 31 linkage groups and a total length of 2,750 cM (average resolution of 3 cM). The NCCCWA map is based on a panel of five families that represent the starting genetic material of the NCCCWA selective breeding program. It has 1,124 microsatellite loci over 29 linkage groups and a total length of 2,927 cM (average resolution of 2.6 cM). The linkage groups from the two microsatellite genetic maps were anchored to the physical chromosomes using fluorescent in-situ hybridization and were found to represent 52 chromosome arms [23, 24].
Qualitative/quantitative trait loci (QTL) mapping experiments in rainbow trout have been very successful because of their high fecundity, external fertilization, and ease of gamete handling and manipulation. Many QTL have been identified for production and life-history traits including resistance to the parasite C. shasta, resistance to IHNV [26, 27] and to IPNV , whirling disease resistance , Killer cell-like activity , upper thermal tolerance [31, 32], embryonic development rate [9, 33, 34], spawning time [35, 36], confinement stress response , early maturation  and smoltification . The availability of a BAC physical map integrated with the genetic map will facilitate fine mapping of QTL, the selection of positional candidate genes and the incorporation of marker-assisted selection (MAS) into rainbow trout breeding programs. A major shortcoming of QTL studies is that they are limited to the variation present in a limited number of families and typically do not detect loci with small effects. This can be overcome by whole genome association studies and other approaches, such as genomic selection, that capture the effects of most QTL that contribute to the population-wide variation in a trait. Recently we demonstrated the feasibility of low resolution LD association studies in rainbow trout [40, 41]. In the absence of a reference genome sequence assembly, a robust integrated physical and genetic map will provide better resolution than the current genetic maps for ordering of genetic markers and estimating physical distances between markers, thus facilitating future whole genome association studies in rainbow trout.
The first BAC-based physical map of the rainbow trout genome was recently assembled using DNA fingerprints of 154,439 clones from the 10X HindIII Swanson library . The map contains 4,173 contigs and 9,379 singletons. The physical length of the map contigs was estimated to be approximately 2.0 Gb, which represents approximately 80% of rainbow trout genome. Here we report the construction of the first integrated physical and genetic map of the rainbow trout genome using microsatellites isolated from BAC end sequences and PCR superpools for library screening and identification of BACs that harbor previously mapped markers. This integrated map provides a frame work for a robust composite genome map and future reference genome sequence assemblies.
BAC end sequencing (BES) microsatellites
Summary of genotyping results of microsatellite markers isolated from BAC end sequences for integration between the genetic and physical maps
No. of markers identified
205 (from 117 contigs)
Informative for linkage analysis
128 (63%; 88 contigs; 129 loci)
Mapped to linkage groups
127 loci (98.5%; 88 contigs)
PCR optimization failed
Monomorphic in mapping panel
15 (9 informative for mapping and 6 non-informative)
Redundancy in contig coverage (optimized, but panel not genotyped)
Library screening with PCR superpools
Previously mapped microsatellites
Summary of BAC library screening results with previously mapped microsatellites using PCR super-pools
No. of markers tested
Localized to a single FPC contig
Localized to two FPC contigs
Singletons or failed DNA fingerprinting
Not validated by single clone PCR
Not positive by PCR screening of superpools
No. of chromosomes covered
Immune response genes
The BAC library was also screened with PCR primers from 12 immune response genes that were not previously mapped to the rainbow trout genome (Additional file 2, Table S1). Positive clones were verified by PCR of the individual clones and direct sequencing from the BAC DNA. The BAC clones that were positive and their corresponding physical map contigs are listed in Additional file 1, sheet 3.
Single nucleotide polymorphism (SNP) markers
Genetic loci sources and linkage mapping statistics
% of Input
Rexroad et al. 2008 
The genetic map
The female:male recombination ratio was 1.65:1, with the female having a map length of 4,775.7 cM and the male map 2,897.8 cM. This ratio varied by chromosome, ranging from 0.53:1 to 11.87:1 (Additional file 3, Worksheet 2). It is noteworthy that this type of sex recombination ratio estimates do not take into account the larger differences in recombination rate that exist between males and females throughout most of the length of the linkage groups. It is likely that female:male ratios will be elevated throughout most of the length of the chromosome arms, while they will be much lower in the more contracted telomeric ends of the linkage groups because of elevated male recombination rates in these regions . It should be pointed out that overall estimates of recombination rate may not be accurately depicted in the current study, because recombination estimates were not obtained by direct comparisons of adjacent intervals. Therefore, the reported recombination distances given in this study are likely an underestimate of the real recombination ratio values.
