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      Large-scale SNP discovery and construction of a high-density genetic map of Colossoma macropomum through genotyping-by-sequencing

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          Abstract

          Colossoma macropomum, or tambaqui, is the largest native Characiform species found in the Amazon and Orinoco river basins, yet few resources for genetic studies and the genetic improvement of tambaqui exist. In this study, we identified a large number of single-nucleotide polymorphisms (SNPs) for tambaqui and constructed a high-resolution genetic linkage map from a full-sib family of 124 individuals and their parents using the genotyping by sequencing method. In all, 68,584 SNPs were initially identified using minimum minor allele frequency (MAF) of 5%. Filtering parameters were used to select high-quality markers for linkage analysis. We selected 7,734 SNPs for linkage mapping, resulting in 27 linkage groups with a minimum logarithm of odds (LOD) of 8 and maximum recombination fraction of 0.35. The final genetic map contains 7,192 successfully mapped markers that span a total of 2,811 cM, with an average marker interval of 0.39 cM. Comparative genomic analysis between tambaqui and zebrafish revealed variable levels of genomic conservation across the 27 linkage groups which allowed for functional SNP annotations. The large-scale SNP discovery obtained here, allowed us to build a high-density linkage map in tambaqui, which will be useful to enhance genetic studies that can be applied in breeding programs.

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          SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries.

          High-density single-nucleotide polymorphism (SNP) arrays have revolutionized the ability of genome-wide association studies to detect genomic regions harboring sequence variants that affect complex traits. Extensive numbers of validated SNPs with known allele frequencies are essential to construct genotyping assays with broad utility. We describe an economical, efficient, single-step method for SNP discovery, validation and characterization that uses deep sequencing of reduced representation libraries (RRLs) from specified target populations. Using nearly 50 million sequences generated on an Illumina Genome Analyzer from DNA of 66 cattle representing three populations, we identified 62,042 putative SNPs and predicted their allele frequencies. Genotype data for these 66 individuals validated 92% of 23,357 selected genome-wide SNPs, with a genotypic and sequence allele frequency correlation of r = 0.67. This approach for simultaneous de novo discovery of high-quality SNPs and population characterization of allele frequencies may be applied to any species with at least a partially sequenced genome.
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            A review on SNP and other types of molecular markers and their use in animal genetics

            During the last ten years, the use of molecular markers, revealing polymorphism at the DNA level, has been playing an increasing part in animal genetics studies. Amongst others, the microsatellite DNA marker has been the most widely used, due to its easy use by simple PCR, followed by a denaturing gel electrophoresis for allele size determination, and to the high degree of information provided by its large number of alleles per locus. Despite this, a new marker type, named SNP, for Single Nucleotide Polymorphism, is now on the scene and has gained high popularity, even though it is only a bi-allelic type of marker. In this review, we will discuss the reasons for this apparent step backwards, and the pertinence of the use of SNPs in animal genetics, in comparison with other marker types.
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              Gains in QTL Detection Using an Ultra-High Density SNP Map Based on Population Sequencing Relative to Traditional RFLP/SSR Markers

              Huge efforts have been invested in the last two decades to dissect the genetic bases of complex traits including yields of many crop plants, through quantitative trait locus (QTL) analyses. However, almost all the studies were based on linkage maps constructed using low-throughput molecular markers, e.g. restriction fragment length polymorphisms (RFLPs) and simple sequence repeats (SSRs), thus are mostly of low density and not able to provide precise and complete information about the numbers and locations of the genes or QTLs controlling the traits. In this study, we constructed an ultra-high density genetic map based on high quality single nucleotide polymorphisms (SNPs) from low-coverage sequences of a recombinant inbred line (RIL) population of rice, generated using new sequencing technology. The quality of the map was assessed by validating the positions of several cloned genes including GS3 and GW5/qSW5, two major QTLs for grain length and grain width respectively, and OsC1, a qualitative trait locus for pigmentation. In all the cases the loci could be precisely resolved to the bins where the genes are located, indicating high quality and accuracy of the map. The SNP map was used to perform QTL analysis for yield and three yield-component traits, number of tillers per plant, number of grains per panicle and grain weight, using data from field trials conducted over years, in comparison to QTL mapping based on RFLPs/SSRs. The SNP map detected more QTLs especially for grain weight, with precise map locations, demonstrating advantages in detecting power and resolution relative to the RFLP/SSR map. Thus this study provided an example for ultra-high density map construction using sequencing technology. Moreover, the results obtained are helpful for understanding the genetic bases of the yield traits and for fine mapping and cloning of QTLs.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                07 April 2017
                2017
                : 7
                : 46112
                Affiliations
                [1 ]Animal Science department, University of São Paulo (USP)/Luiz de Queiroz College of Agriculture (ESALQ) , Piracicaba, São Paulo, Brazil
                [2 ]The Fish Molecular Genetics and Biotechnology Laboratory, Aquatic Genomics Unit, School of Fisheries, Aquaculture and Aquatic Sciences and Program of Cell and Molecular Biosciences, Auburn University , Auburn, AL, 36849, United States of America
                [3 ]Nature and Culture Institute, Federal University of Amazon (UFAM) , Benjamin Constant, Amazonas, Brazil
                [4 ]Unit of Biotechnology, University of Mogi das Cruzes , P.O. Box 411, 08701-970, Mogi das Cruzes, SP, Brazil
                [5 ]Brazilian National Institute for Research of the Amazon, Laboratory of Ecophysiology and Molecular Evolution , Manaus, Amazonas, Brazil
                [6 ]University Nilton Lins, Aquaculture Graduate Program , Manaus, Amazonas, Brazil
                Author notes
                [*]

                These authors contributed equally to this work.

                Article
                srep46112
                10.1038/srep46112
                5384230
                28387238
                36ed3127-3ae8-465b-9ce0-6649a211776a
                Copyright © 2017, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 13 December 2016
                : 06 March 2017
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