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      A Genome-Wide Association Study for Host Resistance to Ostreid Herpesvirus in Pacific Oysters ( Crassostrea gigas)

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          Abstract

          Ostreid herpesvirus (OsHV) can cause mass mortality events in Pacific oyster aquaculture. While various factors impact on the severity of outbreaks, it is clear that genetic resistance of the host is an important determinant of mortality levels. This raises the possibility of selective breeding strategies to improve the genetic resistance of farmed oyster stocks, thereby contributing to disease control. Traditional selective breeding can be augmented by use of genetic markers, either via marker-assisted or genomic selection. The aim of the current study was to investigate the genetic architecture of resistance to OsHV in Pacific oyster, to identify genomic regions containing putative resistance genes, and to inform the use of genomics to enhance efforts to breed for resistance. To achieve this, a population of ∼1,000 juvenile oysters were experimentally challenged with a virulent form of OsHV, with samples taken from mortalities and survivors for genotyping and qPCR measurement of viral load. The samples were genotyped using a recently-developed SNP array, and the genotype data were used to reconstruct the pedigree. Using these pedigree and genotype data, the first high density linkage map was constructed for Pacific oyster, containing 20,353 SNPs mapped to the ten pairs of chromosomes. Genetic parameters for resistance to OsHV were estimated, indicating a significant but low heritability for the binary trait of survival and also for viral load measures (h2 0.12 – 0.25). A genome-wide association study highlighted a region of linkage group 6 containing a significant QTL affecting host resistance. These results are an important step toward identification of genes underlying resistance to OsHV in oyster, and a step toward applying genomic data to enhance selective breeding for disease resistance in oyster aquaculture.

          Most cited references35

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          Family-based association tests for genomewide association scans.

          With millions of single-nucleotide polymorphisms (SNPs) identified and characterized, genomewide association studies have begun to identify susceptibility genes for complex traits and diseases. These studies involve the characterization and analysis of very-high-resolution SNP genotype data for hundreds or thousands of individuals. We describe a computationally efficient approach to testing association between SNPs and quantitative phenotypes, which can be applied to whole-genome association scans. In addition to observed genotypes, our approach allows estimation of missing genotypes, resulting in substantial increases in power when genotyping resources are limited. We estimate missing genotypes probabilistically using the Lander-Green or Elston-Stewart algorithms and combine high-resolution SNP genotypes for a subset of individuals in each pedigree with sparser marker data for the remaining individuals. We show that power is increased whenever phenotype information for ungenotyped individuals is included in analyses and that high-density genotyping of just three carefully selected individuals in a nuclear family can recover >90% of the information available if every individual were genotyped, for a fraction of the cost and experimental effort. To aid in study design, we evaluate the power of strategies that genotype different subsets of individuals in each pedigree and make recommendations about which individuals should be genotyped at a high density. To illustrate our method, we performed genomewide association analysis for 27 gene-expression phenotypes in 3-generation families (Centre d'Etude du Polymorphisme Humain pedigrees), in which genotypes for ~860,000 SNPs in 90 grandparents and parents are complemented by genotypes for ~6,700 SNPs in a total of 168 individuals. In addition to increasing the evidence of association at 15 previously identified cis-acting associated alleles, our genotype-inference algorithm allowed us to identify associated alleles at 4 cis-acting loci that were missed when analysis was restricted to individuals with the high-density SNP data. Our genotype-inference algorithm and the proposed association tests are implemented in software that is available for free.
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            Detection and description of a particular Ostreid herpesvirus 1 genotype associated with massive mortality outbreaks of Pacific oysters, Crassostrea gigas, in France in 2008.

            Ostreid herpesvirus 1 (OsHV-1) infections have been reported around the world and are associated with high mortalities of the Pacific oyster (Crassostrea gigas). In the summer 2008, abnormal mortality rates ranging from 80% to 100% were reported in France and affected only Pacific oysters. Analyses of oyster samples collected during mortality outbreaks demonstrated a significant detection of OsHV-1 (75% of analysed batches), which appeared stronger than previous years. DNA sequencing based on C and IA regions was carried out on 28 batches of OsHV-1 infected Pacific oysters collected in 2008. Polymorphisms were described in both the C and IA regions and characterized a genotype of OsHV-1 not already reported and termed OsHV-1 microVar. A microsatellite zone present in the C region showed several deletions. Additionally, 44 isolates collected in France and in the USA, from 1995 to 2007 were sequenced and compared to the 2008 sequences. The analyses of 76 sequences showed OsHV-1 microVar detection only in 2008 isolates. These data suggest that OsHV-1 microVar can be assumed as an emergent genotype. (c) 2010 Elsevier B.V. All rights reserved.
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              Heritability of Threshold Characters.

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                Author and article information

                Journal
                G3 (Bethesda)
                Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes|Genomes|Genetics
                Genetics Society of America
                2160-1836
                22 February 2018
                April 2018
                : 8
                : 4
                : 1273-1280
                Affiliations
                [* ]The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, United Kingdom
                []Centre for Environment Fisheries and Aquaculture Science (Cefas) Weymouth Laboratory, Dorset DT4 8UB, United Kingdom
                []Department of Biosciences, Ecological Genetics Research Unit, University of Helsinki, Helsinki, Finland
                [§ ]Guernsey Sea Farms Ltd. Parc Lane, Vale, Guernsey GY3 5EQ
                Author notes
                [ ]Corresponding author: Ross Houston, The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, United Kingdom. ross.houston@ 123456roslin.ed.ac.uk
                Author information
                http://orcid.org/0000-0003-1805-0762
                Article
                GGG_200113
                10.1534/g3.118.200113
                5873916
                29472307
                9320cfe4-d89b-40ab-bb87-78d3ebbc8cac
                Copyright © 2018 Gutierrez 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.

                History
                : 21 November 2017
                : 09 February 2018
                Page count
                Figures: 2, Tables: 3, Equations: 2, References: 45, Pages: 8
                Categories
                Investigations

                Genetics
                gwas,oshv-1,snp array,linkage map,oysters
                Genetics
                gwas, oshv-1, snp array, linkage map, oysters

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