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

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

Genes ◽

10.3390/genes12020210 ◽

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.

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EFEKTIVITAS SERBUK DAUN TANAMAN KAYU MANIS (Cinnamomum burmanii) TERHADAP DIFERENSIAL LEUKOSIT DAN AKTIVITAS FAGOSITOSIS IKAN NILA (Oreochoromis niloticus) YANG DIINFEKSI Streptococcus agalactiae

Jurnal Perikanan Pantura (JPP) ◽

10.30587/jpp.v1i1.293 ◽

2018 ◽

Vol 1 (1) ◽

pp. 40

Author(s):

Sri Wulandari ◽

Rahmad Jumadi ◽

Firma Fika Rahmawati

Keyword(s):

Streptococcus Agalactiae ◽

Oreochromis Niloticus ◽

Phagocytic Activity ◽

Challenge Test ◽

Bacterial Disease ◽

Leaf Powder ◽

Alternative Measures ◽

Monocyte Cell ◽

And Control ◽

Phagocytosis Activity

The main problem in the cultivation of tilapia is the attack of bacterial disease Streptococcosiscaused by Streptococcus agalactiae bacteria. Alternative measures to prevent the disease ofStreptocococis can be through the use of phytopharmaca materials. One of the ingredients ofphytopharmaca can be used is cinnamon plant. Cinnamon leaves contain several types of activesubstances such as tannins, eugenol, safrole, calcium oxalate, resin, saponins, tanners, andsinamaldehid. The objective of the study of effectiveness of leaf powder of cinnamon plant toleukocyte differential and phagocytic activity in tilapia (Oreochromis niloticus) infected byStreptococcus agalactiae is to know the influence and dosage of cinnamon leaf powder in feedtoLeukocyte Differentiation and Phagocytosis Activity Postcainfection Streptococcus agalactiaein tilapia (Oreochromis niloticus). This research used 3treatment 4 replication and control withdose K- = challenge test, K+ = without test challenge, A = dose 0,25%, B = dose 0,5%, C = 1%.The parameters observed are Leukocyte Differential and Phagocytosis Activity. Dosage 0.5%addition of leaf powder cinnamon plant on feed is the best dose. Giving of cinnamon leaf powderin feed influenced to increase of leukocyte difference especially on monocyte cell and neutrophilcell was significantly different (P>0,05) than K+ without addition of cinnamon leaf powderwhile phagocytic activity had an effect on Streptococcus agacatiae.

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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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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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Genomic Prediction and Selection for Fruit Traits in Winter Squash

G3 Genes|Genome|Genetics ◽

10.1534/g3.120.401215 ◽

2020 ◽

Vol 10 (10) ◽

pp. 3601-3610

Author(s):

Christopher O. Hernandez ◽

Lindsay E. Wyatt ◽

Michael R. Mazourek

Keyword(s):

Genomic Selection ◽

Fruit Quality ◽

Quality Index ◽

Recurrent Selection ◽

Selection Model ◽

Breeding Cycle ◽

Quality Traits ◽

Genomic Selection Model ◽

Selection For ◽

Winter Squash

Improving fruit quality is an important but challenging breeding goal in winter squash. Squash breeding in general is resource-intensive, especially in terms of space, and the biology of squash makes it difficult to practice selection on both parents. These restrictions translate to smaller breeding populations and limited use of greenhouse generations, which in turn, limit genetic gain per breeding cycle and increases cycle length. Genomic selection is a promising technology for improving breeding efficiency; yet, few studies have explored its use in horticultural crops. We present results demonstrating the predictive ability of whole-genome models for fruit quality traits. Predictive abilities for quality traits were low to moderate, but sufficient for implementation. To test the use of genomic selection for improving fruit quality, we conducted three rounds of genomic recurrent selection in a butternut squash (Cucurbita moschata) population. Selections were based on a fruit quality index derived from a multi-trait genomic selection model. Remnant seed from selected populations was used to assess realized gain from selection. Analysis revealed significant improvement in fruit quality index value and changes in correlated traits. This study is one of the first empirical studies to evaluate gain from a multi-trait genomic selection model in a resource-limited horticultural crop.

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Penggunaan bakteri probiotik untuk pencegahan infeksi bakteri Streptococcus agalactiae pada ikan Nila, Oreochromis niloticus

e-Journal BUDIDAYA PERAIRAN ◽

10.35800/bdp.6.2.2018.20556 ◽

2018 ◽

Vol 6 (2) ◽

Author(s):

Aisin Umasugi ◽

Reiny A. Tumbol ◽

Reni L. Kreckhoff ◽

Henky Manoppo ◽

Novie P.L. Pangemanan ◽

...

