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      Genome-Wide Association and Genomic Selection for Resistance to Amoebic Gill Disease in Atlantic Salmon

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          Abstract

          Amoebic gill disease (AGD) is one of the largest threats to salmon aquaculture, causing serious economic and animal welfare burden. Treatments can be expensive and environmentally damaging, hence the need for alternative strategies. Breeding for disease resistance can contribute to prevention and control of AGD, providing long-term cumulative benefits in selected stocks. The use of genomic selection can expedite selection for disease resistance due to improved accuracy compared to pedigree-based approaches. The aim of this work was to quantify and characterize genetic variation in AGD resistance in salmon, the genetic architecture of the trait, and the potential of genomic selection to contribute to disease control. An AGD challenge was performed in ∼1,500 Atlantic salmon, using gill damage and amoebic load as indicator traits for host resistance. Both traits are heritable (h 2 ∼0.25-0.30) and show high positive correlation, indicating they may be good measurements of host resistance to AGD. While the genetic architecture of resistance appeared to be largely polygenic in nature, two regions on chromosome 18 showed suggestive association with both AGD resistance traits. Using a cross-validation approach, genomic prediction accuracy was up to 18% higher than that obtained using pedigree, and a reduction in marker density to ∼2,000 SNPs was sufficient to obtain accuracies similar to those obtained using the whole dataset. This study indicates that resistance to AGD is a suitable trait for genomic selection, and the addition of this trait to Atlantic salmon breeding programs can lead to more resistant stocks.

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          Most cited references 34

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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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            Establishing an adjusted p-value threshold to control the family-wide type 1 error in genome wide association studies

            Background By assaying hundreds of thousands of single nucleotide polymorphisms, genome wide association studies (GWAS) allow for a powerful, unbiased review of the entire genome to localize common genetic variants that influence health and disease. Although it is widely recognized that some correction for multiple testing is necessary, in order to control the family-wide Type 1 Error in genetic association studies, it is not clear which method to utilize. One simple approach is to perform a Bonferroni correction using all n single nucleotide polymorphisms (SNPs) across the genome; however this approach is highly conservative and would "overcorrect" for SNPs that are not truly independent. Many SNPs fall within regions of strong linkage disequilibrium (LD) ("blocks") and should not be considered "independent". Results We proposed to approximate the number of "independent" SNPs by counting 1 SNP per LD block, plus all SNPs outside of blocks (interblock SNPs). We examined the effective number of independent SNPs for Genome Wide Association Study (GWAS) panels. In the CEPH Utah (CEU) population, by considering the interdependence of SNPs, we could reduce the total number of effective tests within the Affymetrix and Illumina SNP panels from 500,000 and 317,000 to 67,000 and 82,000 "independent" SNPs, respectively. For the Affymetrix 500 K and Illumina 317 K GWAS SNP panels we recommend using 10-5, 10-7 and 10-8 and for the Phase II HapMap CEPH Utah and Yoruba populations we recommend using 10-6, 10-7 and 10-9 as "suggestive", "significant" and "highly significant" p-value thresholds to properly control the family-wide Type 1 error. Conclusion By approximating the effective number of independent SNPs across the genome we are able to 'correct' for a more accurate number of tests and therefore develop 'LD adjusted' Bonferroni corrected p-value thresholds that account for the interdepdendence of SNPs on well-utilized commercially available SNP "chips". These thresholds will serve as guides to researchers trying to decide which regions of the genome should be studied further.
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              Development and validation of a high density SNP genotyping array for Atlantic salmon (Salmo salar)

              Background Dense single nucleotide polymorphism (SNP) genotyping arrays provide extensive information on polymorphic variation across the genome of species of interest. Such information can be used in studies of the genetic architecture of quantitative traits and to improve the accuracy of selection in breeding programs. In Atlantic salmon (Salmo salar), these goals are currently hampered by the lack of a high-density SNP genotyping platform. Therefore, the aim of the study was to develop and test a dense Atlantic salmon SNP array. Results SNP discovery was performed using extensive deep sequencing of Reduced Representation (RR-Seq), Restriction site-Associated DNA (RAD-Seq) and mRNA (RNA-Seq) libraries derived from farmed and wild Atlantic salmon samples (n = 283) resulting in the discovery of > 400 K putative SNPs. An Affymetrix Axiom® myDesign Custom Array was created and tested on samples of animals of wild and farmed origin (n = 96) revealing a total of 132,033 polymorphic SNPs with high call rate, good cluster separation on the array and stable Mendelian inheritance in our sample. At least 38% of these SNPs are from transcribed genomic regions and therefore more likely to include functional variants. Linkage analysis utilising the lack of male recombination in salmonids allowed the mapping of 40,214 SNPs distributed across all 29 pairs of chromosomes, highlighting the extensive genome-wide coverage of the SNPs. An identity-by-state clustering analysis revealed that the array can clearly distinguish between fish of different origins, within and between farmed and wild populations. Finally, Y-chromosome-specific probes included on the array provide an accurate molecular genetic test for sex. Conclusions This manuscript describes the first high-density SNP genotyping array for Atlantic salmon. This array will be publicly available and is likely to be used as a platform for high-resolution genetics research into traits of evolutionary and economic importance in salmonids and in aquaculture breeding programs via genomic selection.
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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
                02 February 2018
                April 2018
                : 8
                : 4
                : 1195-1203
                Affiliations
                [* ]The Roslin Institute and Royal (Dick) School of Veterinary Studies and
                []Landcatch Natural Selection Ltd., Roslin Innovation Centre, University of Edinburgh, EH25 9RG Midlothian, United Kingdom,and
                []Hendrix Genetics Aquaculture BV/ Netherlands, Villa ‘de Körver’, Spoorstraat 69, 5831 CK Boxmeer, The Netherlands
                Author notes
                [1 ]Corresponding author: The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, UK. E-mail: ross.houston@ 123456roslin.ed.ac.uk
                Article
                GGG_200075
                10.1534/g3.118.200075
                5873910
                29420190
                Copyright © 2018 Robledo 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.

                Page count
                Figures: 3, Tables: 4, Equations: 1, References: 53, Pages: 9
                Product
                Categories
                Genomic Selection

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