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

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.

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Genomic Selection for Yield and Seed Protein Content in Soybean: A Study of Breeding Program Data and Assessment of Prediction Accuracy

Crop Science ◽

10.2135/cropsci2016.06.0496 ◽

2017 ◽

Vol 57 (3) ◽

pp. 1325-1337 ◽

Cited By ~ 22

Author(s):

Alexandra Duhnen ◽

Amandine Gras ◽

Simon Teyssèdre ◽

Michel Romestant ◽

Bruno Claustres ◽

...

Keyword(s):

Protein Content ◽

Genomic Selection ◽

Prediction Accuracy ◽

Seed Protein ◽

Breeding Program ◽

Seed Protein Content ◽

Selection For ◽

Program Data

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Telaah morfometrik Pinctada margaritifera untuk pengembangan usaha budidaya

e-Journal BUDIDAYA PERAIRAN ◽

10.35800/bdp.6.1.2018.19546 ◽

2018 ◽

Vol 6 (1) ◽

Author(s):

Karel Sumtaki ◽

Ockstan J. J. Kalesaran ◽

Cyska Lumenta

Keyword(s):

Water Quality ◽

Shell Length ◽

Current Velocity ◽

Quality Parameters ◽

Morphometric Measurements ◽

Shell Weight ◽

Pinctada Margaritifera ◽

Shell Width ◽

Aquaculture Development ◽

Quality Measurements

This study aimed to analyze the size of shell length, shell width and total weight of Pinctada margaritifera shells, and water quality parameters for the aquaculture development. Morphometric measurements include: shell length (PC), and shell width (LC), shell weight (BT). The results showed that PC size 8 - 11.99 cm, LC 8 - 9.99 cm and BT 80 - 99.99 gram dominate the Arakan waters while PC size 6 - 9.99 cm, LC 6 - 7.99 cm and BT 60 - 79.99 gram dominate the Bahoi waters. The results of water quality measurements in both locations are Bahoi waters, namely: 29-32oC, DO 7-8,5 mg/l, pH 7,8-7,9, salinity, 28-32 ppt, 4,5- 7 meters, 11 cm/sec. While in Arakan waters, the temperature was 30-30,8oC, DO 6,3-6,7 mg/l, pH 7,6-7,7 salinity 30 ppt, brightness 4-5 meter, current velocity 4,5 cm / second. Both locations were feasible for the development of pearl aquacultureKeywords: Morphometric, Pinctada margaritifera, Aquaculture

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Bayesian genomic models boost prediction accuracy for survival to Streptococcus agalactiae infection in Nile tilapia (Oreochromus nilioticus)

Genetics Selection Evolution ◽

10.1186/s12711-021-00629-y ◽

2021 ◽

Vol 53 (1) ◽

Author(s):

Rajesh Joshi ◽

Anders Skaarud ◽

Alejandro Tola Alvarez ◽

Thomas Moen ◽

Jørgen Ødegård

Keyword(s):

Genomic Selection ◽

Streptococcus Agalactiae ◽

Nile Tilapia ◽

Prediction Accuracy ◽

Predictive Accuracy ◽

Challenge Test ◽

Bacterial Disease ◽

Nucleotide Polymorphisms ◽

Genomic Selection Model ◽

Selection For

Abstract Background Streptococcosis is a major bacterial disease in Nile tilapia that is caused by Streptococcus agalactiae infection, and development of resistant strains of Nile tilapia represents a sustainable approach towards combating this disease. In this study, we performed a controlled disease trial on 120 full-sib families to (i) quantify and characterize the potential of genomic selection for survival to S. agalactiae infection in Nile tilapia, and (ii) identify the best genomic model and the optimal density of single nucleotide polymorphisms (SNPs) for this trait. Methods In total, 40 fish per family (15 fish intraperitoneally injected and 25 fish as cohabitants) were used in the challenge test. Mortalities were recorded every 3 h for 35 days. After quality control, genotypes (50,690 SNPs) and phenotypes (0 for dead and 1 for alive) for 2472 cohabitant fish were available. Genetic parameters were obtained using various genomic selection models (genomic best linear unbiased prediction (GBLUP), BayesB, BayesC, BayesR and BayesS) and a traditional pedigree-based model (PBLUP). The pedigree-based analysis used a deep 17-generation pedigree. Prediction accuracy and bias were evaluated using five replicates of tenfold cross-validation. The genomic models were further analyzed using 10 subsets of SNPs at different densities to explore the effect of pruning and SNP density on predictive accuracy. Results Moderate estimates of heritabilities ranging from 0.15 ± 0.03 to 0.26 ± 0.05 were obtained with the different models. Compared to a pedigree-based model, GBLUP (using all the SNPs) increased prediction accuracy by 15.4%. Furthermore, use of the most appropriate Bayesian genomic selection model and SNP density increased the prediction accuracy up to 71%. The 40 to 50 SNPs with non-zero effects were consistent for all BayesB, BayesC and BayesS models with respect to marker id and/or marker locations. Conclusions These results demonstrate the potential of genomic selection for survival to S. agalactiae infection in Nile tilapia. Compared to the PBLUP and GBLUP models, Bayesian genomic models were found to boost the prediction accuracy significantly.