In this version of the map, we have added to the map of Rexroad et al.  through multipoint linkage analysis 159 RRL SNPs, 126 microsatellites from BES and 9 microsatellites isolated from BACs that harbor immune response genes (Additional file 2, Table S2). The SNPs were distributed in all the chromosomes (2-10 per chromosome; Additional file 3, worksheet 3) and the BES microsatellites were mapped to all but chromosome 24 (1-10 per chromosome; Additional file 3, worksheet 4). Twenty seven loci that were previously mapped to expand the length of linkage groups  were not mapped in this version, and 29 loci that were previously genotyped but were not linked, were assigned to linkage groups in the current version. A high frequency of duplicated microsatellite loci was observed as previously reported , but in many cases only one locus was successfully ordered on the map. Overall, 88 duplicated markers were successfully mapped to two loci (176 loci), which means that the total number of markers mapped was 1,333.
The integrated map
The combined physical length of the 203 anchored contigs was 138,525 consensus bands (CB) which is equal to 235,493 Kb based on a conversion ratio of 1 CB = 1.7 Kb . Therefore, we estimate that the integrated map covers ~12% of the physical map, or ~10% of the rainbow trout genome, assuming haploid genome size of 2.4 × 109 bp. The length of anchored contigs ranged from 119 Kb to 4,590 Kb with an average length of 1,160 Kb (Additional file 3, worksheet 5). The ratio of physical to genetic linkage distances varied substantially among the 33 anchored contigs that contained spaced markers, which is similar to other vertebrate genomes [45, 47]. The 33 contigs represent segments from 21 of the 29 chromosomes (Additional file 3, worksheet 6). The Kb/cM ratio ranged from 37 to 17,000 with an average of 3,033. In addition, 35 contigs were anchored to linkage groups of the INRA map through markers that were not informative for linkage analysis in the NCCCWA mapping panel (Additional file 3, worksheet 7), bringing the total number of anchored contigs to 238.
An FPC map with all the genetic markers that we have assigned to BAC contigs can be viewed and searched online through: http://www.genome.clemson.edu/activities/projects/rainbowTrout
The integrated map we developed for the rainbow trout genome will facilitate comparative genomics studies with other salmonids and with model fish species. Many microsatellite markers can be used for genetic mapping across salmonid species which is very useful for comparative genome mapping [23, 48] and can benefit research in species with less developed genome maps. In addition, the rainbow trout BAC end sequences can be used to infer conserved synteny with other fish genomes as we have previously shown (Genet et al.: Analysis of BAC-end sequences in rainbow trout: content characterization and assessment of synteny between trout and other fish genomes, submitted), and this integrated map provides a larger frame-work expanding the size of the syntenic blocks that can be identified between fish genomes.
The first generation integrated map of the rainbow trout genome is composed of 238 BAC contigs anchored to chromosomes of the genetic map. It covers more than 10% of the genome across segments from all 29 chromosomes. This map provides a frame work for a robust composite genome map. The availability of an integrated physical and genetic map will enable detailed comparative genome analyses, fine mapping of QTL, positional cloning, selection of positional candidate genes for economically important traits and the incorporation of MAS into rainbow trout breeding programs. A comprehensive integrated map will also provide a minimal tiling path for genome sequencing and a framework for whole genome sequence assembly.
BAC end sequencing and markers development
The 10X HindIII Rainbow trout BAC library  was used for BAC-end sequencing (BES) as previously described (Genet et al.: Analysis of BAC-end sequences in rainbow trout: content characterization and assessment of synteny between trout and other fish genomes, submitted). Briefly, BAC culture was conducted using standard protocols and end sequencing with SP6 and T7 primers was done using standard Sanger technique. The raw, untrimmed files were processed by PHRED software . The PHRED quality score cut-off value was set at 20 for the acquisition of Q20 values. The BESs were trimmed of vector sequences (pBeloBAC11 vector ) and filtered of E. coli sequences. Microsatellites and other simple sequence repeats (SSR) were analyzed using Tandem repeat Finder software . We examined ten classes of SSRs by using a maximum period size of 10. BES reads harboring at least 50 base pairs (bp) flanking sequences on either side of the SSRs were selected for PCR primer design. Primers for BESs containing microsatellites were designed using Primer3 software . The primer product size range was chosen between 150 and 450 nucleotides. The optimum size of primers was set to 20 nucleotides (range from 18 to 27 nucleotides) with an optimum melting temperature of 60.0°C (range from 57 to 63°C).
