scholarly journals Potential of genomic selection for improvement of resistance to Ostreid Herpes virus in Pacific oyster (Crassostrea gigas)

2019 ◽  
Author(s):  
Alejandro P. Gutierrez ◽  
Jane Symonds ◽  
Nick King ◽  
Konstanze Steiner ◽  
Tim P. Bean ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Pacific Oyster ◽  
Snp Array ◽  
Cost Effective ◽  
Snp Markers ◽  
Density Reduction ◽  
Pacific Oysters ◽  
Summer Mortality ◽  
Selection For ◽  
Estimated Breeding Values

AbstractIn genomic selection (GS), genome-wide SNP markers are used to generate genomic estimated breeding values (gEBVs) for selection candidates. The application of GS in shellfish looks promising and has the potential to help in dealing with one of the main issues currently affecting Pacific oyster production worldwide, which is the “summer mortality syndrome”. This causes periodic mass mortality in farms worldwide and has mainly been attributed to a specific variant of the Ostreid herpesvirus (OsHV-1-μvar). In the current study, we evaluated the potential of genomic selection for host resistance OsHV in Pacific oysters, and compared it to pedigree-based approaches. An OsHV-1 disease challenge was performed using an immersion-based virus exposure treatment for oysters for seven days. 768 samples were genotyped using the medium density SNP array for oysters. GWAS was performed for the survival trait using a GBLUP approach in BLUPF90 software. Heritability ranged from 0.25±0.05 to 0.37±0.05 (mean±s.e) based on pedigree and genomic information, respectively. Genomic prediction was more accurate than pedigree prediction, and SNP density reduction had little impact on prediction accuracy until marker densities dropped below ∼500 SNPs. This demonstrates the potential for GS in Pacific oyster breeding programs and importantly, demonstrates that a low number of SNPs might suffice to obtain accurate gEBVs, thus potentially making the implementation of GS more cost effective.

scholarly journals Potential of genomic selection for improvement of resistance to ostreid herpesvirus in Pacific oyster ( Crassostrea gigas )

2020 ◽  
Vol 51 (2) ◽  
pp. 249-257 ◽  
Author(s):  
A. P. Gutierrez ◽  
J. Symonds ◽  
N. King ◽  
K. Steiner ◽  
T. P. Bean ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Crassostrea Gigas ◽  
Pacific Oyster ◽  
Selection For

Bias in variance component estimation in swine crossbreeding schemes using selective genotyping and phenotyping strategies

2021 ◽  
Author(s):  
Garrett M See ◽  
Benny E Mote ◽  
Matthew L Spangler
Keyword(s):  
Variance Components ◽  
Snp Markers ◽  
Selective Genotyping ◽  
Sib Families ◽  
Component Estimation ◽  
Selection For ◽  
Estimated Breeding Values ◽  
Genetic Evaluations ◽  
The Impact ◽  
True Values

Abstract Selective genotyping of crossbred (CB) animals to include in traditionally purebred (PB) dominated genetic evaluations has been shown to provide an increase in the response to selection for CB performance. However, the inclusion of phenotypes from selectively genotyped CB animals, without the phenotypes of their non-genotyped cohorts, could cause bias in estimated variance components (VC) and subsequent estimated breeding values (EBV). The objective of the study was to determine the impact of selective CB genotyping on VC estimates and subsequent bias in EBV when non-genotyped CB animals are not included in genetic evaluations. A swine crossbreeding scheme producing 3-way CB animals was simulated to create selectively genotyped datasets. The breeding scheme consisted of three PB breeds each with 25 males and 450 females, F1 crosses with 1200 females and 12,000 CB progeny. Eighteen chromosomes each with 100 QTL and 4k SNP markers were simulated. Both PB and CB performance were considered to be moderately heritable (h2=0.4). Factors evaluated were, 1) CB phenotype and genotype inclusion of 15% (n=1800) or 35% (n=4200), 2) genetic correlation between PB and CB performance (rpc=0.1, 0.5 or 0.7) and 3) selective genotyping strategy. Genotyping strategies included: a) Random: random CB selection, b) Top: highest CB phenotype and c) Extreme: half highest and half lowest CB phenotypes. Top and Extreme selective genotyping strategies were considered by selecting animals in full-sib (FS) families or among the CB population (T). In each generation, 4320 PB selection candidates contributed phenotypic and genotypic records. Each scenario was replicated 15 times. VC were estimated for PB and CB performance utilizing bivariate models using pedigree relationships with dams of CB animals considered to be unknown. Estimated values of VC for PB performance were not statistically different from true values. Top selective genotyping strategies produced deflated estimates of phenotypic VC for CB performance compared to true values. When using estimated VC, Top_T and Extreme_T produced the most biased EBV, yet EBV of PB selection candidates for CB performance were most accurate when using Extreme_T. Results suggest that randomly selecting CB animals to genotype or selectively genotyping Top or Extreme CB animals within full-sib families can lead to accurate estimates of additive genetic VC for CB performance and unbiased EBV.