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An Overview of Key Factors Affecting Genomic Selection for Wheat Quality Traits

Plants ◽

10.3390/plants10040745 ◽

2021 ◽

Vol 10 (4) ◽

pp. 745

Author(s):

Ivana Plavšin ◽

Jerko Gunjača ◽

Zlatko Šatović ◽

Hrvoje Šarčević ◽

Marko Ivić ◽

...

Keyword(s):

Genomic Selection ◽

Prediction Accuracy ◽

Molecular Genetic ◽

Wheat Quality ◽

Quality Traits ◽

Key Factors ◽

Breeding Values ◽

Factors Affecting ◽

Selection For ◽

Marker Information

Selection for wheat (Triticum aestivum L.) grain quality is often costly and time-consuming since it requires extensive phenotyping in the last phases of development of new lines and cultivars. The development of high-throughput genotyping in the last decade enabled reliable and rapid predictions of breeding values based only on marker information. Genomic selection (GS) is a method that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model. The success of GS depends on the obtained prediction accuracy, which is influenced by various molecular, genetic, and phenotypic factors, as well as the factors of the selected statistical model. The objectives of this article are to review research on GS for wheat quality done so far and to highlight the key factors affecting prediction accuracy, in order to suggest the most applicable approach in GS for wheat quality traits.

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Correlation, regression estimates and heterosis of Archachatina marginata snails in Calabar, Nigeria

Nigerian Journal of Animal Production ◽

10.51791/njap.v46i3.856 ◽

2020 ◽

Vol 46 (3) ◽

pp. 64-72

Author(s):

I. A. Ibom ◽

B. Okon ◽

F. I. Okon

Keyword(s):

Body Weight ◽

Shell Length ◽

Growth Traits ◽

Morphometric Traits ◽

Egg Weight ◽

Shell Width ◽

Mating Group ◽

Selection Programme ◽

The Face ◽

Mouth Width

White skinned ectotypes were used in a study to estimate heterosis, correlation and regression. Data collected on the snails and their eggs included body weight (BWT), shell length (SLH), shell width (SWH), “mouth” length (MLH) and “mouth” width (MWH); and egg weight (EWT), egg length (ELH) and egg width (EWH), respectively. These data were used to estimate correlations between pairs of traits, heterosis and to predict weight from other morphometric traits among three mating groups [black skinned x black skinned (BAM X BAM), white skinned x white skinned (WAM X WAM) and their cross BAM X WAM)]. Results obtained from the study showed that evaluated traits expressed strong, positive and highly significant (P<0.01) correlation values that ranged from rp= 0.86 to rp = 0.99 among the snails mating groups. The correlation values of traits evaluated on eggs laid by these snails ranged from mild (rp = 0.49) through moderate (rp = 0.59, rp= 0.70) to strong/close (rp = 0.89). Regression estimates values obtained ranged from mild (1.30) to high (6.25), an shell width best predicted hatchlings body weight in the black skinned x black skinned (BAM X BAM) mating group, while “mouth” length best predicted hatchlings body weight in the white skinned x white skinned (WAM X WAM) mating group. Traits evaluated for percent heterosis expressed positive and significant (P<0.05) values between the mating groups, with the crossbred (BAM X WAM) mating group having advantage over the purebred (BAM X BAM and WAM X WAM) mating groups. It could therefore be concluded that in the face of appropriate selection programme, heterosis can be exploited to improve snails reproductive and growth traits. It can thus be recommended that having information regarding the association of weight with other contributing traits/parameters is crucial before starting any breeding programme.