The NCCCWA mapping panel of 5 families was genotyped with microsatellites as previously described . A total of 205 microsatellite markers isolated from BAC end sequences (Additional file 1, sheet 1) were genotyped using the tailed protocol of Boutin-Ganache et al.. Primers were obtained from commercial sources (Alpha DNA, Montreal, Quebec, Canada). Three oligonucleotide primers were used in each DNA amplification reaction (Forward: 5' GAGTTTTCCCAGTCACGAC-primer sequence 3'; reverse: 5' GTTT-primer sequence 3'; fluorescent labeled primer with FAM: 5' GAGTTTTCCCAGTCACGAC 3'). Primers were optimized for amplification by varying annealing temperatures and MgCl2 concentrations. PCR reactions (12 μl total volume) included 50 ng DNA, 1.5-2.5 mM MgCl2, 2 pmol of forward primer, 6 pmol of reverse primer, 1 pmol of fluorescent labeled primer, 200 μM dNTPs, 1X manufacturer's reaction buffer, and 0.5 unit Taq Polymerase (ABI, Foster City, CA, USA). Amplifications were conducted in an MJ Research DNA Engine thermal cycler model PTC 200 (MJ Research, Waltham, MA) as follows: an initial denaturation at 95°C for 10 min, 30 cycles consisting of 94°C for 60 sec, annealing temperature for 45 sec, 72°C extension for 45 sec; followed by a final extension of 72°C for 10 min. PCR products were visualized on agarose gels after staining with ethidium bromide. Three μl of each PCR product was added to 20 μl of water, 1 μl of the diluted sample was added to 12.5 μl of loading mixture made up with 12 μl of HiDi formamide and 0.5 μl of Genscan 400 ROX internal size standard. Samples were denatured at 95°C for 5 min and kept on ice until loading on an ABI 3730 DNA Analyzer (ABI, Foster City, CA, USA). Output files were analyzed using GeneMapper version 3.7 (ABI, Foster City, CA, USA), formatted using Microsoft Excel and stored in a Microsoft Access database.
Library screening with PCR superpools
The 10x Swanson BAC library was screened using the NCCCWA or the INRA PCR superpools with microsatellites that were mapped to the NCCCWA or INRA genetic maps [13, 16] as previously described [54, 55]. The screening results were cross-referenced with the physical map to localize the positive clones onto contigs. For microsatellite markers that did not have at least two positive clones from the same FPC BAC contig, the individual positive clones were picked from glycerol stock and confirmed by PCR as previously described .
SNPs discovery using reduced representation libraries (RRL)
Protocols developed and used for SNPs discovery in cattle and swine [56–58] were adapted for rainbow trout using RRL libraries and high throughput parallel 454 GS FLX pyrosequencing. The experimental design and results of the rainbow trout work were recently published . Briefly, DNA from 96 unrelated individuals representing the families of the NCCCWA broodstock was pooled into one sample. The reduced representation library consisted of 440 bp fragments resulting from complete digestion of the pooled DNA with the restriction enzyme Hae III; sequencing produced 2,000,000 reads providing an average 6 fold coverage of the estimated 150,000 unique genomic restriction fragments (300,000 fragment ends). Three independent computational data analyses identified 22,022 to 47,128 putative SNPs on 13,140 to 24,627 contigs. A set of 384 putative SNPs, randomly selected from the sets produced by the three analyses were genotyped on individual fish to determine the validation rate of putative SNPs among analyses, distinguish apparent SNPs that actually represent paralogous loci in the semi-tetraploid genome, examine Mendelian segregation, and place the validated SNPs on the rainbow trout linkage map.
Alignments between SNPs and BES
To find matches we aligned the HaeIII RRL SNP discovery database of Castaño-Sánchez et al.  with the BES database (Genet et al.: Analysis of BAC-end sequences in rainbow trout: content characterization and assessment of synteny between trout and other fish genomes, submitted). Matches were found using SSAHA2  (http://www.sanger.ac.uk/Software/analysis/SSAHA2/) for pairwise sequence alignment with a threshold Smith-Waterman score of 160 (very restrictive and conserved to avoid matches between paralogous loci).