scholarly journals Genomic Prediction for Fusiform Rust Disease Incidence in a Large Cloned Population of Pinus taeda

2021 ◽  
Author(s):  
Mohammad Nasir Shalizi ◽  
W Patrick Cumbie ◽  
Fikret Isik
Keyword(s):  
Genomic Selection ◽  
Pinus Taeda ◽  
Random Sampling ◽  
Cross Validation ◽  
Disease Incidence ◽  
Rust Disease ◽  
Fusiform Rust ◽  
Selection For ◽  
Model Training ◽  
Estimated Breeding Values

Abstract In this study, 723 Pinus taeda L. (loblolly pine) clonal varieties genotyped with 16920 SNP markers were used to evaluate genomic selection for fusiform rust disease caused by the fungus Cronartium quercuum f. sp. fusiforme. The 723 clonal varieties were from five full-sib families. They were a subset of a larger population (1831 clonal varieties), field-tested across 26 locations in the southeast US. Ridge regression, Bayes B and Bayes Cπ models were implemented to study marker-trait associations and estimate predictive ability for selection. A cross-validation scenario based on random sampling of 80% of the clonal varieties for model building had higher (0.71- 0.76) prediction accuracies of genomic estimated breeding values compared with family and within-family cross-validation scenarios. Random sampling within families for model training to predict genomic estimated breeding values of the remaining progenies within each family produced accuracies between 0.38 to 0.66. Using four families out of five for model training was not successful. The results showed the importance of genetic relatedness between the training and validation sets. Bayesian whole genome regression models detected three QTL with large effects on the disease outcome, explaining 54% of the genetic variation in the trait. The significance of QTL was validated with GWAS while accounting for the population structure and polygenic effect. The odds of disease incidence for heterozygous AB genotypes were 10.7 and 12.1 times greater than the homozygous AA genotypes for SNP11965 and SNP6347 loci, respectively. Genomic selection for fusiform rust disease incidence could be effective in P. taeda breeding. Markers with large effects could be fit as fixed covariates to increase the prediction accuracies, provided that their effects are validated further.

scholarly journals Accuracy of Imputation of Microsatellite Markers from an SNP50K Chip in Spanish Assaf Sheep

2020 ◽  
Author(s):  
Hector Marina ◽  
Aroa Suarez-Vega ◽  
Rocio Pelayo ◽  
Beatriz Gutierrez-Gil ◽  
Antonio Reverter ◽  
...  
Keyword(s):  
Microsatellite Markers ◽  
Snp Array ◽  
Cost Effective ◽  
Snp Markers ◽  
Sheep Breed ◽  
Optimum Number ◽  
Low Density ◽  
Snp Chip ◽  
Imputation Procedure ◽  
Genotype Concordance

Abstract Background: Traditional and new genotyping technologies must be combined by applying bridge methodologies that avoid double genotyping costs. This study aims to identify and evaluate a reliable approach to precisely impute microsatellite markers from SNP-chip panels to perform parental verifications in sheep. Moreover, we will assess the optimum number of SNPs necessary to accurately impute microsatellite markers to develop a low-density SNP chip for parentage verification in the Assaf sheep breed.Results: A total of 4,423 animals belonging to the Spanish Assaf sheep breed were genotyped for 19 microsatellites and an ovine custom 49,897 SNP array. The accuracy of microsatellite marker imputation, performed with BEAGLE v5.1 software, was assessed with three metrics, namely, genotype concordance (C), genotype dosage (length r2), and allelic dosage (allelic r2), for all imputation scenarios tested (0.5-10 Mb microsatellite flanking SNP windows). The accuracy of our imputation results for the three metrics analyzed for all haplotype lengths tested was higher than 0.90 (C), 0.80 (length r2), and 0.75 (allelic r2). Considering that the objective of the study was to assess a SNP window length that provides the best accuracy for the microsatellite imputation procedure to design an affordable low-density SNP chip for parentage testing, we considered 2 Mb to be the best SNP haplotype length for further analyses (SNPs/window =74.05, C= 0.970; length r2= 0.952, allelic r2=0.899). We additionally evaluated imputation performance under two null models, naive and random, which showed weak genotype concordance averages in comparison with imputed microsatellites (0.41 and 0.15, respectively).Conclusions: We presented for the first time a precise methodology in dairy sheep to impute multiallelic microsatellite genotypes from biallelic SNP markers. The use of a 2 Mb SNP flanking window for each microsatellite has been shown to achieve high accuracy in the imputation procedure while providing a low-density SNP chip that could be cost-effective. The results from this study will undoubtedly have a significant impact on sheep breeders overcoming the problem of parentage verification when different genotyping platforms have been used across generations.