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An analysis of morphological variation in limpets of the genus Collisella

Canadian Journal of Zoology ◽

10.1139/z78-092 ◽

1978 ◽

Vol 56 (4) ◽

pp. 643-650 ◽

Cited By ~ 3

Author(s):

Paul A. Dehnel

Keyword(s):

Morphological Variation ◽

Shell Length ◽

Shell Height ◽

Shell Morphology ◽

Shell Weight ◽

Shell Width ◽

Significant Relationships ◽

Wet Weight ◽

Animal Growth

Shell morphology, radular length, and wet weight of soft parts were compared between three species (Collisella pelta, Collisella persona, and Collisella scutum) from one geographic locality. Shell length vs. shell height, wet weight of soft parts vs. radular length, and shell length vs. radular length are statistically significant relationships to separate the three species. Shell weight vs. wet weight of soft parts, wet weight of soft parts vs. extravisceral space, and shell volume vs. wet weight of soft parts are statistically significant relationships to separate C. scutum from either C. pelta or C. persona. These comparisons do not separate C. pelta from C. persona. Shell length vs. shell width, shell length vs. shell volume, and shell length vs. shell weight were found not to be statistically significant relationships to separate the three species. The importance of using shell length and wet weight of soft parts as a measure of animal growth is discussed.

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Prediction of body weight using morphometric indices in giant African land snail in three zones of Imo state

Nigerian Journal of Animal Production ◽

10.51791/njap.v47i6.2861 ◽

2021 ◽

Vol 47 (6) ◽

pp. 1-9

Author(s):

S. I. Ahamba ◽

C. U. Ekugba ◽

O. E. Kadurumba ◽

U. E. Ogundu

Keyword(s):

Body Weight ◽

Shell Length ◽

Land Snail ◽

Study Data ◽

Shell Weight ◽

Ecological Zones ◽

Imo State ◽

Shell Width ◽

Poids Corporel ◽

Significant Difference

Prediction of body weight using morphometric indices in giant African land snail (Achatina marginata) was studied for the three (3) agro-ecological zones in Imo State (Owerri, Okigwe and Orlu zones). One hundred and sixty (160) snails in Imo State were surveyed in course of this study. Data were generated through measurement of body weight, shell length, shell width, shell thickness, mouth length, weight of shell, weight of visceral, length of foot and thickness of foot. Data collected were subjected to regression analysis using SPSS statistical package. Results showed generally highly significant value (P<0.01) R2 value and R-value across the zoness. Highly significant difference (P<0.01) was observed in Owerri zone for the weight of shell, and the shell length. In Okigwe zone, only the weight of visceral showed a significant effect (P<0.01), whereas, in Orlu zone also, the shell width, shell mouth length and foot thickness showed a significant effect (P<0.01). The study therefore recommends that the linear model used, fits the data. Hence a comprehensive selection program for improvement of A. marginata in Imo State could be planned using the regressions coefficients obtained from the study. La prédiction du poids corporel à l'aide d'indices morphométriques chez l'escargot terrestre géant africain (Achatina marginata) a été étudiée pour les trois (3) zones agro-écologiques de l'État d'Imo (zones Owerri, Okigwe et Orlu). Cent soixante (160) escargots de l'État d'Imo ont été étudiés au cours de cette étude. Les données ont été générées en mesurant le poids corporel, la longueur de la coquille, la largeur de la coquille, l'épaisseur de la coquille, la longueur de la bouche, le poids de la coquille, le poids du viscéral, la longueur du pied et l'épaisseur du pied. Les données collectées ont été soumises à une analyse de régression à l'aide du progiciel statistique SPSS. Les résultats ont montré une valeur R2 et une valeur R généralement très significatives (P <0,01) dans les zones. Une différence très significative (P <0,01) a été observée dans la zone d'Owerri pour le poids de la coquille et la longueur de la coquille. Dans la zone Okigwe, seul le poids des viscéraux a montré un effet significatif (P<0,01), alors que, dans la zone Orlu, la largeur de la coque, la longueur de la bouche de la coque et l'épaisseur du pied ont également montré un effet significatif (P <0,01) que le modèle linéaire utilisé, ajuste les données. Par conséquent, un programme de sélection complet pour l'amélioration d'A. Marginata dans l'État de l'Imo pourrait être planifié en utilisant les coefficients de régression obtenus à partir de l'étude.

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Genomic Prediction for Whole Weight, Body Shape, Meat Yield, and Color Traits in the Portuguese Oyster Crassostrea angulata

Frontiers in Genetics ◽

10.3389/fgene.2021.661276 ◽

2021 ◽

Vol 12 ◽

Author(s):

Sang V. Vu ◽

Wayne Knibb ◽

Cedric Gondro ◽

Sankar Subramanian ◽

Ngoc T. H. Nguyen ◽

...