The microsatellites and SNPs were placed on the rainbow trout genetic map using the genetic linkage mapping programs MULTIMAP  and CRI-MAP . First, genotype data combined for both sexes were formatted into the standard LINKAGE  file format and checked for Mendelian inheritance using PEDCHECK . RECODE  was then used to convert the allele sizes into number-coded alleles. Using an in-house Perl script, make_gen, the genotype data and the locus names were assembled into CRI-MAP input format. The resulting file was then added to that of Rexroad et al. using another in-house Perl script, join_gens, and MULTIMAP was used to conduct two-point linkage analyses to identify the closest markers with LOD ≥8.75 and recombination fraction r ≤0.2. An additional 192 markers with two-point LOD <8.75 were added to linkage groups manually, of which only six markers had a two-point LOD <3.0 (2.90, 2.89, 2.64, 2.12, 2.10 and 1.80). The specific best of two-point LOD score for each marker is provided in Additional File 3, Worksheet 1. Multipoint linkage analysis was conducted on individual linkage groups to assign LOD scores for the specific position of each marker within the linkage group. Framework maps were constructed at LOD ≥4 for all linkage groups but OMY21, for which the framework map was created at LOD ≥3. Markers were added to comprehensive maps by lowering the LOD threshold one integer at a time and starting with the previous order. Resulting maps are consensus maps, accounting for co-informative meiosis across the five families. Chromosome numbers were assigned to linkage groups using the integrated cytogenetic/linkage map of Phillips et al..
This project was supported by National Research Initiative Competitive Grant no. 2007-35616-17875 from the USDA National Institute of Food and Agriculture and by internal base funds provided by the Agricultural Research Service project no. 1930-31000-009. The INRA BAC library pooling was granted by the French program AGENOP. The BAC end sequencing was made possible by the financial support of Genoscope. We thank Brian Smith, Kristy Shewbridge, Renee Fincham and Roseanna Long for their technical support in the microsatellites genotyping, and Amandine Launay for technical support in the INRA PCR superpools screening. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer.
- Thorgaard GH, Bailey GS, Williams D, Buhler DR, Kaattari SL, Ristow SS, Hansen JD, Winton JR, Bartholomew JL, Nagler JJ, et al: Status and opportunities for genomics research with rainbow trout. Comp Biochem Physiol B Biochem Mol Biol. 2002, 133 (4): 609-646. 10.1016/S1096-4959(02)00167-7.PubMedView ArticleGoogle Scholar
- FAO: Cultured Aquatic Species Information Programme. Oncorhynchus mykiss. FAO Fisheries and Aquaculture Department. 2010, [http://www.fao.org/fishery/culturedspecies/Oncorhynchus_mykiss/en]Google Scholar
- Ng SH, Artieri CG, Bosdet IE, Chiu R, Danzmann RG, Davidson WS, Ferguson MM, Fjell CD, Hoyheim B, Jones SJ, et al: A physical map of the genome of Atlantic salmon, Salmo salar. Genomics. 2005, 86 (4): 396-404. 10.1016/j.ygeno.2005.06.001.PubMedView ArticleGoogle Scholar
- Young WP, Wheeler PA, Coryell VH, Keim P, Thorgaard GH: A detailed linkage map of rainbow trout produced using doubled haploids. Genetics. 1998, 148 (2): 839-850.PubMedPubMed CentralGoogle Scholar
- Allendorf FW, Thorgaard GH: Tetraploidy and the evolution of salmonid fishes. Evolutionary Genetics of Fishes. Edited by: Turner BJ. 1984, New York: Plenum Press, 1-46.View ArticleGoogle Scholar
- Palti Y, Gahr SA, Hansen JD, Rexroad CE: Characterization of a new BAC library for rainbow trout: evidence for multi-locus duplication. Anim Genet. 2004, 35 (2): 130-133. 10.1111/j.1365-2052.2004.01112.x.PubMedView ArticleGoogle Scholar
