Blog
About

25
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Accuracy of Genomic Evaluations of Juvenile Growth Rate in Common Carp ( Cyprinus carpio) Using Genotyping by Sequencing

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Cyprinids are the most important group of farmed fish globally in terms of production volume, with common carp ( Cyprinus carpio) being one of the most valuable species of the group. The use of modern selective breeding methods in carp is at a formative stage, implying a large scope for genetic improvement of key production traits. In the current study, a population of 1,425 carp juveniles, originating from a partial factorial cross between 40 sires and 20 dams, was used for investigating the potential of genomic selection (GS) for juvenile growth, an exemplar polygenic production trait. RAD sequencing was used to identify and genotype SNP markers for subsequent parentage assignment, construction of a medium density genetic map (12,311 SNPs), genome-wide association study (GWAS), and testing of GS. A moderate heritability was estimated for body length of carp at 120 days (as a proxy of juvenile growth) of 0.33 (s.e. 0.05). No genome-wide significant QTL was identified using a single marker GWAS approach. Genomic prediction of breeding values outperformed pedigree-based prediction, resulting in 18% improvement in prediction accuracy. The impact of reduced SNP densities on prediction accuracy was tested by varying minor allele frequency (MAF) thresholds, with no drop in prediction accuracy until the MAF threshold is set <0.3 (2,744 SNPs). These results point to the potential for GS to improve economically important traits in common carp breeding programs.

          Related collections

          Most cited references 41

          • Record: found
          • Abstract: found
          • Article: not found

          Best linear unbiased estimation and prediction under a selection model.

          Mixed linear models are assumed in most animal breeding applications. Convenient methods for computing BLUE of the estimable linear functions of the fixed elements of the model and for computing best linear unbiased predictions of the random elements of the model have been available. Most data available to animal breeders, however, do not meet the usual requirements of random sampling, the problem being that the data arise either from selection experiments or from breeders' herds which are undergoing selection. Consequently, the usual methods are likely to yield biased estimates and predictions. Methods for dealing with such data are presented in this paper.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Mapping genes for complex traits in domestic animals and their use in breeding programmes.

            Genome-wide panels of SNPs have recently been used in domestic animal species to map and identify genes for many traits and to select genetically desirable livestock. This has led to the discovery of the causal genes and mutations for several single-gene traits but not for complex traits. However, the genetic merit of animals can still be estimated by genomic selection, which uses genome-wide SNP panels as markers and statistical methods that capture the effects of large numbers of SNPs simultaneously. This approach is expected to double the rate of genetic improvement per year in many livestock systems.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found

              Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding

              Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade.
                Bookmark

                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                13 March 2018
                2018
                : 9
                Affiliations
                1The Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh , Edinburgh, United Kingdom
                2Faculty of Fisheries and Protection of Waters, South Bohemian Research Centre of Aquaculture and Biodiversity of Hydrocenoses, University of South Bohemia in České Budějovice , Vodňany, Czechia
                Author notes

                Edited by: Riccardo Negrini, Università Cattolica del Sacro Cuore, Italy

                Reviewed by: Nicholas Andrew Robinson, Nofima, Norway; Mario Calus, Wageningen University & Research, Netherlands

                *Correspondence: Ross D. Houston, ross.houston@ 123456roslin.ed.ac.uk

                This article was submitted to Livestock Genomics, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2018.00082
                5859378
                Copyright © 2018 Palaiokostas, Kocour, Prchal and Houston.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 3, Tables: 2, Equations: 0, References: 54, Pages: 9, Words: 0
                Funding
                Funded by: Seventh Framework Programme 10.13039/100011102
                Award ID: Fishboost
                Categories
                Genetics
                Original Research

                Genetics

                genomic prediction, rad-seq, high-throughput sequencing, carps, aquaculture breeding

                Comments

                Comment on this article