Keyword(s):

Statistical Analysis ◽

Survival Rate ◽

Streptococcus Agalactiae ◽

Oreochromis Niloticus ◽

Nile Tilapia ◽

Challenge Test ◽

Probiotic Bacteria ◽

Growth And Survival ◽

Significant Difference ◽

Materials Used

This study aimed to evaluate the effect of probiotic on growth and survival of tilapia (Oreochromis niloticus) against Streptococcus agalactiae infection. The test materials used were commercial probiotic bacteria and Streptococcus agalactiae. The probiotic bacteria were administered by mixing into the feed with a dose of 0 mL (without probiotics), 10 mL / kg of feed, 15 mL / kg of feed and 20 mL / kg of feed. Feed was given 3 times a day at 08.00, 12.00 and 16.00 for 21 days. After that, the challenge test was done with Streptococcus agalactiae bacteria by cohabitation. Bacteria with a density of 107 cells / mL were mixed into the water and allowed for 2 hours. The result showed that B treatment (10 mL / kg of feed) gave the best result with 93,33 percent of survival rate. Statistical analysis showed that the survival of fish in treatments B, C and D was significantly different from treatment A (P <0.1). However, there was no significant difference between treatments B, C, and D. It was also found that the addition of probiotic in feed did not affect the growth of Nile tilapia (Orechromis niloticus). Keywords : Probiotic bacteria, nile tilapia, Streptococcus agalactiae, growth, infection

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Genomic Prediction of Drought Tolerance during Seedling Stage in Maize using Low-Cost Markers

10.21203/rs.3.rs-1172460/v1 ◽

2021 ◽

Author(s):

Ao Zhang ◽

Shan Chen ◽

Zhenhai Cui ◽

Yubo Liu ◽

Yuan Guan ◽

...

Keyword(s):

Drought Tolerance ◽

Genomic Selection ◽

Genomic Prediction ◽

Northeast China ◽

Prediction Accuracy ◽

Phylogenetic Trees ◽

Predictive Accuracy ◽

Seedling Stage ◽

Breeding Cycle ◽

Maize Breeding

Abstract Drought tolerance in maize is a complex and polygenic trait, especially in the seedling stage. In plant breeding, such traits can be improved by genomic selection (GS), which has become a practical and effective tool. In the present study, a natural maize population named Northeast China core population (NCCP) consisting of 379 inbred lines were genotyped with diversity arrays technology (DArT) and genotyping-by-sequencing (GBS) platforms. Target traits of seedling emergence rate (ER), seedling plant height (SPH), and grain yield (GY) were evaluated under two natural drought environments in northeast China. adequate genetic variants have been found for genomic selection, they are not stable enough between two years. Similarly, the heritability of the three traits is not stable enough, and the heritabilities in 2019 (0.88, 0.82, 0.85 for ER, SPH, GY) are higher than that in 2020 (0.65, 0.53, 0.33) and cross-two-year (0.32, 0.26, 0.33). The current research obtained two kinds of marker sets: the SilicoDArT markers were from DArT-seq, and SNPs were from the GBS and DArT-seq. In total, a number of 11,865 SilicoDArT, 7,837 DArT's SNPs, and 91,003 GBS SNPs were used for analysis after quality control. The results of phylogenetic trees showed that the population was rich in consanguinity. Genomic prediction results showed that the average prediction accuracies estimated using the DArT SNP dataset under the 2-fold cross-validation scheme were 0.27, 0.19, and 0.33, for ER, SPH, and GY, respectively. The result of SilicoDArT is close to the SNPs from DArT-seq, those were 0.26, 0.22, and 0.33. For SPH, the prediction accuracies using SilicoDArT were more than ones using DArT SNP, In some cases, alignment to the reference genome results in a loss to the prediction. The trait with lower heritability can improve the prediction accuracy using filtering of linkage disequilibrium. For the same trait, the prediction accuracy estimated with two types of DArT markers was consistently higher than those estimated with the GBS SNPs under the same genotyping cost. Our results show the prediction accuracy has been improved in some cases of controlling population structure and marker quality, even when the density of the marker is reduced. In the initial maize breeding cycle, Silicodart markers can obtain higher prediction accuracy with a lower cost. However, higher marker density platforms i.e. GBS may play a role in the following breeding cycle for the long term. The natural drought experimental station can reduce the difficulty of phenotypic identification in a water-scarce environment. The accumulation of more yearly data will help to stabilize the heritability and improve predictive accuracy in maize breeding. The experimental design and model for drought resistance also need to be further developed.

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