- Katagiri T, Asakawa S, Minagawa S, Shimizu N, Hirono I, Aoki T: Construction and characterization of BAC libraries for three fish species; rainbow trout, carp and tilapia. Anim Genet. 2001, 32 (4): 200-204. 10.1046/j.1365-2052.2001.00764.x.PubMedView ArticleGoogle Scholar
- Palti Y, Luo MC, Hu Y, Genet C, You F, Vallejo R, Thorgaard G, Wheeler P, Rexroad C: A first generation BAC-based physical map of the rainbow trout genome. BMC Genomics. 2009, 10 (1): 462-10.1186/1471-2164-10-462.PubMedPubMed CentralView ArticleGoogle Scholar
- Robison BD, Wheeler PA, Sundin K, Sikka P, Thorgaard GH: Composite interval mapping reveals a major locus influencing embryonic development rate in rainbow trout (Oncorhynchus mykiss). J Hered. 2001, 92 (1): 16-22. 10.1093/jhered/92.1.16.PubMedView ArticleGoogle Scholar
- Robison BD, Wheeler PA, Thorgaard GH: Variation in development rate among clonal lines of rainbow trout (Oncorhynchus mykiss). Aquaculture. 1999, 173 (1-4): 131-141. 10.1016/S0044-8486(98)00481-5.View ArticleGoogle Scholar
- Young WP, Wheeler PA, Fields RD, Thorgaard GH: DNA fingerprinting confirms isogenicity of androgenetically derived rainbow trout lines. J Hered. 1996, 87 (1): 77-80.PubMedView ArticleGoogle Scholar
- Quillet E, Dorson M, Le Guillou S, Benmansour A, Boudinot P: Wide range of susceptibility to rhabdoviruses in homozygous clones of rainbow trout. Fish & Shellfish Immunology. 2007, 22 (5): 510-519.View ArticleGoogle Scholar
- Guyomard R, Mauger S, Tabet-Canale K, Martineau S, Genet C, Krieg F, Quillet E: A type I and type II microsatellite linkage map of rainbow trout (Oncorhynchus mykiss) with presumptive coverage of all chromosome arms. BMC Genomics. 2006, 7: 302-10.1186/1471-2164-7-302.PubMedPubMed CentralView ArticleGoogle Scholar
- Nichols KM, Young WP, Danzmann RG, Robison BD, Rexroad C, Noakes M, Phillips RB, Bentzen P, Spies I, Knudsen K: A consolidated linkage map for rainbow trout (Oncorhynchus mykiss). Anim Genet. 2003, 34: 102-115. 10.1046/j.1365-2052.2003.00957.x.PubMedView ArticleGoogle Scholar
- Sakamoto T, Danzmann RG, Gharbi K, Howard P, Ozaki A, Khoo SK, Woram RA, Okamoto N, Ferguson MM, Holm L-E, et al: A Microsatellite Linkage Map of Rainbow Trout (Oncorhynchus mykiss) Characterized by Large Sex-Specific Differences in Recombination Rates. Genetics. 2000, 155 (3): 1331-1345.PubMedPubMed CentralGoogle Scholar
- Rexroad CE, Palti Y, Gahr SA, Vallejo RL: A second generation genetic map for rainbow trout (Oncorhynchus mykiss). BMC Genet. 2008, 9: 74-10.1186/1471-2156-9-74.PubMedPubMed CentralView ArticleGoogle Scholar
- Rexroad CE, Lee Y, Keele JW, Karamycheva S, Brown G, Koop B, Gahr SA, Palti Y, Quackenbush J: Sequence analysis of a rainbow trout cDNA library and creation of a gene index. Cytogenet Genome Res. 2003, 102 (1-4): 347-354. 10.1159/000075773.PubMedView ArticleGoogle Scholar
- Govoroun M, Le Gac F, Guiguen Y: Generation of a large scale repertoire of Expressed Sequence Tags (ESTs) from normalised rainbow trout cDNA libraries. BMC Genomics. 2006, 7 (1): 196-10.1186/1471-2164-7-196.PubMedPubMed CentralView ArticleGoogle Scholar
- Salem M, Rexroad C, Wang J, Thorgaard G, Yao J: Characterization of the rainbow trout transcriptome using Sanger and 454-pyrosequencing approaches. BMC Genomics. 2010, 11 (1): 564-10.1186/1471-2164-11-564.PubMedPubMed CentralView ArticleGoogle Scholar
- Salem M, Xiao C, Womack J, Rexroad CE, Yao J: MicroRNA repertoire for functional genome research in rainbow trout (Oncorhynchus mykiss). Marine Biotech. 2009, 12 (4): 410-429. 10.1007/s10126-009-9232-z.View ArticleGoogle Scholar
