Construction and Annotation of a High Density SNP Linkage Map of the Atlantic Salmon (Salmo salar) Genome
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Genetics Society of America
Abstract
High density linkage maps are useful tools for fine-scale mapping of quantitative trait loci, and
characterization of the recombination landscape of a species’ genome. Genomic resources for Atlantic
salmon (Salmo salar) include a well-assembled reference genome, and high density single nucleotide polymorphism
(SNP) arrays. Our aim was to create a high density linkage map, and to align it with the reference
genome assembly. Over 96,000 SNPs were mapped and ordered on the 29 salmon linkage groups using a
pedigreed population comprising 622 fish from 60 nuclear families, all genotyped with the ‘ssalar01’ high
density SNP array. The number of SNPs per group showed a high positive correlation with physical chromosome
length (r = 0.95). While the order of markers on the genetic and physical maps was generally
consistent, areas of discrepancy were identified. Approximately 6.5% of the previously unmapped reference
genome sequence was assigned to chromosomes using the linkage map. Male recombination rate was
lower than females across the vast majority of the genome, but with a notable peak in subtelomeric regions.
Finally, using RNA-Seq data to annotate the reference genome, the mapped SNPs were categorized
according to their predicted function, including annotation of 2500 putative nonsynonymous variants.
The highest density SNP linkage map for any salmonid species has been created, annotated, and integrated
with the Atlantic salmon reference genome assembly. This map highlights the marked heterochiasmy of
salmon, and provides a useful resource for salmonid genetics and genomics research
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Hsin Y. Tsai, Diego Robledo, Natalie R. Lowe, Michael Bekaert, John B. Taggart, James E. Bron and View ORCID ProfileRoss D. Houston G3: GENES, GENOMES, GENETICS July 1, 2016 vol. 6 no. 7 2173-2179; https://doi.org/10.1534/g3.116.029009
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https://doi.org/10.1534/g3.116.029009Sponsors
The University of Edinburgh. Edinburgh Genomics is partly supported through core grants from the Natural Environment Research Council (NERC) (R8/H10/56), Medical Research Council (MRC) (MR/K001744/1),and the Biotechnology and Biological Sciences Research Council (BBSRC) (BB/J004243/1). This research was supported by BBSRC grants (BB/H022007/1, BB/F002750/1, BB/F001959/1) awarded to The Roslin Institute and University of Stirling, and by BBSRC Institute Strategic Funding Grants to The Roslin Institute (BB/J004235/1, BB/J004324/1, BB/J004243/1). D.R. was funded by a postgraduate grant from Fundación Barrié. The authors also acknowledge the support of
the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland) in the completion of this study. MASTS is funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions
Rights
Copyright © 2016 Tsai et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.