scholarly journals Prediction Accuracies of Genomic Selection for Nine Commercially Important Traits in the Portuguese Oyster (Crassostrea angulata) Using DArT-Seq Technology

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 210
Author(s):  
Sang V. Vu ◽  
Cedric Gondro ◽  
Ngoc T. H. Nguyen ◽  
Arthur R. Gilmour ◽  
Rick Tearle ◽  
...  
Keyword(s):  
Moisture Content ◽  
Genomic Selection ◽  
Shell Length ◽  
Prediction Accuracy ◽  
Genomic Information ◽  
Morphometric Traits ◽  
Shell Weight ◽  
Shell Width ◽  
Crassostrea Angulata ◽  
Selection For

Genomic selection has been widely used in terrestrial animals but has had limited application in aquaculture due to relatively high genotyping costs. Genomic information has an important role in improving the prediction accuracy of breeding values, especially for traits that are difficult or expensive to measure. The purposes of this study were to (i) further evaluate the use of genomic information to improve prediction accuracies of breeding values from, (ii) compare different prediction methods (BayesA, BayesCπ and GBLUP) on prediction accuracies in our field data, and (iii) investigate the effects of different SNP marker densities on prediction accuracies of traits in the Portuguese oyster (Crassostrea angulata). The traits studied are all of economic importance and included morphometric traits (shell length, shell width, shell depth, shell weight), edibility traits (tenderness, taste, moisture content), and disease traits (Polydora sp. and Marteilioides chungmuensis). A total of 18,849 single nucleotide polymorphisms were obtained from genotyping by sequencing and used to estimate genetic parameters (heritability and genetic correlation) and the prediction accuracy of genomic selection for these traits. Multi-locus mixed model analysis indicated high estimates of heritability for edibility traits; 0.44 for moisture content, 0.59 for taste, and 0.72 for tenderness. The morphometric traits, shell length, shell width, shell depth and shell weight had estimated genomic heritabilities ranging from 0.28 to 0.55. The genomic heritabilities were relatively low for the disease related traits: Polydora sp. prevalence (0.11) and M. chungmuensis (0.10). Genomic correlations between whole weight and other morphometric traits were from moderate to high and positive (0.58–0.90). However, unfavourably positive genomic correlations were observed between whole weight and the disease traits (0.35–0.37). The genomic best linear unbiased prediction method (GBLUP) showed slightly higher accuracy for the traits studied (0.240–0.794) compared with both BayesA and BayesCπ methods but these differences were not significant. In addition, there is a large potential for using low-density SNP markers for genomic selection in this population at a number of 3000 SNPs. Therefore, there is the prospect to improve morphometric, edibility and disease related traits using genomic information in this species.

scholarly journals Genomic selection in French dairy cattle

2012 ◽  
Vol 52 (3) ◽  
pp. 115 ◽  
Author(s):  
D. Boichard ◽  
F. Guillaume ◽  
A. Baur ◽  
P. Croiseau ◽  
M. N. Rossignol ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Genomic Selection ◽  
Reference Population ◽  
Specific Reference ◽  
Nucleotide Polymorphisms ◽  
Genomic Evaluation ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Estimated Breeding Values ◽  
Restricted Use

Genomic selection is implemented in French Holstein, Montbéliarde, and Normande breeds (70%, 16% and 12% of French dairy cows). A characteristic of the model for genomic evaluation is the use of haplotypes instead of single-nucleotide polymorphisms (SNPs), so as to maximise linkage disequilibrium between markers and quantitative trait loci (QTLs). For each trait, a QTL-BLUP model (i.e. a best linear unbiased prediction model including QTL random effects) includes 300–700 trait-dependent chromosomal regions selected either by linkage disequilibrium and linkage analysis or by elastic net. This model requires an important effort to phase genotypes, detect QTLs, select SNPs, but was found to be the most efficient one among all tested ones. QTLs are defined within breed and many of them were found to be breed specific. Reference populations include 1800 and 1400 bulls in Montbéliarde and Normande breeds. In Holstein, the very large reference population of 18 300 bulls originates from the EuroGenomics consortium. Since 2008, ~65 000 animals have been genotyped for selection by Labogena with the 50k chip. Bulls genomic estimated breeding values (GEBVs) were made official in June 2009. In 2010, the market share of the young bulls reached 30% and is expected to increase rapidly. Advertising actions have been undertaken to recommend a time-restricted use of young bulls with a limited number of doses. In January 2011, genomic selection was opened to all farmers for females. Current developments focus on the extension of the method to a multi-breed context, to use all reference populations simultaneously in genomic evaluation.

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