- Rise ML, von Schalburg KR, Brown GD, Mawer MA, Devlin RH, Kuipers N, Busby M, Beetz-Sargent M, Alberto R, Gibbs AR, et al: Development and Application of a Salmonid EST Database and cDNA Microarray: Data Mining and Interspecific Hybridization Characteristics. Genome Res. 2004, 14 (3): 478-490. 10.1101/gr.1687304.PubMedPubMed CentralView ArticleGoogle Scholar
- Salem M, Kenney B, Rexroad CE, Yao J: Development of a 37 k high-density oligonucleotide microarray: a new tool for functional genome research in rainbow trout. Journal of Fish Biology. 2008, 72: 2187-2206. 10.1111/j.1095-8649.2008.01860.x.View ArticleGoogle Scholar
- Phillips R, Keatley K, Morasch M, Ventura A, Lubieniecki K, Koop B, Danzmann R, Davidson W: Assignment of Atlantic salmon (Salmo salar) linkage groups to specific chromosomes: Conservation of large syntenic blocks corresponding to whole chromosome arms in rainbow trout (Oncorhynchus mykiss). BMC Genetics. 2009, 10 (1): 46-10.1186/1471-2156-10-46.PubMedPubMed CentralView ArticleGoogle Scholar
- Phillips RB, Nichols KM, DeKoning JJ, Morasch MR, Keatley KA, Rexroad C, Gahr SA, Danzmann RG, Drew RE, Thorgaard GH: Assignment of rainbow trout linkage groups to specific chromosomes. Genetics. 2006, 174 (3): 1661-1670. 10.1534/genetics.105.055269.PubMedPubMed CentralView ArticleGoogle Scholar
- Nichols KM, Bartholomew J, Thorgaard GH: Mapping multiple genetic loci associated with Ceratomyxa shasta resistance in Oncorhynchus mykiss. Dis Aquat Organ. 2003, 56 (2): 145-154. 10.3354/dao056145.PubMedView ArticleGoogle Scholar
- Barroso RM, Wheeler PA, LaPatra SE, Drew RE, Thorgaard GH: QTL for IHNV resistance and growth identified in a rainbow (Oncorhynchus mykiss)xYellowstone cutthroat (Oncorhynchus. Aquaculture. 2008, 277 (3): 156-163. 10.1016/j.aquaculture.2008.03.001.View ArticleGoogle Scholar
- Rodriguez MF, LaPatra S, Williams S, Famula T, May B: Genetic markers associated with resistance to infectious hematopoietic necrosis in rainbow and steelhead trout. Aquaculture. 2004, 241 (1): 93-115. 10.1016/j.aquaculture.2004.08.003.View ArticleGoogle Scholar
- Ozaki A, Sakamoto T, Khoo S, Nakamura K, Coimbra MR, Akutsu T, Okamoto N: Quantitative trait loci (QTLs) associated with resistance/susceptibility to infectious pancreatic necrosis virus (IPNV) in rainbow trout (Oncorhynchus mykiss). Mol Genet Genomics. 2001, 265 (1): 23-31. 10.1007/s004380000392.PubMedView ArticleGoogle Scholar
- Baerwald MR, Petersen JL, Hedrick RP, Schisler GJ, May B: A major effect quantitative trait locus for whirling disease resistance identified in rainbow trout (Oncorhynchus mykiss). Heredity. 2010,Google Scholar
- Zimmerman A, Evenhuis J, Thorgaard G, Ristow S: A single major chromosomal region controls natural killer cell-like activity in rainbow trout. Immunogenetics. 2004, 55 (12): 825-835. 10.1007/s00251-004-0645-6.PubMedView ArticleGoogle Scholar
- Perry GM, Danzmann RG, Ferguson MM, Gibson JP: Quantitative trait loci for upper thermal tolerance in outbred strains of rainbow trout (Oncorhynchus mykiss). Heredity. 2001, 86 (Pt 3): 333-341. 10.1046/j.1365-2540.2001.00838.x.PubMedView ArticleGoogle Scholar
- Perry GM, Ferguson MM, Sakamoto T, Danzmann RG: Sex-linked quantitative trait loci for thermotolerance and length in the rainbow trout. J Hered. 2005, 96 (2): 97-107. 10.1093/jhered/esi019.PubMedView ArticleGoogle Scholar
- Nichols KM, Broman KW, Sundin K, Young JM, Wheeler PA, Thorgaard GH: Quantitative trait loci × maternal cytoplasmic environment interaction for development rate in Oncorhynchus mykiss. Genetics. 2007, 175 (1): 335-347. 10.1534/genetics.106.064311.PubMedPubMed CentralView ArticleGoogle Scholar