Keyword(s):

Soft Tissue ◽

Body Shape ◽

Condition Index ◽

Genomic Information ◽

Meat Yield ◽

Tissue Weight ◽

Crassostrea Angulata ◽

Color Traits ◽

Sib Families ◽

Selection For

Genetic improvement for quality traits, especially color and meat yield, has been limited in aquaculture because the assessment of these traits requires that the animals be slaughtered first. Genotyping technologies do, however, provide an opportunity to improve the selection efficiency for these traits. The main purpose of this study is to assess the potential for using genomic information to improve meat yield (soft tissue weight and condition index), body shape (cup and fan ratios), color (shell and mantle), and whole weight traits at harvest in the Portuguese oyster, Crassostrea angulata. The study consisted of 647 oysters: 188 oysters from 57 full-sib families from the first generation and 459 oysters from 33 full-sib families from the second generation. The number per family ranged from two to eight oysters for the first and 12–15 oysters for the second generation. After quality control, a set of 13,048 markers were analyzed to estimate the genetic parameters (heritability and genetic correlation) and predictive accuracy of the genomic selection for these traits. The multi-locus mixed model analysis indicated high estimates of heritability for meat yield traits: 0.43 for soft tissue weight and 0.77 for condition index. The estimated genomic heritabilities were 0.45 for whole weight, 0.24 for cup ratio, and 0.33 for fan ratio and ranged from 0.14 to 0.54 for color traits. The genetic correlations among whole weight, meat yield, and body shape traits were favorably positive, suggesting that the selection for whole weight would have beneficial effects on meat yield and body shape traits. Of paramount importance is the fact that the genomic prediction showed moderate to high accuracy for the traits studied (0.38–0.92). Therefore, there are good prospects to improve whole weight, meat yield, body shape, and color traits using genomic information. A multi-trait selection program using the genomic information can boost the genetic gain and minimize inbreeding in the long-term for this population.

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Bayesian genomic models boost prediction accuracy for resistance against Streptococcus agalactiae in Nile tilapia (Oreochromus nilioticus)

10.1101/2020.01.09.900134 ◽

2020 ◽

Author(s):

Rajesh Joshi ◽

Anders Skaaurd ◽

Alejandro Tola Alvarez ◽

Thomas Moen ◽

Jørgen Ødegård

Keyword(s):

Genomic Selection ◽

Streptococcus Agalactiae ◽

Nile Tilapia ◽

Prediction Accuracy ◽

Challenge Test ◽

Practical Implementation ◽

Bacterial Disease ◽

Genetic Strains ◽

Genomic Selection Model ◽

Selection For

AbstractStreptococcosis due to Streptococcus agalactiae is a major bacterial disease in Nile tilapia, and development of the resistant genetic strains can be a sustainable approach towards combating this problematic disease. Thus, a controlled disease trial was performed on 120 full-sib families to i) quantify and characterize the potential of genomic selection for S. agalactiae resistance in Nile tilapia and to ii) select the best genomic model and optimal SNP-chip for this trait.In total, 40 fish per family (15 fish intraperitoneally injected and 25 fish as cohabitants) were selected for the challenge test and mortalities recorded every 3 hours, until no mortalities occurred for a period of 3 consecutive days. Genotypes (50,690 SNPs) and phenotypes (0 for dead and 1 for alive) for 2472 cohabitant fish were available. The pedigree-based analysis utilized a deep pedigree, going 17 generations back in time. Genetic parameters were obtained using various genomic selection models (GBLUP, BayesB, BayesC, BayesR and BayesS) and traditional pedigree-based model (PBLUP). The genomic models were further analyzed using 10 different subsets of SNP-densities for optimum marker density selection. Prediction accuracy and bias were evaluated using 5 replicates of 10-fold cross-validation.Using an appropriate Bayesian genomic selection model and optimising it for SNP density increased prediction accuracy up to ∼71%, compared to a pedigree-based model. This result is encouraging for practical implementation of genomic selection for S. agalactiae resistance in Nile tilapia breeding programs.

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Emerging issues in genomic selection

Journal of Animal Science ◽

10.1093/jas/skab092 ◽

2021 ◽

Author(s):

I Misztal ◽

I Aguilar ◽

D Lourenco ◽

L Ma ◽

J Steibel ◽

...

Keyword(s):

Genomic Selection ◽

Sparse Matrices ◽

Large Datasets ◽

Single Step ◽

Genomic Information ◽

Long Term Effects ◽

Additive Variance ◽

Recent Developments ◽

Emerging Issues ◽

Using Data

Abstract Genomic selection is now practiced successfully across many species. However, many questions remain such as long-term effects, estimations of genomic parameters, robustness of GWAS with small and large datasets, and stability of genomic predictions. This study summarizes presentations from at the 2020 ASAS symposium. The focus of many studies until now is on linkage disequilibrium (LD) between two loci. Ignoring higher level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWAS studies using small genomic datasets frequently find many marker-trait associations whereas studies using much bigger datasets find only a few. Most current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit computation of p-values from GBLUP, where models can be arbitrarily complex but restricted to genotyped animals only, and to single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as one SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. While many issues in genomic selection have been solved, many new issues that require additional research continue to surface.

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