- Sundin K, Brown KH, Drew RE, Nichols KM, Wheeler PA, Thorgaard GH: Genetic analysis of a development rate QTL in backcrosses of clonal rainbow trout, Oncorhynchus mykiss. Aquaculture. 2005, 247 (1-4): 75-83. 10.1016/j.aquaculture.2005.02.054.View ArticleGoogle Scholar
- O'Malley KG, Sakamoto T, Danzmann RG, Ferguson MM: Quantitative trait Loci for spawning date and body weight in rainbow trout: testing for conserved effects across ancestrally duplicated chromosomes. J Hered. 2003, 94 (4): 273-284.PubMedView ArticleGoogle Scholar
- Sakamoto T, Danzmann RG, Okamoto N, Ferguson MM, Ihssen PE: Linkage analysis of quantitative trait loci associated with spawning time in rainbow trout (Oncorhynchus mykiss). Aquaculture. 1999, 173 (1-4): 33-43. 10.1016/S0044-8486(98)00463-3.View ArticleGoogle Scholar
- Drew RE, Schwabl H, Wheeler PA, Thorgaard GH: Detection of QTL influencing cortisol levels in rainbow trout (Oncorhynchus mykiss). Aquaculture. 2007, 272 (Supplement 1): S183-S194. 10.1016/j.aquaculture.2007.08.025.View ArticleGoogle Scholar
- Haidle L, Janssen J, Gharbi K, Moghadam H, Ferguson M, Danzmann R: Determination of Quantitative Trait Loci (QTL) for Early Maturation in Rainbow Trout (Oncorhynchus mykiss). Marine Biotechnology. 2008, 10 (5): 579-592. 10.1007/s10126-008-9098-5.PubMedPubMed CentralView ArticleGoogle Scholar
- Nichols KM, Edo AF, Wheeler PA, Thorgaard GH: The Genetic Basis of Smoltification-Related Traits in Oncorhynchus mykiss. Genetics. 2008, 179 (3): 1559-1575. 10.1534/genetics.107.084251.PubMedPubMed CentralView ArticleGoogle Scholar
- Johnson NA, Vallejo RL, Silverstein JT, Welch TJ, Wiens GD, Hallerman EM, Palti Y: Suggestive Association of Major Histocompatibility IB Genetic Markers with Resistance to Bacterial Cold Water Disease in Rainbow Trout (Oncorhynchus mykiss). Mar Biotechnol. 2008, 10 (4): 429-437. 10.1007/s10126-007-9080-7.PubMedView ArticleGoogle Scholar
- Rexroad C, Vallejo R: Estimates of linkage disequilibrium and effective population size in rainbow trout. BMC Genetics. 2009, 10 (1): 83-10.1186/1471-2156-10-83.PubMedPubMed CentralView ArticleGoogle Scholar
- Castano-Sanchez C, Smith T, Wiedmann R, Vallejo R, Salem M, Yao J, Rexroad C: Single nucleotide polymorphism discovery in rainbow trout by deep sequencing of a reduced representation library. BMC Genomics. 2009, 10 (1): 559-10.1186/1471-2164-10-559.View ArticleGoogle Scholar
- Ning Z, Cox AJ, Mullikin JC: SSAHA: A Fast Search Method for Large DNA Databases. Genome Research. 2001, 11 (10): 1725-1729. 10.1101/gr.194201.PubMedPubMed CentralView ArticleGoogle Scholar
- Voorrips RE: MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered. 2002, 93 (1): 77-78. 10.1093/jhered/93.1.77.PubMedView ArticleGoogle Scholar
- Quiniou S, Waldbieser G, Duke M: A first generation BAC-based physical map of the channel catfish genome. BMC Genomics. 2007, 8 (1): 40-10.1186/1471-2164-8-40.PubMedPubMed CentralView ArticleGoogle Scholar
- Nelson WM, Bharti AK, Butler E, Wei F, Fuks G, Kim H, Wing RA, Messing J, Soderlund C: Whole-Genome Validation of High-Information-Content Fingerprinting. Plant Physiol. 2005, 139 (1): 27-38. 10.1104/pp.105.061978.PubMedPubMed CentralView ArticleGoogle Scholar
- Nievergelt CM, Smith DW, Kohlenberg JB, Schork NJ: Large-Scale Integration of Human Genetic and Physical Maps. Genome Research. 2004, 14 (6): 1199-1205. 10.1101/gr.1475304.PubMedPubMed CentralView ArticleGoogle Scholar
- Danzmann RG, Cairney M, Davidson WS, Ferguson MM, Gharbi K, Guyomard R, Holm LE, Leder E, Okamoto N, Ozaki A, et al: A comparative analysis of the rainbow trout genome with 2 other species of fish (Arctic charr and Atlantic salmon) within the tetraploid derivative Salmonidae family (subfamily: Salmoninae). Genome. 2006, 48: 1037-1051. 10.1139/g05-067.View ArticleGoogle Scholar
- Ewing B, Green P: Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 1998, 8 (3): 186-194.PubMedView ArticleGoogle Scholar
- Kim UJ, Birren BW, Slepak T, Mancino V, Boysen C, Kang HL, Simon MI, Shizuya H: Construction and characterization of a human bacterial artificial chromosome library. Genomics. 1996, 34 (2): 213-218. 10.1006/geno.1996.0268.PubMedView ArticleGoogle Scholar
- Benson G: Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999, 27 (2): 573-580. 10.1093/nar/27.2.573.PubMedPubMed CentralView ArticleGoogle Scholar
- Rozen S, Skaletsky H: Primer3 on the www for general users and for biologist programmers. Methods Mol Biol. 2000, 132: 365-386.PubMedGoogle Scholar
- Boutin-Ganache I, Raposo M, Raymond M, Deschepper CF: M13-tailed primers improve the readability and usability of microsatellite analyses performed with two different allele-sizing methods. Biotechniques. 2001, 31 (1): 24-26. 28PubMedGoogle Scholar
- Coulibaly I, Gahr SA, Palti Y, Yao J, Rexroad CE: Genomic structure and expression of uncoupling protein 2 genes in rainbow trout (Oncorhynchus mykiss). BMC Genomics. 2006, 7 (203): 203-10.1186/1471-2164-7-203.PubMedPubMed CentralView ArticleGoogle Scholar
- Palti Y, Gahr SA, Purcell MK, Hadidi S, Rexroad Iii CE, Wiens GD: Identification, characterization and genetic mapping of TLR7, TLR8a1 and TLR8a2 genes in rainbow trout (Oncorhynchus mykiss). Developmental & Comparative Immunology. 2010, 34 (2): 219-233.View ArticleGoogle Scholar
- Van Tassell CP, Smith TP, Matukumalli LK, Taylor JF, Schnabel RD, Lawley CT, Haudenschild CD, Moore SS, Warren WC, Sonstegard TS: SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat Methods. 2008, 5 (3): 247-252. 10.1038/nmeth.1185.PubMedView ArticleGoogle Scholar
- Wiedmann RT, Smith TP, Nonneman DJ: SNP discovery in swine by reduced representation and high throughput pyrosequencing. BMC Genet. 2008, 9: 81-10.1186/1471-2156-9-81.PubMedPubMed CentralView ArticleGoogle Scholar
- Ramos AM, Crooijmans RPMA, Affara NA, Amaral AJ, Archibald AL, Beever JE, Bendixen C, Churcher C, Clark R, Dehais P, et al: Design of a High Density SNP Genotyping Assay in the Pig Using SNPs Identified and Characterized by Next Generation Sequencing Technology. PLoS ONE. 2009, 4 (8): e6524-10.1371/journal.pone.0006524.PubMedPubMed CentralView ArticleGoogle Scholar
- Matise TC, Perlin M, Chakravarti A: Automated construction of genetic linkage maps using an expert system (MultiMap): a human genome linkage map. Nat Genet. 1994, 6 (4): 384-390. 10.1038/ng0494-384.PubMedView ArticleGoogle Scholar
- Lander ES, Green P: Construction of multilocus genetic linkage maps in humans. Proc Natl Acad Sci USA. 1987, 84 (8): 2363-2367. 10.1073/pnas.84.8.2363.PubMedPubMed CentralView ArticleGoogle Scholar
- Lathrop GM, Lalouel JM, Julier C, Ott J: Strategies for multilocus linkage analysis in humans. Proc Natl Acad Sci USA. 1984, 81 (11): 3443-3446. 10.1073/pnas.81.11.3443.PubMedPubMed CentralView ArticleGoogle Scholar
- O'Connell JR, Weeks DE: PedCheck: a program for identification of genotype incompatibilities in linkage analysis. Am J Hum Genet. 1998, 63 (1): 259-266.PubMedPubMed CentralView ArticleGoogle Scholar
- O'Connell JR, Weeks DE: The VITESSE algorithm for rapid exact multilocus linkage analysis via genotype set-recoding and fuzzy inheritance. Nat Genet. 1995, 11 (4): 402-408.PubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.